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Investigations into the total antioxidant capacities of cultivars of gluten-free grains using near-infrared spectroscopy
Accepted Manuscript Investigations into the total antioxidative capacities of cultivars of gluten-free grains using near-infrared spectroscopy Verena Wiedemair, Reinhold Ramoner, Christian W. Huck PII:
S0956-7135(18)30382-7
DOI:
10.1016/j.foodcont.2018.07.045
Reference:
JFCO 6258
To appear in:
Food Control
Received Date: 2 May 2018 Revised Date:
5 July 2018
Accepted Date: 27 July 2018
Please cite this article as: Wiedemair V., Ramoner R. & Huck C.W., Investigations into the total antioxidative capacities of cultivars of gluten-free grains using near-infrared spectroscopy, Food Control (2018), doi: 10.1016/j.foodcont.2018.07.045. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Investigations into the total antioxidative capacities of
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cultivars of gluten-free grains using near-infrared
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spectroscopy
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Verena WIEDEMAIRa, Reinhold RAMONERb, Christian W. HUCK*a
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Institute of Analytical Chemistry and Radiochemistry, CCB – Center for Chemistry and Biomedicine, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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Health University of Applied Sciences Tyrol, Innrain 98, 6020 Innsbruck, Austria
Mag. Verena Wiedemair, MA MSc
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E-Mail: [email protected] Tel.: +43 512 507 57372 Adress: Innrain 80/82, 6020 Innsbruck, Austria
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Priv.Doz. Reinhold Ramoner
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E-Mail: [email protected] Tel.: +43 50 8648 4779 Adress: Innrain 98, 6020 Innsbruck
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Univ.-Prof. Christian W. Huck (corresponding author)
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E-Mail: [email protected] Tel.: +43 512 507 57304 Adress: Innrain 80/82, 6020 Innsbruck, Austria
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*
Corresponding author
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Abstract
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26 Millet, buckwheat and oat are considered to be minor crops, hence chemical profiles for
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different cultivars are rare. The examination of a sum parameter, like the total antioxidative
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capacity (TAC) can thus be a first step to systematically assess the quality of different cultivars of
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mentioned gluten-free grains and thereby serve as an indicator for the selection of cultivars for
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food processing.
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TAC of 20 common buckwheat, 14 proso millet and six common oat cultivars was analysed using
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Folin-Ciocalteu assay and an optimized NIRS methodology. PLS regressions for milled and intact
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samples were established and yielded a R2(CV) of 0.892 and 0.929. The SEPs for milled and intact
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samples were approximately 1.7 and 1.6 mgGAE/g. Multivariate LOD and LOQ were also
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calculated. LODmax for intact and milled samples was 1.72 and 2.80 mgGAE/g.
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TAC varies considerably among cultivars of one species, emphasising the need for full chemical
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profiles. Values for LOD and LOQ show that established PLS-R models can be used to quantify
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TAC of buckwheat and oat cultivars. TAC of millet cultivars can be detected, however not
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quantified.
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Keywords: NIRS, total antioxidative capacity, gluten-free, buckwheat, millet, oat, Folin-Ciocalteu
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1. Introduction
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In the past, only patients suffering from coeliac disease used to be on a gluten-free diet, as it is
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the only cure for their illness (Alaedini & Green, 2005; Ludvigsson et al., 2013). Coeliac disease is
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a chronic autoimmune disorder, which is triggered in genetically sensitive individuals by
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exposure to gluten. The result are small intestinal inflammations and a severe ablation of
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intestinal villi (Alaedini & Green, 2005; Ludvigsson et al., 2013).
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Nowadays however, the gluten-free market benefits from the notion that going gluten-free is
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healthier (Kim et al., 2016), which is why many people who do not suffer from coeliac disease
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choose to cut out or reduce gluten in their diets. A study by the National Health and Nutrition
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Examination found that in 2014 more than three times more people without coeliac disease
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were avoiding gluten in their diets than in 2009 (Kim et al., 2016). Another study indicates that
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around 22% of adults in North America are trying to avoid gluten in their diet, thus creating a $
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8,8 billion market (Fromartz, 2015). Even more so, the market of gluten-free products is
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expected to grow 10.2% over each year between 2015–2019 (Markets and Markets Web site,
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2014). This market growth resulted in an increase in availability of gluten-free products in stores,
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however many customers following this health trend are unaware of its downsides. Studies on
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patients with coeliac disease, who follow a strict diet, have shown that many of them suffer from
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a poor vitamin and mineral status (Hallert et al., 2002; Thompson, 2000; Udachan & Sahoo,
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2017).
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In order to improve the quality of gluten-free food, new cultivars with more favourable
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properties must not only be bred, but also be analysed thoroughly and consequently be
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implemented in foodstuff production. However, exhaustive chemical profiles of different
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cultivars of gluten-free grains are still missing, leaving companies often clueless about the quality
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of their primary products. The establishment of a complete nutritional profile is a tedious task.
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the quality of different cultivars. It is the hypothesis of this study that cultivars of one grain
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species differ significantly from one another and thus it is important to review various cultivars,
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in order to make informed decisions, when buying grains for food processing or breeding.
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Antioxidants are substances, which inhibit oxidation, reactions promoted by oxygen or reactive
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oxygen species (ROS) (Halliwell & Gutteridge, 2015), thus reducing the risk of several diseases,
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including chronic inflammation, cardiovascular diseases, type 2 diabetes and cancer (Adom,
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Sorrells, & Liu, 2005; Kris-Etherton et al., 2002; McKeown, Meigs, Liu, Wilson, & Jacques, 2002;
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Scalbert, Johnson, & Saltmarsh, 2005). But natural ROS also have important function in the body
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as second messengers, meaning that a delicate equilibrium of radical production and scavenging
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is in existence (Clara et al., 2016; Mittler, 2002). Typical antioxidant substances are polyphenols,
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vitamins and phenolic acids (Choi, Jeong, & Lee, 2007; Pietta, 2000; Scalbert et al., 2005).
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The total antioxidative capacity can be estimated by various reactions and methods, which can
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be divided into two groups according to their reaction mechanism: hydrogen atom transfer
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(HAT) or single electron transfer (SET) (Prior, Wu, & Schaich, 2005). One widely used technique
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for the determination of total antioxidative capacity is the Folin-Ciocalteu method, which
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exhibits a SET mechanism. It was developed by Folin and Ciocalteu (Folin & Ciocalteu, 1927) and
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later improved by Singleton et al. (Singleton, Orthofer, & Lamuela-Raventós) to monitor total
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phenolic content. Later on studies showed that this assay is sensitive towards a variety of
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molecules besides phenols, like vitamins, thiols and nitrogen containing moleclues (Amorati &
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Valgimigli, 2015; Everette et al., 2010; Ikawa, Schaper, Dollard, & Sasner, 2003; Prior et al., 2005;
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Rao, Kanjilal, & Mohan, 1978). This is why this assay is suitable for the analysis of the total
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antioxidant capacity. Nevertheless, this method is still often used to determine only phenolic
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content, as the extraction reagent used is often only sensitive towards phenols. This is not the
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case in this study, which is why the Folin-Ciocalteu assay was chosen to determine total
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2005; Prior et al., 2005).
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Folin-Ciocalteu’s method is a wet chemical analysis, thus sample preparation as well as the
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measurements themselves are quite time-consuming. Samples have first to be extracted and
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normalized to a certain volume, before they can be exposed to Folin-Ciocalteu’s reagent and
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lastly be measured with a UV/VIS-photometer at 750 nm. This is why in recent years, many
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studies using near-infrared spectroscopy to determine the antioxidative capacity have been
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published (Clara et al., 2016; Lu et al., 2011; Lucas, Andueza, Rock, & Martin, 2008; Schmutzler &
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Huck, 2016; Wang, Yu, Fan, & Qu, 2009; Zhang, Shen, Chen, Xiao, & Bao, 2008). Near-infrared
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spectroscopy provides a non-invasive and fast tool and is due to its broad bands often used for
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the determination of sum parameters. Furthermore, overtones and combination vibrations
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appearing in NIR spectra hold additional information (Blanco & Villarroya, 2002), which can be
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interpreted using multivariate data analysis (MVA) – a known powerful tool for establishing
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calibration and validation models (Næs, Isaksson, Fearn, & Davies, 2002). However, near-infrared
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spectroscopy also has its limits which are usually estimated using well-established quality
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parameters like the standard error of prediction. This study uses a multivariate, yet UIPAC
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conform approach on LOD and LOQ prediction, in order to more accurately assess the
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possibilities and limits for the use of near-infrared spectroscopy for grain analysis. The
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application of near-infrared spectroscopy to estimate the antioxidative capacity of different
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cultivars of gluten-free buckwheat, millet and oat grains is new, as differences within one species
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might be too small to be recognized by NIRS.
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Knowing which cultivar holds more favourable properties is very important to breeders, as well
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as companies producing gluten-free foods. Thereby the study also investigates differences of
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hulled and dehulled grains of one cultivars, as grains need to be dehulled for food production.
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Additionally, the partial least square regression (PLS-R) model established using NIR data may be
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used to estimate the antioxidative capacity for other cultivars, not surveyed in this study as well
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and has thus the potential to replace the time consuming and invasive wet-chemical Folin-
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Ciocalteu measurements.
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2. Methods and Materials
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2.1. Samples management
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20 buckwheat, 14 millet and six oat cultivars were provided by Research Center Laimburg
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(Bolzano, Italy) and Dr. Schär AG / SPA (Burgstall, Italy). All cultivars were provided as grains with
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husk, but for some also dehulled grains were received. The supplementary material gives a
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detailed overview over all cultivars used in this study and further shows in what state and from
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what year they were. In total, 77 samples was analysed:
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Twelve buckwheat samples with husk, harvested in 2016
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Twelve dehulled buckwheat samples, harvested in 2016
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Sixteen buckwheat samples with husk, harvested in 2016
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Six oat samples with husk, harvested in 2016
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Ten millet samples with husk, harvested in 2016 Five millet samples with husk, harvested in 2015
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Five dehulled millet samples, harvested in 2015
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Eleven millet samples with husk, harvested in 2016
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grid in a Retsch mill ZM200 (Haan, Germany). Both milled and intact samples were analysed
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using NIR spectroscopy. 1 g of milled samples were further used for acidified methanol
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extraction and consequently for Folin-Ciocalteu analyses.
