RETRACTED: Vibrational spectroscopy used in milk products analysis: A review

Food Chemistry 196 (2016) 877–884

Contents lists available at ScienceDirect

Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Review

Vibrational spectroscopy used in milk products analysis: A review Andrei A. Bunaciu a, Hassan Y. Aboul-Enein b, Vu Dang Hoang c,⇑ a

SCIENT – Research Center for Instrumental Analysis, (CROMATEC_PLUS S.R.L.), Tancabesti, Snagov, Romania Pharmaceutical and Medicinal Chemistry Department, Pharmaceutical and Drug Industries Research Division, National Research Centre, Giza 12622, Egypt c Department of Analytical Chemistry and Toxicology, Hanoi University of Pharmacy, Hanoi, Viet Nam b

a r t i c l e

i n f o

Article history: Received 8 June 2015 Received in revised form 20 August 2015 Accepted 5 October 2015 Available online 9 October 2015 Keywords: Milk analysis Vibrational spectroscopy Milk composition Infrared and Raman applications

a b s t r a c t Milk is a fluid containing several substances, and its composition depends on several factors. Vibrational spectroscopy is a powerful tool to determine the constituent concentrations and qualitative characteristics of dairy products. Vibrational spectrometry covers a series of well-established analytical methodologies suitable to be employed for both qualitative and quantitative purposes. In the first part of this review, theoretical aspects on vibrational techniques are presented; in the second part, the most important papers, published during the period 2009–2015, related to milk analysis are discussed. Ó 2015 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theoretical aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Vibrational spectroscopy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Milk analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analytical applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Chemical composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Adulterant analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Other methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Vibrational spectroscopy includes several techniques, but the most important are mid-infrared (MIR), near-IR (NIR) and Raman spectroscopy. Both mid-IR (MIR) and Raman spectroscopy provide characteristic fundamental vibrations that are employed for the elucidation of molecular structure. Near-IR spectroscopy measures the broad overtone and combination bands of some of the fundamental vibrations (only the higher frequency modes), and is an excellent technique for rapid and accurate quantitation. Milk is a food that is rich in nutrients; it is considered to be a complete nutrient source for humans, and is widely marketed ⇑ Corresponding author. E-mail address: [email protected] (V.D. Hoang). http://dx.doi.org/10.1016/j.foodchem.2015.10.016 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.

877 878 878 879 880 880 882 883 883 883

and consumed by populations around the world. Milk powder also has an important role in the diets of human populations, as well as in the world economy. Milk can be defined as the fluid secreted by the female of all mammalian species, primarily to meet the complete nutritional requirement of the neonate. The increasing public demand for high-quality and safe food, together with the globalization of food production, have resulted in a global and interconnected system for the production and distribution of food, followed by an enormous increase in food standards (Trienekens & Zuurbier, 2008). In order to satisfy this demand, appropriate analytical tools for food analysis during and after production are required. An increase in milk consumption has occurred in recent years, owing to its popularity as a nutritional food. The proportion of every nutritional component in milk products is determined

878

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

according to the needs of different people, and the nutrition components greatly affect the market price of the product. Tragedies caused by malnutrition can be avoided by enhancing analytical methods for determining the quality of milk powders, so that high quality safe products can be ensured. Milk is a mixture of several substances (lactose, lipids, proteins, amino acids, urea, creatinine, etc.), and its composition depends on several factors, such as genetic breeding programs, feeding schemes and climatic conditions among others. Approximately 80% of total milk protein is casein and 20% whey protein. Caseins include alpha-casein, beta-casein, kappa-casein and gammacasein. Whey proteins (also called milk serum proteins) remain soluble when caseins are coagulated by either enzymes or acid (Cavalcanti Inácio, de Fátima Vitória de Moura, & Kássio Michell Gomes de Lima, 2011). They include beta-lactoglobulin, alphalactalbumin, blood serum albumins, immunoglobulins and protease-peptone fractions. The building blocks of protein are amino acids, and there are about 20 amino acids, of which at least nine are dietary essentials. Milk powders contain significant amounts of these essential amino acids, in addition to others that are required by children. Lysine is of particular interest, as this is the first limiting amino acid in individuals whose diets are based mainly on cereals and vegetable protein sources (US Dairy Export Council). Consumers commonly judge the quality of milk powder by using touch, colour, smell, taste, rate of solubility and other factors. Although these methods of evaluation are simple and accepted by consumers, they are subjective and have low accuracy. Therefore, a rapid, accurate and non-destructive quantification device, or method, is needed. Consumers are nowadays increasingly interested in information about the origin of their foods, including information on dairy cows’ diet, housing and herd management system. This is because specific feeding regimen and herd management practices, such as fresh grass feeding or pasture grazing, are linked to superior organoleptic and nutritional quality attributes of milk and dairy products (Elgersma, Tamminga, & Ellen, 2006). The most widely used methods for milk analysis are UVphotometry with wavelengths at 280 nm and 220 nm (Stoscheck, 1990), the Biuret method (340–550 nm) (Gornall, Bardawill, & David, 1949), and other classical methods (Bradford, 1976; Lowry, Rosebrough, Farr, & Randall, 1951; Zaia, Verri, & Zaia, 1999). Most of these methods, along with the Kjeldahl method, are tedious owing to the large number of analytical steps, resulting in time-consuming analysis and the need for reagents and/or expensive equipment, involving high operating costs and maintenance. Among the panoply of analytical tools used are vibrational spectroscopic techniques (near-infrared [NIR], mid-infrared [MIR], and Raman), now attracting growing interest, and based on the fact that food products have a specific composition or characteristics that gives them an individual ‘‘fingerprint”. Genetic evaluation of dairy animals requires rather large data sets, possibly larger than what is feasible to observe directly through chemical analyzes of milk composition and quality traits, because these are costly and time consuming. An alternative and more convenient method, the use of infrared (IR) spectra, has been proposed (Goulden, 1964). The common practice used currently is that milk composition phenotypes are collected using FTIR spectra. These phenotypes are obtained through application of calibration equations on the spectral data. In the food industry, food safety and quality are an important issue all over the world, which are directly related to people’s health and social progress. Consumers are gradually looking for quality seals and trust marks on food products, and expect manufacturers and retailers to provide products of high quality. All of

