Detection of adulteration in Chinese monofloral honey using 1H nuclear magnetic resonance and chemometrics

Journal Pre-proof Detection of adulteration in Chinese monofloral honey using 1 H nuclear magnetic resonance and chemometrics Xiaoying Song (Writing - original draft) (Formal analysis) (Investigation), Seng She (Writing - original draft) (Formal analysis) (Investigation), Manman Xin (Methodology) (Software) (Investigation), Lei Chen (Methodology) (Software) (Investigation), Yi Li (Project administration) (Funding acquisition), Yvan Vander Heyden (Methodology) (Writing - review and editing), Karyne M. Rogers (Methodology)Writing - review and editing), Lanzhen Chen (Conceptualization) (Methodology) (Investigation) (Resources)Writing - review and editing) (Supervision) (Project administration) (Funding acquisition)

PII:

S0889-1575(19)30833-6

DOI:

https://doi.org/10.1016/j.jfca.2019.103390

Reference:

YJFCA 103390

To appear in:

Journal of Food Composition and Analysis

Received Date:

5 June 2019

Revised Date:

13 December 2019

Accepted Date:

13 December 2019

Please cite this article as: Song X, She S, Xin M, Chen L, Li Y, Heyden YV, Rogers KM, Chen L, Detection of adulteration in Chinese monofloral honey using 1 H nuclear magnetic resonance and chemometrics, Journal of Food Composition and Analysis (2019), doi: https://doi.org/10.1016/j.jfca.2019.103390

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Detection of adulteration in Chinese monofloral honey using 1H nuclear magnetic resonance and chemometrics Xiaoying Songa, Seng Shea, Manman Xind, Lei Chend*, Yi Lia,b,c , Yvan Vander Heydene, Karyne M. Rogersf, Lanzhen Chena,b,c* a

Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China

b

Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture,

Beijing 100093, China c

Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing

d

Wuhan Institute of Physics and Mathematics ,Chinese Academy of Sciences, National Center for

Magnetic Resonance in Wuhan, Wuhan 430071, China e

Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije

Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium

National Isotope Center, GNS Science, 30 Gracefield Road, Lower Hutt 5040, New Zealand

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100093, China

* Corresponding Author Tel./fax: +86 10 62594643

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E-mail addresses: [email protected] chenlei @ wipm.ac.cn

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Notes

The authors declare no competing financial interest.

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147 authentic monofloral honeys from China were investigated by NMR and chemometrics.

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Highlights

NMR data from δ 0.00 to 6.00 ppm is most suitable for performing CDA model.

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NMR spectra of pure acacia honey was compared with rape-honey adulterated samples.

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PLS model was satisfactory to predict rape honey concentration in mixed samples.

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ABSTRACT Incidents of fraud are emerging on the domestic Chinese honey market, especially where lower value rape honey is mixed with high-value acacia honey. In this study, we use a combination of 1H NMR spectroscopy combined with chemometric techniques to detect and quantify adulteration of acacia honey with cheaper rape honey. The honey 1H NMR spectroscopy was split into three regions i.e. the aliphatic (0.00-3.00 ppm), carbohydrate (3.00-6.00 ppm) and aromatic (6.00-9.50 ppm) regions, to investigate which region provided

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the best qualitative and quantitative indicators in terms of rape honey adulteration in acacia honey. Results showed that the highest prediction accuracy for rape honey addition is 89.7 % using canonical discriminant analysis (CDA), determined from compounds located in 0.00 to 6.00 ppm spectral range. Orthogonal projection to latent structures discriminant analysis

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(OPLS-DA) was used to further discriminate samples of pure acacia honey adulterated with different amounts of rape honey. A partial least squares model (PLS) established a linear fit

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between the actual and predicted adulterant concentration with an R2 value up to 0.9996.

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When the content of rape honey added was less than 100 g/kg, the purity of acacia honey could still be estimated using 1H NMR spectroscopy with chemometric method.