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2.2 Extraction
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In order to measure the total antioxidative capacity, samples had to be extracted first. The
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extraction reagent consisted of 95% methanol (≥99.9%) and 5% hydrochloric acid (35%)
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(Chethan & Malleshi, 2007; Pradeep & Guha, 2011), and was always prepared on the same day
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the extraction was performed. 1 g of milled sample was weighed into a 50 mL Falcon tube and
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then 10 mL of extraction reagent was added. The tube was put into an ultrasonic bath at 60 °C
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for 20 min. Next, the sample was centrifuged at 3500 rpm (25473 rcf) for 10 min. Preliminary
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extraction studies showed that usually six repetitions are needed to extract all compound
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contributing to the antioxidative capacity. Only for buckwheat samples with husk a total of nine
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extraction cycles was needed. Lastly, the volume of the extracts was adjusted to 60 and 90 mL
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with acidified methanol, respectively. All samples were stored at -80 °C overnight then Folin-
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Ciocalteu measurements were performed.
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2.3. Folin-Ciocalteu measurements
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The previously prepared extracts were thawed and then 1.5 mL distilled water, 100 µL extract,
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100 µL Folin-Coicalteu’s phenol reagent (2N, Sigma Aldrich) and 1.3 mL sodium carbonate (50
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mg/mL; made of sodium carbonate 99.0%, Sigma Aldrich) were mixed together in PMMA
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(Polymethyl(methacrylate)) cuvettes (d = 1 cm) (Brand, Wertheim, Germany). Next, the cuvettes
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were put in an oven at 60°C for 30 minutes. After that, the samples cooled down to room 8
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(Genova Plus, Jenway, Stone, Staffordshire, UK). Three replicates were prepared for each
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extract. The standard calibration curve was established using gallic acid in concentrations
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between 0 and 250 mg/L gallic acid in equidistant 25 mg/L steps. Each concentration was
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prepared as a triplicate and the calibration curve yielded a R2 of 0.995. The standard error of the
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regression was 0.02 mg/L.
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2.4. Near-infrared spectroscopy (NIRS)
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All samples were measured with NIRS in intact and milled form at 21°C air-conditioner controlled
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room temperature. The device used was a NIRFlex N-500 (Büchi, Flawil, Switzerland) with the
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solids add-on. Samples were filled in a rotating cylindrical quartz cuvette (h = 25 mm, inner
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diameter = 31.6 mm) and measured six times with 64 scans in the wavelength range between
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10000–4000 cm-1 (1000–2500 nm) in diffuse reflection mode. The spectral resolution was 8 cm-1,
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whereas the digital resolution was 4 cm-1.
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2.5. Multivariate data analysis
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All NIRS spectra recorded were exported and then analysed using the multivariate data analysis
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software The Unscrambler X 10.4 (Camo, Oslo, Norway). First, spectra were transformed into -
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logR spectra, then all of them were averaged by a factor of six, in order to get one spectrum per
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sample. The descriptive statistics tool was applied, in order to determine necessary spectral pre-
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treatments. Consequently, Savitzky Golay 2nd derivation was applied to remove additive baseline
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offset, enhance small peaks and differences and smooth spectra. The number of smoothing
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points was adjusted, according to the state of the sample. For milled samples the number of
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smoothing points was chosen to be 17, and when intact grains were measured 13 smoothing
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8948–4032 cm-1 (1118–2480 nm) to reduce multiplicative scatter effects. Data outside the
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selected wavenumber range was noise and thus not included in further spectral pre-treatments
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or the establishment of PLS-R models. Spectra were then split into a calibration and a test set by
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Kennard-Stone sample selection (Galvão et al., 2005). This algorithm maximizes the Euclidean
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distance between the instrumental response vectors of the selected samples. This means that
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the algorithm first generates a group consisting of the two most distant objects in the sample
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set. Then, the object with the largest minimal distance to the group is added and so on (Galvão
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et al., 2005; Rajer-Kanduč, Zupan, & Majcen, 2003). Last, orthogonal signal correction (OSC)
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according to Fearn (Fearn, 2000) was used on the calibration set in the same wavenumber range
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to filter out variables, which are orthogonal to the y data, i.e. the reference data. The established
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OSC model was then used on the test set.
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The calibration set consisted of two thirds of the samples (ncal=51). 35 of these samples are
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unique hulled or dehulled cultivars. The remaining 16 spectra stem from eight unique cultivars.
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and was used to build a PLS-R model, which was first validated using random leave one out cross
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validation (LOOCV) with 20 segments and then using the test set, which consisted of the
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remaining third of the samples (nval=26). The latter is called test set validation (TV). For the
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establishment of the PLS-R model for intact and milled grains two and four factors were needed,
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respectively.
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Lastly, the limit of detection (LOD) was determined in a multivariate way, as proposed by
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Allegrini and Olivieri (Allegrini & Olivieri, 2014). The two authors explain that since the matrix of
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each sample is a little different, a univariate LOD is unfit to cope with a multivariate problem.
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Hence, they introduced an LOD range, with a LODmin and an LODmax, which are calculated
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according to equation (1) and (2)
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(1)
= 3.3
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(2)
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SEN refers to the sensitivity, which in a PLS-R is given by the inverse length of the regression
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coefficient. var(x) is the variance of the x data, hence the variance in the spectral data, whereas
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var(ycal) refers to the variance in the calibration data. h refers to the sample leverage and can be
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calculated according to equations (3) and (4):
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(3) ℎ
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= ∑) #$%!&
(4) ℎ
= max ℎ
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Values below LODmin indicate that no antioxidative capacity can be detected. If a value above
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LODmax is reached, the sample is certain to hold antioxidative capacity. Between LODmin and
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LODmax the existence of any antioxidative capacity is not certain, as different values may be due
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to matrix effects. Additionally, the limit of quantification (LOQ) can also be calculated in a
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multivariate way, by tripling LODmin and LODmax. It is not possible to quantify antioxidative
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capacity, if it is below LOQmin. A value above LOQmax indicates that quantification is possible.
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Values between LOQmin and LOQmax cannot be quantified with certainty. If the matrix is very
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homogenous throughout a sample set, then the values LODmin and LOQmin will be close to their
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respective maxima.
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3. Results and Discussion
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3.1 Folin-Ciocalteu measurements
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As mentioned before, gallic acid was used to establish a calibration curve for all Folin-Ciocalteu
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measurements. Consequently, the results are given in mg gallic acid equivalents per g sample 11
ACCEPTED MANUSCRIPT (mgGAE/g). The samples ranged from 1.4 to 18.8 mgGAE/g, which corresponds to 23 to 212
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mg/L. The sample data collected had a mean of 7.19 mgGAE/g, a median of 6.18 mgGAE/g and a
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standard deviation of 4.86 mgGAE/g. On average oat samples had an antioxidative capacity of
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5.76 mgGAE/g (SD=1.29) and buckwheat and millet samples with husk an antioxidative capacity
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of 12.90 mgGAE/g (SD=2.49) and 2.74 mgGAE/g (SD=0.36), respectively. Dehulled buckwheat
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samples had an antioxidative capacity of 6.43 mgGAE/g (SD=0.55) and dehulled millet samples a
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capacity of 1.45 mgGAE/g (SD=0.04). Figure 1 shows more detailed results of the Folin-Ciocalteu
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measurements.
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Samples with husk generally had a higher total antioxidative capacity than their dehulled
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counterparts. Buckwheat samples with husk hold 2.3 times more antioxidants on average than
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their complement. Millet samples with husk have an average 1.8 times higher total antioxidative
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capacity than their dehulled counterpart. This poses a challenge, as most of the husk is removed
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for food production. Buckwheat has the highest antioxidative capacity and the values for
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dehulled buckwheat are still in the same range as oat samples with husk. Millet samples on the
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other hand have a very low total antioxidative capacity. Additionally, millet cultivars had a very
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homogenous distribution of antioxidative capacity throughout all samples. Buckwheat cultivars
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on the other hand showed greater variance, even when just looking at samples with husk or
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dehulled samples. Oat cultivars have quite similar total antioxidative capacities, however cv.
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Irina and cv. ITA-7 seem to underperform in comparison with the other oat samples.
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To compare the results with similar studies of antioxidative capacities of grains is difficult, as
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most previously performed studies do either not state the exact cultivar studied or do not use
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the same species. Nevertheless, a comparison of the data at hand with similar literature showed,
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that the results for millet cultivars are in good agreement with previous, similar studies
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examining other millet species in respect to their antioxidative capacity (Dykes & Rooney, 2006;
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Pradeep & Guha, 2011). Little millet (Panicum sumatrense) has a reported antioxidative capacity
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mgGAE/g of the investigated proso millets with husk. The cultivar Tiroler Rispenhirse has the
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highest TAC of the examined proso millets with a value of 3.68 mgGAE/g. Another study showed
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that different species of millet vary greatly in regard to their total antioxidative capacity with
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proso millet only holding 0.5–1.0 mgCatechin/g and finger millet containing 5.5–5.9
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mgCatechin/g (Dykes & Rooney, 2006).
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The values for buckwheat samples are also in good accordance with previously performed
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similar studies (Li, Yuan, Yang, Tao, & Ming, 2013). The reported antioxidant capacities for
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buckwheat flours ranged from 8.05–15.11 mgGAE/g (Li et al., 2013). The buckwheat cultivars
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with husk analysed in this study ranged from 9.08–18.78 mgGAE/g.
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Looking at oat studies it becomes clear that these grains have rarely been investigated towards
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their total antioxidant capacity. Furthermore, it seems that the oat cultivars used in this study
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carry more antioxidants, than the cultivars used in other studies (Fardet, Rock, & Rémésy, 2008).
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This may be due to investigating other oat species, other cultivars or because of this study using
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oat samples with husk.
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In general, it becomes clear that the intraspecific variance of total antioxidant capacity of
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buckwheat and oat is high, thus establishing chemical profiles of each cultivar is important. The
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selection of oat and buckwheat varieties with favourable properties is also important for
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breeders, as high quality grains will lead to higher quality food products. Millet samples have less
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variance, meaning that the selection of favourable grains is even more important. By effectively
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choosing millet cultivars the quality can be improved and thus support individuals living on a
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gluten-free diet.
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Spectra were recorded between with the NIRFlex N-500 (Büchi, Flawil, Switzerland) between
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10000–4000 cm-1. The samples rotated in the cylindrical quartz cuvettes during measurements.
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An average spectrum of all intact samples is shown in figure 2. Table 1 lists important maxima
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and their respective vibration according to Workman and Weyer (Workman & Weyer, 2008). It is
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evident that the upper part of the NIR spectrum is determined by overtones, whereas
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combination vibrations mainly contribute to the lower part of the spectrum.
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Table 1: Important vibrations in the NIR spectrum of millet, buckwheat and oat grains (Workman & Weyer, 2012).
Vibration C-H str. 2 overtone O-H & N-H str. 1st overtone C-H str. 1st overtone C-H str. 1st overtone O-H str. & O-H def. N-H str. C-H str. & C-H def. C-H str. & C-H def. comb.