these factors have underlined the need for reliable techniques to evaluate the food quality. Considering the demands in practice, it is more necessary to develop a fast and efficient method, such as vibrational spectroscopy technology, to accomplish the food quality detection. Different vibrational spectroscopy methods for the analysis of milk powder constituents have been reviewed recently (McGoverin, Clark, Holroyd, & Gordon, 2009; Moros, Garrigues, & de la Guardia, 2007; Moros, Garrigues, & de la Guardia, 2010; Okazaki, Hiramatsu, Gonmori, Suzuki, & Tu, 2009). The objective of this article is to review new developments in applications of vibrational spectroscopy in milk analysis investigations, covering the period between 2009 and 2015. Prior to a review on this subject, it is useful to give a short introduction to the concept of the vibrational spectroscopy and milk analysis, followed by discussion of the quantitative and qualitative milk investigations of the techniques.

2. Theoretical aspects 2.1. Vibrational spectroscopy Vibrational spectroscopy is the term used to describe studies of the interaction between electromagnetic radiation and the vibrational states of atomic nuclei within their respective molecules. It is regarded as a rapid and reliable means of investigating food quality and safety, that offers quick analysis, minimal sample preparation requirements, and low cost (Larkin, 2011; Sun, 2009). The principal advantages of vibrational spectroscopy include its speed of analysis, and its potential selectivity when coupled with chemometric data analysis techniques. They can be used to detect compositional differences between food samples on the basis of vibrations of various chemical groups at specific wavelengths, in the mid-infrared region of the spectrum. A notable advancement in IR spectroscopy technology is represented by Fourier transform infrared (FTIR) spectroscopy. FTIR differs from standard IR technology, because it employs the interferometric modulation of radiation to measure multiple frequencies simultaneously. The resulting interferogram is then converted to the original spectrum using complex algorithms. The major advantage of FTIR over dispersive-based instrumentations are: improved sensitivity, due to higher signal to noise ratio, improved speed of analysis, higher energy throughput and superior accuracy (Rodriguez-Saona & Allendorf, 2011). Infrared (IR) spectroscopy expresses typical vibration modes of covalent bonds in molecules, and thus contains quantitative information about all the constituents that absorb IR radiation, including proteins (Etzion, Linker, Cogan, & Shmulevich, 2004). FTIR spectroscopy is routinely used by laboratories specializing in milk analysis, because it is a fast, non-destructive, and easy procedure that enables simultaneous measurement of several components in a complex natural media (Andersen, Hansen, & Andersen, 2002; Griffiths & deHaseth, 1986). The MIR range (wave number range 400–4000 cm 1) is especially attractive, since measurements in this range provide direct information concerning the specific constituents in the sample, as well as their characteristic molecular structure. However, the use of the MIR spectrum for quantitative analysis of protein components is difficult for two fundamental reasons. Firstly, water, which is the continuous phase in milk as well as in many other biological media, has intense absorbance bands centered on approximately 1640 cm 1 (resulting from H2O bending vibrations) and 3300 cm 1 (resulting from O–H stretching vibrations) (Libnau, Kvalheim, Christy, & Toft, 1994). The first water band overlaps with much smaller bands characteristic of proteins, known as amide I and amide II, respectively

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

(Bunaciu, Fleschin, & Aboul-Enein, 2014; Jung, 2000; Susi, 1969). These bands result from different combinations of vibrations in the peptide bonds, and have a major role in protein analysis by MIR spectroscopy (Curley, Kumosinski, Unruh, & Farrell, 1998). Secondly, the formation of both intra- and intermolecular hydrogen bonds by proteins affects the position, magnitude, and shape of the amide bands. Difficulties arise from a number of sources. Firstly, in a biological sample such as food, the number and types of compounds containing, for example, C@O bonds is large and variable. Therefore, spectra are usually very complex, containing information about most, if not all, of the molecular absorbers present in a given sample. When dealing with food samples, for example, it is often not possible to definitively relate absorbance at any specific frequency or wavelength to an individual molecular species; rather, the approach has been to accept that some, or all, of the recorded spectra include information about the complete molecular composition of the material under test, and may be considered to be a fingerprint of it. Secondly, there are effects arising from the fact that the molecular absorbers are not harmonic (ideal) so that in addition to the fundamental (strongest) absorption, there will also be other absorptions arising from combinations and overtones, Fermi resonances and combinations. Overtone and combination band absorbances are particularly important in near infrared spectra and are found at 1690–1755 nm (first overtone), 1127–1170 nm (second overtone) and 845–878 nm (third overtone) (Workman & Weyer, 2007). Mid-infrared spectroscopy allows structural elucidation and compound identification; functional groups absorb photons at characteristic frequencies of MIR radiation. The MIR region is between 4000 and 400 cm 1. Near-infrared spectroscopy is widely used to determine organic matter constituents. It is based on the absorption of electromagnetic radiation by a sample at wavelengths in the 800–2500 nm range. Raman spectroscopy provides chemical and structural information. For MIR and NIR techniques, the overlapping of many different overtone and combination vibrations results in broad bands with low structural selectivity in NIR spectra compared with MIR spectra where fundamentals are more resolved, allowing the structure of a sample to be better elucidated (Karoui, Mazerolles, & Dufour, 2003). While MIR spectra contain information arising from fundamental molecular vibrational frequencies, in the near-infrared (NIR) region, information arises from overtones and combinations of such vibrations, rendering them more difficult to interpret. Spectroscopic methods give complementary information about a molecule vibration. In the MIR and Raman techniques, vibrations that results in changes in the dipole moment are MIR active, whereas vibrations that results in a change in polarizability are

Fig. 1. Absorption spectra of five milk samples with varying fat content. Spectra were collected in the transmission mode.