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KEYWORDS: Chinese honey, NMR, adulteration, OPLS-DA, PLS, authentication, food

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composition analysis

1. Introduction Honey is produced by bees from plant nectar or honeydew without processing. As an entirely natural product, honey is very popular all over the world for its nutritional value and bioactive properties (Cabañero et al., 2006; Simsek et al., 2012). Honey is widely used in the food

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industry as a sweetener and is also incorporated into cosmetics and medicinal products (Corbella et al., 2006). Unfortunately, there is a growing occurrence of adulterated and falsely-labelled Chinese and international monofloral honeys. In order to seek higher profits, high-quality honey is subjected to sugar adulteration through the addition of cheaper sweeteners, such as refined cane sugar, beet sugar, corn sugar, high fructose corn syrup (HFCS) or through substitution of lower-grade honey. Honey adulteration is not only unfair to consumers, but may also cause severe health and safety problems depending on the source and ingredients of the adulterants. In the longer term, due to decreased consumer confidence,

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authentic honey sales could decline and the bee-keeping industry could shrink. Since bees are the main pollinators of wild and cultivated plants, a downturn in the honey industry could pose a significant threat to agricultural and ecosystems (Wu et al., 2016).

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In China, more than 20 types of monofloral honeys, such as acacia, rape, chaste, lychee, jujube, citrus, longan and linden honey, are commonly produced. Each honey is from a

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specific floral origin and contains unique substances related to the plant, which is believed to promote human health (Dezmirean et al., 2012). Acacia honey is one of the most popular

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types of monofloral honey because of its distinctive flavor, great taste and high nutritional value. Yet, the annual production of acacia honey is relatively low, and accordingly, its

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market price is higher than other honey types. Now, more frequent cases where acacia honey is subjected to adulteration with inexpensive rape honey is observed. This results in a growing

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need to find suitable and robust methods for the detection of various adulterants to determine honey purity.

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Several techniques have been reported to identify adulteration in honey. Stable carbon isotopic ratio analysis (13C/ 12C) is mainly used to determine the adulteration by C4 plant syrups, such as cane sugar or HFCS (Simsek et al., 2012; Padovan et al., 2003). Additionally, quantitative analytical approaches, such as high-performance liquid chromatography (HPLC) (Naila et al., 2018; Wang et al., 2015; Cotte et al., 2014), gas chromatography-mass spectrometry (GC-MS) (Ruiz-Matute et al., 2007; Ruiz-Matute et al., 2010; Montilla et al., 3

2006 ) and quadrupole time-of-flight mass spectrometry (Q-TOF-MS) (Du et al., 2015) have also been used to determine honey adulteration. However, these destructive methods are timeconsuming, and may not accurately identify the actual adulterant. Moreover, they are generally unsuitable for large numbers of samples. More recently research has focused on using rapid and non-invasive techniques to identify adulterants. Near infrared (NIR) (Siddiqui et al., 2017; Zhu et al., 2010; Chen et al., 2010), fourier transform infrared (FTIR) (Irudayaraj et al., 2003; Subari et al., 2012) and raman spectroscopy (Li et al., 2012; Özbalic et al., 2013) combined with chemometrics for data treatment, were successfully demonstrated

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to be excellent tools. However, while these methods have been reported to identify honey mixed with sugar syrups or the determination of monofloral honey purity, less research is

seen on high-value honey fraudulently blended with low-value honey. Due to the different

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levels of blending and the changing composition of nectars collected by bees over a flowering season, the previously mentioned techniques have limitations in the detection of fraud, and

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are less adapted to determine more sophisticated adulterations.