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Wavelength / nm 1203 1469 1728 1766 1929 2106 2283 2336
3.3. Prediction of antioxidative capacity using NIRS
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The data collected during Folin-Ciocalteu measurements was used as reference data, in order to
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establish a prediction model for the total antioxidative capacity using NIRS. Necessary spectral
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pre-treatments were identified using the descriptive statistics tool implemented in the software
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The Unscrambler X 10.4 (see section 2.5). Two and four PLS-R factors were used for intact and
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milled samples, respectively. Table 2 gives an overview over the statistical quality parameters
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regression coefficient (R2), bias, standard error of cross validation (SECV), and standard error of
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prediction (SEP) (Williams, 1987) of the established PLS-R models. Figure 3 shows the PLS-R
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model of the calibration set for grains. The SEP was calculated using the 26 test set samples.
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Table 2: Overview over statistical parameters of the established PLS-R models. CV refers to cross validation, whereas depicts test set validation.
State of the grains Intact Milled
SECV / mgGAE/g 1.1794 1.6156
R2 (CV) 0.9288 0.8918
Bias (CV) / mgGAE/g -0.0004 -0.0206
SEP / mgGAE/g 1.6381 1.6925
R2 (TV) 0.8911 0.8831
Bias (TV) / mgGAE/g -0.4534 0.0592
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of 0.89 for cross validation and 0.88 for test set validation, which is satisfactory for NIRS analyses
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and indicates good correlation between the spectra and the reference values. The regression
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coefficient for the cross validated model for intact samples reaches a value of 0.93 and for the
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test set validated model a value of 0.89. This is even higher than for the respective results for
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milled samples. Consequently, the intact samples also indicate a good correlation between the
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spectra and the reference data.
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For cross validation, the bias, which indicates the presence of systematic error, is close to zero,
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as it should be. Looking at the test set validation, the bias value for intact and milled samples is
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much higher. This has to be expected as samples used for validation are not part of the
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calibration model.
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SECV and SEP values are acceptable, considering that oat and buckwheat samples have an
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antioxidative capacity between 5.8 and 18.8 mgGAE/g. Only millet samples with an antioxidative
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capacity between 1.4 and 3.7 mgGAE/g are in the range of SECV and SEP values. When looking at
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the LODmin and LODmax values (Table 3) for each model, it becomes clear that dehulled millet
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samples can just barely be detected with certainty, as most of them have a value just above the
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LODmax. The antioxidative capacity of millet samples with husk are well above the LODmax,
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however quite similar to the LOQmin value, meaning that although the presence of antioxidative
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capacity is certain, quantification is not yet possible. All of this suggest, that only the total
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antioxidative capacities of buckwheat and oat can be quantified using NIRS. Proso millet cultivars
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ACCEPTED MANUSCRIPT seem to be too poor in antioxidants to quantify them. To further lower the LOD, solid phase
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extraction (SPE) or a reflector, which enhances the signal has to be applied. Interestingly,
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LODmin/LODmax and LOQmin/LOQmax are lower for intact samples. This seems to be
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counterintuitive, as milled samples are usually more homogeneous. However, the milling
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process might have an influence on the matrix, thus effecting some samples more, some less.
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Furthermore, even intact grains are quite small, meaning that near-infrared radiation can
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penetrate quite far into the sample and thus give accurate feedback about chemical properties.
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Table 3: LOD and LOQ ranges for the established PLS-R models. All values are given in mgGAE/g.
State of the seeds Intact Milled
LODmin 0.8666 1.2381
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LODmax
LOQmin
LOQmax
1.7160 2.7969
2.5997 3.7144
5.1480 8.3906
Another way to evaluate SEP values is the calculation of the ratio of performance to deviation
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(RPD). This value was first proposed by Williams (Williams, 1987) and indicates how accurate a
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model is and for what purposes it can be used. The RPD is calculated according to equation (5): 23
(5) 01 = 2564
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The RPD value for intact samples is 2.7 and for milled samples 2.8. According to the quality
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attribution, a RPD below 3 indicates that the model is adequate for screening analyses (Williams,
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1987). Intact and milled samples show very similar values for SEP and RPD, indicating that near-
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infrared radiation is able to penetrate even intact samples well. This shows that no sample
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preparation is necessary to establish a calibration model for total antioxidative capacity for
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gluten-free grains. Thus, the implementation of this model is very practical.
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only in buckwheat and oat samples they can be accurately quantified. Furthermore, sample
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milling is not necessary, as it has little effect on the quality of the PLS-R model. By measuring
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even more samples, it might be possible to further lower the SEP, thus improving the RPD and
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the robustness of the model.
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4. Conclusion
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This study examines 40 cultivars of buckwheat, millet and oat in different forms towards their
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antioxidative capacity using Folin-Ciocalteu measurements and NIRS, in order to get first insights
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into the chemical profile of different cultivars of certain species of gluten-free grains. A total of
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77 samples were investigated. Folin-Coicalteu measurements proved the importance of
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investigating cultivars of one species separately, as the total antioxidative capacity differs
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significantly. The differences are especially prominent among buckwheat samples, whereas
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millet samples show fewer variation.
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Furthermore, it was possible to establish PLS-R models using Folin-Ciocalteu measurements as
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reference data with a R2 of about 0.9, which show good correlation between spectra and
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reference data. By calculating LOD and LOQ in a multivariate way, it was demonstrated that the
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antioxidative capacity of most millet cultivars can be detected with certainty, however it is too
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low to be quantified. The antioxidative capacity of buckwheat and oat cultivars on the other
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hand can be quantified in milled and intact grains using the established PLS-R models.
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Calculation of RPD showed that the state of the grains – milled or intact – has only little influence
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on the established models. Thus an approach with no sample preparation is suggested for future
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studies.
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quality of buckwheat, oat and millet grains. This study showed that cultivars of one species differ
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in terms of their antioxidative capacites, meaning that the selection of a specific grain has great
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influence on gluten-free products. But, examining one sum parameter – the total antioxidative
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capacity – makes it possible to only get an indication for which cultivar to use in breeding, as well
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as food production. Naturally, also agricultural properties have to be taken into account when
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breeding, however the balance between favourable agricultural and nutritional properties will
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become more and more important as gluten-free market grows.
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5. Acknowledgement
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The authors want to thank the European Union, the European Regional Development Fund and
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the cross-border programme Interreg V-A Italy-Austria 2014–2020 (project “RE-Cereal”, ITAT
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1005, P-7250-013-042) for financial support.
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For sample management we want to thank Dr. Schär AG / SPA (Burgstall, Italy) and Research
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Center Laimburg (Bolzano, Italy).
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6. Conflicts of Interest
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The authors declare no conflicts of interest.
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References
394
Adom, K. K., Sorrells, M. E., & Liu, R. H. (2005). Phytochemicals and antioxidant activity of milled
395
fractions of different wheat varieties. Journal of agricultural and food chemistry, 53(6), 2297–
396
2306. https://doi.org/10.1021/jf048456d
398 399
Alaedini, A., & Green, P. H. R. (2005). Narrative review: Celiac disease: understanding a complex
RI PT
397
autoimmune disorder. Annals of internal medicine, 142(4), 289–298.
Allegrini, F., & Olivieri, A. C. (2014). IUPAC-consistent approach to the limit of detection in partial least-squares calibration. Analytical chemistry, 86(15), 7858–7866.
401
https://doi.org/10.1021/ac501786u
M AN U
402
SC
400
Amorati, R., & Valgimigli, L. (2015). Advantages and limitations of common testing methods for
403
antioxidants. Free radical research, 49(5), 633–649.
404
https://doi.org/10.3109/10715762.2014.996146
Blanco, M., & Villarroya, I. (2002). NIR spectroscopy: A rapid-response analytical tool. TrAC
TE D
405 406
Trends in Analytical Chemistry, 21(4), 240–250. https://doi.org/10.1016/S0165-
407
9936(02)00404-1
Chethan, S., & Malleshi, N. (2007). Finger millet polyphenols: Optimization of extraction and the
EP
408
effect of pH on their stability. Food Chemistry, 105(2), 862–870.
410
https://doi.org/10.1016/j.foodchem.2007.02.012
411
AC C
409
Choi, Y., Jeong, H.-S., & Lee, J. (2007). Antioxidant activity of methanolic extracts from some
412
grains consumed in Korea. Food Chemistry, 103(1), 130–138.
413
https://doi.org/10.1016/j.foodchem.2006.08.004
414
Clara, D., Pezzei, C. K., Schönbichler, S. A., Popp, M., Krolitzek, J., Bonn, G. K., & Huck, C. W.
415
(2016). Comparison of near-infrared diffuse reflectance (NIR) and attenuated-total-
416
reflectance mid-infrared (ATR-IR) spectroscopic determination of the antioxidant capacity of
19
ACCEPTED MANUSCRIPT 417
Sambuci flos with classic wet chemical methods (assays). Anal. Methods, 8(1), 97–104.
418
https://doi.org/10.1039/c5ay01314c
420 421
Dykes, L., & Rooney, L. W. (2006). Sorghum and millet phenols and antioxidants. Journal of Cereal Science, 44(3), 236–251. https://doi.org/10.1016/j.jcs.2006.06.007 Everette, J. D., Bryant, Q. M., Green, A. M., Abbey, Y. A., Wangila, G. W., & Walker, R. B. (2010).
RI PT
419
Thorough study of reactivity of various compound classes toward the Folin-Ciocalteu reagent.
423
Journal of agricultural and food chemistry, 58(14), 8139–8144.
424
https://doi.org/10.1021/jf1005935
Fardet, A., Rock, E., & Rémésy, C. (2008). Is the in vitro antioxidant potential of whole-grain
M AN U
425
SC
422
426
cereals and cereal products well reflected in vivo? Journal of Cereal Science, 48(2), 258–276.
427
https://doi.org/10.1016/j.jcs.2008.01.002
431 432 433 434
TE D
430
Systems, 50(1), 47–52. https://doi.org/10.1016/S0169-7439(99)00045-3 Folin, O., & Ciocalteu, V. (1927). On tyrosine and tryptophane determinations in proteins. Journal of biological chemistry, 73(2), 627–650.
Fromartz, S. (2015). The Gluten Enigma: Unraveling the gluten-free trend. Retrieved from
EP
429
Fearn, T. (2000). On orthogonal signal correction. Chemometrics and Intelligent Laboratory
http://www.eatingwell.com/article/285160/unraveling-the-gluten-free-trend/
AC C
428
Galvão, R. K. H., Araujo, M. C. U., José, G. E., Pontes, M. J. C., Silva, E. C., & Saldanha, T. C. B.
435
(2005). A method for calibration and validation subset partitioning. Talanta, 67(4), 736–740.
436
https://doi.org/10.1016/j.talanta.2005.03.025
437
Hallert, C., Grant, C., Grehn, S., Grännö, C., Hultén, S., Midhagen, G.,. . . Valdimarsson, T. (2002).
438
Evidence of poor vitamin status in coeliac patients on a gluten-free diet for 10 years.
