Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy

Accepted Manuscript Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy Carmen L. Manuelian, Sarah Currò, Mauro Penasa, Martino Cassandro, Massimo De Marchi PII:

S0958-6946(17)30072-9

DOI:

10.1016/j.idairyj.2017.03.011

Reference:

INDA 4162

To appear in:

International Dairy Journal

Received Date: 27 November 2016 Revised Date:

22 March 2017

Accepted Date: 23 March 2017

Please cite this article as: Manuelian, C.L., Currò, S., Penasa, M., Cassandro, M., De Marchi, M., Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy, International Dairy Journal (2017), doi: 10.1016/ j.idairyj.2017.03.011. 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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Prediction of minerals, fatty acid composition and cholesterol content of commercial

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cheeses by near infrared transmittance spectroscopy

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Carmen L. Manuelian, Sarah Currò, Mauro Penasa, Martino Cassandro, Massimo De

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Marchi*

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Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE),

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University of Padova, Viale dell’Università 16, 35020 Legnaro (PD), Italy

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E-mail address: [email protected]. (M. De Marchi)

Corresponding author. Tel.: +39 334 6958869

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ACCEPTED MANUSCRIPT _________________________________________________________________________ ABSTRACT

Prediction models for the mineral, fatty acid (FA) and cholesterol contents of commercial

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European cheeses using near infrared transmittance spectroscopy were developed. Cheese samples (n=145) were from different dairy species and ripening time. Sample spectra were matched with mineral, FA and cholesterol reference data to develop prediction models.

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Modified partial least squares regressions were validated through cross-validation procedure on the complete dataset (n=145) and through external validation after dividing the data into

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calibration (74%) and external validation (26%) sets. Satisfactory models were developed for Ca, P, S, Mg and Zn, and for FA groups (saturated, unsaturated, monounsaturated and polyunsaturated FAs), major FAs (myristic, palmitic and oleic acids) and some minor FAs, whereas cholesterol content could not be predicted with adequate accuracy. Results of the

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present study are a precursor to at-line utilisation of prediction models for the most abundant cheese minerals and FAs at an industry level.

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ACCEPTED MANUSCRIPT 1.

Introduction

Cheese supplies essential nutrients such as bioactive peptides, fatty acids (FAs), minerals and vitamins (Ash & Wilbey, 2010; Walther, Schmid, Sieber, & Wehr, 2008).

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Cheese consumption has grown in the last five years, especially in the European Union (17.4 kg of cheese per capita) and the United States (15.7 kg of cheese per capita) (CLAL, 2016). Several health food authorities recommend a fat daily intake below 30–35% of the total

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daily energy intake, and recommend a move from saturated fatty acids (SFAs) to unsaturated fatty acids (UFAs), and to avoid trans fat (FSA, 2016; WHO, 2016). A low omega-6 to

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omega-3 ratio (ω-6/ω-3) decreases the risk of cancer, cardiovascular, inflammatory, and autoimmune diseases (Simopoulos, 2002) because ω-3 leads to anti-inflammatory eicosanoids (DeFilippis & Sperling, 2006). Cholesterol is the main sterol in milk and it is highly correlated with total milk fat content (Palmquist, 2006), but its role in the onset of

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cardiovascular diseases is still controversial (Ash & Wilbey, 2010). Milk and dairy products provide between 60 and 70% of dietary Ca, which is essential for bone and teeth health (Bonjour, Guéguen, Palacios, Shearer, & Weaver, 2009; Walther et al., 2008). Regarding

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Na, high intake of this mineral has been related to an increase in the risk of cardiovascular diseases (Aburto et al., 2013). The current mandatory labelling regulation compels

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declaration of total “salt” (defined as Na × 2.5) and SFAs on the product label (EU, 2011). However, it is voluntary to declare monounsaturated- and polyunsaturated fatty acids (MUFAs and PUFAs, respectively; EU, 2011). Therefore, the quantification of these components is interesting for human health and for the industry to achieve new mandatory labelling requirements. Near infrared (NIR) spectroscopy offers a rapid, objective, non-destructive and simultaneous analysis of several traits at much lower cost compared with the common

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ACCEPTED MANUSCRIPT reference laboratory methods. Because of this, the food industry has increased its interest in NIR spectroscopy implementation for process control by at-line, on-line, and in-line measurements (Porep, Kammerer, & Carle, 2015). Cheese is the most difficult dairy product to analyse using NIR spectroscopy because of the wide variability of cheese types and the

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heterogeneity of the product (Holroyd, 2013). While all organic components have absorption bands in the NIR region, minerals can only be detected if they are in organic complexes, or indirectly due to changes produced in the amount of hydrogen bonding by minerals which

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affect the water spectrum (Begley, Lanza, Norris, & Hruschka, 1984; Buing-Pfaue, 2003). In cheese, studies have dealt mainly with the prediction of fat, moisture and protein content

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(Holroyd, 2013), and only a few papers have predicted minor cheese compounds (Holroyd, 2013) such as volatile FAs (González-Martín et al., 2014), FA profile (Lucas, Andueza, Ferlay, & Martin, 2008a; Ottavian et al., 2012) and mineral content (González-Martín, Hernández-Hierro, Revilla, Vivar-Quintana, & Lobos Ortega, 2011; Lucas, Andueza, Rock,

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& Martin, 2008b). However, those studies analysed cheeses manufactured in standardised laboratory conditions (González-Martín et al., 2011, 2014), or involved less than 4 different varieties of cheese (Lucas et al., 2008a,b; Ottavian et al., 2012).