Nuclear magnetic resonance (NMR) spectroscopy has several advantages over other

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techniques such as its capability for providing information on a wide of range of components in a single analysis, its non-invasive approach and its relatively fast data acquisition

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(Agiomyrgianaki et al., 2010; Monteiro et al., 2009; Schievano et al., 2017). NMR measurements combined with chemometric methods have already been suggested to be

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effective tools in the authenticity assessments of food products, such as saffron, roasted coffee, edible oils, milk and honey (Petrakis et al., 2016; Vinã-Cius et al., 2017; Zhu et al.,

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2017; Santos et al., 2016; Bertelli et al., 2010 ). Amiry et al. (Amiry et al., 2016) measured 102 honey samples adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (70 g/kg, 150 g/kg, 300 g/kg) and successfully distinguished the different adulterant levels using principal components analysis (PCA) and linear discriminant analysis (LDA). To our knowledge, no research has been performed on the qualitative and quantitative analysis of high-priced acacia honey, 4

adulterated with cheaper rape honey, using untargeted NMR spectrometry. The main goal of this study was to detect the adulteration of acacia honey with different levels of rape honey by combining 1H NMR spectroscopy and chemometrics. One-hundred and forty-seven samples, including thirty-four monofloral acacia honeys, forty-eight monofloral rape honeys and sixtyfive acacia honeys mixed with varying amounts of rape honey, were analyzed by 600 MHz 1H NMR spectroscopy. Canonical discriminant analysis (CDA) models were built using the 1H NMR data to explore the best bucket width. Then, orthogonal projection to latent structures discriminant analysis (OPLS-DA) and partial least squares (PLS) models were built as

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classification and calibration models, respectively, to detect and measure adulteration, and to predict adulteration levels.

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2. Materials and methods

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2.1 Honey Samples

Eighty-two honey samples with two botanical origins were directly collected from different

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apiaries, including 34 acacia honeys (Robinia pseudoacacia L) and 48 rape honeys (Brassica campestris L.). The botanical origin of each sample was declared by the provider, identified

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through sensorial analysis, and in several cases, by melissopalynology. The honey samples were stored at 4 °C before analysis.

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Five acacia honeys and five rape honeys were picked randomly. Pure rape honey was added to pure acacia honey at percentages ranging from 5 to 90 %. Sixty-five mixed honey samples

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were prepared in five batches with 13 incremental additions of rape honey for each batch (50 g/kg, 100 g/kg, 150 g/kg, 200 g/kg, 250 g/kg, 300 g/kg, 350 g/kg, 400 g/kg, 500 g/kg, 600 g/kg, 700 g/kg, 800 g/kg, 900 g/kg, w/w of rape honey), respectively.

2.2 Reagents and solutions

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All chemicals used in this study were of analytical reagent grade and were purchased from Sinopharm Chemical Reagent Co., Ltd (Beijing, China). Deionized water (18.2 MΩ/cm) was obtained from a Milli-Q Plus system (Millipore, Bedford, MA, USA). 2.3 Sample preparation Monofloral acacia and rape honey, and rape-honey adulterated samples were placed in a

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water bath at 40 °C to dissolve any crystals and ensure homogeneity. Around 100 mg of honey was precisely weighed and dissolved in 1.2 mL deuterated phosphate buffer (0.15 mol/L, pH 7.40) containing 0.05 g/100 mL sodium 3-(trimethylsilyl)- 2, 2, 3, 3- 2H4 propionate (TSP). The honey-buffer mixture was put in a vortex for 5 min until

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homogenization and then centrifuged at 7155 ×g for 10 min. A 600 μL aliquot of each

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solution was transferred into a 5 mm NMR tube for measurement.

2.3 1H NMR spectroscopy

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All NMR experiments were performed at 25 °C on a Bruker AVANCE 600 MHz spectrometer (Bruker Biospin, Fallanden, Switzerland) equipped with a 5 mm cryoprobe. 1D 1

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H NMR spectra were acquired using a water presaturation NOESY pulse scheme (recycle

delay-90°-t1-90°- tm-90°-acquisition). The water peak was suppressed by CW irradiation

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during the recycle delay of 2.0 s and a mixing time (tm) of 100.0 ms. The 90° pulse width, the fixed interval t1 and the acquisition time were set at 14.5 μs, 4.0 μs and 2.27 s, respectively.