439
Alimentary pharmacology & therapeutics, 16(7), 1333–1339.
20
ACCEPTED MANUSCRIPT
441 442 443 444
Halliwell, B., & Gutteridge, J. M. (2015). Free Radicals in Biology and Medicine (5th ed.). chap. 3. p. 77. Oxford: OUP Oxford. Huang, D., Ou, B., & Prior, R. L. (2005). The chemistry behind antioxidant capacity assays. Journal of agricultural and food chemistry, 53(6), 1841–1856. https://doi.org/10.1021/jf030723c Ikawa, M., Schaper, T. D., Dollard, C. A., & Sasner, J. J. (2003). Utilization of Folin-Ciocalteu
RI PT
440
445
phenol reagent for the detection of certain nitrogen compounds. Journal of agricultural and
446
food chemistry, 51(7), 1811–1815. https://doi.org/10.1021/jf021099r
Kim, H.-S., Patel, K. G., Orosz, E., Kothari, N., Demyen, M. F., Pyrsopoulos, N., & Ahlawat, S. K.
448
(2016). Time Trends in the Prevalence of Celiac Disease and Gluten-Free Diet in the US
449
Population: Results From the National Health and Nutrition Examination Surveys 2009-2014.
450
JAMA internal medicine, 176(11), 1716–1717.
451
https://doi.org/10.1001/jamainternmed.2016.5254
M AN U
Kris-Etherton, P. M., Hecker, K. D., Bonanome, A., Coval, S. M., Binkoski, A. E., Hilpert, K. F.,. . .
TE D
452
SC
447
453
Etherton, T. D. (2002). Bioactive compounds in foods: Their role in the prevention of
454
cardiovascular disease and cancer. The American journal of medicine, 113 Suppl 9B, 71S-88S. Li, F.-h., Yuan, Y., Yang, X.-l., Tao, S.-y., & Ming, J. (2013). Phenolic Profiles and Antioxidant
EP
455
Activity of Buckwheat (Fagopyrum esculentum Möench and Fagopyrum tartaricum L. Gaerth)
457
Hulls, Brans and Flours. Journal of Integrative Agriculture, 12(9), 1684–1693.
458
https://doi.org/10.1016/S2095-3119(13)60371-8
459
AC C
456
Lu, X., Wang, J., Al-Qadiri, H. M., Ross, C. F., Powers, J. R., Tang, J., & Rasco, B. A. (2011).
460
Determination of total phenolic content and antioxidant capacity of onion (Allium cepa) and
461
shallot (Allium oschaninii) using infrared spectroscopy. Food Chemistry, 129(2), 637–644.
462
https://doi.org/10.1016/j.foodchem.2011.04.105
21
ACCEPTED MANUSCRIPT 463
Lucas, A., Andueza, D., Rock, E., & Martin, B. (2008). Prediction of dry matter, fat, pH, vitamins,
464
minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses
465
by visible-near-infrared reflectance spectroscopy. Journal of agricultural and food chemistry,
466
56(16), 6801–6808. https://doi.org/10.1021/jf800615a Ludvigsson, J. F., Rubio-Tapia, A., van Dyke, C. T., Melton, L. J., Zinsmeister, A. R., Lahr, B. D., &
468
Murray, J. A. (2013). Increasing incidence of celiac disease in a North American population.
469
The American journal of gastroenterology, 108(5), 818–824.
470
https://doi.org/10.1038/ajg.2013.60
SC
Markets and Markets Web site. (2014). Gluten-free products market by type (bakery &
M AN U
471
RI PT
467
confectionery, snacks, breakfast cereals, baking mixes & flour and meat & poultry products),
473
sales channel (natural & conventional) & geography - global trends & forecasts to 2019.
474
Retrieved from http://www.marketsandmarkets.com/Market-Reports/gluten-free-products-
475
market-738.html
476
TE D
472
McKeown, N. M., Meigs, J. B., Liu, S., Wilson, P. W. F., & Jacques, P. F. (2002). Whole-grain intake is favorably associated with metabolic risk factors for type 2 diabetes and cardiovascular
478
disease in the Framingham Offspring Study. The American journal of clinical nutrition, 76(2),
479
390–398.
481 482 483 484 485
Mittler, R. (2002). Oxidative stress, antioxidants and stress tolerance. Trends in Plant Science,
AC C
480
EP
477
7(9), 405–410. https://doi.org/10.1016/S1360-1385(02)02312-9 Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2002). A user-friendly guide to multivariate calibration and classification. Chichester: NIR Publications. Pietta, P.-G. (2000). Flavonoids as Antioxidants. Journal of Natural Products, 63(7), 1035–1042. https://doi.org/10.1021/np9904509
22
ACCEPTED MANUSCRIPT 486
Pradeep, S. R., & Guha, M. (2011). Effect of processing methods on the nutraceutical and
487
antioxidant properties of little millet (Panicum sumatrense) extracts. Food Chemistry, 126(4),
488
1643–1647. https://doi.org/10.1016/j.foodchem.2010.12.047
489
Prior, R. L., Wu, X., & Schaich, K. (2005). Standardized methods for the determination of antioxidant capacity and phenolics in foods and dietary supplements. Journal of agricultural
491
and food chemistry, 53(10), 4290–4302. https://doi.org/10.1021/jf0502698
492
RI PT
490
Rajer-Kanduč, K., Zupan, J., & Majcen, N. (2003). Separation of data on the training and test set for modelling: a case study for modelling of five colour properties of white pigment.
494
Chemometrics and Intelligent Laboratory Systems, 65(2), 221–229.
M AN U
495
SC
493
Rao, G. R., Kanjilal, G., & Mohan, K. R. (1978). Extended application of Folin-Ciocalteu reagent in
496
the determination of drugs. The Analyst, 103(1230), 993.
497
https://doi.org/10.1039/AN9780300993
499 500
Scalbert, A., Johnson, I. T., & Saltmarsh, M. (2005). Polyphenols: antioxidants and beyond.
TE D
498
American Journal of clinical nutrition, 81(1), 215–217. Schmutzler, M., & Huck, C. W. (2016). Simultaneous detection of total antioxidant capacity and total soluble solids content by Fourier transform near-infrared (FT-NIR) spectroscopy: A quick
502
and sensitive method for on-site analyses of apples. Food Control, 66, 27–37.
503
https://doi.org/10.1016/j.foodcont.2016.01.026
AC C
EP
501
504
Singleton, V. L., Orthofer, R., & Lamuela-Raventós, R. M. Analysis of total phenols and other
505
oxidation substrates and antioxidants by means of folin-ciocalteu reagent, 299, 152–178.
506
https://doi.org/10.1016/S0076-6879(99)99017-1
507
Thompson, T. (2000). Folate, Iron, and Dietary Fiber Contents of the Gluten-free Diet. Journal of
508
the American Dietetic Association, 100(11), 1389–1396. https://doi.org/10.1016/S0002-
509
8223(00)00386-2
23
ACCEPTED MANUSCRIPT 510
Udachan, I., & Sahoo, A. K. (2017). Quality evaluation of gluten free protein rich broken rice
511
pasta. Journal of Food Measurement and Characterization, 11(3), 1378–1385.
512
https://doi.org/10.1007/s11694-017-9516-3
513
Wang, Y., Yu, L.-y., Fan, X.-h., & Qu, H.-b. (2009). An approach to predicting antioxidative activities of natural products based on near infrared spectroscopy. Spectroscopy and spectral
515
analysis, 9(29), 2401–2404. https://doi.org/10.3964/j.issn.1000-0593(2009)09-2401-04
516
Williams, P. (1987). Variables Affecting Near-Infrared Reflectance Spectroscopic Analysis. In P.
517
Williams & K. Norris (Eds.), Near Infrared Technology in the Agriculture and Food Industries
518
(Chap. 8, 143–167). St. Paul: American Association of Cereal Chemists.
521 522 523
SC
M AN U
520
Workman, J., & Weyer, L. (2008). Practical guide to interpretive near-infrared spectroscopy. Boca Raton: Taylor & Francis.
Workman, J., & Weyer, L. (2012). Practical guide and spectral atlas for interpretive near-infrared spectroscopy (2nd ed.). Boca Raton, FL: CRC Press.
TE D
519
RI PT
514
Zhang, C., Shen, Y., Chen, J., Xiao, P., & Bao, J. (2008). Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared
525
spectroscopy. Journal of agricultural and food chemistry, 56(18), 8268–8272.
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https://doi.org/10.1021/jf801830z
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Figure 1: Overview of the total antioxidative capacities of all samples. H refers to samples with husk, DH to samples without husk.
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Figure 2: Averaged spectrum of all intact samples after spectroscopic transformation to absorbance spectra.
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Figure 3: PLS-R model for grains established using the calibration set.
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Early Bird−DH Early Bird−H−1 Early Bird−H−2 Early Bird−H−3 Gelbhirse−H Gierczyckie−H−1 Gierczyckie−H−2 GL RH 16106−H Horizon−DH Horizon−H−1 Horizon−H−2 Horizon−H−3 Huntsman−DH Huntsman−H−1 Huntsman−H−2 Huntsman−H−3 ITA−4−DH ITA−4−H−1 ITA−4−H−2 ITA−5−H Jagna−H−1 Jagna−H−2 Kornberger−H−1 Kornberger−H−2 Quartett−H Silberhirse−H Sunrise−DH Sunrise−H−1 Sunrise−H−2 Sunrise−H−3 Tiroler Rispenhirse−H
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Bamby−DH Bamby−H−1 Bamby−H−2 Billy−H Cebelica−H Chinese Landrace−DH Chinese Landrace−H Devyatka−H Dikul−H ITA−1−DH ITA−1−H ITA−2−DH ITA−2−H ITA−3−DH ITA−3−H Kaerntner Hadn−H Koma−DH Koma−H−1 Koma−H−2 Kora−DH Kora−H−1 Kora−H−2 Koto−DH Koto−H−1 Koto−H−2 La Harpe−DH La Harpe−H−1 La Harpe−H−2 Lileja−DH Lileja−H−1 Lileja−H−2 Panda−DH Panda−H−1 Panda−H−2 Spacinska−H Temp−H VB Nojai−H VB Vokiai−DH VB Vokiai−H−1 VB Vokiai−H−2
Total antioxidant capacity / mgGAE/g
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ACCEPTED MANUSCRIPT Highlights: First investigation into different cultivars of millet, buckwheat and oat
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Total antioxidative capacity varies significantly between cultivars of one grain species
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NIR model development for antioxidants of different cultivars of three grain species
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Calculation of UIPAC conform multivariate LOD and LOQ for established PLS-R models
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S0956-7135(18)30382-7
DOI:
10.1016/j.foodcont.2018.07.045
Reference:
JFCO 6258
To appear in:
Food Control
Received Date: 2 May 2018 Revised Date:
5 July 2018
Accepted Date: 27 July 2018
Please cite this article as: Wiedemair V., Ramoner R. & Huck C.W., Investigations into the total antioxidative capacities of cultivars of gluten-free grains using near-infrared spectroscopy, Food Control (2018), doi: 10.1016/j.foodcont.2018.07.045. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Investigations into the total antioxidative capacities of
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cultivars of gluten-free grains using near-infrared
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spectroscopy
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Verena WIEDEMAIRa, Reinhold RAMONERb, Christian W. HUCK*a
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a
Institute of Analytical Chemistry and Radiochemistry, CCB – Center for Chemistry and Biomedicine, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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b
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Health University of Applied Sciences Tyrol, Innrain 98, 6020 Innsbruck, Austria
Mag. Verena Wiedemair, MA MSc
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E-Mail: [email protected] Tel.: +43 512 507 57372 Adress: Innrain 80/82, 6020 Innsbruck, Austria
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Priv.Doz. Reinhold Ramoner
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E-Mail: [email protected] Tel.: +43 50 8648 4779 Adress: Innrain 98, 6020 Innsbruck
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Univ.-Prof. Christian W. Huck (corresponding author)
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E-Mail: [email protected] Tel.: +43 512 507 57304 Adress: Innrain 80/82, 6020 Innsbruck, Austria
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*
Corresponding author
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Abstract
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26 Millet, buckwheat and oat are considered to be minor crops, hence chemical profiles for
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different cultivars are rare. The examination of a sum parameter, like the total antioxidative
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capacity (TAC) can thus be a first step to systematically assess the quality of different cultivars of
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mentioned gluten-free grains and thereby serve as an indicator for the selection of cultivars for
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food processing.
