Recent advances on determination of milk adulterants

Accepted Manuscript Recent advances on determination of milk adulterants Carina F. Nascimento, Poliana M. Santos, Edenir Rodrigues Pereira-Filho, Fábio R.P. Rocha PII: DOI: Reference:

S0308-8146(16)31857-X http://dx.doi.org/10.1016/j.foodchem.2016.11.034 FOCH 20167

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

31 May 2016 7 October 2016 7 November 2016

Please cite this article as: Nascimento, C.F., Santos, P.M., Rodrigues Pereira-Filho, E., R.P. Rocha, F., Recent advances on determination of milk adulterants, Food Chemistry (2016), doi: http://dx.doi.org/10.1016/j.foodchem. 2016.11.034

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Recent advances on determination of milk adulterants

Carina F. Nascimento a, Poliana M. Santos b, Edenir Rodrigues Pereira-Filhoc, Fábio R.P. Rochaa,*

a

Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, P.O. Box 96, 13400-970 Piracicaba, SP, Brazil b

Departamento Acadêmico de Química e Biologia, Universidade Tecnológica Federal do Paraná, Curitiba, PR, Brazil. c

Grupo de Análise Instrumental Aplicada, Departamento de Química, Universidade Federal de São Carlos, P.O. Box 676, 13565-905 São Carlos, SP, Brazil

Corresponding author E-mail address: [email protected] 1

ABSTRACT Milk adulteration is a current fraudulent practice to mask the quality parameters (e.g. protein and fat content) and increase the product shelf life. Milk adulteration includes addition of toxic substances, such as formaldehyde, hydrogen peroxide, hypochlorite, dichromate, salicylic acid, melamine, and urea. In order to assure the food safety and avoid health risks to consumers, novel analytical procedures have been proposed for detection of these adulterants. The innovations encompass sample pretreatment and improved detection and data processing, including chemometric tools. This review focuses on critical evaluation of analytical approaches for assay of milk adulteration, with emphasis on applications published after 2010. Alternatives for fast, environmentally friendly and in-situ detection of milk adulterants are highlighted.

Keywords: Food adulteration, Milk analysis, Spectrometry, Chemometrics, Electroanalysis, Melamine, Hydrogen peroxide, Fats

2

1. Introduction Milk is an important constituent of the human diet that is rich in proteins, carbohydrates, minerals, and vitamins, all of which are essential to human health. Due to its high nutritional value, global production and consumption of milk have increased, especially in the developing world (Food and Agriculture Organization of the United Nations, FAO). As a result, milk is a constant target of adulteration, causing not only economic loss, but also a significant risk to consumers’ health. In addition, as an important feedstock for the food industry, cumulative effects can be observed in lacteous derivatives, thus increasing the demand for stringent evaluation of the product authenticity. Milk adulteration typically involves dilution and/or addition of inexpensive, low-quality, and sometimes dangerous products in order to increase the volume, mask inferior quality, or replace the natural substances in milk for economic gain. The simplest case is addition of water to increase the volume. Nowadays, more sophisticated frauds (and thus more difficult to detect) have been documented in both scientific and gray literature, including addition of melamine to increase the nitrogen content of milk after dilution with water (Lu et al., 2016). Other substances, such as formaldehyde, hydrogen peroxide, hypochlorite, dichromate, and salicylic acid have been added to increase the product shelf life (Jeong et al., 2015; Souza, Silva, Leotério, Paim, & Lavorante, 2014), vegetable oils and surfactants are used for adulteration of the fat content (Garcia et al., 2012, Rani, Sharma, Arora, Lal, & Kumar, 2015) as well as cheese whey and urea are used to artificially alter the protein content (Carvalho et al., 2015; Jha, Jaiswal, Borah, Gautam, & Srivastava, 2015).

3

In response to these fraudulent practices and health effects, regulatory agencies worldwide have established maximum residue limits (MRLs) and tolerable daily intakes (TDI) for some chemical contaminants. For example, the World Health Organization (WHO) and the US Food and Drug Administration (FDA) have set a MRL of 1 mg kg-1 for melamine in infant formula and 2.5 mg kg-1 for other products. The same limit was established in China, while in Europe, the Food Safety Authority (EFSA) has set the limit at 2.5 mg kg-1 for all products containing more than 15% milk. Meanwhile, WHO, FDA and EFSA have established TDI of 0.2 mg, 0.063 mg or 0.5 mg melamine kg-1 body weight. Generally, the use of preservative substances, such as potassium dichromate, salicylic acid, and hydrogen peroxide is not allowed (Souza et al., 2014). In the United States, hydrogen peroxide is allowed only in milk intended for cheese making and the concentration should be lower than 0.05% (FDA). Conversely, the threshold limit for formaldehyde is 2.5 g kg-1 (Wahed, Razzaq, Dharmapuri, & Corrales, 2016). The EFSA established a TDI of 100 mg kg-1 for formaldehyde in food from animal and vegetable sources (Wahed et al., 2016). Over the last decade, several analytical procedures have been proposed for rapid screening or selective confirmation of the quality and authenticity of milk. Liquid and gas chromatography (HPLC, GC) are conventionally employed for highly selective determinations, especially coupled with mass spectrometry (MS) (Abernethy & Higgs, 2013). Spectroscopic techniques, including near infrared (NIR), mid infrared (MIR), ultraviolet-visible (UV-Vis), and Raman spectroscopy, as well as digital image analysis, mainly scanometry, and electrochemical sensors, have also been employed to detect milk adulteration and assess the authenticity and intrinsic quality parameters (Bunaciu et al.,

4

2016; Santos, & Pereira-Filho, 2013; Wu, Wang, Zhao, & Lu, 2012). These methods have also been employed for simultaneous detection of multiple adulterants (Botelho, Reis, Oliveira, & Sena, 2015; Santos, Pereira-Filho, & Rodriguez-Saona, 2013a). The studies are often supported by chemometric approaches, allowing the development of reliable qualitative (classification) and quantitative (univariate or multivariate calibration) procedures. In 2015, a comprehensive index (termed index Q) was proposed to discriminate adulterated milk from unadulterated one. This index is established by Principal Component Analysis (PCA) and is based on the correlation of seven parameters: fat, protein, lactose, total solids, non-fat solid, freezing point, and somatic cell count. In blind tests, the index Q was successfully utilized for detection of milk adulteration with maltodextrin and water in concentrations as low as 1.0% (v/v) (Liu, Ren, Liu, Guo, 2015). Herein, we present an overview of the latest analytical innovations (from 2010 to 2016) for assay of the most common adulterants in liquid and powdered cow’s milk. In order to achieve a comprehensive highlight of the new developments, Section 2 provides a general overview of papers published in the aforementioned

period,

emphasizing

the

main

adulterants,

analytical

techniques, and sample treatment strategies. Sections 3 to 6 introduce recent advances in the determination of specific categories of adulterants: nitrogenous substances (Section 3), fats and surfactants (Section 4), substances that are used to increase the product shelf life (Section 5) and dilution to increase the final volume (Section 6). Fig. 1 shows the most common milk adulterants, highlighting the main analytical techniques and a pictorial description of the complexity of the required sample pretreatment.

5

Chemometrics are involved in applications discussed in all sections, thus showing the importance of these tools in food authenticity analysis either to optimize the experimental conditions or to develop classification or calibration models. Indeed, chemometrics is already a reality in food analysis and mathematical

packages

are

available

for

commercial

instruments.

In

chromatographic applications, for example, application of scripts for peak alignment is usual. In exploratory analysis, PCA allows a global vision of high dimensional data sets. Multivariate calibration can be accomplished by Partial least squares (PLS) and for classification models some chemometric techniques like Soft independent modeling of class analogy (SIMCA), Partial least squares discrimination analysis (PLS-DA), k-nearest-neighbors (KNN), and Linear discriminant analysis (LDA) can be used. It is important to mention the need for validation in order to evaluate the application of these mathematical/statistical techniques in real situations. The use of one or another technique will depend on the nature of the data set and figures of merit can be used to compare them. A more detailed discussion of applications of chemometrics for food authenticity analysis can be found in a recent review (Kamal & Karoui, 2015).

2. General overview A literature survey using the Web of Science database revealed 124 articles focused on the determination of milk adulterants in the period of 2010– 2016. These articles are authored by researchers worldwide (e.g. China, Brazil, India, USA, Spain, Portugal, Italy, Iran, and Germany), demonstrating that milk adulteration is a topic of general concern. The increasing number of 6

publications (Fig. 2a) and the predominance of articles in journals with high impact factors (79% in journals with IF > 2.0) reinforce the importance of this research field. In spite of the recent publication, the number of citations of these articles is notable (1934, which corresponds to a mean of 15.6 citations per article). Applications of specific techniques, such as liquid chromatography– tandem

mass

spectrometry (Abernethy et

al., 2013)

and vibrational

spectroscopy (Bunaciu et al., 2016) to the determination of milk adulterants have also been reviewed. Other reviews have focused on specific adulterants, such as melamine (Lu et al., 2016). El-Salam (2014) reviewed the use of proteomics to evaluate milk contamination and adulterations. The usual analytical techniques (e.g. HPLC-MS, HPLC-MS/MS, and MALDI-TOF) required a time-consuming sample preparation, but the chemical information obtained was very useful to identify milk protein changes during the lactation or when the animal presents mastitis. Identification of milk allergen substances is also feasible. The distribution of analytical applications in relation to the main milk adulterants is shown in Fig. 2b. The large number of studies on the determination of hydrogen peroxide and melamine (total of 50.3%) is remarkable.