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To our knowledge, only a few studies have used NIR spectroscopy to predict a cheese FA profile, with successful predictions for SFAs, MUFAs, PUFAs and major FAs

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(Lucas et al., 2008a; Ottavian et al., 2012). Moreover, only a few authors have developed equations for minerals, achieving good results for Na (González-Martín et al., 2011; Lucas et al., 2008b; Ottavian et al., 2012), Ca and Zn (Lucas et al., 2008b), and K (GonzálezMartín et al., 2011). On the other hand, there is a lack of information on the prediction of cholesterol content in cheese. Although Paradkar and Irudayaraj (2002) developed an excellent prediction model using pure cholesterol, poor predictions have been reported for

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ACCEPTED MANUSCRIPT meat (Berzaghi, Dalle Zotte, Jansson, & Andrighetto, 2005; De Marchi, Berzaghi, Boukha, Mirisola, & Gallo, 2007). Therefore, the aim of the present study was to evaluate the feasibility of near infrared transmittance (NIT) spectroscopy to predict mineral, FA and cholesterol content of

Materials and methods

2.1.

Samples

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commercial European cheeses.

One hundred and forty-five European cheeses were purchased according to store availability and commercial trends in Italy from July to October 2015. Several cheese varieties from different dairy species and ripening time were included to ensure a wide

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variety of samples, which is useful for the development of robust prediction models (Table 1). Full details of chemical composition of each cheese variety can be retrieved from Manuelian, Currò, Penasa, Cassandro, and De Marchi (2017). Cheeses were transported in

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portable refrigerators (4 °C) to the laboratory of the Department of Agronomy, Food, Natural Resources, Animals and Environment of the University of Padova (Legnaro, Italy).

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Here, 1.5 cm of the rind were removed where required and each cheese was homogenised with a knife mill RetschGrindomix GM200 (Retch GmbH & Co, Haa, Germany). To preserve the products features, the ground samples were stored in sealed plastic bags at refrigeration temperature and analysed within 24 h of purchase.

2.2.

Chemical analyses of cheeses

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ACCEPTED MANUSCRIPT Cheese moisture and protein content were determined by FoodScanTM Dairy Analyzer (Foss, Electric A/S, Hillerød, Denmark) precalibrated with FOSS Artificial Neural Networks Dairy Calibration.

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2.2.1. Mineral composition

Major minerals (Ca, Na, P, S, K and Mg) and trace minerals (Zn, Fe, Se, Cu, Cr and Pb) were quantified after mineralisation with nitric acid (HNO3) in closed vessels by a

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microwave system (Ethos 1600 Milestone S.r.l. Sorisole, BG, Italy) using inductively

coupled plasma optical emission spectrometry (ICP-OES) Ciros Vision EOP (SPECTRO

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Analytical Instruments GmbH, Kleve, Germany). The ICP-OES determined Ca at 317.933 nm, Na at 589.592 nm, P at 178.287 nm, S at 182.034 nm, K at 766.941 nm, Mg at 285.213 nm, Zn at 213.856 nm, Fe at 259.941 nm, Se at 196.090 nm, Cu at 324.754 nm, Cr at 220.353 nm and Pb at 220.353 nm. Instrument operating parameters were optimised for acid

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solution and calibration standards were matched with 5% HNO3 (v/v) solution using 65% HNO3 Suprapur® (100441, Merck, Darmstadt, Germany). Operating conditions of ICP-OES were 2 mL min-1 of sample aspiration rate, plasma power 1350 W, coolant flow 11 L min-1,

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auxiliary flow 0.60 L min-1, nebuliser flow 0.75 L min-1 and integration time of 28 s. The calibration solutions for each mineral were prepared from single element solutions

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(Inorganic Ventures, Christiansburg, VA, USA) in a concentration range between 0 and 100 mg L-1.