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TAC of 20 common buckwheat, 14 proso millet and six common oat cultivars was analysed using
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Folin-Ciocalteu assay and an optimized NIRS methodology. PLS regressions for milled and intact
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samples were established and yielded a R2(CV) of 0.892 and 0.929. The SEPs for milled and intact
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samples were approximately 1.7 and 1.6 mgGAE/g. Multivariate LOD and LOQ were also
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calculated. LODmax for intact and milled samples was 1.72 and 2.80 mgGAE/g.
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TAC varies considerably among cultivars of one species, emphasising the need for full chemical
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profiles. Values for LOD and LOQ show that established PLS-R models can be used to quantify
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TAC of buckwheat and oat cultivars. TAC of millet cultivars can be detected, however not
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quantified.
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Keywords: NIRS, total antioxidative capacity, gluten-free, buckwheat, millet, oat, Folin-Ciocalteu
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1. Introduction
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In the past, only patients suffering from coeliac disease used to be on a gluten-free diet, as it is
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the only cure for their illness (Alaedini & Green, 2005; Ludvigsson et al., 2013). Coeliac disease is
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a chronic autoimmune disorder, which is triggered in genetically sensitive individuals by
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exposure to gluten. The result are small intestinal inflammations and a severe ablation of
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intestinal villi (Alaedini & Green, 2005; Ludvigsson et al., 2013).
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Nowadays however, the gluten-free market benefits from the notion that going gluten-free is
55
healthier (Kim et al., 2016), which is why many people who do not suffer from coeliac disease
56
choose to cut out or reduce gluten in their diets. A study by the National Health and Nutrition
57
Examination found that in 2014 more than three times more people without coeliac disease
58
were avoiding gluten in their diets than in 2009 (Kim et al., 2016). Another study indicates that
59
around 22% of adults in North America are trying to avoid gluten in their diet, thus creating a $
60
8,8 billion market (Fromartz, 2015). Even more so, the market of gluten-free products is
61
expected to grow 10.2% over each year between 2015–2019 (Markets and Markets Web site,
62
2014). This market growth resulted in an increase in availability of gluten-free products in stores,
63
however many customers following this health trend are unaware of its downsides. Studies on
64
patients with coeliac disease, who follow a strict diet, have shown that many of them suffer from
65
a poor vitamin and mineral status (Hallert et al., 2002; Thompson, 2000; Udachan & Sahoo,
66
2017).
67
In order to improve the quality of gluten-free food, new cultivars with more favourable
68
properties must not only be bred, but also be analysed thoroughly and consequently be
69
implemented in foodstuff production. However, exhaustive chemical profiles of different
70
cultivars of gluten-free grains are still missing, leaving companies often clueless about the quality
71
of their primary products. The establishment of a complete nutritional profile is a tedious task.
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the quality of different cultivars. It is the hypothesis of this study that cultivars of one grain
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species differ significantly from one another and thus it is important to review various cultivars,
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in order to make informed decisions, when buying grains for food processing or breeding.
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Antioxidants are substances, which inhibit oxidation, reactions promoted by oxygen or reactive
77
oxygen species (ROS) (Halliwell & Gutteridge, 2015), thus reducing the risk of several diseases,
78
including chronic inflammation, cardiovascular diseases, type 2 diabetes and cancer (Adom,
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Sorrells, & Liu, 2005; Kris-Etherton et al., 2002; McKeown, Meigs, Liu, Wilson, & Jacques, 2002;
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Scalbert, Johnson, & Saltmarsh, 2005). But natural ROS also have important function in the body
81
as second messengers, meaning that a delicate equilibrium of radical production and scavenging
82
is in existence (Clara et al., 2016; Mittler, 2002). Typical antioxidant substances are polyphenols,
83
vitamins and phenolic acids (Choi, Jeong, & Lee, 2007; Pietta, 2000; Scalbert et al., 2005).
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The total antioxidative capacity can be estimated by various reactions and methods, which can
85
be divided into two groups according to their reaction mechanism: hydrogen atom transfer
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(HAT) or single electron transfer (SET) (Prior, Wu, & Schaich, 2005). One widely used technique
87
for the determination of total antioxidative capacity is the Folin-Ciocalteu method, which
88
exhibits a SET mechanism. It was developed by Folin and Ciocalteu (Folin & Ciocalteu, 1927) and
89
later improved by Singleton et al. (Singleton, Orthofer, & Lamuela-Raventós) to monitor total
90
phenolic content. Later on studies showed that this assay is sensitive towards a variety of
91
molecules besides phenols, like vitamins, thiols and nitrogen containing moleclues (Amorati &
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Valgimigli, 2015; Everette et al., 2010; Ikawa, Schaper, Dollard, & Sasner, 2003; Prior et al., 2005;
93
Rao, Kanjilal, & Mohan, 1978). This is why this assay is suitable for the analysis of the total
94
antioxidant capacity. Nevertheless, this method is still often used to determine only phenolic
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content, as the extraction reagent used is often only sensitive towards phenols. This is not the
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case in this study, which is why the Folin-Ciocalteu assay was chosen to determine total
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2005; Prior et al., 2005).
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Folin-Ciocalteu’s method is a wet chemical analysis, thus sample preparation as well as the
100
measurements themselves are quite time-consuming. Samples have first to be extracted and
101
normalized to a certain volume, before they can be exposed to Folin-Ciocalteu’s reagent and
102
lastly be measured with a UV/VIS-photometer at 750 nm. This is why in recent years, many
103
studies using near-infrared spectroscopy to determine the antioxidative capacity have been
104
published (Clara et al., 2016; Lu et al., 2011; Lucas, Andueza, Rock, & Martin, 2008; Schmutzler &
105
Huck, 2016; Wang, Yu, Fan, & Qu, 2009; Zhang, Shen, Chen, Xiao, & Bao, 2008). Near-infrared
106
spectroscopy provides a non-invasive and fast tool and is due to its broad bands often used for
107
the determination of sum parameters. Furthermore, overtones and combination vibrations
108
appearing in NIR spectra hold additional information (Blanco & Villarroya, 2002), which can be
109
interpreted using multivariate data analysis (MVA) – a known powerful tool for establishing
110
calibration and validation models (Næs, Isaksson, Fearn, & Davies, 2002). However, near-infrared
111
spectroscopy also has its limits which are usually estimated using well-established quality
112
parameters like the standard error of prediction. This study uses a multivariate, yet UIPAC
113
conform approach on LOD and LOQ prediction, in order to more accurately assess the
114
possibilities and limits for the use of near-infrared spectroscopy for grain analysis. The
115
application of near-infrared spectroscopy to estimate the antioxidative capacity of different
116
cultivars of gluten-free buckwheat, millet and oat grains is new, as differences within one species
117
might be too small to be recognized by NIRS.
118
Knowing which cultivar holds more favourable properties is very important to breeders, as well
119
as companies producing gluten-free foods. Thereby the study also investigates differences of
120
hulled and dehulled grains of one cultivars, as grains need to be dehulled for food production.
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Additionally, the partial least square regression (PLS-R) model established using NIR data may be
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used to estimate the antioxidative capacity for other cultivars, not surveyed in this study as well
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and has thus the potential to replace the time consuming and invasive wet-chemical Folin-
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Ciocalteu measurements.
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2. Methods and Materials
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2.1. Samples management
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20 buckwheat, 14 millet and six oat cultivars were provided by Research Center Laimburg
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(Bolzano, Italy) and Dr. Schär AG / SPA (Burgstall, Italy). All cultivars were provided as grains with
131
husk, but for some also dehulled grains were received. The supplementary material gives a
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detailed overview over all cultivars used in this study and further shows in what state and from
133
what year they were. In total, 77 samples was analysed:
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Twelve buckwheat samples with husk, harvested in 2016
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Twelve dehulled buckwheat samples, harvested in 2016
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Sixteen buckwheat samples with husk, harvested in 2016
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Six oat samples with husk, harvested in 2016
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Five dehulled millet samples, harvested in 2015
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Eleven millet samples with husk, harvested in 2016
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grid in a Retsch mill ZM200 (Haan, Germany). Both milled and intact samples were analysed
144
using NIR spectroscopy. 1 g of milled samples were further used for acidified methanol
145
extraction and consequently for Folin-Ciocalteu analyses.
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2.2 Extraction
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In order to measure the total antioxidative capacity, samples had to be extracted first. The
149
extraction reagent consisted of 95% methanol (≥99.9%) and 5% hydrochloric acid (35%)
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(Chethan & Malleshi, 2007; Pradeep & Guha, 2011), and was always prepared on the same day
151
the extraction was performed. 1 g of milled sample was weighed into a 50 mL Falcon tube and
152
then 10 mL of extraction reagent was added. The tube was put into an ultrasonic bath at 60 °C
153
for 20 min. Next, the sample was centrifuged at 3500 rpm (25473 rcf) for 10 min. Preliminary
154
extraction studies showed that usually six repetitions are needed to extract all compound
155
contributing to the antioxidative capacity. Only for buckwheat samples with husk a total of nine
156
extraction cycles was needed. Lastly, the volume of the extracts was adjusted to 60 and 90 mL
157
with acidified methanol, respectively. All samples were stored at -80 °C overnight then Folin-
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Ciocalteu measurements were performed.