Whereas

the

former

has

been

determined

mainly

by

electroanalytical techniques, several methods have been proposed for melamine determination, especially after the adulteration incidents reported in China (Wu, & Zhang, 2013). Determination of other substances used to increase the product shelf life (e.g. salicylic acid and formaldehyde) and adulteration of the nitrogen content (e.g. with whey and urea) have also received significant attention. A large variety of analytical techniques have been

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exploited for determination of the adulterants; the main techniques are highlighted in Fig. 2c. Because of the diversity of studies on the determination of hydrogen peroxide, electroanalysis (essentially amperometry, but also other voltammetric approaches) predominates as the detection technique of adulterants (25.4%). Emphasis has been placed on the development of modified electrodes and biosensors aiming to perform analysis directly (i.e. without sample pretreatment) or after a simple dilution/adjustment of the ionic strength; such approaches account for 24.2% and 21.0% of the applications (Fig. 2d), respectively. Often, calibration requires the standard additions method to circumvent matrix effects. However, application of electrochemical detection to the determination of other milk adulterants has been scarce. Often, direct analysis is also feasible in vibrational spectroscopy, e.g. infrared (IR) and Raman, although sonication to obtain a sample with a homogenous distribution of fat globules (Jha et al., 2015) or sample drying (Santos et al., 2013a) is generally

required

prior

to

IR

measurements.

On

the

other

hand,

chromatographic, spectrophotometric, and luminometric analyses often require complex

and

time-consuming

sample

pretreatments,

involving

protein

precipitation and, sometimes, fat removal. This is needed to avoid irreversible retention of sample components in chromatographic columns or light scattering in the spectrometric techniques. The risks of adsorption of species on the electrode surfaces and clogging of sample introduction devices in atomic spectrometry must also be taken into account. For protein precipitation, sample treatment with trichloroacetic acid (TCA) or an organic solvent (e.g. methanol or acetonitrile) is usual, while fat removal is often carried out via liquid-liquid extraction. Solid-phase extraction (SPE) is also generally employed in the

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clean-up of milk samples before chromatographic separation. The group termed “other techniques” in Fig. 2c involves a diversity of alternatives, such as capillary zone electrophoresis (Wen et al., 2010), immunoassays (Lutter et al., 2011), and nuclear magnetic resonance (Santos, Pereira-Filho, & Colnago, 2016), as well as more exotic approaches, such as measurement of the dielectric properties (Zhu, Guo, & Liang, 2015) and solidification time (Kumar, Lal, Seth, & Sharma, 2010) for determination of the fat content. Both NIR and MIR spectroscopy have been exploited for determination of milk adulterants and these techniques are often associated with chemometric data treatment. As a rule, a group of adulterants is determined, which is useful for arriving at conclusions about the product authenticity. The relatively poor detectability (i.e. high limits of detection, LODs) of these techniques can be highlighted as a drawback. On the other hand, highly sensitive detection, e.g. fluorimetry (Peng, Liu, Xie, 2015) and amperometry (Sun et al., 2013) for detection of hydrogen peroxide, allows extensive dilution of samples, which minimizes

matrix effects. Applications involving HPLC predominate in

comparison to those involving GC due to the low volatility of most of the adulterants. In fact, GC applications generally require prior chemical derivatization. Mass spectrometry (and mainly tandem MS) is predominantly used for detection in HPLC and it has also been exploited without coupling with separation techniques. Although desirable in view of the large analytical demand, flow analysis has been exploited in a restricted number of applications (4.9%).

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3. Adulteration of nitrogen content Milk adulteration generally involves the addition of nitrogenous compounds to increase the apparent protein content. This type of adulteration is very usual because the non-protein nitrogen cannot be distinguished by Kjeldahl and Dumas methods that are commonly used for determining total protein content in dairy products. Melamine, urea, and whey are the main adulterants for this purpose due to their high nitrogen content and low cost. Melamine (1,3,5-triazine-2,4,6-triamine, C3H6N6), a nitrogen-rich (66.6% m/m nitrogen) organic compound, is commonly used to increase the apparent protein content of liquid and powdered milk. Recently, this adulterant has become an important issue and attracted worldwide attention as a result of several food safety incidents (Wu et al., 2013). Whey is a very cheap by-product of cheese manufacturing that is added to liquid milk, not only to increase the volume but also the protein content. Urea is naturally present in milk in low concentration, but is also extensively used in frauds because of its low cost. In the period 2010-2016, numerous analytical procedures have been developed for detection of theses nitrogenous compounds in milk and infant formulas. Liquid and gas chromatography (HPLC and GC), sensor technology (e.g. exploitation of molecularly imprinted polymers), NIR and MID infrared and Raman spectroscopies are the most reported methods. Less frequently used techniques to quantify this type of milk adulteration are UV-Vis, Fluorescence, low field nuclear magnetic resonance (LF-NMR), and digital image analysis. Tables 1 and 2 provide an overview of the procedures proposed in the last seven years.

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A series of analytical methods are available to determine melamine in liquid and powdered milk, but HPLC is widely applied and accepted by regulatory agencies for determination of this adulterant. Several detectors can be used, such as UV–vis spectrophotometry, fluorescence, and mass spectrometry

(MS).

Currently,

liquid

chromatography–tandem

mass

spectrometry (HPLC-MS/MS) is the method established by the FDA for detection of melamine in infant formula due to the molecular specificity and high sensitivity. A zwitterionic HILIC column is generally used, along with 0.1% formic acid in acetonitrile (5:95 v/v) and 20 mmol L-1 ammonium formate in acetonitrile (50:50 v/v) as mobile phases. Melamine has been detected by using a triple quadrupole mass detector with an electrospray ionization source (ESI) in positive mode. The limit of quantification (LOQ) of the procedure was 0.25 mg kg-1 (FDA, 2008). In spite of the good analytical performance, the extensive sample pretreatment (extraction in an aqueous formic acid solution followed by filtration, centrifugation, and dilution) limited its application in routine analysis. Accordingly, a simpler procedure was proposed by Lutter et al. (2011). The sample preparation was limited to protein precipitation in acetonitrile:water (70:30 v/v) followed by centrifugation. The analysis was also performed in the HPLC-MS/MS with ESI, using a HILIC TSKgel Amide-80 column and a LOD of 0.05 mg kg-1 was achieved. Also aiming to avoid time-consuming sample pretreatment, He et al. (2014) proposed a selective method for melamine detection by using magnetic molecularly imprinted polymer (MMIP) as sorbent. For melamine extraction, 100 mg of MMIP were mixed with diluted milk samples and stirred for 5 min; melamine was eluted using a methanolic solution with 5.0% acetic acid. The analysis was carried out by HPLC-MS/MS, equipped with

11

Zorbax 300-SCX column. Isocratic elution was carried out with methanol and ammonium acetate as the mobile phase. The main advantage of the procedure is the simpler sample pretreatment, which consumes low volumes of organic solvent. Moreover, the procedure showed a LOQ as low as 0.096 mg kg-1. HPLC with UV or fluorescence detection have also been proposed for determination of melamine in liquid milk and powdered infant formula (Finete, Gouvêa, Marques, & Neto, 2015; Sun, Wang, Liang, & Wu, 2010; Yan et al., 2012; Venkatasami & Sowa, 2010; Lutter et al., 2011). These methods require relatively simpler and cheaper equipment compared to HPLC-MS/MS. However, they can be less selective, especially with HPLC-UV, because many organic compounds absorb in the same spectral region of melamine (ca. 240 nm). Therefore, the analytical procedure involves laborious sample treatment using acetonitrile or trichloroacetic acid, followed by filtration, clean-up and preconcentration by solvent evaporation, solid phase extration (SPE) or matrix solid phase dispersion (MSPD) (Lutter et al., 2011; Finete et al., 2015; Sun et al., 2010; Yan et al., 2012). In this sense, sample preparation achieved in an acetonitrile-free procedure (Venkatasami et al., 2010) stands out due to the practical aspects and environmental friendliness. Sequential injection chromatography (SIC) in a micellar medium is an alternative to replace toxic organic solvents used as mobile phases, in the sample pretreatment or both, such as chloroform, methanol and acetonitrile (Batista, Nascimento, Melchert, & Rocha, 2014). The procedure involves on-line sample pretreatment by a simple dilution with aqueous sodium dodecyl sulfate using a multiposition valve, thus minimizing the risks of sample contamination and analyte loss. Another alternative is capillary electrophoresis (CE) (Wen et