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Each spectrum was recorded with a spectral width of 12 kHz and 32 K data points using 64 scans and 4 dummy scans. All NMR data were processed with Topspin v.3.5 software (Bruker Biospin, Fallanden, Switzerland). The free induction decay (FID) of 1H NMR was weighted by a decaying exponential function with 0.3-Hz line broadening for sensitivity enhancement before

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undergoing Fourier transformation. Phase and baseline corrections were performed manually for each spectrum. All spectra were calibrated with the TSP signal at 0.00 ppm. The processed 1D 1H NMR spectra were then automatically integrated with a bin width of 0.004 ppm using AMIX v3.9.2 (Bruker Biospin, Fallanden, Switzerland). The NMR spectral region between 4.73 and 4.93 ppm was removed to eliminate the effects of residual water resonance. All integrated bins were normalized to the total integral of the spectral regions, and then converted into ASCII

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format for further chemometric analyses.

2.4 Chemometric analyses

CDA was performed using SPSS v.19.0 (IBM Inc., New York, USA) and established the

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discrimination model relating the spectral data characteristics of the honey samples. PLS was carried out in Matlab v7.0 (The MathWorks, Massachusetts, USA) and OPLS-DA was

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performed using SIMCA v13.0 (Umetrics AB, Sweden). By calculating the distance of a

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sample to the mean value of a set of standards, samples can be classified into several classes (Torre et al., 2008; Heaton et al., 2008).

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OPLS-DA models (a supervised discrimination method) were built to optimize the separation between pairwise classified groups. Three parameters, R2X, R2Y and Q2, are often used to evaluate these models. R2X and R2Y represent the fraction of variance in matrices X and Y

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explained by the model. When they are close to 1, the model is accurate and robust. Q2

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represents the predictive accuracy of the model. Values > 0.5 indicate a good predictive result from the OPLS-DA models (Bylesjö et al., 2006; Triba et al., 2014). PLS is a multivariate calibration technique that relates changes in spectral data with quantitative proportions of the samples. The model is based on latent variable (PLS factors) and regression features (Coury et al., 2008). It extracts the maximum information from the

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data in the model to make the best calibration. The accuracy of the model can be evaluated from the root mean squared error of estimation (RMESS) and root mean squared error of prediction (RMSEP). If both statistical values are small, then a good predictive ability of the model may be expected. The fit of the model is also represented by R2, and when R2 > 0.99, the model is considered good (Gautz et al., 2006; Chen et al., 2012).

3. Results and Discussion

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3.1 Characterization of honey by NMR spectroscopy Typical 1H NMR spectra of acacia and rape honey are shown in Figure 1. The honey

spectrum was divided into three regions: the aliphatic (0.00-3.00 ppm), carbohydrate (3.006.00 ppm), and aromatic (6.00-9.50 ppm). These three representative intervals (0.50-3.00

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ppm, 5.00-5.50 ppm, 6.50-9.00 ppm) regions were selected and enlarged in Figures 1B-D.

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At the low frequency range (0.50-3.00 ppm, Figure 1B), aliphatic signals from ethanol, lactic acid, alanine, proline, acetic acid, valine and succinic acid can be observed in varying

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amounts in the honeys. Proline was the most abundant in this region and can be seen in both floral types. Multiplets at 1.985 and 2.037 ppm were assigned to γ-CH2 proline, whereas these

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at 2.075 and 2.348 ppm were assigned to β-CH2 proline. For the carbohydrates, 5.00-5.50 ppm was regarded as representative region. Fructose and

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glucose were the two main sugars detected in this region while several isomers were also present. From the NMR spectrum, α-glucopyranose (α-Glcp H-1, d, δ=5.24) and β-

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glucopyranose (β-Glcp H-1, d, δ=4.63) were observed for free glucose, while free fructose was demonstrated by its H-3 at δ=4.10. The singlet at δ =5.24 ppm corresponded to αglucopyranose (α-Glcp H-1, J1, 2=3.3Hz). Resonance signals of maltulose, turanose, nigerose, kojibiose and sucrose were also observed.