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2.3. Folin-Ciocalteu measurements
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The previously prepared extracts were thawed and then 1.5 mL distilled water, 100 µL extract,
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100 µL Folin-Coicalteu’s phenol reagent (2N, Sigma Aldrich) and 1.3 mL sodium carbonate (50
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mg/mL; made of sodium carbonate 99.0%, Sigma Aldrich) were mixed together in PMMA
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(Polymethyl(methacrylate)) cuvettes (d = 1 cm) (Brand, Wertheim, Germany). Next, the cuvettes
165
were put in an oven at 60°C for 30 minutes. After that, the samples cooled down to room 8
ACCEPTED MANUSCRIPT temperature for 35 minutes and were measured at 750 nm with an UV/VIS spectrometer
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(Genova Plus, Jenway, Stone, Staffordshire, UK). Three replicates were prepared for each
168
extract. The standard calibration curve was established using gallic acid in concentrations
169
between 0 and 250 mg/L gallic acid in equidistant 25 mg/L steps. Each concentration was
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prepared as a triplicate and the calibration curve yielded a R2 of 0.995. The standard error of the
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regression was 0.02 mg/L.
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2.4. Near-infrared spectroscopy (NIRS)
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All samples were measured with NIRS in intact and milled form at 21°C air-conditioner controlled
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room temperature. The device used was a NIRFlex N-500 (Büchi, Flawil, Switzerland) with the
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solids add-on. Samples were filled in a rotating cylindrical quartz cuvette (h = 25 mm, inner
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diameter = 31.6 mm) and measured six times with 64 scans in the wavelength range between
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10000–4000 cm-1 (1000–2500 nm) in diffuse reflection mode. The spectral resolution was 8 cm-1,
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whereas the digital resolution was 4 cm-1.
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All NIRS spectra recorded were exported and then analysed using the multivariate data analysis
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software The Unscrambler X 10.4 (Camo, Oslo, Norway). First, spectra were transformed into -
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logR spectra, then all of them were averaged by a factor of six, in order to get one spectrum per
185
sample. The descriptive statistics tool was applied, in order to determine necessary spectral pre-
186
treatments. Consequently, Savitzky Golay 2nd derivation was applied to remove additive baseline
187
offset, enhance small peaks and differences and smooth spectra. The number of smoothing
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points was adjusted, according to the state of the sample. For milled samples the number of
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smoothing points was chosen to be 17, and when intact grains were measured 13 smoothing
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ACCEPTED MANUSCRIPT points were needed. Then, standard normal variate (SNV) was employed in the range between
191
8948–4032 cm-1 (1118–2480 nm) to reduce multiplicative scatter effects. Data outside the
192
selected wavenumber range was noise and thus not included in further spectral pre-treatments
193
or the establishment of PLS-R models. Spectra were then split into a calibration and a test set by
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Kennard-Stone sample selection (Galvão et al., 2005). This algorithm maximizes the Euclidean
195
distance between the instrumental response vectors of the selected samples. This means that
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the algorithm first generates a group consisting of the two most distant objects in the sample
197
set. Then, the object with the largest minimal distance to the group is added and so on (Galvão
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et al., 2005; Rajer-Kanduč, Zupan, & Majcen, 2003). Last, orthogonal signal correction (OSC)
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according to Fearn (Fearn, 2000) was used on the calibration set in the same wavenumber range
200
to filter out variables, which are orthogonal to the y data, i.e. the reference data. The established
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OSC model was then used on the test set.
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The calibration set consisted of two thirds of the samples (ncal=51). 35 of these samples are
203
unique hulled or dehulled cultivars. The remaining 16 spectra stem from eight unique cultivars.
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and was used to build a PLS-R model, which was first validated using random leave one out cross
205
validation (LOOCV) with 20 segments and then using the test set, which consisted of the
206
remaining third of the samples (nval=26). The latter is called test set validation (TV). For the
207
establishment of the PLS-R model for intact and milled grains two and four factors were needed,
208
respectively.
209
Lastly, the limit of detection (LOD) was determined in a multivariate way, as proposed by
210
Allegrini and Olivieri (Allegrini & Olivieri, 2014). The two authors explain that since the matrix of
211
each sample is a little different, a univariate LOD is unfit to cope with a multivariate problem.
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Hence, they introduced an LOD range, with a LODmin and an LODmax, which are calculated
213
according to equation (1) and (2)
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(1)
= 3.3
+ℎ
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(2)
= 3.3
+ℎ
/
+ℎ
/
+ℎ
SEN refers to the sensitivity, which in a PLS-R is given by the inverse length of the regression
217
coefficient. var(x) is the variance of the x data, hence the variance in the spectral data, whereas
218
var(ycal) refers to the variance in the calibration data. h refers to the sample leverage and can be
219
calculated according to equations (3) and (4):
220
(3) ℎ
!" &
= ∑) #$%!&
(4) ℎ
= max ℎ
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Values below LODmin indicate that no antioxidative capacity can be detected. If a value above
223
LODmax is reached, the sample is certain to hold antioxidative capacity. Between LODmin and
224
LODmax the existence of any antioxidative capacity is not certain, as different values may be due
225
to matrix effects. Additionally, the limit of quantification (LOQ) can also be calculated in a
226
multivariate way, by tripling LODmin and LODmax. It is not possible to quantify antioxidative
227
capacity, if it is below LOQmin. A value above LOQmax indicates that quantification is possible.
228
Values between LOQmin and LOQmax cannot be quantified with certainty. If the matrix is very
229
homogenous throughout a sample set, then the values LODmin and LOQmin will be close to their
230
respective maxima.
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3. Results and Discussion
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3.1 Folin-Ciocalteu measurements
235
As mentioned before, gallic acid was used to establish a calibration curve for all Folin-Ciocalteu
236
measurements. Consequently, the results are given in mg gallic acid equivalents per g sample 11
ACCEPTED MANUSCRIPT (mgGAE/g). The samples ranged from 1.4 to 18.8 mgGAE/g, which corresponds to 23 to 212
238
mg/L. The sample data collected had a mean of 7.19 mgGAE/g, a median of 6.18 mgGAE/g and a
239
standard deviation of 4.86 mgGAE/g. On average oat samples had an antioxidative capacity of
240
5.76 mgGAE/g (SD=1.29) and buckwheat and millet samples with husk an antioxidative capacity
241
of 12.90 mgGAE/g (SD=2.49) and 2.74 mgGAE/g (SD=0.36), respectively. Dehulled buckwheat
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samples had an antioxidative capacity of 6.43 mgGAE/g (SD=0.55) and dehulled millet samples a
243
capacity of 1.45 mgGAE/g (SD=0.04). Figure 1 shows more detailed results of the Folin-Ciocalteu
244
measurements.
245
Samples with husk generally had a higher total antioxidative capacity than their dehulled
246
counterparts. Buckwheat samples with husk hold 2.3 times more antioxidants on average than
247
their complement. Millet samples with husk have an average 1.8 times higher total antioxidative
248
capacity than their dehulled counterpart. This poses a challenge, as most of the husk is removed
249
for food production. Buckwheat has the highest antioxidative capacity and the values for
250
dehulled buckwheat are still in the same range as oat samples with husk. Millet samples on the
251
other hand have a very low total antioxidative capacity. Additionally, millet cultivars had a very
252
homogenous distribution of antioxidative capacity throughout all samples. Buckwheat cultivars
253
on the other hand showed greater variance, even when just looking at samples with husk or
254
dehulled samples. Oat cultivars have quite similar total antioxidative capacities, however cv.
255
Irina and cv. ITA-7 seem to underperform in comparison with the other oat samples.
256
To compare the results with similar studies of antioxidative capacities of grains is difficult, as
257
most previously performed studies do either not state the exact cultivar studied or do not use
258
the same species. Nevertheless, a comparison of the data at hand with similar literature showed,
259
that the results for millet cultivars are in good agreement with previous, similar studies
260
examining other millet species in respect to their antioxidative capacity (Dykes & Rooney, 2006;
261
Pradeep & Guha, 2011). Little millet (Panicum sumatrense) has a reported antioxidative capacity
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mgGAE/g of the investigated proso millets with husk. The cultivar Tiroler Rispenhirse has the
264
highest TAC of the examined proso millets with a value of 3.68 mgGAE/g. Another study showed
265
that different species of millet vary greatly in regard to their total antioxidative capacity with
266
proso millet only holding 0.5–1.0 mgCatechin/g and finger millet containing 5.5–5.9
267
mgCatechin/g (Dykes & Rooney, 2006).
268
The values for buckwheat samples are also in good accordance with previously performed
269
similar studies (Li, Yuan, Yang, Tao, & Ming, 2013). The reported antioxidant capacities for
270
buckwheat flours ranged from 8.05–15.11 mgGAE/g (Li et al., 2013). The buckwheat cultivars
271
with husk analysed in this study ranged from 9.08–18.78 mgGAE/g.
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Looking at oat studies it becomes clear that these grains have rarely been investigated towards
273
their total antioxidant capacity. Furthermore, it seems that the oat cultivars used in this study
274
carry more antioxidants, than the cultivars used in other studies (Fardet, Rock, & Rémésy, 2008).
275
This may be due to investigating other oat species, other cultivars or because of this study using
276
oat samples with husk.
277
In general, it becomes clear that the intraspecific variance of total antioxidant capacity of
278
buckwheat and oat is high, thus establishing chemical profiles of each cultivar is important. The
279
selection of oat and buckwheat varieties with favourable properties is also important for
280
breeders, as high quality grains will lead to higher quality food products. Millet samples have less
281
variance, meaning that the selection of favourable grains is even more important. By effectively
282
choosing millet cultivars the quality can be improved and thus support individuals living on a
283
gluten-free diet.
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286
Spectra were recorded between with the NIRFlex N-500 (Büchi, Flawil, Switzerland) between
287
10000–4000 cm-1. The samples rotated in the cylindrical quartz cuvettes during measurements.
288
An average spectrum of all intact samples is shown in figure 2. Table 1 lists important maxima
289
and their respective vibration according to Workman and Weyer (Workman & Weyer, 2008). It is
290
evident that the upper part of the NIR spectrum is determined by overtones, whereas
291
combination vibrations mainly contribute to the lower part of the spectrum.
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Table 1: Important vibrations in the NIR spectrum of millet, buckwheat and oat grains (Workman & Weyer, 2012).
Vibration C-H str. 2 overtone O-H & N-H str. 1st overtone C-H str. 1st overtone C-H str. 1st overtone O-H str. & O-H def. N-H str. C-H str. & C-H def. C-H str. & C-H def. comb.