2.2.2. Fatty acid composition and cholesterol content Accelerated solvent extraction method by ASE 200 (Dionex corporation, Sunnyvale. CA, USA) with hexane:isopropanol (3:2) as solvent was used for lipid extraction. Percentage of total fat content was obtained after solvent evaporation. The FAs were

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ACCEPTED MANUSCRIPT methylated following an internal method adapted from Christie (1993) with CH3ONa as catalyst and n-heptane as internal standard. Fatty acid methyl esters solution was centrifuged at 693 × g for 10 min at 10 °C, and transferred to 1.5 mL vial for GC analysis, using Agilent 7820A GC System (Agilent Technologies, Santa Clara, CA, USA) equipped with an

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automatic sampler G4567A (Agilent Technologies) and a flame ionisation detector. An Omegawax® capillary GC column (24136 Supelco; Sigma-Aldrich, Castle Hill, Australia) 30 m long, 0.25 mm inner diameter, and 0.25 µm film thickness was used. The carrier gas

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was hydrogen at a constant flow rate at 100 °C of 30 cm s-1. The injector and detector

temperature was set at 250 °C. Oven temperature was initially 50 °C for 2 min, and then

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increased at 4 °C min-1 to 220 °C and held for 18 min. The individual FAs were identified by comparing their retention times with those of a standard FA mix (FAME mixC4–C24 #18919-1AMP and octadecadienoic acid conjugated methyl ester; Supelco, Sigma-Aldrich). Peaks areas were calculated using GC/MSD ChemStation Software (Agilent Technologies)

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and expressed as percentage of total identified FAs. In addition, FAs were also expressed as absolute concentration (g 100 g-1 cheese) through the following formula: (FA%/100) × 0.945 (Greenfield & Southgate, 2003). Cholesterol content was determined according to Fletouris,

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Botsoglou, Psomas, and Mantis (1998) with a capillary column GC system (model GC-15A, Shimadzu Corp., Kyoto, Japan).

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To facilitate the description of the FA profile, individual FAs were classified as

major (≥8% of total identified FAs) and minor (≤8% of total identified FAs). Moreover, individual FAs were summed to obtain the following FA groups: SFAs, which included C4:0, C6:0, C7:0, C8:0, C10:0, C11:0, C12:0, C13:0 (iso and anteiso form), C14:0 (iso and anteiso form), C15:0 (iso and anteiso form), C16:0 (iso and anteiso form), C17:0 (iso and anteiso form), C18:0 (iso and anteiso form), C19:0, C20:0, C21:0, C22:0, C23:0, and C24:0; MUFAs, which included C10:1, C12:1, C14:1 (and isomers), C15:1, C16:1n9, C16:1n7,

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C18:2n6; ω-3, which was the sum of C18:3n3, C20:3n3, C20:5n3, C22:5n3, and C22:6n3; and ω-6, which included C18:2n6, C18:3n6, C20:2n6, C20:3n6, C20:4n6, and C22:2n6.

Near infrared transmittance spectra acquisition

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The ground samples were brought to room temperature (20 °C), introduced in a Petri cup (diameter 100 mm, depth 15mm) and scanned with the FoodScanTM Dairy Analyzer (Foss). The spectrometer operated at room temperature scanning every 2 nm from 850 to 1050 nm wavelength. Each spectrum was an average of 16 sub-spectra recorded at different

log(transmittance-1).

Spectral data analysis

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points by rotating the Petri cup automatically in the analyser and recorded as

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Spectral chemometric analysis was performed using WinISI software (Infrasoft

International, Port Matilda, PA, USA). Two prediction equations were developed for each trait using (i) the complete dataset (n=145) or (ii) a subset of the complete data termed the calibration set. Prediction equations for the complete dataset were validated using a 5-fold cross-validation, and prediction equations developed using the calibration set were validated by means of an external validation. Calibration and validation sets were obtained by splitting the complete data into 2 subsets with similar mean and standard deviation (SD) for each

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ACCEPTED MANUSCRIPT trait. The calibration set (75% of the samples) was used to generate the prediction models and the validation set (25% of the samples) was used to validate the calibrations. Calibration models were developed using modified partial least squares regression. For FAs, spectra information was correlated with the percentage of total identified FAs and

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with the absolute concentration in cheese. Samples with a predicted value that differed more than 2.5 SD from the reference value (T-statistics) were considered outliers and removed from the dataset. Combinations of scatter corrections (NONE, no correction; D, detrending;

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SNV, standard normal variate; SNV+D, standard normal variate + detrending; MSC,

multiplicative scatter correction) and derivative mathematical treatments (0,0,1,1; 1,4,4,1;

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1,8,8,1; 2,5,5,1 and 2,10,10,1; where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated, the third is the number of data points in the first smoothing and the fourth is the number of data points in the second smoothing) were tested.

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The prediction models were evaluated based on the number of latent factors (LF); the standard error of calibration (SEC), cross-validation (SECV), and external validation (SEP); the coefficient of determination of calibration (R2), cross-validation (R2CV), and external

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validation (R2ExV); and the residual predictive deviation of cross-validation (RPDCV) and of external validation (RPDExV) calculated as the ratio between SD and SECV or SEP,

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respectively (Williams & Sobering, 1993). Predictions were considered excellent when R2 was greater than 0.91, good when R2 ranged from 0.82 to 0.90, approximate when R2 was between 0.66 and 0.81, and poor when R2 was less than 0.66 (Karoui et al., 2006a). Prediction models with RPD greater than 2.5 were considered adequate for analytical purposes (Sinnaeve, Dardenne, Agneessens, & Biston, 1994), whereas prediction models with RPD smaller than 1.5 were considered unsatisfactory (Karoui et al., 2006a).