879

Raman active (Pistorius, 1995). Some vibrations can be both MIR and Raman active. For example, the C@C bond is generally more intense in Raman than in IR spectra, because the double bond connects two identical parts of the molecule that lead to high polarizability changes, whereas a C@O bond exhibits a high electric dipole moment, leading to a more intense band in MIR than in Raman spectra (Abbas, Dardenne, & Baeten, 2012). Major food compounds (fat, protein, and carbohydrates) present C–H bands in both Raman and MIR, but with variable intensities. Vibrational spectroscopy techniques, both infrared (IR) and Raman, are gaining attention since they could solve some of the problems presented by traditional techniques (Craig, Franca, & Irudayaraj, 2015). FT-Raman spectroscopy offers some advantages for the characterization of food products when compared with infrared spectroscopy. For instance, it is not destructive, does not require sample preparation (thus saving on time and reagents), can determine more than one component at a time, and is free from water interference in the analysis. 2.2. Milk analysis Breast milk is the first and best food for an infant. According to WHO (World Health Organization), women should breastfeed until 6 months after delivery and then can continue breastfeeding up to 12 months (together with infant formula supplementation) (WHO, 2007). Milk is made up of more than 80% water and has a spectrum that looks very similar to water (as can be seen in Fig. 1) (McClure & Stanfield, 2002). The most obvious difference between the spectra of water (spectrum 5 in Fig. 1) and milk is that the milk spectra have higher absorbances than water at all wavelengths. Water bands are very weak in Raman spectra, but they present a high broad band in MIR spectra because of the weakness of O–H bond polarizability. A careful look at milk spectra reveals an interesting correlation. As the concentration of fat globules and protein micelles in the milk samples decreases, the absorption spectra shift downward. This shift is a ‘‘light scattering” effect, an expected occurrence in turbid liquid samples. Light scattering causes the light to travel further than it would in a sample (like water) free of this peculiarity. This idiosyncrasy is always present in turbid (milk and muddy water) and solid (plant tissue) samples. The second obvious difference between the spectra of milk and water is the presence of a dominant linear term that tilts the milk spectra upward in the 400–1300 nm region, an effect caused by colour transitions associated with the milk complex. This is another anomaly associated with turbid samples, and it becomes more prominent as the sample becomes darker in colour. For example, if one were to add brown soluble dye to the milk, the colour change would be exhibited as an increase in the slope of the spectra between 400 and 1300 nm. A less obvious, but still noticeable difference is the presence of absorption bands due to fat in the region of 1700 and 2300 nm. These peaks can be clearly observed in the spectra of ‘‘half-and-half” (a product containing half cream and half milk). Half-and-half certainly has the highest concentration of fat, a fact that is substantiated by the obvious ‘‘fat bands” at 1730, 1778 and 2312 and 2354 nm. The two bands at 1730 and 1778 nm have been assigned to first overtone CH2 stretching vibrations of fat and the two bands at 2312 and 2354 nm to combination stretching and bending of CH2 and CH3 in the fat molecules. The second-derivative spectra (presented also in Fig. 1) clearly indicate the quantitative relationship between the magnitude of the bands and fat concentration. The investigation of the authenticity of milk powder is extremely important because alterations can compromise the nutritional value of school meals that contain milk powder, milk

880

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

powder containing supplements for children and lactating women, and milk powder-containing food for convalescents and the elderly. The adulteration and food authenticity problems arise from a competitive market and the urge for illegal commercial benefits. For example, the adulteration of products of good quality could lead to lower pricing and cause unfair competition (Liu, Ye, He, & Wang, 2009), thus hampering legitimate business. Because milk powder is a highly complex matrix, the corresponding infrared spectral data contain highly overlapped peaks regardless of bandwidth. It is necessary to use chemometric methods to extract the relevant quantitative information. In recent decades, spectroscopic methods have been developed to assess the quality of milk, with some earlier reviews being published (Borin, Ferrão, Mello, Maretto, & Poppi, 2006; Inon, Garrigues, & de la Guardia, 2004; Karoui & Baerdemaeker, 2007; Kasemsumran, Thanapare, & Kiatsoonthan, 2007; Moros et al., 2007). 3. Analytical applications The applications that will be presented next can be divided in several fields, such as: chemical composition, adulteration, especially melamine content and other different milk analysis using vibrational spectroscopy methods. 3.1. Chemical composition Knowing the composition of milk has great value for the dairy farmer. It determines the economic value of the milk and provides valuable information about the metabolism of particular cows. The composition of raw milk largely determines its nutritional value and the physicochemical properties during milk treatment and processing. Therefore, milk composition is very important both for the milk industry and the consumer. Beside this, the composition (mainly fat and protein content) and quality defines the economic value of the milk and, with that, also the income of the farmer. Moreover, the composition of the extracted milk contains valuable information about the metabolic status of the cow, due to the very intensive interaction between blood circulation and milk production (Bramley, Dodd, Mein, & Bramley, 1992).

Fig. 3. MIR-microspectroscopy spectra of dried films for, (a) control milk and milk adulterated with, (b) hydrogen peroxide (0.15 g/L), (c) synthetic milk (0.8 g/L of urea), (d) whey (30 g/L) and (e) urea (12.5 g/L).

Mottram et al. (2002) listed fat, protein, urea, and acetone as most useful milk constituents with regard to metabolic and nutritional status of the cow, which are easily monitored at the same time. A chemometric study on the prediction of the main nutritional aspects of milk has been carried out by using FTIR, attenuated total reflectance (ATR) measurements of commercially available milk samples of different types (Inon et al., 2004). Whole, semi and skimmed milks, enriched or not with calcium, vitamins or modified by alteration of lipid or sugar composition were considered. A critical evaluation has also been made for the application of near infrared Fourier transform-Raman spectroscopy for the simultaneous determination of the most important nutritional parameters, such as energetic value, carbohydrate, protein and fat contents of infant formula and powdered milk samples based on the use of partial least squares (PLS) regression analysis (Moros et al., 2007). The

Fig. 2. Raw FT-Raman spectra of (a) skimmed powdered milk, (b) breast-feeding milk enhanced with iron, (c) continuation milk enhanced with minerals and vitamins, (d) whole instantaneous powdered milk and (e) breast-feeding powdered milk enhanced with iron.