12

al., 2010; Xia et al., 2010), which is a cost-effective method relative to HPLC and GC due to the faster separation and lower consumption of solvents. Gas chromatography coupled with mass spectrometry (GC-MS) is another efficient method for melamine quantification. This technique has been successfully applied to assay cow’s milk and powdered infant formula, with LOD of 0.019 mg kg-1 (Pan et al., 2013; Lutter et al., 2011). The main weakness of GC-MS is the need for analyte derivatization (e.g. trimethylsilyl derivatives), which requires expensive reagents and time-consuming sample pretreatments. Other MS techniques (e.g. matrix-assisted laser-desorption ionizationtime of flight mass spectrometry, MALDI-TOF MS and electrospray ionization tandem mass spectrometry, ESI–MS/MS) are powerful alternatives for melamine detection, with LODs of 0.1 (Su et al., 2013) and 0.26 mg kg-1 (Domingo et al., 2015). In spite of the high selectivity and sensitivity of these methods, their application is limited due to the time-consuming sample pretreatment and expensive instrumentation. On the other hand, enzyme-linked immunosorbent assays (ELISA) can provide a cost-effective and fast alternative for melamine analysis by using commercial kits (Lutter et al., 2011). A variety of commercial ELISA kits is available, but they are restricted to semi-quantitative (screening) methods due to the false positives results. Numerous researchers have attempted to develop sensors for melamine detection. Molecularly imprinted polymer (MIP) based sensors have received considerable attention because of their high stability, low cost, and easy preparation. Examples of electrochemical sensors based on MIP can be found in the literature (Liu et al., 2011; Wu, Wang, Zhao, & Lu, 2012). They are prepared by electrochemical polymerization of p-aminobenzoic acid and poly-

13

(2-mercaptobenzimidazole) on glassy carbon and Au electrodes, respectively. The MIP sensor showed good selectivity, sensitivity, and reproducibility, with LOQ of 0.4 µg kg-1 (Wu, Wang, Zhao, & Lu, 2012). However, its preparation requires different steps, making the procedures complex and time-consuming. Another study explored a non-modified copper electrode (Araujo & Paixão, 2014) for electrochemical monitoring of the reduction of the melamine/copper chloride ion-pair (MELH+[CuCl2]−). The quantification of melamine in milk was performed by differential pulse voltammetry, without dilution or any sample pretreatment. In the last few years, increasing attention has been paid to the use of spectroscopic techniques (i.e. UV-vis, infrared, Raman, and fluorescence) for development of greener analytical procedures for food analysis. These methods are rapid, non-destructive and allow a direct analysis, with little (or without) sample preparation (Kamal et al., 2015; Bunaciu et al., 2016). In addition, the combination of these methods with chemometric tools has facilitated the development of on-line and in-line procedures for food quality control. In spite of these advantages, spectroscopic methods usually show lower sensitivity compared to chromatography, thus limiting their application to the proposition of classification models for an initial sample inspection. Table 2 summarizes some illustrative applications of spectroscopic techniques to milk authenticity analysis Infrared spectroscopy, with applications in the NIR (14,000–4,000 cm−1) and MIR (4,000–400 cm−1) spectral ranges, is one of the most widely used spectroscopic techniques for melamine, urea, and whey detection because of the simplicity, cost-effectiveness and environmental friendliness. Advances in the instrumentation of Fourier transform spectrometers (FTIR) as well as the

14

use of attenuated total reflectance (ATR) or transflectance have allowed fast data collection from solid and liquid samples. Moreover, the development of portable spectrometers has allowed in-situ analysis with analytical performance similar to the benchtop instruments. Rapid determination of melamine, urea and whey with minimal (Jawaid, Talpur, Sherazi, Nizamani, & Khaskheli, 2013; Jha et al., 2015; Carvalho et al., 2015; Santos, Pereira-Filho, Rodriguez-Saona, 2013b) or without (Yang, Liu, & Xu, 2013; Capuano, Boerrigter-Eenling, Koot, & van Ruth, 2015; Santos et al., 2013a) sample preparation have been reported. Calibration models obtained with different regression algorithms (PLS; Least square support vector machine, LS-SVM and multi-way partial least squares, NPLS) have generated reliable models to estimate melamine in milk with correlation coefficients (R2) higher than 0.94 and root mean square errors of prediction (RMSEP) of 1.55 mg kg-1 for melamine (Jawaid, Talpur, Sherazi, Nizamani, & Khaskheli, 2013), 254.23 mg kg-1 for urea (Jha et al., 2015) and 15.000 mg kg-1 for whey (Capuano, Boerrigter-Eenling, Koot, & van Ruth, 2015). These RMSEP values indicated that the results of multivariate models presented good agreement with reference methods. To avoid water interference, adulterated milk samples were placed on a 192-well microarray slide and air-dried. The analysis was performed by MIR micro-spectroscopy, allowing the identification of the type of adulterant due to the distinct absorption spectra (Santos et al., 2013a). Quantification of the adulterant levels in liquid milk was performed by PLS regression analysis and RMSEP values of 300 and 2330 mg kg-1 were obtained for urea and whey, respectively. Similar results were obtained using portable MIR spectrometry (Santos et al., 2013b) with lowcost and better analytical performance in relation to MIR micro-spectroscopy.

15

The water signal was also suppressed by drying the samples under vacuum until formation of a thin film. NIR hyperspectral imaging has also been investigated as an alternative method for melamine detection. Compared to conventional NIR spectroscopy, this technique simultaneously provides spectral and spatial information, which is useful for assay of non-homogeneous samples. With this approach, a PLS model for powdered milk showed RMSEP of 810 mg kg-1 (Lim et al., 2016).The main disadvantage over the conventional NIR spectroscopy is the higher cost; data collection and analysis requires sensitive detectors and fast computers. UV spectroscopy (in the range of 200 to 290 nm) was also applied to the detection of melamine in both liquid and powdered milk. Regression models evaluated using non-linear regression algorithms (support vector machine, SVM, or LS-SVM) yielded LOD values of 14.2 mg kg-1 and 9.94 mg L-1 for powdered and liquid milk, respectively (Zou, Zhang, Feng, & Liang, 2014). Another study explored the quenching of Triton X-114 fluorescence; the surfactant was used for fat removal from the samples by cloud point extraction and the amount remaining after extraction acted as fluorophore (Nascimento, Rocha, & Rocha, 2015). The LOD was estimated at 0.8 mg L-1, which was significantly lower than the achieved by vibrational methods (i.e. NIR, MIR and UV). Thus, this is a fast, practical and environmentally friendly alternative for determination of melamine in adulterated milk. Recently, chemiluminescence was explored for the highly sensitive detection of melamine in liquid milk based on the effect of gold nanoparticles (AuNPs) on the bis(2,4,6-trichlorophenyl) oxalate-hydrogen peroxide-fluorescein reaction (Du, Wang, & Zhang, 2015). Chemiluminescence is quenched in the presence of the AuNPs due to the

16

spectral overlap of the emission and absorption spectra. Melamine induced aggregation of the nanoparticles leading to a red-shift of the absorption band, which increases proportionally the chemiluminescence signal. Fast and environmentally friendly methods for determination of melamine in different matrices have exploited Raman spectroscopy. The major advantage over other spectroscopic techniques (NIR, MIR and UV-Vis) is the absence of interference from water and the better analytical performance (e.g. improved sensitivity). Surface-enhanced Raman spectroscopy (SERS) generally provides LODs similar to those obtained with HPLC-MS (Zhang et al., 2010; Lou et al., 2011). Zhang et al. (2010) achieved a LOD of 0.5 µg mL-1 for assay of melamine in liquid milk, with a greener sample preparation, limited to the dilution with distilled water followed by centrifugation. On the other hand, Lou et al. (2011) achieved a LOD < 3.0 µg L-1, but the procedure was less environmentally friendly because of the use of acetonitrile for sample preparation. Low field nuclear magnetic resonance (LF-NMR) (Santos et al., 2016) and digital images (Santos et al., 2013) were also applied for detection of urea and whey in liquid milk. In these studies, the milk samples were spiked with solutions of the target adulterant and the final concentration range for whey or urea varied from 5–50% (v/v). The LF-NMR procedure showed better analytical performance, with RMSEP of 2.84% (v/v). Although the LF-NMR does not require any sample preparation and has the potential for through-package analysis, the instrumentation is expensive and discrimination of the types of adulterants was not feasible.