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Results

3.1.

Cheese composition

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Cheese moisture, fat, protein, total solids and cholesterol averaged 43.24 ± 0.97%, 27.24 ± 0.47%, 24.87 ± 0.54%, 56.76 ± 0.97% and 0.07 ± 0.001%, respectively (results not shown). Among major minerals, Ca, Na and P were the most abundant, and among trace

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minerals, Zn was 16 to 30 times more abundant than other elements (Table 2). For all

samples, Cr and Pb were under the level of detection (< 0.002 ppm). Cheeses were richer in

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SFAs than UFAs, richer in MUFAs than PUFAs, and richer in ω-6 than ω-3 (Table 3). About 70% of total identified FAs were composed of palmitic (C16:0), oleic (C18:1n9), myristic (C14:0) and stearic (C18:0) acids, which were considered major FAs (data not shown). The most abundant minor FAs were lauric (C12:0) and capric (C10:0), followed by

Prediction models for cheese minerals

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butyric (C4:0) and linoleic (C18:2n6) (Table 3).

Calibration and cross-validation statistics for prediction equations of cheese mineral

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content are summarised in Table 2, and calibration and external validation statistics for the same traits are presented in Table 4. The LF for the calibration models ranged from 6 to 10 (Tables 2 and 4). Outliers were less than 14% in all cases except for Mg, which reached 21% in the external validation. We developed more accurate prediction equations using crossvalidation than external validation. However, in both cases adequate predictions for analytical purposes were developed for Ca, P, S, Mg and Zn. Sodium prediction achieved RPDCV greater than 2.5 (Table 2), and RPDExV close to 2.5 (Table 4). On the contrary, Cu

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ACCEPTED MANUSCRIPT prediction showed RPDExV greater than 2.5 (Table 4), and RPDCV close to 2.5 (Table 2). Almost all the best calibrations were obtained after mathematical treatments, being the most used MSC, SNV and the second derivative (2,5,5,1).

Prediction models for fatty acids and cholesterol content

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3.3.

Prediction statistics for absolute content of FA and cholesterol validated using cross-

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validation procedure are reported in Table 3, and those validated using external validation are in Table 5. The LF kept on the selected equations were below 10 in both cases. Outliers

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were lower than 16% for the calibration model using all 145 cheese samples (Table 3), and lower than 20% for the calibration model developed using 75% of the total data (Table 5). Overall, we obtained more accurate predictions using the cross-validation than the external validation procedure. However, in both cases, we built adequate equations for SFAs, UFAs,

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MUFAs, PUFAs, C14:0, C16:0, C18:1n9, C4:0, C6:0, C15:0 and C16:1 (Tables 4 and 5). We also obtained adequate predictions for C18:0 and C10:0 in cross-validation (Table 3). The best prediction models were developed for SFA, being adequate for quality control

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when validated using cross-validation (Table 3). Also, the best equations were primarily obtained with the first derivative (1,4,4,1) in cross-validation (Table 3) and without

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derivative (0,0,1,1) in external validation (Table 5). Inadequate prediction equations were obtained for FAs expressed as % of total identified FAs (data not shown) and for cholesterol content (Tables 3 and 5).

4.

Discussion

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ACCEPTED MANUSCRIPT Cheese samples included 19 different types of cheese manufactured from different milks (water buffalo, cow, sheep or goat; whole or partial skimmed milk), technological processes and ripening times. All these aspects ensured huge variability to the dataset, which is essential to develop robust prediction models (De Marchi, Toffanin, Cassandro, & Penasa,

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2014). Recently, Eskildsen et al. (2014) have reported covariance structures between milk individual FAs and total fat that lead to biased mid-infrared FA predictions. Because of that, it is necessary to maintain the same indirect correlations in the calibration and the validation

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set (Eskildsen et al., 2014). Although external validation is generally recommended

external validation.

4.1.

Prediction of cheese minerals

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(O’Donnell, Fagan, & Cullen, 2014), we obtained similar results from cross-validation and

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Mineral calibrations are difficult to achieve because NIR spectroscopy could only detect minerals if they are in organic complexes (Buing-Pfaue, 2003; Woodcock, Fagan, O’Donnell, & Downey, 2008). In addition, Begley et al. (1984) demonstrated that NaCl

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changes the NIR water spectrum producing a wavelength shift in the absorption band of water that could be detected. It has been reported that for most constituents the limit of

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sensitivity for infrared spectroscopy is 0.1% (Iwamoto, Kawano, & Ozaki, 1995), which could explain the unsatisfactory calibrations for Fe and Se. The LF retained for building calibrations were in agreement with previous studies

(González-Martín et al., 2011; Karoui et al., 2006a,b; Lucas et al., 2008b; Ottavian et al., 2012). In addition, LF between 1 and 12 have been considered adequate to avoid overfitting issues (De Marchi, 2013). We reported better results with the second derivate, similarly to González-Martín et al. (2011). However, Lucas et al. (2008b) reported better results with