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The resonance signals of tyrosine, phenylalanine were present in the aromatic region (6.509.00 ppm). This region also contained some unidentified signals. The characteristic proton NMR resonances found in these honeys were listed in Table 1. In the spectra, some notable differences could be observed between acacia and rape honey, but to extract the less obvious differences and increase accuracy, a more comprehensive multivariate data analysis is required.

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3.2 Discrimination of adulteration by CDA First, when using 1H NMR spectrum modeling, the appropriate chemical shift regions should

be selected. If the selected region is too small, the predictive accuracy of the model is reduced due to insufficient discriminative peak information. If the selected region is too large, the

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introduction of random noise signals decreases the modeling and predictive efficiencies. Thus, CDA models were derived testing five data matrices in order to establish the best

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spectral information: matrix A, the entire spectrum (0.10-9.50 ppm); matrix B, the aliphatic

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and carbohydrate regions (0.10-6.00 ppm); matrix C, the carbohydrate and aromatic regions (3.00-9.50 ppm); matrix D, the aliphatic and aromatic regions (0.10-3.00 ppm and 6.00-9.50

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ppm), and matrix E, the carbohydrate region (3.00-6.00 ppm). Leave-one-out cross validation was used to evaluate the predictive ability of the models. The technique showed a limited ability to classify the samples. Table 2 shows the correct

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classification results for the different honey samples, applying the different data matrices. The

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correct classification rate for each type of honey by the different models is given. Key spectral differences between the pure honey and adulterated samples were mainly found in the carbohydrate region (3.00-6.00 ppm). In matrix D, the spectrum without the 3.00-6.00 ppm region, the correct classification rate of the adulterated samples was not very high, and they were frequently misclassified as acacia honey. For the matrices A, C, D and E, the classification accuracy of adulterated honey was lower than in matrix B.

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Matrix B (0.10-6.00 ppm) provided the most accurate classification and the best ability to discriminate adulterated samples, using a CDA model (Figure 2). It clearly shows the separation of pure acacia and rape honeys, while the mixed (adulterated) samples are intermediately located. Mostly are well separated from the pure samples, resulting in an overall classification accuracy rate of 89.7%. Rape honey was rather well separated from the adulterated samples, while, some pure acacia and adulterated samples overlapped. Matrix B resulted also is the best classification accuracy rate for acacia honey, i.e. 85.3%. In five misclassified samples, four pure acacia honeys were misjudged as mixed (adulterated) honey.

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From the four acacia honey samples misclassified, three as adulterated used in the preparation of the mixed (adulterated) honeys. For the rape honey, five samples were misclassified, two as acacia honey, and three as mixed (adulterated) honey, two of these three correctly

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classified rape honey samples were used to prepar the mixed (adulterated) samples. Using the

CDA model, the classification accuracy rate for rape honeys was 89.6%. For the misclassified

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adulterated samples, two samples containing 800 g/kg and 900 g/kg added rape honey were classified as pure rape honey, while three samples containing only 50 g/kg, 100 g/kg, and 200

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g/kg rape honey were identified as pure acacia honey.

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3.3 Discrimination of adulteration by OPLS-DA In the CDA model, the overall correct classification rate using cross validation was about

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90%. This supervised multivariate method requires further strengthening to better differentiate between low and high levels of adulteration and to improve discrimination and

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classification accuracy (Schievano et al., 2010). OPLS-DA, a common supervised technique, was applied to the NMR data. OPLS-DA score plots for the aliphatic and carbohydrate regions (0.00-6.00 ppm) are shown in Figure 3A. Comparisons between these specify major data pre processing methods, frequently applied to NMR analyses, showed that found OPLSDA models with unit variance scaling (UV) were the most useful (ZieliSki et al., 2014).