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4. Adulteration of the fat content of milk and addition of detergents Fat is one of the major components of milk and generally constitutes 3 to 5% (m/m) of cow’s milk (Yoshinaga, Nagai, Mizobe, Kojima, & Gotoh, 2013). Triacylglycerols constitute about 97–98% of the fat in milk, and are important component that provide the characteristic flavor and texture. Fat has been one of the main targets of adulteration (Rani et al., 2015) due to the value for production of milk derivatives and to compensate the effect of fraudulent dilution. In this sense, the major adulterants are vegetable oils (e.g. soybean, sunflower, groundnut, coconut, palm, and peanut oil) and animal fat (e.g. cow tallow and pork lard) as highlighted in Table 3 (Kumar et al., 2010, Rani et al., 2015, Garcia et al., 2012, Upadhyay, Kumar, Rathod, Goyal, & Lal, 2015). Detecting adulteration with vegetable oils is often difficult because of the variation in the chemical composition of these oils. On the other hand, detecting adulteration with animal fat is difficult because of the chemical composition similar to milk fat (Rebechi, Vélez, Vaira, & Perotti, 2016). A recently developed procedure for detecting adulteration of milk fat is based on HPLC coupled with atmospheric pressure chemical ionization tandem mass spectrometry (HPLC-APCI-MS/MS). The compound employed as a model of milk fat was 1,2-dipalmitoyl-3-butyroyl-glycerol, and separation was carried out by using a C28 column after lipid extraction with organic solvents (e.g. chloroform and methanol). A linear response was achieved in the concentration range of 1 to 250 µg mL-1 with recoveries within 99.9% and 105.0% (Yoshinaga et al., 2013). Gas chromatography with flame ionization detection (GC-FID) (Kala, 2013; Rebechi et al., 2016; Kim, Kim, & Park, 2015) was also exploited for determination of milk fat. This is a rapid alternative because the fatty acids 18

and triacylglycerols can be monitored and compared with the reference triglyceride profile (Kala, 2013). The procedure can be used to discriminate the source of adulteration, i.e. either animal fat or vegetable oil, and facilitates detection of more than 5.0% and 6.3% of vegetable oil and lard in milk, respectively (Kala, 2013). Multiple linear regression (MLR) yielded reliable calibration models for evaluation of the adulterants based on the fatty acid profiles. Adulteration was detected at a level higher than 10% for tallow and 5% for lard with application of the models (Rebechi et al., 2016), while detection of fat added to milk at 10% was achieved in another study (Kim et al., 2015). A procedure employing matrix-assisted laser desorption/ionization-quadrupole time of flight mass spectrometry (MALDI-QTOF MS) was also used to evaluate the authenticity of milk fat. This technique facilitated detection of the addition of soybean oil to milk powder by monitoring the triacylglycerol profile of the sample. The procedure was validated using time-consuming procedures recommended by Brazilian regulations, such as gravimetric analysis (Garcia et al., 2012). Reversed-phase thin layer chromatography (RP-TLC) (Rani et al., 2015, Upadhyay et al., 2015) facilitated detection of vegetable oil added to milk at concentrations as low as 1% (m/m) via a fast and easily implemented procedure (Rani et al., 2015). The vegetable oil content (peanut, soybean, and sunflower oil) was quantified by monitoring the structure of the sterols present in the plants (e.g. differences in the number of unsaturated groups) by using β-sitosterol as a marker (Upadhyay et al., 2015). Direct analysis via real time ionization high resolution mass spectrometry (DART-HRMS) was used to distinguish the type of milk (cattle or sheep), the animal feed (organic or traditional) utilized, and the

19

presence of fat or vegetable oil adulterants. It was possible to detect oil added to milk also at concentrations as low as 1% (m/m) (Hrbek, Vaclavik, Elich, & Hajslova, 2014). Derivative spectrophotometry was exploited to verify the authenticity of milk based on the absorption peaks at 238 and 297 nm (Jirankalgikar & De, 2014). With the addition of vegetable oils and tallow, the peak at 238 nm remained constant, while that at 297 nm decreased with the addition of nondairy fats. Quantification was based on the second derivative of the spectra by using the ratio of the analytical signals at 238 and 297 nm. Raman spectroscopy combined with PLS has been applied to the determination of milk fat, without sample preparation (El-Abassy, Eravuchira, Donfack, von der Kammer & Materny, 2011). The strategy was based on the shifts in the relative Raman band intensities, which allowed detection of fat at concentrations as low as 0.3% with good precision (El-Abassy et al., 2011). Less usual alternatives have also been exploited for the detection of milk fat adulteration, such as the fractionation technique followed by measurement of the apparent solidification time of fat (Kumar et al., 2010); the oscillating system (Gupta, Sadat & Khan, 2014); and analysis based on measurement of the dielectric properties, i.e. dielectric constant and dielectric loss factor (Zhu et al., 2015). The method based on the apparent solidification time can be used to detect adulteration of milk with 5% vegetable oils as well as the (goat and pig) body fats in cow’s milk, but not in buffalo milk (Kumar et al., 2010). The procedure is relatively simple, but the uncertainty of the visual observation of the solidification time can be a significant source of error. The oscillating system is based on the motion of a pendulum (a uniform metallic rod); the oscillation

20

period of which depends on the fat content of the medium (Gupta et al., 2014). The procedure based on the dielectric properties is a fast alternative for measurement of the fat content of milk in the range of 0.06 to 4.04% (m/v). Detection of adulterants is feasible because both the dielectric constant and loss factor are inversely proportional to the fat content at a given frequency (varied from 20 to 4500 MHz) and temperature (varied from 25 to 45 °C). For example, when the fat content in milk increased from 0.06 to 4.04 %, the dielectric constant decreased from 66.60 to 60.59 at a frequency of 41 MHz (Zhu et al., 2015). Because detergents are essential components for emulsification of added fat in the preparation of synthetic milk, they are considered a new class of milk adulterants (Barui, Sharma, & Rajput, 2012, Barui, Sharma, Rajput, Singh, 2013). Synthetic milk has the same color and consistency as the genuine product, and has thus been used for adulteration of dairy milk at levels of 5– 10%. The presence of detergents in infant milk formula can sometimes be distinguished by means of color and smell (Barui et al., 2012, Barui et al., 2013, Tay, Fang, Chia, & Li, 2013). Long-term consumption of this kind of adulterated milk can cause serious deleterious health effects, such as heart and digestive problems (Tay et al., 2013). The main procedure for determining anionic detergents in milk is based on the use of methylene blue or Azure A, which are cationic dyes that form ion-pairs with anionic detergents. The ion-pair is more soluble into the organic phase of a water–chloroform system, and the absorbance of the organic phase is measured at 635 nm (Barui et al., 2012, Barui et al., 2013). The LOD and LOQ of this procedure for assay of sodium

21

dodecylbenzene sulfonate (SDBS) in milk were 10 and 20 mg L-1, respectively, with recoveries ranging from 97.5 to 101.3%. In order to avoid the use of chloroform, a simple procedure based on paper chromatographic analysis of the anionic detergent/methylene blue ionpair was proposed for the detection of the adulterants in milk (Barui et al., 2013). The method is based on the difference in the solubility of the free and associated dye, and results are obtained in less than 10 min. Whereas only one spot is observed for unadulterated milk, at least one additional spot is expected for milk adulterated with surfactants. The color intensity of the spots is proportional to the detergent concentration and the LOD values were 0.1 and 0.01 % (m/v) for detection of a laboratory grade detergent (labolene) and SDBS in milk, respectively. Another procedure for determination of detergent in milk exploited liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-QTOF– MS) (Tay et al., 2013). The proposed method involved simple solid–liquid extraction and facilitated detection of more than 5% (m/m) of detergent in infant milk formula.

Dodecylbenzenesulfonate was

used

as

a

marker,

the

quantification was based on the standard additions method, and the LOD and LOQ were 0.176 and 0.585 µg L-1, respectively.

5. Adulteration to increase the product shelf life A common method of milk adulteration involves the addition of substances to decrease microbial growth and thus increase the product shelf life. This group includes several substances, such as hydrogen peroxide, formaldehyde, hypochlorite, salicylic acid, and even potassium dichromate. 22

These substances are toxic to humans and their monitoring is required for quality control. However, this task is often hindered by addition of a diversity of substances with the same aim. Recently, several alternative procedures have been proposed, mainly for determination of hydrogen peroxide. Some illustrative examples are highlighted in Table 4. The use of salicylic acid in animals producing milk for human consumption is forbidden (e.g. by the European Community) and this species is often determined in multiresidue analysis of non-steroidal anti-inflammatory drugs. Salicylic acid is a signalizing molecule in plants that is released in response to pathogen attacks. Thus, it may be introduced into the food chain of animals and low amounts (µg kg-1) can be found in milk for human consumption; higher amounts are indicative of adulteration. Multiresidue analysis has been carried out by HPLC-MS/MS; the main difficulty is analyte isolation due to its non-covalent interactions with matrix proteins. In this sense, a procedure involving deproteinization/analyte extraction with acetonitrile and sample clean-up by SPE was proposed and validated (Gentili, et al., 2012). More practical and inexpensive approaches involved an amperometric biosensor (Zavar, Heydari, & Rounaghi, 2013) and flow injection analysis (Souza et al., 2014). The former was based on a graphite disk electrode coated with polypyrrole and entrapping a banana tissue as a polyphenol oxidase source. The electrocatalytic oxidation of salicylic acid was observed at a lower potential (0.11 versus 0.91 V in the graphite electrode) and yielded a 33-fold higher peak current, thus improving the selectivity and sensitivity of the analysis. The flow-based procedure exploited the multi-pumping approach for spectrophotometric determination based on formation of an iron(III) complex.