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ACCEPTED MANUSCRIPT first derivative. In addition, while we selected MSC and SNV as the best mathematical treatments for almost all the minerals, which agreed with González-Martín et al. (2011) and Lucas et al. (2008b) who developed better models using SNV+D. Results for Na in cross-validation agreed with those of González-Martín et al. (2011)

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who reported RPDCV of 3.8 for Na content of intact cheese samples. We reported lower RPDExV for Na than Lucas et al. (2008b) (RPDExV = 2.94) did in ground cheese, but greater values than those reported by Karoui et al. (2006a) for NaCl in Emmentaler cheese (RPDExV

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= 1.59 and 1.33 for mid-infrared and near-infrared spectroscopy, respectively) and Ottavian et al. (2012) for NaCl in Asiago d’Allevo cheese (RPDExV = 2.00). Ottavian et al. (2012)

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argued that the most important region for NaCl calibration was between 1850 and 1950 nm. The promising predictions for Na obtained in the present work suggested that wavelength range between 850 and 1050 nm and the use of the instrument in transmittance instead of reflectance mode were important to develop the model, which agreed with Ellekjær

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Hildrum, Næs, and Isaksson (1993), who showed that both reflectance and transmittance could be used to predict NaCl in sausages.

Our successful predictions for Ca and Zn using cross-validation and external

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validation agreed with Lucas et al. (2008b), but not with González-Martín et al. (2011) who reported unsatisfactory predictions for Ca (RPDCV = 2.00). We obtained better results for P

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and Mg prediction models than Lucas et al. (2008b) (RPDExV = 1.93 for Mg) and GonzálezMartín et al. (2011) (RPDCV = 1.8 and 2.0 for P and Mg, respectively). On the other hand, predictions for K were worse than those of Lucas et al. (2008b) (RPDExV = 2.06) and González-Martín et al. (2011) (RPDCV = 2.70). The differences observed between our results and those of González-Martín et al. (2011) could be related to the sample preparation. While we ground the samples before scanning with the FoodScan instrument, González-Martín et al. (2011) applied the NIR technology directly to the samples without any treatment

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ACCEPTED MANUSCRIPT (González-Martín et al., 2011). Previous studies pointed out that the milling process improved the predictive ability of NIR spectroscopy in food because of the homogenisation of the sample (Riovanto, De Marchi, Cassandro, & Penasa, 2012).

Prediction of fatty acid composition and cholesterol content of cheese

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4.2.

The poor predictions for FAs expressed as percentage of total identified FAs

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compared with FAs expressed as g 100 g-1 cheese agreed with authors who compared

relative and absolute FA predictions in cow milk (De Marchi et al., 2014) and poultry meat

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(Riovanto et al., 2012; Zhou, Wu, Li, Wang, & Zhang, 2012). Although we unsuccessfully predicted relative FA content, Ottavian et al. (2012) built satisfactory models (RPDCV > 2.5) for SFAs, MUFAs, myristic, oleic, palmitic, capric, and lauric acids in Asiago d’Allevo cheese. Nevertheless, we achieved more accurate predictions for absolute FA content than

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Ottavian et al. (2012). The inaccurate predictions for cholesterol content agreed with previous results on beef (De Marchi et al., 2007) and chicken meat (Berzaghi et al., 2005). To our knowledge, only few studies have dealt with NIR spectroscopy as a tool to

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predict FA composition of cheese. Lucas et al. (2008a) reported that the absorption regions that mainly contributed to the SFA, MUFA and PUFA models were 1032 to 2188 nm, 1500

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to 1900 nm and 2000 to 2250 nm, respectively. In addition, Núñez-Sánchez et al. (2016) suggested that FA and fat absorption bands of goat milk were at 1210, 1726, 1760, 2308 and 2348 nm, being around 1725 nm related to oleic acid and 1760 nm related to saturated components. Although the FoodScan instrument operated at a lower wavelength range (850 to 1050 nm), we obtained very good predictions for almost all groups and major FAs, which suggested the presence of other important absorption bands regions for FAs using the near infrared technology in transmittance mode. The LF considered in the present study for the

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ACCEPTED MANUSCRIPT prediction of the traits was slightly lower than that reported for French cheeses (Lucas et al., 2008a) and Asiago d’Allevo cheese (Ottavian et al., 2012). Moreover, Lucas et al. (2008a) argued that the best prediction models were developed using the second derivative, whereas we obtained better results with the first derivative.