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As shown in Figure 3A, pure acacia, rape and adulterated honey samples were separated. The corresponding parameters R2X(cum), R2Y(cum), Q2(cum) of the model were 0.75, 0.75 and 0.63, respectively, which indicated a good fitness and high predictive ability. To highlight the class separation and to determine the discriminatory variables, pairwise OPLS-DA models were also created (Figures 3B-D). The supervised OPLS-DA pattern recognition models distinguish different groups, but external validation is needed to demonstrate the validity of the model. In order to verify the

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reliability of the model, the permutation test method was adopted (Figure 4). In the permutation test, the regression intercept values for R2 and Q2 regression lines represent the degree of fit to the data and the predictive ability of the model, respectively. The intercept value (R2) should not be higher than 0.3-0.4, and for Q2, it should not be more than 0.05,

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otherwise the model is overfitting (And et al., 2002). When the intercept value of

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regression line Q2 for the original differential model on the right side of Figure 4 is larger than the intercept value of regression line Q2 for the random permutation model of any y

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variable on the left side, it indicates that though differences between sample types were small, the model may have good stability and predictability, and therefore be statistically significant.

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When acacia honey was adulterated with 50 g/kg of rape honey, the overlap between the pure acacia honey sample and the mixed (adulterated) sample demonstrated the detection limit of

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the model (Figure 3C). Two acacia honey samples adulterated with 900 g/kg of rape honey were identified as rape honey, while the rape honey sample closest to the adulterated honey

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group in Figure 3D was used to prepare the mixed (adulterated) honey samples. To improve the discriminative capacity between the honey types, S-plots of OPLS-DA models were built (Figure 5). The variables selected in the S-plot are highlighted with a dotted rectangle. Figure 5A shows the most relevant variables affecting the discrimination between acacia and rape honey. For acacia honey, the key spectral intervals which defined its

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classification were 3.72-3.82 ppm and 4.00-4.10 ppm, whereas, rape honey was defined by characteristic peaks in the 3.20-3.30 ppm, 3.40-3.50 ppm and 4.60-4.70 ppm intervals. When comparing acacia and rape honey with mixed (adulterated) samples (Figures 5B and C), the characteristic peaks for acacia honey were concentrated in the 3.50-3.60 ppm, 3.70-3.8 ppm and 4.00-4.10 ppm intervals while rape honey peaks were concentrated in the 3.25-3.35 ppm, 3.40-3.50 ppm and 4.60-4.70 ppm intervals. The spectral intervals of mixed (adulterated) samples classification were 3.55-3.60 ppm, 3.70-3.82 ppm and 4.00-4.10 ppm. Therefore, peaks found in 3.70-3.82 ppm and 4.00-4.10 ppm intervals were classified as potential

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markers for acacia honey, while those in 3.40-3.50 ppm, 3.20-3.35 ppm and 4.60-4.70 ppm were classified as potential markers for rape honey. Because it is difficult to know these

intervals represents which chemicals in the presented stage. But for a future researcher, it is

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compounds that can be used to identify adulterants.

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opportunity to assign those spectral regions and that time we can know it is what specific

3.4 Prediction of the adulteration levels by a PLS model

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A PLS model was built to quantify the degree of adulteration (Shi et al., 2017) and to predict the content of rape honey in the mixed (adulterated) samples. The linearity between the actual

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and the predicted addition is shown in Figure 6. The x-axis represents the actual level of rape honey in 45 samples and the y-axis represents the predicted value obtained by the leave-one-

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out cross validation method for the PLS model. The model resulted in a high linearity in Figures 6, with R2 of 0.9996 and a slope of 1.0011 indicating that the differences between the

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actual and estimated values were very small. For acacia honey with lower adulteration levels of rape honey (50 g/kg, 100 g/kg, 200 g/kg, 300 g/kg, 400 g/kg), the model showed a better ability to accurately estimate the rape honey addition. For acacia honey with higher adulteration levels of rape honey (500 g/kg, 600 g/kg, 700 g/kg, 800 g/kg), a larger error occurred. Nonetheless the actual content of rape honey in adulterated acacia samples can be estimated using the PLS model, while appeared best suited at lower additions of rape honey. 12