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

procedure developed provided figures of merit, which complied with the statutory values declared by the United States Food and Drug Administration (US FDA) (see Figs. 2 and 3). Real-time information about milk composition is useful for managing the milking process. Mid-infrared spectroscopy, which relies on fundamental modes of molecular vibrations, is routinely used for off-line analysis of milk, and the purpose of this study was to investigate the potential of attenuated total reflectance mid-infrared spectroscopy for real-time analysis of milk in milking lines (Linker & Etzion, 2009). Principal component analysis, wavelets and neural networks were used to develop various models for predicting protein and fat concentration. Although reasonable protein models were obtained for some seasonal sub-datasets (determination errors <0–15% protein), the models lacked robustness, and it was not possible to develop a model suitable for all the data. These results show that the potential of mid-infrared attenuated total reflectance spectroscopy for in-line milk analysis is quite limited. Near-infrared (NIR) spectroscopy is an analytical tool that offers the possibility of analyzing milk during the daily milking routine, and can analyze multiple constituents in a given substrate at the same time. In a recent study, a special NIR in-line milk analyzing device was designed, and its ability to predict the contents of fat, protein, lactose, urea and the somatic cell count in milk during the milking process was evaluated (Melfsen, Hartung, & Haeussermann, 2012). Taking into account the need of a method that will enable dairy farmers to monitor milk quality of individual cow during milking, a near-infrared (NIR) spectroscopic sensing system for online monitoring of milk quality on an experimental basis was constructed (Kawamura, Kawasaki, Nakatsuji, & Natsuga, 2007). The calibration models predicted three major milk constituents (fat, protein and lactose), somatic cell count (SCC) and milk urea nitrogen (MUN) of unhomogenized milk. The system can provide dairy farmers with information on milk quality and physiological condition of each cow and therefore give them feedback control for producing milk of high quality, and for optimizing dairy farm management. A simple and economical method to isolate whey protein from fresh raw milk was developed by serial defatting, casein eliminating, lactose removing, and separating by gel filtration chromatography (Liang, Chen, Chen, & Chen, 2006). The small non-protein molecule separated are rich in aromatic amino acids and thiol groups, and were structural characterized with near infrared Fourier transform Raman spectroscopy (FT-Raman). Differences in the Raman profile for each whey component reflects their intrinsic compositional differences and distinct spatial arrangement. Fat is an important nutritional component in milk. Improper content of fat ingestion can cause obesity in humans, and consequently increase the incidence of hypertension, coronary heart disease and diabetes. Currently, there are several methods widely applied for measuring the fat content in dairy products, including the Röse-Gottlieb, Soxhlet extraction, Babcock and Gerber methods (Rosenthal & Baruch, 1993) and gas chromatography (Deeth, FitzGerald, & Snow, 1983). However, there are several problems inherent in these methods in that they are time consuming, complex, and they require sample destruction. The functionality of anhydrous milk fat (AMF) is determined from solid fat content (SFC) and triacylglycerol (TG) profiles, parameters traditionally measured using nuclear magnetic resonance and high pressure liquid chromatography, respectively. Raman spectroscopy, coupled with partial least squares (PLS) analysis, has been assessed as an alternative method for SFC and TG class quantification (McGoverin et al., 2009). In contrast to SFC calibrations, physically homogenous samples in a liquid form were ideal for TG class concentration predictions, however, not all TG classes could be reliably predicted.

881

It is very important to be able to detect the fat content in milk powder using a rapid and non-destructive method (Wu, Feng, & He, 2007). Near and mid infrared spectroscopy techniques were used to achieve this purpose. The results based on LS-SVM (leastsquares support vector machine) were better than those of backpropagation artificial neural networks. It was concluded that infrared spectroscopy technique could quantify the fat content in milk powder rapidly and non-destructively. The results could be beneficial for designing a simple and non-destructive spectral sensor for the quantification of fat content in milk powder. It was demonstrated that VIS Raman spectroscopy in combination with partial least square regression (PLS) is a suitable rapid technique for direct milk fat determination (El-Abassy, Eravuchira, Donfack, von der Kammer, & Materny, 2011). Raman spectra of milk samples revealed contributions from proteins, but mainly from their fat content with different spectral characteristics. The results show that Raman spectroscopy is suited for inline monitoring purposes. Near (NIR) and medium (MIR) infrared reflectance spectroscopy predictions of fatty acid (FA) composition, expressed as g/kg of milk or g/100 g of FA, on fresh and thawed milk were compared (Coppa et al., 2014). The NIR predictions on fresh liquid and oven-dried milk were similar, but the reliability decreased for thawed liquid milk. The high performance shown by NIR and MIR allows their use for routine milk FA composition recording. FTIR analyzes the vibrational motions of molecules, and can be used for determination of FA in different ways. As there is no need for pre-preparation of the sample for analysis, the method is advantageous because of the low cost of reagents, time and specialized labour skills (Petersen Rodriguez et al., 2014). Furthermore, FTIR is important for studies involving cellular responses, and it can be used as biochemical screening technique for explorative research. It requires minimal sample preparation and preserves the components in their natural environment (Najbjerg et al., 2011). Recently, a novel approach for FTIR characterization of the milk fatty acid composition based on dried film measurements has been presented and compared to a standard FTIR approach, based on liquid milk measurements (Afseth et al., 2010). It was concluded that there is no equivalency between the measurements of milk FA yielded by GC and FTIR. However, it was observed that both methods indicate a similar pattern of milk composition. Protein is another important component of milk. The potential use of attenuated total reflectance spectroscopy in the midinfrared range for determining protein concentration in raw cow milk was investigated (Etzion et al., 2004). The determination of protein concentration was based on the characteristic absorbance of milk proteins, but in order to minimize the influence of the strong water band (centered around 1640 cm 1) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk. Fourier transform infrared (FTIR) spectroscopy was used to examine the conformation of proteins in spray-dried milk protein concentrate (MPC) powders, also to determine if the spectral changes could be related to nitrogen solubility of these powders (Kher, Udabage, McKinnon, McNaughton, & Augustin, 2007). Changes in nitrogen solubility of individual MPC powders during storage could be correlated to changes in FTIR spectra. FTIR data