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Prediction equations were more accurate for the more abundant FAs than minor FAs, which is mainly a consequence of the concentration effect (Coppa et al., 2010). We obtained more accurate equations for SFAs, which included 68% of the identified FAs, than PUFAs,

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which represented only 5% of the identified FAs. Coppa et al. (2010) also reported lower accuracy for PUFAs (RPDCV = 2.57) than SFAs (RPDCV = 6.25) in oven-dried milk. In the

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present study, RPDExV values for SFAs, MUFAs, PUFAs, myristic, oleic and butyric acids were greater than those reported by Lucas et al. (2008a) (RPDExV = 2.84, 2.90, 2.54, 2.94, 2.01, and 2.43, respectively), and RPDExV values for ω-3 and ω-6 were greater than those reported by Coppa et al. (2010) in milk (RPDCV = 1.65 and 0.20, respectively). Regarding

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CLAs, Coppa et al. (2010) and Lucas et al., (2008a) reported better predictions in milk (RPDCV = 2.77) and rumenic acid (the main acid of CLAs) in French cheeses (RPDExV =

Conclusions

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2.70), respectively, compared with our findings in cheese.

Results of the present study demonstrated the feasibility of NIT spectroscopy to

predict minerals and FA composition of cheese. The huge variability of the initial data in terms of type of cheese, species that originated milk for cheese production and ripening time helped to build robust calibrations. However, the prediction models could fail when applying to samples of different population. Cross-validation and external validation performed similarly confirming the great potential of prediction models. Although minerals

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ACCEPTED MANUSCRIPT are difficult to predict, excellent prediction models were developed for Ca, P, S, Mg and Zn. This work also supported the contention that more accurate predictions were obtained for FAs expressed as absolute than relative content in cheese. Successful predictions were achieved for SFAs, followed by UFAs, MUFAs, PUFAs, major FAs (myristic, palmitic, and

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oleic) and some minor FAs, whereas cholesterol content could not be predicted with

satisfactory accuracy. Overall, these findings are a precursor to at-line implementation of the prediction models for the most abundant cheese minerals and FAs, which in view of its

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speed, could be an appropriate method for process control and optimisation during the

the label of their products.

Acknowledgement

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production of cheese. This would help the dairy industry to incorporate this information on

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We thank Luciano Magro and Massimo Cagnin (Department of Agronomy, Food,

References

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Natural Resources, Animals and Environment - University of Padova) for technical support.

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ACCEPTED MANUSCRIPT Table 1 Description of characteristics of commercial cheeses and cheese-making technologies. a Species Country Milk

Milk fat

Asiago PDO Brie Casatella PDO Cheddar Emmentaler PDO Feta PDO Fontina PDO Robiola Goat Gorgonzola PDO Grana Padano PDO Maasdam Montasio PDO Mozzarella PDO/TSG Parmigiano Reggiano PDO Piave PDO Provolone PDO Pecorino Spun Paste Taleggio PDO

C C, S C C C C, G, S C C, G, S C C C C B, C C C C S C C

YN YN Y Y Y Y Y Y Y N Y N Y N Y Y Y Y Y

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RP R P P R RP R RP P R P R P R P RP RP P P

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a

IT FR IT UK CH GR IT IT IT IT NL IT IT IT IT IT IT IT IT

Ripening (months) 0.7–24 1–1.7 0.1–0.3 9–24 4–12 <2 3 0.3–1.3 3–6 9–20 4–12 2–6 12–24 0.7–18 0.3–9 0.7–12 ≥1.2

Texture H, SH S S H, SH SH S SH S S H SH H S H H SH H, SH S S

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Cheese type

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Abbreviation are: PDO, Protected Denomination of Origin; TSG, Traditional Specialities Guaranteed; B, water buffalo; C, cow; G, goat; S, sheep; IT, Italy; FR, France; UK, United Kingdom; CH, Switzerland; GR, Greece; NL, the Netherlands; R, raw milk; P, pasteurised milk; RP, raw or pasteurised milk; Y, whole milk; N, partially skimmed milk; YN, whole milk or/and partially skimmed milk; H, hard; SH, semi-hard; S, soft.

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ACCEPTED MANUSCRIPT Table 2 Calibration and cross-validation statistics for modified partial least squares regression models developed to predict minerals content of commercial cheeses.a N

Mean SD

LF

SEC

R2

SECV

R2CV

RPDCV

135 134 137 134 139 124

5.53 4.71 3.38 1.16 1.31 0.19

2.41 2.07 1.16 0.32 0.52 0.08

8 10 8 9 10 7

0.50 0.59 0.26 0.07 0.24 0.02

0.96 0.92 0.95 0.95 0.79 0.93

0.53 0.63 0.29 0.08 0.26 0.02

0.95 0.91 0.94 0.94 0.74 0.93

4.57 3.28 4.02 4.22 1.97 3.71

Trace elements (µg g-1) Zn MSC(0011) Fe SNV(1441) Se MSC(2551) Cu MSC(2551)

136 140 142 126

24.17 1.47 0.80 1.02

9.12 0.84 0.15 1.96

8 7 1 10

0.93 0.30 0.05 0.82

3.90 1.20 1.01 2.38

SC 2.19 0.64 0.15 0.70

0.94 0.41 0.09 0.87

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a

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Trait Math Major minerals (mg g-1) Ca D(1441) Na MSC(2551) P SNV(0011) S SNV(2551) K NONE(2551) Mg NONE(0011)

2.34 0.70 0.15 0.82

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Abbreviation are: Math, mathematical treatment; D, detrend; MSC, multiplicative scatter correction; SNV, standard normal variate; NONE, no correction; N, number of samples used to develop the model; SD, standard deviation of reference data; LF, number of latent factors selected; SEC, standard error of calibration; R2, coefficient of determination of calibration; SECV, standard error of cross-validation; R2CV, coefficient of determination of crossvalidation; RPDCV, residual predictive deviation of cross-validation calculated as the ratio of SD in SECV.