23

The procedure stands out in terms of the high sample throughput (80 determinations per hour) and the capability to determine multiple adulterants (also dichromate, hydrogen peroxide, and starch), but the LOQ for salicylic acid is relatively high (ca. 100 mg L-1). These procedures also involved simplified sample preparation that required protein precipitation (Zavar et al., 2013) or simple dilution (Souza et al., 2014). Addition of dichromate or formaldehyde to milk is critical because of the toxicity and carcinogenicity of these species. Detecting these adulterants thus requires reliable analytical procedures capable of identifying these adulterants even at low concentrations. Recent studies exploiting ATR-MIR (Botelho, Reis, Oliveira, & Sena, 2015) or flow analysis (Souza et al., 2014) failed to achieve this goal, with LOQ estimated as 5 g L-1 for formaldehyde and 1 mg L-1 for dichromate. A 100-fold lower LOQ could be achieved for Cr(VI) determination by exploiting long pathlength spectrophotometry, but the effect of light scattering by the milk sample should be critically evaluated. Low LODs were achieved for formaldehyde determination by GC-MS, but a time-consuming sample preparation, involving chemical derivatization with o-(2,3,4,5,6-pentafluorobenzyl)-hydroxylamine

hydrochloride

and

headspace

solid-phase

microextraction were required (Jeong et al., 2015). Because of the multiple steps involved, poor recovery was estimated for some samples. A suitable LOD was achieved by exploiting dispersive liquid-liquid microextraction in a flowbased system (Nascimento et al., 2015). The dispersion of the extractant in a temperature-controlled lab-made chamber was improved by the pulsed flows provided by solenoid micro-pumps, thus yielding an extraction efficiency of 82%. The procedure is environmentally friendly due to the use of a low toxicity

24

ionic liquid as an extractant (only 120 µL per extraction) and reduced consumption

of

the

derivatizing

reagent

(3.5

µL

acetylacetone

per

determination). Low amounts of hydrogen peroxide are sometimes added to milk in order to activate the natural enzyme lactoperoxidase, which has antimicrobial activity and increases the shelf life of pasteurized milk. Although this activity is legal, e.g. the US FDA established a limit of 0.05% (Reanpang, Themsirimongkon, Saipanya, Chailapakul, Jakmunee, 2015), addition of H2O2 is forbidden in some countries due to the toxic effects. As a requirement for production of lacteous derivatives, H2O2 must be eliminated in a pretreatment step. The need for monitoring hydrogen peroxide has fostered the recent development of several analytical procedures by exploiting spectrophotometric (Han, Zhang, & Serpe, 2015), fluorimetric (Peng et al., 2015), and mainly electrochemical (Reanpang et al., 2015; Thandavan et al., 2015) detection. Some representative examples are presented in Table 4. Amperometric sensors have been predominantly utilized because the analyte is naturally electroactive. Biosensors are usually based on peroxidase enzymes (Thandavan et al., 2015), which catalyze the oxidation of a substrate by H2O2 to yield other electroactive species, thus improving the sensor selectivity. Non-enzymatic sensors that mimic peroxidase have also been proposed, and one of the lowest LODs was achieved by this strategy (Sun et al., 2013). In this sense, Prussian blue has been termed an “artificial peroxidase” because of the selectivity of electrodes modified with this substance to H2O2 (Silva, Montes, Richter, & Munoz, 2012). The main aim of utilizing electrochemical sensors for adulterant assay is to develop inexpensive devices for direct detection of target analytes in milk, i.e. without complex

25

sample pretreatments. Recent developments have focused on the use of nanomaterials, especially of multiwalled carbon nanotubes (Thandavan et al., 2015) and metal nanoparticles (Reanpang et al., 2015). For example, an enhanced electron transfer rate was achieved with a nanocomposite of carbon nanotubes and iron oxide (response time < 1s) and improved sensitivity was reported for a biosensor based on the catalase enzyme (Thandavan et al., 2015). The feasibility of flow injection amperometric detection was also demonstrated (Reanpang et al., 2015). Colorimetric tests are of interest as they offer the possibility to visually detect the presence of adulterants and even facilitate semi-quantitative applications. Toward this end, the synthesis and characterization of a microgel composed of Ag nanoparticles (AgNP) was described and the nanomaterial was used as a sensor for hydrogen peroxide (Han et al., 2015). After sample deproteinization and removal of fats, quantitative measurements were feasible due to the bleaching effect of the analyte on the absorption peak at 400 nm related to surface plasmon resonance of the AgNPs. Silver nanoparticles were also used in an interesting fluorimetric assay for determination of hydrogen peroxide by exploiting the dependence of the physicochemical properties with morphology of the nanomaterial (Peng et al., 2015). Ag nanoprisms adsorbed the bovine serum albumin-fluorescein isothiocyanate complex, whereas the fluorescent species were released due to morphological transformation of the material to nanodiscs in the presence of hydrogen peroxide. Based on this principle, very low concentrations of H2O2 (nmol L-1 level) could be detected in diluted milk samples, but the risk of interference from ascorbic acid or iodide (at concentrations higher than 50 and

26

2 µmol L-1, respectively) should be taken into account. An alternative procedure exploited the oxidation of coumarin by the hydroxyl radical produced in the Fenton reaction (Abbasa, Luo, Zhu, Zou, & Tang, 2010), yielding a highly fluorescent 7-hydroxycoumarin. The procedure requires a 9 min reaction and is relatively tolerant to other oxidants or reducing species in the sample.

6. Adulteration with water and other substances Bovine milk adulteration with water is a common activity, but detection of this fraud is not a trivial task and several studies related to this undertaking have been recently reported in the literature (Singh & Gandhi, 2013). Measurement of the osmolality is a simple alternative for detection of water as a milk adulterant. Milk samples presented an average value of (290.2±8.0) mOsmoles kg-1 (Musara & Pote 2014). On the other hand, hypo-osmotic milk samples (i.e. values below 277 mOsmoles kg-1) were typical for milk diluted with water (Büttel, Fuchs, & Holz, 2008). Digital images have also been employed for identification and quantification of water and other adulterants (whey, synthetic milk, synthetic urine and hydrogen peroxide) in bovine milk (Santos et al., 2013). A 5 mL aliquot of bovine milk sample was mixed with bromophenol blue or bromothymol blue in a 10 mL transparent beaker in order to monitor the color changes in response to different level of adulteration. Subsequently, the beakers were placed directly above a scanner and a black cover was used to block spurious light from the laboratory. Images with resolutions of 300 dots per inch (dpi) were obtained, and information about the color composition (red (R), green (G), blue (B), luminosity (L= R + G + B), relative colors (r = R/L, g = G/L,

27

and b = B/L) as well as hue (H), saturation (S), and value (V)) was extracted using a Matlab (Natick, USA) function. More than 1000 milk samples were assayed and the effects of the adulterants were evaluated individually and when combined in binary, ternary or quaternary mixtures. By using chemometric tools for classification (SIMCA, PLS-DA, and KNN) and calibration (PLS), adulterants were identified in concentrations higher than 5% (v/v). The percentage of correct predictions achieved with the classification models was around 90%. It is important to mention that the goal was not identify a specific adulterant and its concentration, but the presence of adulterants individually or in combination. A similar approach was proposed to detect the addition of NaOH in milk, which aims to avoid the sour formation (Santos, Wentzell, & Pereira-Filho, 2012). Fresh milk samples (pH ~ 6.8) were put at ambient temperature up to turn sour (pH ~ 4.8). Later, NaOH was added into the sourish milk to establish its original pH. An acid–base indicator (methyl red) was added to the samples aiming at colorimetric sample analysis. Principal component analysis (PCA) revealed a good separation between the control and adulterated milk samples. The proposed method is simple, presents high analytical throughput and the instruments can be miniaturized aiming at field analysis. In addition, combination of digital image analysis and chemometrics reduces the human eyes subjectivity. Chemometric classification models are a good alternative when spectroscopic

techniques

are

considered.