4. Conclusions In this study, NMR spectroscopy combined with multivariate data analysis was used to qualitatively and quantitatively evaluate the purity of acacia honey samples, either or not adulterated with rape honey. A CDA model was used to determine the most suitable analysis interval. Using only the best spectral interval (0.00-6.00 ppm) greatly improved the predictive accuracy of the model (up to 89.7%). Further, OPLS-DA with unit variance scaling demonstrated that when rape adulteration level in acacia honey was more than 100 g/kg (10%

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w/w rape adulteration), this addition could be effectively distinguished from pure acacia honey. The adulteration level of rape honey can be estimated using a PLS model and

predictions were found to be better for smaller adulteration levels. Given the tendency for

partial, rather than total substitution, most adulteration with cheaper floral honey will occur

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from 10 to 50%, making this model particularly useful.

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Although this integrated NMR chemometrics approach used acacia and rape honey to generate the ‘adulterated’ samples, other low-value honey types are regularly used as

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adulterants in China to stretch the yield of higher value honey. In the future, our methods will be expanded to other botanical honey that is either a target for adulteration or used as an

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adulterant to maximize utilization of this method. Meanwhile, this model provides a reliable method to reduce adulteration of acacia honey with rape honey. In conclusion, these results

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show that 1H NMR spectroscopy combined with chemometric data analysis provides a feasible approach to detect the problem of high-grade honey mixed with lower quality honey,

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and helps to combat growing fraudulent practices in the honey industry.

CRediT author statement

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Lanzhen Chen: Conceptualization, Methodology, Investigation, Resources, Resources, Writing – Review and Editing, Supervision, Project Administration, Funding Acquisition, Xiaoying Song: Writing - Original Draft, Formal analysis, Investigation, Seng She: Writing - Original Draft, Formal analysis, Investigation, Manman Xin: Methodology, Software, Investigation, Lei Chen: Methodology, Software, Investigation, Yi Li: Project administration, Funding acquisition, Yvan

Vander Heyden: Methodology, Writing-Review and Editing, Karyne Rogers: Methodology,

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Writing-Review and Editing

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Figure 1.

1

H NMR spectra of acacia and rape honey: A. entire spectra; B. representative

interval of the aliphatic region (0.50-3.00 ppm); C. representative interval of the carbohydrate region (5.00-5.50 ppm); D. representative interval of the aromatic region (6.50-9.00 ppm). Figure 2. CDA plot using the 0.10-6.00 ppm interval (matrix B) of the NMR spectra for model building. Figure 3.

OPLS-DA score plots of pure and adulterated samples: A. acacia, rape and

adulterated honey samples (R2X=0.749, R2Y=0.746, Q2=0.628); B. acacia and rape honey

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samples (R2X=0.705, R2Y=0.964, Q2=0.910); C. acacia and adulterated honey samples

(R2X=0.649, R2Y=0.836, Q2=0.623); D. rape and adulterated honey samples (R2X=0.591, R2Y=0.809, Q2=0.747).

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Figure 4. Model validation plots from the OPLS-DA models based on 200 permutation test: A.

acacia and rape honey samples (R2=0.538, Q2=-0.519); B. acacia and adulterated honey samples

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(R2=0.588, Q2=-0.491); B. rape and adulterated honey samples (R2=0.182, Q2=-0.344).

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Figure 5. S-plots generated from OPLS-DA models: A. acacia and rape honey samples; B. acacia and adulterated honey samples; C. rape and adulterated honey samples

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Figure 6. Linear relationship between the actual and predicted adulteration levels of rape honey

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in mixed (adulterated) honey samples.

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Figure 1. 1H NMR spectra of acacia and rape honey: A. entire spectra; B. representative interval of the aliphatic region (0.50-3.00 ppm); C. representative interval of the carbohydrate region (5.00-5.50 ppm); D. representative interval of the aromatic region (6.50-9.00 ppm).