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ACCEPTED MANUSCRIPT Table 3 Calibration and cross-validation statistics for modified partial least squares regression models developed to predict absolute concentration of fatty acids (FAs) (g 100 g-1 cheese) and cholesterol content of commercial cheeses (g 100 g-1 cheese). a

a

Math

N

Mean SD

LF SEC R2

SECV R2CV RPDCV

MSC(1441) SNV(1441) MSC(1441) MSC(2551) SNV+D(1441) SNV(2551) MSC(2551)

123 130 128 130 122 127 130

17.01 8.31 7.08 1.23 0.20 0.21 0.70

3.50 1.85 1.55 0.37 0.06 0.08 0.21

10 10 10 7 10 9 9

0.67 0.43 0.39 0.12 0.04 0.04 0.09

0.96 0.95 0.94 0.89 0.65 0.78 0.80

5.25 4.30 4.03 3.00 1.67 2.14 2.25

MSC(1441) SNV(1441) MSC(0011) NONE(1441)

125 128 136 127

2.68 7.47 2.64 5.10

0.59 1.58 0.58 1.10

D(2551) D(0011) D(1441) SNV(1441) SNV+D(0011) SNV(1441) NONE(1441) MSC(2551) D(1141) D(1441)

131 130 122 121 129 128 128 131 127 138

0.64 0.46 0.30 0.70 0.83 0.28 0.39 0.63 0.05 0.07

0.13 0.09 0.06 0.19 0.21 0.07 0.09 0.19 0.01 0.02

SC 10 9 10 10

0.14 0.39 0.21 0.29

0.95 0.94 0.88 0.93

0.14 0.41 0.23 0.32

0.94 0.93 0.85 0.91

4.11 3.83 2.57 3.30

6 6 6 10 5 10 8 8 10 3

0.05 0.03 0.02 0.07 0.09 0.02 0.03 0.09 0.01 0.01

0.87 0.86 0.86 0.88 0.83 0.91 0.91 0.77 0.73 0.59

0.05 0.04 0.03 0.07 0.09 0.02 0.03 0.10 0.01 0.01

0.85 0.85 0.83 0.86 0.82 0.90 0.89 0.75 0.68 0.57

2.62 2.62 2.40 2.69 2.37 3.11 3.08 1.99 1.75 1.53

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0.97 0.95 0.95 0.90 0.67 0.81 0.83

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0.61 0.40 0.36 0.12 0.04 0.03 0.09

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Trait FA groups SFA UFA MUFA PUFA CLA ω-3 ω-6 Major FAs C14:0 C16:0 C18:0 C18:1n9 Minor FAs C4:0 C6:0 C8:0 C10:0 C12:0 C15:0 C16:1 C18:2n6 C20:0 Cholesterol

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Abbreviation are: Math, mathematical treatment; D, detrend; MSC, multiplicative scatter correction; SNV, standard normal variate; NONE, no correction; N, number of samples used to develop the model; SD, standard deviation of reference data; LF, number of latent factors selected; SEC, standard error of calibration; R2, coefficient of determination of calibration; SECV, standard error of cross-validation; R2CV, coefficient of determination of crossvalidation; RPDCV, residual predictive deviation of cross-validation calculated as ratio of SD in SECV.

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Table 4

Trait

Math

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Calibration and external validation statistics for modified partial least squares regression models developed to predict minerals content in commercial cheeses.a Calibration set N Mean (SD)

LF

SEC

R2

Validation set N Mean (SD)

99 94 103 100 101 94

5.48 (2.39) 4.85 (2.31) 3.29 (1.19) 1.15 (0.33) 1.28 (0.54) 0.19 (0.07)

6 10 9 8 10 7

0.55 0.54 0.26 0.07 0.23 0.02

0.95 0.94 0.95 0.95 0.82 0.94

36 35 36 32 35 29

100 101 106 96

23.69 (9.21) 1.47 (0.83) 0.79 (0.16) 1.18 (2.22)

9 8 1 10

1.97 0.63 0.15 0.78

0.95 0.43 0.11 0.88

34 37 37 32

a

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RPDExV

5.89 (2.24) 4.46 (1.70) 3.57 (1.05) 1.26 (0.30) 1.37 (0.45) 0.21 (0.08)

0.60 0.73 0.28 0.10 0.29 0.02

0.93 0.83 0.94 0.90 0.65 0.90

3.73 2.35 3.73 3.02 1.54 3.17

25.80 (8.86) 1.47 (0.88) 0.84 (0.15) 1.40 (2.60)