ATR-MIR

spectroscopy

in

combination with PLS-DA was used to detect the presence of five adulterants in milk - water, starch, sodium citrate, formaldehyde, and sucrose (Botelho, Reis, Oliveira & Sena (2015). Only 30 µL of sample was required and the

28

classification error varied from 0.04% (m/v) for formaldehyde to 0.07% for water, starch, and sucrose. Chemometric tools and NIR spectroscopy were recently combined to identify proteins in milk samples (Zhang et al., 2014; Ni, Zhong, Zhang, Zhang, & Huang, 2014), yielding an experimental setup with a huge number of samples (more than 200 and up to 800). A refractive index (RI) below 37.00 oZeiss at 20 oC is indicative of milk dilution. From this observation, a method to determine the RI of milk serum was optimized from the technique proposed in 1915, thereby increasing the sample throughput from 3 to 12 samples per hour (Rezende, Carmo & Esteves, 2015). A sensor based on a printed circuit board coated with a film of polymethyl-methacrylate was proposed for detection of irregular milk dilution. The sensor is inserted directly in the milk sample and was able to identify adulterations with tap water in concentrations higher than 10% (v/v). On the other hand, in concentrations higher than 10% the changes in signal (phase angle) were very tenue: -24.96 θ for pure milk, -26.73 θ for milk with 10% tap water, and -27.05 θ for milk with 20% tap water (the phase angle for tap water was -28.91 θ). For adulteration with urea the signal did not increase proportionally with the concentration and, despite the simplicity of the system, more developments are necessary (Das, Sivaramakrishna, Biswas, & Gowami, 2015). Another relevant concern is the accidental or intentional contamination of high price milk (e.g. she-donkey milk) with cow or goat milk, for example. MALDI-TOF-MS was used to monitor the protein profiles based on substances such as α-lactalbumin (αLA) and β-lactoglobulin (βLA) (Cunsolo, Muccilli, Saletti, & Foti, 2013). Samples were analyzed without treatment and a ratio for

29

αLA equal to 1.2 was estimated when goat and she-donkey milk were compared. Recent studies proposed a moisture sensor based on microwave absorption at 40 GHz for direct determination of the water content of milk. This technique was based on the relative reduction of a signal transmitted through the sample (Agranovich et al., 2016). The proposed method has potential for the detection of milk adulteration, although the result of this application was not presented.

Conclusions and trends Milk adulteration is a common illegal activity that increases the demand for product monitoring in order to assure food safety and human health. This observation has fostered the development of novel analytical procedures for adulterant assay and a diversity of approaches has been presented in the last five years. These include a variety of analytical techniques and procedures with highly different equipment costs and various degrees of complexity of sample preparation. Powerful analytical tools (e.g. liquid chromatography tandem mass spectrometry and Raman spectroscopy) are rarely available in most laboratories, thus necessitating the development of inexpensive alternatives. In view of the high analytical demand, development of rapid procedures for direct sample analysis has been one of the main goals. While this objective has been achieved for the assay of some adulterants, such as hydrogen peroxide, melamine, urea, and whey, simpler and reliable analytical procedures for determination of other species (e.g. fat and oils) are yet required.

30

Another interesting trend is the development of procedures for determination of a group of adulterants. This contributes to product quality assurance and is useful for cross-checking results (e.g. as adulteration by fat or vegetal oil is often combined with addition of surfactants, the detection of both species confirms the fraud). Infrared spectroscopy has been distinguished as a powerful tool for this task, especially when combined with chemometrics, with interesting applications also in the period covered by this review. Another challenge is in situ analysis, which requires simpler sample treatment and portable equipment, such as hand-held IR spectrometers or amperometric detection. The use of digital images (scanometry) is also a promising alternative that combines simplicity and low cost. This analytical strategy when combined with chemometry can be used in the field for an initial inspection (screening) of the raw milk. Suspicious samples can then be submitted for confirmatory tests in the laboratory. However, for adulterants with deleterious effects even in low concentrations, this screening strategy is not feasible because of the limitations in sensitivity.

Acknowledgments The authors acknowledge the fellowships and financial support from the Brazilian agencies São Paulo Research Foundation (FAPESP), National Council of Technological and Scientific Development (CNPq) and Coordination for the Improvement of Higher Education Personnel (CAPES).

31

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Ni, L. J., Zhong, L., Zhang, X., Zhang, L. G., & Huang, S. X. (2014). Identification of adulterants in adulterated milks by near infrared spectroscopy combined with non-linear pattern recognition methods. Spectroscopy and Spectral Analysis, 34, 2673-2678. Pan, X. D., Wu, P. G., Yang, D. J., Wang, L. Y., Shen, X. H., & Zhu, C. Y. (2013). Simultaneous determination of melamine and cyanuric acid in dairy products by mixed-mode solid phase extraction and GC-MS. Food Control, 30, 545-548. Peng, C., Liu, C., & Xie, Z. (2015). Preparation of a fluorescent silver nanoprism–dye complex for detection of hydrogen peroxide in milk. Analytical Methods, 7, 9749 – 9752. Rani, A., Sharma, V., Arora, S., Lal, D., & Kumar, A. (2015). A rapid reversedphase thin layer chromatographic protocol for detection of adulteration in ghee (clarified milk fat) with vegetable oils. Journal of Food Science and Technology, 52(4), 2434–2439. Reanpang, P., Themsirimongkon, S., Saipanya, S., Chailapakul, O., & Jakmunee, J. (2015). Cost-effective flow injection amperometric system with metal nanoparticle loaded carbon nanotube modified screen printed carbon electrode for sensitive determination of hydrogen peroxide. Talanta, 144, 868–874. Rebechi, S. R., Vélez, M. A., Vaira, S., & Perotti, M. C. (2016). Adulteration of Argentinean milk fats with animal fats: Detection by fatty acids analysis and multivariate regression techniques. Food Chemistry, 192, 1025–1032. Rezende, P. S., Carmo, G. P., & Esteves, E. G. (2015). Optimization and validation of a method for the determination of the refractive index of milk serum based on the reaction between milk and copper(II) sulfate to detect milk dilutions. Talanta, 138, 196-202. Santos, P. M., & Pereira-Filho, E. R. (2013). Digital image analysis – an alternative tool for monitoring milk authenticity. Analytical Methods, 5, 3669–3674. Santos, P. M., Pereira-Filho, E. R., & Colnago, L. A. (2016). Detection and quantification of milk adulteration using time domain nuclear magnetic resonance (TD-NMR). Microchemical Journal, 124, 15-19. Santos, P. M., Pereira-Filho, E. R., & Rodriguez-Saona, L. E. (2013a). Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food Chemistry, 138, 19– 24. Santos, P. M., Pereira-Filho, E. R., & Rodriguez-Saona, L. E. (2013b). Application of hand-held and portable infrared spectrometers in bovine milk analysis. Journal of Agricultural and Food Chemistry, 61, 1205−1211. 36

Santos, P. M., Wentzell, P. D., & Pereira-Filho, E. R. (2012). Scanner digital images combined with color parameters: A case study to detect adulterations in liquid cow’s milk. Food Analytical Methods, 5, 89–95. Silva, R. A. B., Montes, R. H. O., Richter, E. M., & Munoz, R. A. A. (2012). Rapid and selective determination of hydrogen peroxide residues in milk by batch injection analysis with amperometric detection. Food Chemistry, 133, 200–204. Singh, P., & Gandhi, N. (2013). Milk preservatives and adulterants: processing, regulatory and safety issues. Food Reviews International, 31, 236-261. Souza, G. C. S., Silva, P. A. B., Leotério, D. M. S., Paim, A. P .S., & Lavorante, A. F. (2014). A multicommuted flow system for fast screening/sequential spectrophotometric determination of dichromate, salicylic acid, hydrogen peroxide and starch in milk samples. Food Control, 46, 127-135. Su, X., Zhou, H. Y., Chen, F. C., Gao, B. X., Liu, Z. W., Zhang, Y. H., Liu, F., Liu, F., Li, Z. R., & Gao, Z. X. (2013). Modified SBA-15 matrices for highthroughput screening of melamine in milk samples by MALDI-TOF MS. International Journal of Mass Spectrometry, 338, 39-44. Sun, H., Wang, L., Ai, L., Liang, S., & Wu, H. (2010). A sensitive and validated method for determination of melamine residue in liquid milk by reversed phase high-performance liquid chromatography with solid-phase extraction. Food Control, 21, 686-691. Sun, W., Ju, X., Zhang, Y., Sun, X., Li, G., & Sun, Z. (2013). Application of carboxyl functionalized graphene oxide as mimetic peroxidase for sensitive voltammetric detection of H2O2 with 3,3′,5,5′-tetramethylbenzidine. Electrochemistry Communications, 26, 113–116. Tay, M., Fang, G., Chia, P. L., & Li, S. F. Y. (2013). Rapid screening for detection and differentiation of detergent powder adulteration in infant milk formula by LC–MS. Forensic Science International, 232, 32–39. Thandavan, K., Gandhi, S., Nesakumar, N., Sethuraman, S., Rayappana, J. B. B., & Krishnan, U. M. (2015). Hydrogen peroxide biosensor utilizing a hybrid nano-interface of iron oxide nanoparticles and carbon nanotubes to assess the quality of milk. Sensors and Actuators B, 215, 166–173. Upadhyay, N., Kumar, A., Rathod, G., Goyal, A., & Lal, D. (2015). Development of a method employing reversed-phase thin-layer chromatography for establishing milk fat purity with respect to adulteration with vegetable oils. International Journal of Dairy Technology, 68 (2), 207-217. Venkatasami, G., & Sowa, J. R. (2010). A rapid, acetonitrile-free, HPLC method for determination of melamine in infant formula. Analytica Chimica Acta, 665, 227-230.