Figure. 1. 1H NMR spectra of acacia and rape honey: A. The entire spectra; B. The representative interval of the aliphatic region (0.50-3.00 ppm); C. The representative intervals of the carbohydrate region (5.00-5.50 ppm); D. The representative interval of the aromatic region (6.50-9.00 ppm). 20

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Fig.2 CDA results using the of 0.10-6.00 ppm (matrix B) of rape honey, acacia honey and

model building.

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Figure 2. CDA plot using the 0.10-6.00 ppm interval (matrix B) of the NMR spectra for

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Figure. 3. OPLS-DA score plots of pure and adulterated samples: A. acacia, rape and adulterated honey samples (R2X=0.749, R2Y=0.746, Q2=0.628); B. acacia and rape honey

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samples (R2X=0.705, R2Y=0.964, Q2=0.910); C. acacia and adulterated honey samples (R2X=0.649, R2Y=0.836, Q2=0.623); D. rape and adulterated honey samples (R2X=0.591,

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R2Y=0.809, Q2=0.747).

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Figure 4. Model validation plots from the OPLS-DA models based on 200 permutation test: A. acacia and rape honey samples (R2=0.538, Q2=-0.519); B. acacia and adulterated honey

0.344).

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samples (R2=0.588, Q2=-0.491); B. rape and adulterated honey samples (R2=0.182, Q2=-

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Figure 5. S-plots generated from OPLS-DA models: A. acacia and rape honey samples; B.

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acacia and adulterated honey samples; C. rape and adulterated honey samples

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Figure 6. Linear relationship between the actual and predicted adulteration levels of rape

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honey in mixed (adulterated) honey samples.

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Table 1 1H NMR signals of minor compounds present in honey. Table 2 Classification results of acacia, rape and adulterated honey samples based on CDA

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model.

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Table 1 1H NMR signals of minor compounds present in honey Group

Multiplicity a

δ (ppm)

Valine

γ-CH3

d

0.997

γ’-CH3

d

1.050

2,3-Butandiol

CH3

m

1.139

Ethanol

CH3

t

1.188

Ethyl acetate

CH3

d

1.239

3-Hydroxybutanone

CH3

d

1.308

Lactic acid

CH3

d

1.338

Alanine

CH3

d

Acetic acid

CH3

s

Proline

γ-CH2

m

β-CH2

m

CH2

Turanose

C(1)H

Nigerose

C(1)H

2.075, 2.348

2.649-2.759

d

5.308

m

5.367-5.389

C(1)H

d

5.455

Ar-C(3)H C(5)H

d

6.907

Ar-C(2)H C(6)H

d

7.198

Ar-C(2)H C(6)H

d

7.334

Ar-C(3)H C(5)H

d

7.428

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s, singlet; d, doublet; t, triplet; m, multiplet; dd, doublet-doublet

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a

1.985, 2.037

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Phenylalanine

1.925

2.408

Kojibiose Tyrosine

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Citric acid

1.483

s

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CH2

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Succinic acid

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Compounds name

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Table 2

Classification results of acacia, rape and adulterated honey samples based on CDA model. Predicted class

Matrix

Correct

True class

classification Acacia

Rape

Average

Adulterated rate

D

6

82.4%

Rape

1

44

3

91.7%

Adulterated

7

3

55

84.6%

Acacia

29

1

4

Rape

2

43

3

Adulterated

3

2

60

Acacia

29

0

Rape

1

44

Adulterated

6

5

54

83.1%

Acacia

22

5

7

64.7%

1

42

5

87.5%

13

5

47

72.3%

Rape

89.6% 92.3%

5

85.3%

3

91.7%

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1

5

82.4%

Rape

1

44

3

91.7%

7

5

53

81.5%

Adulterated

89.7%

86.7%

74.8%

85.2%

Matrix represents the applied regions for modeling, A: 0.10-9.50 ppm, B: 0.10-6.00 ppm , C:

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a

85.3%

Acacia

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E

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Adulterated

86.3%

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C

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B

Acacia

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A

3.00-9.50 ppm, D: 0.10-3.00 ppm and 6.00-9.50 ppm, and E: 3.00-9.50 ppm.

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