882

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

were pre-processed to remove physical effects causing discrimination between samples using firstly, second derivatives and normalization and secondly, the extended multiplicative scatter correction (EMSC) technique. The results also showed that EMSC data pre-processing presented comparable results when compared with more complicated data pre-processing for the removal of physical effects. The fast and non-destructive detection of protein content in milk powder is very important, so infrared spectroscopy technique was applied to achieve this (Wu, He, Feng, & Sun, 2008). Leastsquares support vector machine (LS-SVM) was applied to building the protein prediction model based on spectral transmission rate. The process is simple and easy to operate, and the prediction ability of LS-SVM is better than that of partial least square. Moreover, the comparison of prediction results showed that the performance of model with mid-infrared spectra data was better than that with near infrared spectra data. The feedback of milk composition might be directly used for nutrition monitoring and concomitant adaptation of feeding. Samples were collected from four French regions to study the potential capability of mid-infrared (MIR) and near-infrared (NIR) spectroscopy data, to differentiate milk according to the feeding system, breed of cow and altitude of the farm (Valenti et al., 2013). The MIR method demonstrated an excellent capability to distinguish milk from hay- and pasture-based systems and those from maize silage- and pasture-based systems. The MIR and NIR spectroscopies are capable of distinguishing between milk samples from different feeding systems, particularly when hay from pasture systems and maize silage from pasture systems were compared. It was shown that FTIR spectra of raw herd bulk milk contain valuable information on cows’ diets that can be used for the authentication of cow feeding regime and herd management system (Capuano, Rademaker, van den Bijgaart, & van Ruth, 2014). The results reported suggest that FTIR spectroscopy might be used as a screening technique for the authentication of fresh grass feeding before more accurate, but time-consuming, techniques are applied (e.g. FA profiling) for confirmatory purposes. Rapid measurement of milk properties and discrimination of milk origin is essential for quality control of milk products. Visible (Vis) and near infrared (NIR) spectroscopy is a proven technology to provide intensive, cost effective and fast analysis with high accuracy for many materials (Mouazen, Dridi, Rouissi, De Baerdemaeker, & Ramon, 2009). It was recommended to adopt a full wavelength range Vis–NIR instrument for good accuracy measurement of key properties in ewe’s milk of different feeding systems and genotypes. 3.2. Adulterant analysis FT-Raman spectroscopy was explored as a fast and reliable screening method for the assessment of milk powder quality, and also the identification of samples adulterated with whey (1–40% w/w) (Almeida, Oliveira, Stephani, & de Oliveira, 2010). Raman measurements can easily differentiate milk powders without the need of sample preparation, whereas the traditional methods of quality control, including high-performance liquid chromatography, are laborious and slow. Multivariate analysis was also developed to classify the adulterated milk powder samples using PCA and partial least squares discriminate analysis (PLS-DA). The resulting PLS-DA model correctly classified 100% of the adulterated samples. These results clearly demonstrate the utility of FT-Raman spectroscopy, combined with chemometrics as a rapid method for screening milk powder. Food ingredient fraud and economically motivated adulteration are emerging risks, being addition of low cost ingredients creates not only an economical problem but also a health risk for

consumers (Sharma & Paradakar, 2010). Food fraud often has been considered to be, foremost, an economic issue and less a concern of the traditional food safety or food protection intervention and response infrastructure. However, the melamine incidents in 2007 and 2008 showed how adulteration can cause the safety of food to collapse and the hazards that can be introduced by economically motivated adulteration (Moore, Spink, & Lipp, 2012). Advances in infrared (IR) spectroscopic instrumentation, and the combination with chemometric methods, have made this technique a powerful tool for determination of food quality and authenticity (Rodriguez-Saona & Allendorf, 2011). The main advantages of IR spectroscopy include its ability to analyze samples with little or no sample preparation, ease of use, fast data collection, high sensitivity and specificity, as well as capability to serve as a ‘‘fingerprint” technique. The content of additive components is a crucial factor, which affects the entire quality of powdered milk. Traditional analytical methods, such as high-performance liquid chromatography or liquid chromatography–mass spectroscopy (LC–MS), are time consuming, destructive, and costly. Hence, spectral analyzes are preferred as simple and direct methods in evaluating the quality of powdered milk. The express method of evaluating the quality of powdered milk based on the fingerprint of the FTIR spectra were studied (Deng, Zhou, & Sun, 2006; Santos, Pereira-Filho, & Rodriguez-Saona, 2013). Spectra showed variations in the peak positions and shapes between the control and adulterated milk, demonstrating the potential of the method to detect milk adulteration. Melamine, (2,4,6-triamino-s-triazin, MEL), is a raw ingredient for the manufacture of plastics, but it is frequently misused by adding it to food to raise the nitrogen content, thereby giving the false impression of a high protein content. Although MEL has low toxicity, it may lead to kidney stones, eventual renal failure, and ultimately death, when it forms an insoluble compound with the analogue cyanuric acid (CYA) (see Fig. 4) (Oxford University Press, 2008). In 2007, MEL was found in pet-food products, and led to kidney toxicity in dogs and cats in the USA. Later, MEL contamination was found in milk-based products in China. Because China is a major exporter of milk products and ingredients, the events created a widespread food-safety scare. More than 294,000 children in China have reportedly been affected by adulterated formula. Over 50,000 were hospitalized, and at least 6 died. Reports of MELcontaminated foods manufactured in the USA and other countries occurred in the subsequent months (Ingelfinger, 2008). These MELcontamination incidents prompted the US Food and Drug Administration (FDA), the European Community and other countries and regions to establish the criteria of Maximum Residue Limits (MRLs) for MEL in various everyday products. Simple, cost-effective and environmentally friendly analytical methods were developed for the quantification of melamine (MEL) in liquid milk and infant powder, by using transmission FT-IR (Domingo, Tirelli, Nunes, Guerreiro, & Pinto, 2014; Jawaid et al., 2014; Lin, 2009; Okazaki et al., 2009; Yang et al., 2014). Standards and samples were analyzed in the form of a KBr pellet for recording FT-IR spectra. Milk analysis by vibrational spectroscopy provides overall chemical composition of the tested sample; therefore, it is widely considered to give highly reliable and empirical fingerprints for the samples. There are some other adulterants that were determined using vibrational spectroscopy, such as: starch, whey or sucrose (Borin et al., 2006), whey, hydrogen peroxide, synthetic urine, urea and synthetic milk (Santos et al., 2013), pseudo proteins and thickeners (Zhang et al., 2014). All the results presented above indicated that vibrational spectroscopy could provide the dairy industry with a simple, rapid and non-destructive technique for detection and quantification of milk adulteration.