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Xia, J., Zhou, N., Liu, Y., Chen, B., Wu, Y., & Yao, S. (2010). Simultaneous determination of melamine and related compounds by capillary zone electrophoresis. Food Control, 21, 912-918. Wahed, P., Razzaq, M.A., Dharmapuri S., & Corrales, M. (2016). Determination of formaldehyde in food and feed by an in-house validated HPLC method. Food Chemistry, 202, 476–483. Wen, Y., Liu, H., Han, P., Gao, Y., Luan, F., & Li, X. (2010). Determination of melamine in milk powder, milk and fish feed by capillary electrophoresis: a good alternative to HPLC. Journal of the Science and Food Agriculture, 90, 2178-2182. World Health Organization. http://www.who.int. Acessed 01.04.16. Wu, B., Wang, Z., Zhao, D., & Lu, X. (2012). A novel molecularly imprinted impedimetric sensor for melamine determination. Talanta, 101, 374-381. Wu, Y., & Zhang, Y. (2013). Analytical chemistry, toxicology, epidemiology and health impact assessment of melamine in infant formula: Recent progress and developments. Food and Chemical Toxicology, 56, 325-335. Yan, H., Cheng, X., Sun, N., Cai, T., Wu, R., & Han, K. (2012). Rapid and selective screening of melamine in bovine milk using molecularly imprinted matrix solid-phase dispersion coupled with liquid chromatography-ultraviolet detection. Journal of Chromatography B, 908, 137-142. Yang, R., Liu, R., & Xu, K. (2013). Detection of adulterated milk using twodimensional correlation spectroscopy combined with multi-way partial least squares. Food Bioscience, 2, 61-67. Yoshinaga, K., Nagai, T., Mizobe, H., Kojima, K., & Gotoh, N. (2013). Simple method for the quantification of milk fat content in foods by LC-APCIMS/MS using 1,2-dipalmitoyl-3-butyroyl-glycerol as an indicator. Journal of Oleo Science, 3, 215-221. Zavar, M. H. A., Heydari, S., & Rounaghi, G. H. (2013). Electrochemical determination of salicylic acid at a new biosensor based on polypyrrolebanana tissue composite. Arabian Journal for Science and Engineering, 38, 29–36. 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, 342348. Zhang, X. F., Zou, M. Q., Qi, X. H., Liu, F., Zhu, X. H., & Zhao, B. H. (2010). Detection of melamine in liquid milk using surface-enhanced Raman scattering spectroscopy. Journal of Raman Spectroscopy, 41, 1655–1660. 38

Zhu, X., Guo, W., & Liang, Z. (2015). Determination of the fat content in cow’s milk based on dielectric properties. Food and Bioprocess Technology, 8, 1485-1494. Zou, H., Zhang, W., Feng, Y., & Liang, B. (2014) Simultaneous determination of melamine and dicyandiamide in milk by UV spectroscopy coupled with chemometrics. Analytical Methods, 6, 5865-5871.

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Table 1. Overview of analytical procedures for determination of melamine in milk. Technique

Sample preparation

Linear range -1 a (mg kg )

LOD -1 a (mg kg ) 

Recoveries (%) 72–110

RSD (%) 5.7–24.9

Remarks

Reference

HPLC-MS/MS

Protein precipitation, filtration, centrifugation and dilution

0.25–5.00

HILIC column and electrospray ionization

FDA (2008)

HPLC-MS/MS

Melamine extraction using magnetic molecularly imprinted polymer as sorbent

0.01 – 1.0

0.026

87.1-93.9

4.3 – 7.2

Zorbax 300 - SCX column and electrospray ionization

He et al. (2014)

HPLC-UV

Centrifugation, dilution in methanol 50%and filtration

1.0–78

0.1

97.2–101.2

< 1.0

C18 column and detection at 240 nm

Venkatasami et al. (2010)

HPLC-UV

Matrix solid-phase dispersion with molecularly imprinted polymers

0.24–60.0

0.05

86.0–96.2

≤ 4.0

UV detection at 210 nm

Yan et al. (2012)

HPLC-DAD

Protein precipitation, sonication, centrifugation, SPE and filtration

0.1–49

0.018

85.5–99.3

2.3–3.7

C18 column and detection at 235 nm

Sun et al. (2010)

HPLC-FL

Protein precipitation

0.05–9.7

0.023

94.2–96.6

0.65–2.7

Zorbax SB-C18 column; fluorescence at 365 nm

Finete et al. (2015)

Micellar chromatography

Dilution with aqueous sodium dodecyl sulfate

1.9–5.8

0.6



2.9

sequential injection chromatography

Batista et al. (2014)

CE

Protein precipitation, sonication, filtration, and SPE

0.8–78

0.08

94.5–103.7

1.22–3.26

High performance capillary electrophoresis and detection at 210 nm

Wen et al. (2010)

CE

Protein precipitation, 0.24–97 0.053 94.2–97.1 1.84–2.45 UV detection at 214 nm Xia et al. (2010) centrifugation and filtration -1 -1 -1 a. Milk density = 1.03 g mL was considered to convert mg L to mg kg ; pABA: para-aminobenzoic acid; SPB: saline phosphate buffer, TCA: trichloroacetic acid

40

Table 1. Cont. Technique

Sample preparation

Linear range -1 a (mg kg )

LOD -1 a (mg kg )

Recoveries (%)

RSD (%)

Remarks

Reference

GC-MS

Protein precipitation, SPE, and chemical derivatization

0.05–0.78

0.01

80.8–101.5

3.6–7.9

VF-5 ms quartz capillary column

Pan et al. (2013)

GC-MS

Protein precipitation, SPE, and derivatization

< 0.500

0.019

95.0–101.0

7.6–29.9

HB-5MS capillary column

Lutter et al. (2011)

MALDI-TOF MS

Protein precipitation, sonication, filtration, and SPE



0.10



≤ 14

Modified SBA-15 matrix − 3+ ([CHC ] [NH -Si-SBA-15-Si3+ − NH ] [CHC ])

Su et al. (2013)

ESI–MS/MS

Protein precipitation, centrifugation, and filtration

< 19.4

0.26

109–114

< 7.86

Fragmentation of m/z 127.1 confirms the presence of melamine

Domingo et al. (2015)

ELISA

Dilution in SPB/CH3OH MeOH (10%), phosphate buffer, NaCl, and CH3OH)

0.02–0.5

0.06

97–110

10.0–21.1

Screening procedure

Lutter et al. (2011)

Electrochemical sensor

Protein precipitation, centrifugation and filtration

0.05–0.48

0.455

95.6–105.2

2.42–4.38

MIP of pABA

Liu et al.(2011)

Electrochemical sensor

Not informed

0.001–6.1

0.0004

92.0–97.0

3.0–4.2

Au modified with MIP poly (2- mercaptobenzimidazole)

Wu et al.(2012)

Electrochemical None 0.6–11 0.1 94.0–103.0 1.0 non-modified copper Araujo et al. sensor electrode (2014) -1 -1 -1 a. Milk density = 1.03 g mL was considered to convert mg L to mg kg ; pABA: para-aminobenzoic acid; SPB: saline phosphate buffer, TCA: trichloroacetic acid

41

Table 2. Summary of spectroscopic procedures for determination of adulterants of the nitrogen content of milk. -1 a

Adulterant

Method of analysis

Data analysis

Results (mg kg )

Reference

Melamine

MIR

PLS

RMSECV = 5.75; RMSEP = 1.55

Jawaid et al. (2013)

Melamine

MIR

PLS

RMSECV = 162; RMSEP = 160

Yang et al. (2013)

Melamine

NIR hyperspectral imaging

PLS

RMSEC = 660; RMSEP = 810

Lim et al. (2016)

Melamine

UV

PLS

RMSECV < 0.22156; RMSEP < 0.21585

Zou et al. (2014)

Melamine

Fluorescence

Linear regression

LOD = 0.8; RSD = 1.4% (n=11); Recoveries ≥ 95%

Nascimento et al. (2015)

-12

Melamine

Chemiluminescence

Linear regression

LOD = 3.7 x 10 ; Recoveries ≥ 94.1%.