2.77 0.75 0.15 0.86

0.91 0.32 0.03 0.90

3.19 1.18 0.99 3.02

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Major minerals (mg g ) Ca MSC(0011) Na SNV(2551) P SNV(2551) S MSC(1441) K NONE(2551) Mg MSC(0011) Trace elements (µg g-1) Zn SNV(0011) Fe SNV+D(1441) Se SNV+D(2551) Cu MSC(2551)

R2ExV

SC

-1

SEP

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Abbreviation are: Math, mathematical treatment; D, detrend; MSC, multiplicative scatter correction; SNV, standard normal variate; NONE, no correction; N, number of samples used to develop the model; SD, standard deviation of reference data; LF, number of latent factors selected; SEC, standard error of calibration; R2, coefficient of determination of calibration; SECV, standard error of cross-validation; R2CV, coefficient of determination of cross-validation; RPDCV, residual predictive deviation of cross-validation calculated as ratio of SD in SECV.

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Table 5

LF

SEC

R2

MSC(1441) SNV+D(1441) SNV(0011) SNV(2551) MSC(2551) MSC(2551) MSC(0011)

93 94 91 99 94 94 95

17.21 (3.40) 8.44 (1.82) 7.19 (1.56) 1.23 (0.38) 0.19 (0.05) 0.20 (0.07) 0.70 (0.21)

9 8 10 8 7 8 9

0.58 0.33 0.28 0.12 0.03 0.03 0.10

0.97 0.97 0.97 0.90 0.67 0.85 0.76

MSC(1441) MSC(0011) NONE(1441) D(2551)

95 98 93 92

2.70 (0.57) 7.55 (1.58) 2.65 (0.55) 5.14 (1.07)

7 10 9 10

0.14 0.40 0.18 0.23

D(2551) SNV(0011) SNV(0011) MSC(0011) SNV+D(0011) SNV(0011) NONE(1441) MSC(2551) D(2551) D(0011)

95 92 86 89 97 96 95 101 96 101

0.64 (0.13) 0.46 (0.09) 0.30 (0.07) 0.70 (0.19) 0.84 (0.22) 0.28 (0.07) 0.39 (0.10) 0.62 (0.19) 0.05 (0.01) 0.07 (0.02)

6 7 5 7 10 8 9 9 8 3

0.05 0.03 0.02 0.07 0.07 0.02 0.03 0.10 0.00 0.01

Validation set N Mean (SD)

SC

Calibration set N Mean (SD)

SEP

R2ExV

RPDExV

27 34 31 35 30 31 33

16.54 (4.19) 8.23 (1.91) 6.98 (1.63) 1.30 (0.41) 0.19 (0.07) 0.21 (0.07) 0.71 (0.22)

0.89 0.53 0.47 0.15 0.04 0.04 0.11

0.97 0.92 0.92 0.87 0.58 0.71 0.76

4.73 3.58 3.50 2.74 1.57 1.87 2.02

0.94 0.94 0.90 0.95

29 33 34 32

2.60 (0.62) 7.34 (1.60) 2.48 (0.56) 5.03 (1.09)

0.19 0.50 0.24 0.32

0.91 0.90 0.82 0.91

3.25 3.17 2.30 3.38

0.88 0.90 0.90 0.87 0.89 0.91 0.90 0.73 0.78 0.62

35 31 26 26 37 31 29 35 30 37

0.63 (0.15) 0.46 (0.11) 0.29 (0.07) 0.68 (0.17) 0.83 (0.19) 0.28 (0.07) 0.37 (0.09) 0.65 (0.20) 0.0 (0.01) 0.07 (0.02)

0.06 0.04 0.03 0.08 0.11 0.02 0.03 0.09 0.07 0.01

0.89 0.87 0.77 0.83 0.66 0.89 0.88 0.85 0.57 0.50

2.71 2.62 2.00 2.12 1.68 3.04 2.76 2.22 0.16 1.36

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FA groups SFA UFA MUFA PUFA CLA ω-3 ω-6 Major FAs C14:0 C16:0 C18:0 C18:1n9 Minor FAs C4:0 C6:0 C8:0 C10:0 C12:0 C15:0 C16:1 C18:2n6 C20:0 Cholesterol

Math

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Trait

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Calibration and external validation statistics for modified partial least squares regression models developed to predict absolute concentration of fatty acids (FAs) (g 100 g-1 cheese) and cholesterol content of commercial cheeses (g 100 g-1 cheese).a

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ACCEPTED MANUSCRIPT

a

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EP

TE D

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SC

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Abbreviation are: Math, mathematical treatment; D, detrend; MSC, multiplicative scatter correction; SNV, standard normal variate; NONE, no correction; N, number of samples used to develop the model; SD, standard deviation of reference data; LF, number of latent factors selected; SEC, standard error of calibration; R2, coefficient of determination of calibration; SECV, standard error of cross-validation; R2CV, coefficient of determination of cross-validation; RPDCV, residual predictive deviation of cross-validation calculated as ratio of SD in SECV.

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