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Wavenumber / cm-1
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8312 6808 5788 5664 5184 4748 4380 4280
Wavelength / nm 1203 1469 1728 1766 1929 2106 2283 2336
3.3. Prediction of antioxidative capacity using NIRS
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The data collected during Folin-Ciocalteu measurements was used as reference data, in order to
297
establish a prediction model for the total antioxidative capacity using NIRS. Necessary spectral
298
pre-treatments were identified using the descriptive statistics tool implemented in the software
299
The Unscrambler X 10.4 (see section 2.5). Two and four PLS-R factors were used for intact and
300
milled samples, respectively. Table 2 gives an overview over the statistical quality parameters
301
regression coefficient (R2), bias, standard error of cross validation (SECV), and standard error of
302
prediction (SEP) (Williams, 1987) of the established PLS-R models. Figure 3 shows the PLS-R
303
model of the calibration set for grains. The SEP was calculated using the 26 test set samples.
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Table 2: Overview over statistical parameters of the established PLS-R models. CV refers to cross validation, whereas depicts test set validation.
State of the grains Intact Milled
SECV / mgGAE/g 1.1794 1.6156
R2 (CV) 0.9288 0.8918
Bias (CV) / mgGAE/g -0.0004 -0.0206
SEP / mgGAE/g 1.6381 1.6925
R2 (TV) 0.8911 0.8831
Bias (TV) / mgGAE/g -0.4534 0.0592
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308
of 0.89 for cross validation and 0.88 for test set validation, which is satisfactory for NIRS analyses
309
and indicates good correlation between the spectra and the reference values. The regression
310
coefficient for the cross validated model for intact samples reaches a value of 0.93 and for the
311
test set validated model a value of 0.89. This is even higher than for the respective results for
312
milled samples. Consequently, the intact samples also indicate a good correlation between the
313
spectra and the reference data.
314
For cross validation, the bias, which indicates the presence of systematic error, is close to zero,
315
as it should be. Looking at the test set validation, the bias value for intact and milled samples is
316
much higher. This has to be expected as samples used for validation are not part of the
317
calibration model.
318
SECV and SEP values are acceptable, considering that oat and buckwheat samples have an
319
antioxidative capacity between 5.8 and 18.8 mgGAE/g. Only millet samples with an antioxidative
320
capacity between 1.4 and 3.7 mgGAE/g are in the range of SECV and SEP values. When looking at
321
the LODmin and LODmax values (Table 3) for each model, it becomes clear that dehulled millet
322
samples can just barely be detected with certainty, as most of them have a value just above the
323
LODmax. The antioxidative capacity of millet samples with husk are well above the LODmax,
324
however quite similar to the LOQmin value, meaning that although the presence of antioxidative
325
capacity is certain, quantification is not yet possible. All of this suggest, that only the total
326
antioxidative capacities of buckwheat and oat can be quantified using NIRS. Proso millet cultivars
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ACCEPTED MANUSCRIPT seem to be too poor in antioxidants to quantify them. To further lower the LOD, solid phase
328
extraction (SPE) or a reflector, which enhances the signal has to be applied. Interestingly,
329
LODmin/LODmax and LOQmin/LOQmax are lower for intact samples. This seems to be
330
counterintuitive, as milled samples are usually more homogeneous. However, the milling
331
process might have an influence on the matrix, thus effecting some samples more, some less.
332
Furthermore, even intact grains are quite small, meaning that near-infrared radiation can
333
penetrate quite far into the sample and thus give accurate feedback about chemical properties.
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Table 3: LOD and LOQ ranges for the established PLS-R models. All values are given in mgGAE/g.
State of the seeds Intact Milled
LODmin 0.8666 1.2381
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LODmax
LOQmin
LOQmax
1.7160 2.7969
2.5997 3.7144
5.1480 8.3906
Another way to evaluate SEP values is the calculation of the ratio of performance to deviation
338
(RPD). This value was first proposed by Williams (Williams, 1987) and indicates how accurate a
339
model is and for what purposes it can be used. The RPD is calculated according to equation (5): 23
(5) 01 = 2564
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The RPD value for intact samples is 2.7 and for milled samples 2.8. According to the quality
342
attribution, a RPD below 3 indicates that the model is adequate for screening analyses (Williams,
343
1987). Intact and milled samples show very similar values for SEP and RPD, indicating that near-
344
infrared radiation is able to penetrate even intact samples well. This shows that no sample
345
preparation is necessary to establish a calibration model for total antioxidative capacity for
346
gluten-free grains. Thus, the implementation of this model is very practical.
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348
only in buckwheat and oat samples they can be accurately quantified. Furthermore, sample
349
milling is not necessary, as it has little effect on the quality of the PLS-R model. By measuring
350
even more samples, it might be possible to further lower the SEP, thus improving the RPD and
351
the robustness of the model.
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4. Conclusion
355
This study examines 40 cultivars of buckwheat, millet and oat in different forms towards their
356
antioxidative capacity using Folin-Ciocalteu measurements and NIRS, in order to get first insights
357
into the chemical profile of different cultivars of certain species of gluten-free grains. A total of
358
77 samples were investigated. Folin-Coicalteu measurements proved the importance of
359
investigating cultivars of one species separately, as the total antioxidative capacity differs
360
significantly. The differences are especially prominent among buckwheat samples, whereas
361
millet samples show fewer variation.
362
Furthermore, it was possible to establish PLS-R models using Folin-Ciocalteu measurements as
363
reference data with a R2 of about 0.9, which show good correlation between spectra and
364
reference data. By calculating LOD and LOQ in a multivariate way, it was demonstrated that the
365
antioxidative capacity of most millet cultivars can be detected with certainty, however it is too
366
low to be quantified. The antioxidative capacity of buckwheat and oat cultivars on the other
367
hand can be quantified in milled and intact grains using the established PLS-R models.
368
Calculation of RPD showed that the state of the grains – milled or intact – has only little influence
369
on the established models. Thus an approach with no sample preparation is suggested for future
370
studies.
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372
quality of buckwheat, oat and millet grains. This study showed that cultivars of one species differ
373
in terms of their antioxidative capacites, meaning that the selection of a specific grain has great
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influence on gluten-free products. But, examining one sum parameter – the total antioxidative
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capacity – makes it possible to only get an indication for which cultivar to use in breeding, as well
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as food production. Naturally, also agricultural properties have to be taken into account when
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breeding, however the balance between favourable agricultural and nutritional properties will
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become more and more important as gluten-free market grows.
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5. Acknowledgement
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The authors want to thank the European Union, the European Regional Development Fund and
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the cross-border programme Interreg V-A Italy-Austria 2014–2020 (project “RE-Cereal”, ITAT
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1005, P-7250-013-042) for financial support.
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For sample management we want to thank Dr. Schär AG / SPA (Burgstall, Italy) and Research
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Center Laimburg (Bolzano, Italy).
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6. Conflicts of Interest
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The authors declare no conflicts of interest.
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References
394
Adom, K. K., Sorrells, M. E., & Liu, R. H. (2005). Phytochemicals and antioxidant activity of milled
395
fractions of different wheat varieties. Journal of agricultural and food chemistry, 53(6), 2297–
396
2306. https://doi.org/10.1021/jf048456d
398 399
Alaedini, A., & Green, P. H. R. (2005). Narrative review: Celiac disease: understanding a complex
RI PT
397
autoimmune disorder. Annals of internal medicine, 142(4), 289–298.
Allegrini, F., & Olivieri, A. C. (2014). IUPAC-consistent approach to the limit of detection in partial least-squares calibration. Analytical chemistry, 86(15), 7858–7866.
401
https://doi.org/10.1021/ac501786u
M AN U
402
SC
400
Amorati, R., & Valgimigli, L. (2015). Advantages and limitations of common testing methods for
403
antioxidants. Free radical research, 49(5), 633–649.
404
https://doi.org/10.3109/10715762.2014.996146
Blanco, M., & Villarroya, I. (2002). NIR spectroscopy: A rapid-response analytical tool. TrAC
TE D
405 406
Trends in Analytical Chemistry, 21(4), 240–250. https://doi.org/10.1016/S0165-
407
9936(02)00404-1
Chethan, S., & Malleshi, N. (2007). Finger millet polyphenols: Optimization of extraction and the
EP
408
effect of pH on their stability. Food Chemistry, 105(2), 862–870.
410
https://doi.org/10.1016/j.foodchem.2007.02.012
411
AC C
409
Choi, Y., Jeong, H.-S., & Lee, J. (2007). Antioxidant activity of methanolic extracts from some
412
grains consumed in Korea. Food Chemistry, 103(1), 130–138.
413
https://doi.org/10.1016/j.foodchem.2006.08.004
414
Clara, D., Pezzei, C. K., Schönbichler, S. A., Popp, M., Krolitzek, J., Bonn, G. K., & Huck, C. W.
415
(2016). Comparison of near-infrared diffuse reflectance (NIR) and attenuated-total-
416
reflectance mid-infrared (ATR-IR) spectroscopic determination of the antioxidant capacity of
19
ACCEPTED MANUSCRIPT 417
Sambuci flos with classic wet chemical methods (assays). Anal. Methods, 8(1), 97–104.
418
https://doi.org/10.1039/c5ay01314c
420 421
Dykes, L., & Rooney, L. W. (2006). Sorghum and millet phenols and antioxidants. Journal of Cereal Science, 44(3), 236–251. https://doi.org/10.1016/j.jcs.2006.06.007 Everette, J. D., Bryant, Q. M., Green, A. M., Abbey, Y. A., Wangila, G. W., & Walker, R. B. (2010).
RI PT
419
Thorough study of reactivity of various compound classes toward the Folin-Ciocalteu reagent.
423
Journal of agricultural and food chemistry, 58(14), 8139–8144.
424
https://doi.org/10.1021/jf1005935
Fardet, A., Rock, E., & Rémésy, C. (2008). Is the in vitro antioxidant potential of whole-grain
M AN U
425
SC
422
426
cereals and cereal products well reflected in vivo? Journal of Cereal Science, 48(2), 258–276.
427
https://doi.org/10.1016/j.jcs.2008.01.002
431 432 433 434
TE D
430
Systems, 50(1), 47–52. https://doi.org/10.1016/S0169-7439(99)00045-3 Folin, O., & Ciocalteu, V. (1927). On tyrosine and tryptophane determinations in proteins. Journal of biological chemistry, 73(2), 627–650.
Fromartz, S. (2015). The Gluten Enigma: Unraveling the gluten-free trend. Retrieved from
EP
429
Fearn, T. (2000). On orthogonal signal correction. Chemometrics and Intelligent Laboratory
http://www.eatingwell.com/article/285160/unraveling-the-gluten-free-trend/
AC C
428
Galvão, R. K. H., Araujo, M. C. U., José, G. E., Pontes, M. J. C., Silva, E. C., & Saldanha, T. C. B.