Du et al. (2015)

Melamine

Raman

Linear regression

LOQ = 0.5; RSD ≤ 10%; Recoveries ≥ 93%

Zang et al. (2010)

-4

Melamine

Raman

Linear regression

LOD = 1 x 10 ; RSD = 4.7% (n=6); Recoveries ≥ 88.5%

Lou et al. (2011)

Melamine

Raman

Linear regression

LOD = 0.003; RSD < 10%; Recoveries ≥ 89%

Ma et al. (2013)

Whey

NIR

PLS

RMSEC = 5900; RMSEP = 15000

Capuano et al. (2015)

Urea

MIR

MLR

RMSEC = 183.77; RMSEP = 254.23

Jha et al. (2015)

Whey and urea

MIR

PLS

RMSECV = 1160; RMSEP = 1180

Santos et al. (2013a)

RMSECV = 210; RMSEP = 230

Whey and urea

MIR

PLS

RMSECV = 1910; RMSEP = 2330

Santos et al. (2013b)

Whey

MIR

PLS

RMSEP = 11700

Carvalho et al. (2015)

Whey and urea

Digital image

PLS

RMSECV = 58400; RMSEP = 58600

Santos et al. (2013)

Whey and urea

LF-NMR

PLS

RMSEP = 28400

Santos et al. (2016)

RMSECV = 250; RMSEP = 300

-1

-1

-1

a. milk density = 1.03 g mL was considered to convert mg L to mg kg

42

Table 3. Procedures for analysis of adulterants of fat content of milk. Adulterant Fats

Detection LC-APCI/MS-MS

Vegetable oils and animal body fats

Solidification time

Tallow and lard fats.

GC-FID

n-Hexane and H 2SO4–EtOH

2-15

Vegetable oils Vegetable oils

RP-TLC RP-TLC

Methanolic KOH and hexane Alcoholic potassium hydroxide and extraction with diethyl ether

1-10 1-10

Vegetable oils Vegetable oils or fats

GC-FID MALDI-QTOF MS

Chloroform–methanol n-hexano

0-90 

Vegetable oils

DART-HRMS

Toluene and methanol



Animal body’s fat

Oscillating system



0-100

Tallow

Derivative spectrophotometry

Chloroform

0-100

GC

Hexane



Dielectric properties



0.06-4.04

Beef tallow and vegetable Oils Fat

Reagents for sample preparation Chloroform and methanol

Response range (%) 0.0001–0.025 5-15



Milk fat

Remarks PPBu as a model of fat; LOD = 0.11 mg L-1 Fractionation technique and the apparent solidification time test

Reference Yoshinaga et al. (2013)

Multiple Linear Regression applied to FA profiles  Solvent systems with alumina to separate the sterols,  

Rebechi et al. (2016) Rani et al. (2015) Upadhyay et al. (2015)

DART–HRMS identifies triacylglycerols in milk and dairy products Oscillation frequency affected by the medium Alteration of the ratio of the maximum absorption (238 and 297nm) with an increase of added fat Based on comparison of triglyceride profile Dielectric constant and loss factor related to the fat content Chemometric analysis

Kumar et al. (2010)

Kim et al. (2015) Garcia et al. (2012) Hrbek et al. (2014) Gupta et al. (2014) Jirankalgikar et al. (2014)

Kala (2013) Zhu et al. (2015)

Raman 0.3-4.0 El-Abassy et al.  spectroscopy (2011) DART-HRMS: Direct analysis in real time ionization high resolution mass spectrometry; GC-FID: Gas chromatography with flame ionization detector; LC-APCI/MS-MS: Highperformance liquid chromatography coupled with atmospheric pressure chemical ionization tandem mass spectrometry; MALDI-QTOF MS: Matrix-assisted laser desorption/ionization-quadrupole time of flight mass spectrometry flight; PPBu: 1,2-Dipalmitoyl-3-butyroyl-glycerol; .RP-TLC : Rapid reversed-phase thin layer chromatography

Table 4. Analytical features of procedures for determination of adulterants added to increase product shelf life.

43

a

Adulterant

Detection

Sample preparation

Salicylic acid

HPLC-MS/MS

Flow-injection spectrophotometry

Acetonitrile/vortex(2)/so nication(10)/centrifugation(10)/solvent evaporation/ centrifugation(10)/SPE Methanol+water/ centrifugation (20)/pH adjustment 20-fold dilution with water

Dichromate

Flow-injection spectrophotometry

Formaldehyde

Flow injection with dispersive liquid– liquid microextraction GC-MS

Amperometric biosensor

MID-IR attenuated total reflectance (ATR)

Linear range -1 (mg kg ) < 4.5

LOD -1 (µg kg ) 4.6

Recoveries (%) 100

RSD (%) 5

Remarks

Reference

Multi-residue method

Gentili et al. (2012)

0.013-13.4

0.012

97

NI

Using banana tissue coated with polypyrrole

Zavar et al. (2013)

100-402

2.6

95-107.5

2.2

Souza et al. (2014)

20-fold dilution with water

1.0-10

0.12

90.1-108.7

2.3

TCA/centrifugation(5)/ filtration

0.5-4.9

0.1

91-106

3.3

Complexation reaction of Fe(III) dichromate, salicylic acid, hydrogen peroxide, and starch Reaction with 1,5diphenylcarbazide dichromate, salicylic acid, hydrogen peroxide, and starch Extraction with ionic liquid (120 µL per determination)

Headspace SPME (15) after derivatization with O-(2,3,4,5,6pentafluoro-benzyl)hydroxylamine hydrochloride (40) None

0.005-1.0

12.2

68-128%

1.4-15

Determination of acetaldehyde and formaldehyde

Jeong et al. (2015)

(4.8–97)x103







Simultaneous determination of up to 5 adulterants without any sample pretreatment

Botelho et al. (2015)

a. Numbers between brackets indicate time in minutes

*Estimated for spiked milk samples

Table 4. Cont.

44

Souza et al. (2014)

Nascimento et al. (2015)

a

Adulterant

Detection

Sample preparation

Hydrogen peroxide

Flow-injection spectrophotometry

20-fold dilution with water

Spectrophotometry

Methanol/sonication (10)/phase separation (30)/centrifugation (10)/n-hexane 50-fold dilution with water

Fluorimetry

Linear range -1 (mg kg ) (9.7-194)

LOD -1 (µg kg ) 3 1.08x10

Recoveries (%) 90.2- 107.4

RSD (%) 1.1

(9.9-99)x10-3

NI

90.9-92.0

4.4

-2

0.13

82.1-92.8

6.9-9.3*

-2

0.16

93.8-105.7

2.1-6.2*

(0.06-16)x10 and 0.16-3.3

Fluorimetry

TCA/stirring(40)/filtratio n

(0.06-66) 10

Amperometric biosensor

Direct analysis

0.04-0.71

0.12

NI

4

Flow-injection amperometry

Dilution in phosphate buffer

3.3-33

0.66

92.2–93.3

2.3

Amperometry

NI

(0.02-2.6)x10

0.03

94.3-103

4.5

Batch injection amperometry

10-fold dilution

3.3-132

3.3x10

85-107

0.8

-2

a. Numbers between brackets indicate time in minutes; *Estimated for spiked milk samples

45

2

Remarks

Reference

Reaction with vanadium oxide (V) in acid medium Diminution of the peak absorption due to silver nanoparticles

Souza et al. (2014)

Release of a fluorescent species from silver nanoparticles Oxidation of coumarine by hydroxyl radicals generated in a Fenton reaction Electrocatalytic reduction of H2O 2 on a catalase-based H 2O2 on a catalase-based biosensor Screen-printed electrode based on metal nanoparticle loaded carbon nanotube Carboxyl functionalized graphene oxide mimics peroxidase with 3,3′,5,5′tetramethylbenzidine as substrate Graphite-composite electrode modified with Prussian-blue

Peng et al. (2015)

Han et al. (2015

Abbasa et al. (2010) Thandavan et al. (2015)

Reanpang et al. (2015) Sun et al. (2013

Silva et al. (2012)

Figure captions Fig. 1. Overview of the main milk adulterants indicating the analytical techniques predominantly used for determination and the complexity of sample pretreatment. FL: molecular fluorescence; GC: gas chromatography; HPLC: high performance liquid chromatography; IR: infrared spectrometry; RI: measurement

of

refractive

index;

Scan:

scanometry;

SP:

UV-vis

spectrophotometry.

Fig. 2. Overview of the publications focusing on determination of milk adulterants in the period 2010-2016 in relation to (a) publication year, (b) adulterant species, (c) sample pretreatment, and (d) analytical technique. Source: Web of Science database using “milk”, “adulterant” (or derivative words), and the specific substances as keywords. Numbers between brackets correspond to the percentages of the articles in the period.

46

Figure 1

47

48

Figure 2

Highlights

- Analytical procedures for determination of milk adulterants (2010-2016) - Review of the strategies for sample preparation - Proposals to make fraud detection simpler, faster and greener - Strategies for direct determination and in-situ analysis

49