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

883

Fig. 4. Melamine, cyanuric acid and melamine-cyanuric acid complex structures.

3.3. Other methods Microbiological safety plays a very significant role in the quality control of milk and dairy products worldwide. Current methods used in the detection and enumeration of spoilage bacteria in pasteurized milk in the dairy industry, although accurate and sensitive, are time-consuming. FT-IR spectroscopy is a metabolic fingerprinting technique that can potentially be used to deliver results with the same accuracy and sensitivity, within minutes after minimal sample preparation. Taking into account these facts, attenuated total reflectance (ATR) and high throughput (HT) FT-IR techniques were used for such determinations (Nicolaou & Goodacre, 2008). FT-IR ATR data for all milks showed reasonable results for bacterial loads above 105 cfu ml 1. By contrast, FT-IR HT provided more accurate results for lower viable bacterial counts down to 103 cfu ml 1 for whole milk and, 4  102 cfu ml 1 for semi-skimmed and skimmed milk. Micro-Raman spectroscopy, in combination with chemometric analysis, was used to identify Brucella from agar plates and directly from milk (Meisel et al., 2012). Moreover, the measurements were performed at the single-cell level, thus allowing fast identification within a few hours without a demanding process for sample preparation. It was demonstrated that the transmittance data from the FTIR spectra of milk have genetic variability, that may prove useful for the direct genetic improvement of dairy species, rather than only through indirect phenotypic predictions of individual milk quality and technological traits (Bittante & Cecchinato, 2013; Dagnachew, Kohler, & Adnøy, 2013; Dagnachew, Meuwissen, & Ådnøy, 2013). The applicability of FTIR-spectroscopy as a high throughput screening method for detection of biochemical changes in intact liver cells, in bulk upon fatty acid exposure, was investigated (Najbjerg et al., 2011). HepG2 cells adapted to serum free (HepG2-SF) growth were exposed to four different fatty acids, three octadecenoic acids, differing in cis/trans-configuration or double bond position (oleic acid, elaidic acid and vaccenic acid), as well as palmitic acid, over three days. The results demonstrate the applicability of high-throughput FTIR spectroscopy as an explorative method in in vitro systems, from which biologically relevant hypotheses can be generated and further investigated.

4. Conclusion The vibrational spectra, exhibiting reduced cost of analysis, high throughput, and possibly large-scale application, are widely used today in milk recording programs for milk payment and prediction of major milk components (e.g., protein, fat, and lactose percentages). Vibrational spectroscopy techniques also offer rapid, nondestructive, and inexpensive analysis. The results presented above

show that the milk analysis can be successfully determined based on vibrational spectroscopy data with or without chemometrics. No complicated sample preparation or extraction of specific components is required. Infrared spectroscopy techniques, such as near infrared and mid infrared spectroscopy, offer quick analysis, minimal sample preparation requirements, and low cost. In summary, the vibrational spectroscopy techniques showed a good performance in fast and non-destructive measurement of protein content in milk powder. The operation of spectra measurement is simple and more convenient than chemical methods. References Abbas, O., Dardenne, P., & Baeten, V. (2012). Near-infrared, mid-infrared, and Raman spectroscopy. In Y. Picó (Ed.), Chemical analysis of food: Techniques and applications. Elsevier Inc.: Academic Press. Afseth, N. K., Martens, H., Randby, A., Gidskehaug, L., Narum, B., Jørgensen, K., Lien, S., & Kohler, A. (2010). Predicting the fatty acid composition of milk: A comparison of two Fourier transform infrared sampling techniques. Applied Spectroscopy, 64(7), 700–707. Almeida, M. R., Oliveira, K. de S., Stephani, R., & de Oliveira, L. F. C. (2010). Fouriertransform Raman analysis of milk powder: A potential method for rapid quality screening. Journal of Raman Spectroscopy, 42(7), 1548–1552. Andersen, S. K., Hansen, P. W., & Andersen, H. V. (2002). Vibrational spectroscopy in the analysis of dairy products and wine. In J. M. Chalmers & P. R. Griffiths (Eds.), Handbook of vibrational spectroscopy. Chichester, UK: John Wiley & Sons. Bittante, G., & Cecchinato, A. (2013). Genetic analysis of the Fourier-transform infrared spectra of bovine milk with emphasis on individual wavelengths related to specific chemical bonds. Journal of Dairy Science, 96(9), 5991–6006. Borin, A., Ferrão, M. F., Mello, C., Maretto, D. A., & Poppi, R. J. (2006). Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta, 579, 25–32. Bradford, M. M. (1976). A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein–dye binding. Analytical Biochemistry, 72, 248–254. Bramley, A. J., Dodd, F. H., Mein, G. A., & Bramley, J. A. (1992). Machine milking and lactation. Berkshire, UK: Insight Books. Bunaciu, A. A., Fleschin, S., & Aboul-Enein, H. Y. (2014). Evaluation of the protein secondary structures using Fourier Transform Infrared Spectroscopy. Gazi University Journal of Science, 27, 637–644. Capuano, E., Rademaker, J., van den Bijgaart, H., & van Ruth, S. M. (2014). Verification of fresh grass feeding, pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk. Food Research International, 60, 59–65. Cavalcanti Inácio, M. R., de Fátima Vitória de Moura, M., & Kássio Michell Gomes de Lima, K. M. (2011). Classification and determination of total protein in milk powder using near-infrared reflectance spectrometry and the successive projections algorithm for variable selection. Vibrational Spectroscopy, 57, 342–345. Coppa, M., Revello-Chion, A., Giaccone, D., Ferlay, A., Tabacco, E., & Borreani, G. (2014). Comparison of near and medium infrared spectroscopy to predict fatty acid composition on fresh and thawed milk. Food Chemistry, 150, 49–57. Craig, A. P., Franca, A. S., & Irudayaraj, J. (2015). Vibrational spectroscopy for food quality and safety screening. In High throughput screening for food safety assessment (pp. 165–194). Curley, D. M., Kumosinski, T. F., Unruh, J. J., & Farrell, H. M. Jr., (1998). Effect of calcium and temperature on casein. Journal of Dairy Science, 81, 3154–3162. Dagnachew, B. S., Kohler, A., & Adnøy, T. (2013b). Genetic and environmental information in goat milk Fourier transform infrared spectra. Journal of Dairy Science, 96(6), 3973–3985. Dagnachew, B. S., Meuwissen, T. H. E., & Ådnøy, T. (2013a). Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk

884

A.A. Bunaciu et al. / Food Chemistry 196 (2016) 877–884

composition and quality traits in dairy goats. Journal of Dairy Science, 96(9), 5933–5942. Deeth, H. C., Fitz-Gerald, C. H., & Snow, A. J. (1983). A gas chromatographic method for the quantitative determination of free fatty acids in milk and milk products. N.Z. Journal of Dairy Science and Technology, 18, 13–20. Deng, Y. E., Zhou, Q., & Sun, S. Q. (2006). Analysis and discrimination of infant powdered milk via FTIR spectroscopy. Spectroscopy and Spectral Analysis, 26(4), 636–639. Domingo, E., Tirelli, A. A., Nunes, C. A., Guerreiro, M. C., & Pinto, S. M. (2014). Melamine detection in milk using vibrational spectroscopy and chemometrics analysis: A review. Food Research International, 60, 131–139. El-Abassy, R. M., Eravuchira, P. J., Donfack, P., von der Kammer, B., & Materny, A. (2011). Fast determination of milk fat content using Raman spectroscopy. Vibrational Spectroscopy, 56(1), 3–8. Elgersma, A., Tamminga, S., & Ellen, G. (2006). Modifying milk composition through forage – A review. Animal Feed Science and Technology, 131, 207–225. Etzion, Y., Linker, R., Cogan, U., & Shmulevich, I. (2004). Determination of protein concentration in raw milk by mid-infrared Fourier transform infrared/ attenuated total reflectance spectroscopy. Journal of Dairy Science, 87, 2779–2788. Gornall, A. G., Bardawill, C. J., & David, M. M. (1949). Determination of serum proteins by means of the biuret reaction. Journal of Biological Chemistry, 177, 751–766. Goulden, J. D. S. (1964). Analysis of milk by infra-red absorption. Journal of Dairy Research, 31, 273–284. Griffiths, P. R., & deHaseth, J. A. (1986). Fourier transform infrared spectroscopy. In Chemical analysis (Vol. 83). New York, NY: John Wiley InterScience. Ingelfinger, J. R. (2008). Melamine and the global implications of food contamination. New England Journal of Medicine, 359, 2745–2748. Inon, F. A., Garrigues, S., & de la Guardia, M. (2004). Nutritional parameters of commercially available milk samples by FTIR and chemometric techniques. Analytica Chimica Acta, 513, 401–412. Jawaid, S., Talpur, F. N., Imran Afridi, H., Nizamani, S. M., Khaskheli, A. A., & Naz, S. (2014). Quick determination of melamine in infant powder and liquid milk by Fourier transform infrared spectroscopy. Analytical Methods, 6, 5269–5273. Jung, C. (2000). Insight into protein structure and protein-ligand recognition by Fourier transform infrared spectroscopy. Journal of Molecular Recognition, 13, 325–351. Karoui, R., & Baerdemaeker, J. D. (2007). A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry, 102, 621–640. Karoui, R., Mazerolles, G., & Dufour, E. (2003). Spectroscopic techniques coupled with chemometric tools for structure and texture determinations in dairy products. International Dairy Journal, 13(8), 607–620. Kasemsumran, S., Thanapare, W., & Kiatsoonthan, A. (2007). Feasibility of NearInfrared Spectroscopy to detect and to quantify adulterants in cow milk. Analytical Sciences, 23, 907–910. Kawamura, S., Kawasaki, M., Nakatsuji, H., & Natsuga, M. (2007). Near-infrared spectroscopic sensing system for online monitoring of milk quality during milking. Sensing and Instrumentation for Food Quality and Safety, 1(1), 37–43. Kher, A., Udabage, P., McKinnon, I., McNaughton, D., & Augustin, M. A. (2007). FTIR investigation of spray-dried milk protein concentrate powders. Vibrational Spectroscopy, 44(2), 375–381. Larkin, P. J. (2011). Infrared and Raman spectroscopy. Principles and spectral interpretation. Elsevier. Liang, M., Chen, V. Y. T., Chen, H.-L., & Chen, W. (2006). A simple and direct isolation of whey components from raw milk by gel filtration chromatography and structural characterization by Fourier transform Raman spectroscopy. Talanta, 69(5), 1269–1277. Libnau, F. O., Kvalheim, O. M., Christy, A. A., & Toft, J. (1994). Spectra of water in the near- and mid-infrared region. Vibrational Spectroscopy, 7, 243–254. Lin, M. (2009). A review of traditional and novel detection techniques for melamine and its analogues in foods and animal feed. Frontiers of Chemical Engineering in China, 3(4), 427–435. Linker, R., & Etzion, Y. (2009). Potential and limitation of mid-infrared attenuated total reflectance spectroscopy for real time analysis of raw milk in milking lines. Journal of Dairy Research, 76(1), 42–48. Liu, F., Ye, X., He, Y., & Wang, L. (2009). Application of visible/near infrared spectroscopy and chemometric calibrations for variety discrimination of instant milk teas. Journal of Food Engineering, 93, 127–133. Lowry, O. H., Rosebrough, N. J., Farr, A. L., & Randall, R. J. (1951). Protein measurement with the Folin phenol reagent. Journal of Biological Chemistry, 193, 265–275. McClure, W. F., & Stanfield, D. L. (2002). Near-infrared spectroscopy of biomaterials. In J. M. Chalmers & P. R. Griffiths (Eds.), Handbook of vibrational spectroscopy. John Wiley & Sons Ltd.. McGoverin, C. M., Clark, A. S. S., Holroyd, S. E., & Gordon, K. C. (2009). Raman spectroscopic prediction of the solid fat content of New Zealand anhydrous milk fat. Analytical Methods, 1, 29–38. Meisel, S., Melzer, F., Stockel, S., Rosch, P., Elschner, M., & Popp, J. (2012). Raman spectroscopy as a potential tool for detection of Brucella spp. in milk. Applied and Environment Microbiology, 78(15–16), 5575–5583. Melfsen, A., Hartung, E., & Haeussermann, A. (2012). Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy. Journal of Dairy Science, 95(11), 6465–6476.