435
(2005). A method for calibration and validation subset partitioning. Talanta, 67(4), 736–740.
436
https://doi.org/10.1016/j.talanta.2005.03.025
437
Hallert, C., Grant, C., Grehn, S., Grännö, C., Hultén, S., Midhagen, G.,. . . Valdimarsson, T. (2002).
438
Evidence of poor vitamin status in coeliac patients on a gluten-free diet for 10 years.
439
Alimentary pharmacology & therapeutics, 16(7), 1333–1339.
20
ACCEPTED MANUSCRIPT
441 442 443 444
Halliwell, B., & Gutteridge, J. M. (2015). Free Radicals in Biology and Medicine (5th ed.). chap. 3. p. 77. Oxford: OUP Oxford. Huang, D., Ou, B., & Prior, R. L. (2005). The chemistry behind antioxidant capacity assays. Journal of agricultural and food chemistry, 53(6), 1841–1856. https://doi.org/10.1021/jf030723c Ikawa, M., Schaper, T. D., Dollard, C. A., & Sasner, J. J. (2003). Utilization of Folin-Ciocalteu
RI PT
440
445
phenol reagent for the detection of certain nitrogen compounds. Journal of agricultural and
446
food chemistry, 51(7), 1811–1815. https://doi.org/10.1021/jf021099r
Kim, H.-S., Patel, K. G., Orosz, E., Kothari, N., Demyen, M. F., Pyrsopoulos, N., & Ahlawat, S. K.
448
(2016). Time Trends in the Prevalence of Celiac Disease and Gluten-Free Diet in the US
449
Population: Results From the National Health and Nutrition Examination Surveys 2009-2014.
450
JAMA internal medicine, 176(11), 1716–1717.
451
https://doi.org/10.1001/jamainternmed.2016.5254
M AN U
Kris-Etherton, P. M., Hecker, K. D., Bonanome, A., Coval, S. M., Binkoski, A. E., Hilpert, K. F.,. . .
TE D
452
SC
447
453
Etherton, T. D. (2002). Bioactive compounds in foods: Their role in the prevention of
454
cardiovascular disease and cancer. The American journal of medicine, 113 Suppl 9B, 71S-88S. Li, F.-h., Yuan, Y., Yang, X.-l., Tao, S.-y., & Ming, J. (2013). Phenolic Profiles and Antioxidant
EP
455
Activity of Buckwheat (Fagopyrum esculentum Möench and Fagopyrum tartaricum L. Gaerth)
457
Hulls, Brans and Flours. Journal of Integrative Agriculture, 12(9), 1684–1693.
458
https://doi.org/10.1016/S2095-3119(13)60371-8
459
AC C
456
Lu, X., Wang, J., Al-Qadiri, H. M., Ross, C. F., Powers, J. R., Tang, J., & Rasco, B. A. (2011).
460
Determination of total phenolic content and antioxidant capacity of onion (Allium cepa) and
461
shallot (Allium oschaninii) using infrared spectroscopy. Food Chemistry, 129(2), 637–644.
462
https://doi.org/10.1016/j.foodchem.2011.04.105
21
ACCEPTED MANUSCRIPT 463
Lucas, A., Andueza, D., Rock, E., & Martin, B. (2008). Prediction of dry matter, fat, pH, vitamins,
464
minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses
465
by visible-near-infrared reflectance spectroscopy. Journal of agricultural and food chemistry,
466
56(16), 6801–6808. https://doi.org/10.1021/jf800615a Ludvigsson, J. F., Rubio-Tapia, A., van Dyke, C. T., Melton, L. J., Zinsmeister, A. R., Lahr, B. D., &
468
Murray, J. A. (2013). Increasing incidence of celiac disease in a North American population.
469
The American journal of gastroenterology, 108(5), 818–824.
470
https://doi.org/10.1038/ajg.2013.60
SC
Markets and Markets Web site. (2014). Gluten-free products market by type (bakery &
M AN U
471
RI PT
467
confectionery, snacks, breakfast cereals, baking mixes & flour and meat & poultry products),
473
sales channel (natural & conventional) & geography - global trends & forecasts to 2019.
474
Retrieved from http://www.marketsandmarkets.com/Market-Reports/gluten-free-products-
475
market-738.html
476
TE D
472
McKeown, N. M., Meigs, J. B., Liu, S., Wilson, P. W. F., & Jacques, P. F. (2002). Whole-grain intake is favorably associated with metabolic risk factors for type 2 diabetes and cardiovascular
478
disease in the Framingham Offspring Study. The American journal of clinical nutrition, 76(2),
479
390–398.
481 482 483 484 485
Mittler, R. (2002). Oxidative stress, antioxidants and stress tolerance. Trends in Plant Science,
AC C
480
EP
477
7(9), 405–410. https://doi.org/10.1016/S1360-1385(02)02312-9 Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2002). A user-friendly guide to multivariate calibration and classification. Chichester: NIR Publications. Pietta, P.-G. (2000). Flavonoids as Antioxidants. Journal of Natural Products, 63(7), 1035–1042. https://doi.org/10.1021/np9904509
22
ACCEPTED MANUSCRIPT 486
Pradeep, S. R., & Guha, M. (2011). Effect of processing methods on the nutraceutical and
487
antioxidant properties of little millet (Panicum sumatrense) extracts. Food Chemistry, 126(4),
488
1643–1647. https://doi.org/10.1016/j.foodchem.2010.12.047
489
Prior, R. L., Wu, X., & Schaich, K. (2005). Standardized methods for the determination of antioxidant capacity and phenolics in foods and dietary supplements. Journal of agricultural
491
and food chemistry, 53(10), 4290–4302. https://doi.org/10.1021/jf0502698
492
RI PT
490
Rajer-Kanduč, K., Zupan, J., & Majcen, N. (2003). Separation of data on the training and test set for modelling: a case study for modelling of five colour properties of white pigment.
494
Chemometrics and Intelligent Laboratory Systems, 65(2), 221–229.
M AN U
495
SC
493
Rao, G. R., Kanjilal, G., & Mohan, K. R. (1978). Extended application of Folin-Ciocalteu reagent in
496
the determination of drugs. The Analyst, 103(1230), 993.
497
https://doi.org/10.1039/AN9780300993
499 500
Scalbert, A., Johnson, I. T., & Saltmarsh, M. (2005). Polyphenols: antioxidants and beyond.
TE D
498
American Journal of clinical nutrition, 81(1), 215–217. Schmutzler, M., & Huck, C. W. (2016). Simultaneous detection of total antioxidant capacity and total soluble solids content by Fourier transform near-infrared (FT-NIR) spectroscopy: A quick
502
and sensitive method for on-site analyses of apples. Food Control, 66, 27–37.
503
https://doi.org/10.1016/j.foodcont.2016.01.026
AC C
EP
501
504
Singleton, V. L., Orthofer, R., & Lamuela-Raventós, R. M. Analysis of total phenols and other
505
oxidation substrates and antioxidants by means of folin-ciocalteu reagent, 299, 152–178.
506
https://doi.org/10.1016/S0076-6879(99)99017-1
507
Thompson, T. (2000). Folate, Iron, and Dietary Fiber Contents of the Gluten-free Diet. Journal of
508
the American Dietetic Association, 100(11), 1389–1396. https://doi.org/10.1016/S0002-
509
8223(00)00386-2
23
ACCEPTED MANUSCRIPT 510
Udachan, I., & Sahoo, A. K. (2017). Quality evaluation of gluten free protein rich broken rice
511
pasta. Journal of Food Measurement and Characterization, 11(3), 1378–1385.
512
https://doi.org/10.1007/s11694-017-9516-3
513
Wang, Y., Yu, L.-y., Fan, X.-h., & Qu, H.-b. (2009). An approach to predicting antioxidative activities of natural products based on near infrared spectroscopy. Spectroscopy and spectral
515
analysis, 9(29), 2401–2404. https://doi.org/10.3964/j.issn.1000-0593(2009)09-2401-04
516
Williams, P. (1987). Variables Affecting Near-Infrared Reflectance Spectroscopic Analysis. In P.
517
Williams & K. Norris (Eds.), Near Infrared Technology in the Agriculture and Food Industries
518
(Chap. 8, 143–167). St. Paul: American Association of Cereal Chemists.
521 522 523
SC
M AN U
520
Workman, J., & Weyer, L. (2008). Practical guide to interpretive near-infrared spectroscopy. Boca Raton: Taylor & Francis.
Workman, J., & Weyer, L. (2012). Practical guide and spectral atlas for interpretive near-infrared spectroscopy (2nd ed.). Boca Raton, FL: CRC Press.
TE D
519
RI PT
514
Zhang, C., Shen, Y., Chen, J., Xiao, P., & Bao, J. (2008). Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared
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spectroscopy. Journal of agricultural and food chemistry, 56(18), 8268–8272.
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https://doi.org/10.1021/jf801830z
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Figure 1: Overview of the total antioxidative capacities of all samples. H refers to samples with husk, DH to samples without husk.
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Figure 2: Averaged spectrum of all intact samples after spectroscopic transformation to absorbance spectra.
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Figure 3: PLS-R model for grains established using the calibration set.
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Early Bird−DH Early Bird−H−1 Early Bird−H−2 Early Bird−H−3 Gelbhirse−H Gierczyckie−H−1 Gierczyckie−H−2 GL RH 16106−H Horizon−DH Horizon−H−1 Horizon−H−2 Horizon−H−3 Huntsman−DH Huntsman−H−1 Huntsman−H−2 Huntsman−H−3 ITA−4−DH ITA−4−H−1 ITA−4−H−2 ITA−5−H Jagna−H−1 Jagna−H−2 Kornberger−H−1 Kornberger−H−2 Quartett−H Silberhirse−H Sunrise−DH Sunrise−H−1 Sunrise−H−2 Sunrise−H−3 Tiroler Rispenhirse−H
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Bamby−DH Bamby−H−1 Bamby−H−2 Billy−H Cebelica−H Chinese Landrace−DH Chinese Landrace−H Devyatka−H Dikul−H ITA−1−DH ITA−1−H ITA−2−DH ITA−2−H ITA−3−DH ITA−3−H Kaerntner Hadn−H Koma−DH Koma−H−1 Koma−H−2 Kora−DH Kora−H−1 Kora−H−2 Koto−DH Koto−H−1 Koto−H−2 La Harpe−DH La Harpe−H−1 La Harpe−H−2 Lileja−DH Lileja−H−1 Lileja−H−2 Panda−DH Panda−H−1 Panda−H−2 Spacinska−H Temp−H VB Nojai−H VB Vokiai−DH VB Vokiai−H−1 VB Vokiai−H−2
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Total antioxidative capacity varies significantly between cultivars of one grain species
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NIR model development for antioxidants of different cultivars of three grain species
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Calculation of UIPAC conform multivariate LOD and LOQ for established PLS-R models
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