Moore, J. C., Spink, J., & Lipp, M. (2012). Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. Journal of Food Science, 77, R118–R126. Moros, J., Garrigues, S., & de la Guardia, M. (2007). Evaluation of nutritional parameters in infant formulas and powdered milk by Raman spectroscopy. Analytica Chimica Acta, 593, 30–38. Moros, J., Garrigues, S., & de la Guardia, M. (2010). Vibrational spectroscopy provides a green tool for multi-component analysis. TrAC, Trends in Analytical Chemistry, 29(7), 578–591. Mottram, T., Velasco-Garcia, M., Berry, P., Richards, P., Ghesquiere, J., & Masson, L. (2002). Automatic on-line analysis of milk constituents (urea, ketones, enzymes and hormones) using biosensors. Comparative Clinical Pathology, 11, 50–58. Mouazen, A. M., Dridi, S., Rouissi, H., De Baerdemaeker, J., & Ramon, H. (2009). Prediction of selected ewe’s milk properties and differentiating between pasture and box feeding using visible and near infrared spectroscopy. Biosystems Engineering, 104(3), 353–361. Najbjerg, H., Afseth, N. K., Young, J. F., Bertram, H. C., Pedersen, M. E., Grimmer, S., Vogt, G., & Kohler, A. (2011). Monitoring cellular responses upon fatty acid exposure by Fourier transform infrared spectroscopy and Raman spectroscopy. Analyst, 136, 1649–1658. Nicolaou, N., & Goodacre, R. (2008). Rapid and quantitative detection of the microbial spoilage in milk using Fourier transform infrared spectroscopy and chemometrics. Analyst, 133, 1424–1431. Okazaki, S., Hiramatsu, M., Gonmori, K., Suzuki, O., & Tu, A. (2009). Rapid nondestructive screening for melamine in dried milk by Raman spectroscopy. Forensic Toxicology, 27(2), 94–97. Oxford University Press, (2008) Sci. Daily [online], 16 October 2008. (Accessed at: http://www.sciencedaily.com/releases/2008/10/081015110840.htm). Petersen Rodriguez, M. A., Petrini, J., Ferreira, E. M., Mangeti Barreto Mourão, L. R., Salvian, M., Cassoli, L. D., Vaz Pires, A., Machado, P. F., & Barreto Mourão, G. (2014). Concordance analysis between estimation methods of milk fatty acid content. Food Chemistry, 156, 170–175. Pistorius, A. M. A. (1995). Biochemical applications of FT-IR spectroscopy. Spectroscopy Europe, 7, 8–15. Rodriguez-Saona, L. E., & Allendorf, M. E. (2011). Use of FTIR for rapid authentication and detection of adulteration of food. Annual Review in Food Science and Technology, 2, 467–483. Rosenthal, I., & Baruch, R. (1993). 100 years of measuring the fat content of milk. Journal of Chemical Education, 70, 480–482. Santos, P. M., Pereira-Filho, E. R., & Rodriguez-Saona, L. E. (2013). Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food Chemistry, 138, 19–24. Sharma, K., & Paradakar, M. (2010). The melamine adulteration scandal. Food Security, 2, 97–107. Stoscheck, M. (1990). General methods for handling proteins and enzymes: Quantification of protein. In M. P. Deutscher (Ed.), Methods in enzymology: Guide to protein purification. New York: Academic Press. Sun, D. W. (2009). Infrared spectroscopy for food quality analysis and control. Academic Press, Elsevier. Susi, H. (1969). Infrared spectra of biological macromolecules and related systems. In S. N. Timasheff & G. D. Fasman (Eds.), Structure and stability of biological macromolecules (pp. 575–663). New York, NY: Marcel Dekker. Trienekens, J., & Zuurbier, P. (2008). Quality and safety standards in the food industry, developments and challenges. International Journal of Production Economics, 113, 107–122. US Dairy Export Council, http://www.usdec.org. Valenti, B., Martin, B., Andueza, D., Leroux, C., Labonne, C., Lahalle, F., Larroque, H., Brunschwig, P., Lecomte, C., Brochard, M., & Ferlay, A. (2013). Infrared spectroscopic methods for the discrimination of cows’ milk according to the feeding system, cow breed and altitude of the dairy farm. International Dairy Journal, 32(1), 26–32. Workman, J., Jr., & Weyer, L. (2007). Introduction to near-infrared spectra. In J. Workman, Jr. & L. Weyer (Eds.), Practical guide to interpretive near-infrared spectroscopy. London: CRC Press. World Health Organization http://www.who.int/nutrition/topics/global_stra-tegy/ en/index.html. Wu, D., Feng, S., & He, Y. (2007). Infrared spectroscopy technique for the nondestructive measurement of fat content in milk powder. Journal of Dairy Science, 90(8), 3613–3619. Wu, D., He, Y., Feng, S., & Sun, D. W. (2008). Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LSSVM. Journal of Food Engineering, 84(1), 124–131. Yang, Q., Liang, F., Wang, D., Ma, P., Gao, D., Han, J., Li, Y., Yu, A., Song, D., & Wang, X. (2014). Simultaneous determination of thiocyanate ion and melamine in milk and milk powder using surface-enhanced Raman spectroscopy. Analytical Methods, 6, 8388–8395. Zaia, A. M., Verri, W. A., Jr., & Zaia, C. T. B. V. (1999). Determination of total proteins: A study of reaction between quinones and proteins. Talanta, 49, 373–376. Zhang, L.-G., Zhang, X., Ni, L.-J., Xue, Z.-B., Gu, X., & Huang, S.-X. (2014). Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy. Food Chemistry, 145, 342–348.