Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Identification of pasteurized mare milk and powder adulteration with bovine milk using quantitative proteomics and metabolomics approaches

Adulteration in dairy products presents food safety challenges, driven by economic factors. Processing may change specific biomarkers, thus affecting their effectiveness in detection. In this study, proteomics and metabolomics approaches were to investigate the detection of bovine milk (BM) constituents adulteration in pasteurized mare milk (PMM) and mare milk powder (MMP). Several bovine proteins and metabolites were identified, with their abundances in PMM and MMP increasing upon addition of BM. Proteins like osteopontin (OPN) and serotransferrin (TF) detected adulteration down to 1 % in PMM, whereas these proteins in MMP were utilized to identify 10 % adulteration. Biotin and N6-Me-adenosine were effective in detecting adulteration in PMM as low as 10 % and 1 % respectively, while in MMP, their detection limits extend down to 0.1 %. These findings offer insights for authenticating mare milk products and underscore the influence of processing methods on biomarker levels, stressing the need to consider these effects in milk product authentication.

Authenticity identification of animal species in characteristic milk by integration of shotgun proteomics and scheduled multiple reaction monitoring (MRM) based on tandem mass spectrometry

Milk is one of the oldest natural dairies with high value, which has different species including cow, camel, donkey, goat, sheep, buffalo, yak and et al. However, economically motivated adulteration of non-cow milk with cheaper cow milk occurs frequently. To develop a high-throughput approach for milk species authentication, integration of shotgun proteomics and scheduled multiple reaction monitoring (MRM) was developed. In total, 37 specific peptides were screened as unique to different species. Specific peptides processing stability was investigated under different treatment (heat, pressure, fermentation). Subsequently, four quantitative ion pairs of peptides from cow milk and six quantitative ion pairs of peptides from six non-cow milks were selected for the adulteration quantitative analysis. The method is capable of detection adulteration in the range of 1%-100%, and the quantitative recoveries ranged from 91.07% to 111.75%. The results suggested that combination of shotgun proteomics and MRM had potential for the milk species authentication.

Food Safety Assessment and Nutraceutical Outcomes of Dairy By-Products: Ovine Milk Whey as Wound Repair Enhancer on Injured Human Primary Gingival Fibroblasts

The valorization of milk whey appears to be a promising strategy for managing by-products from dairy food industries, which incur demanding economic costs for treatment and/or disposal. Thanks to its numerous bioactive components, whey is expected to be increasingly incorporated into foods in the future. We investigated the safety of ovine milk whey through in vitro experiments on human primary gingival fibroblast (HGF-1) proliferation and wound healing. Fibroblasts play a crucial role in the repair processes from the late inflammatory phase until the final stages. Cells treated with varying concentrations of ovine whey (0.01%, 0.1%, 1%, and 10%) were able to close wounds more rapidly than vehicle-treated cells. Time- and dose-dependent responses were observed in cell populations exposed to ovine whey. Specifically, wounds treated with 0.1% and 10% milk whey showed better migratory capabilities compared to those treated with 0.01% and 1% milk whey after 24 and 48 h. In addition, ovine milk whey stimulates extracellular matrix deposition, as evidenced by the increasing levels of CD44 antigen density evaluated through FACS analysis, as well as COL1A1 expression measured both via RT-qPCR and immunofluorescence. This phenomenon was particularly evident at concentrations of 0.01% and 10%. Ensuring quality and safety has become a major concern for health authorities in the food industry. Our findings suggest that ovine milk whey is safe and possesses regenerative properties. It facilitates tissue re-establishment following exposure to environmental stress, particularly accelerating gingival wound closure.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Advancements in portable instruments based on affinity-capture-migration and affinity-capture-separation for use in clinical testing and life science applications

The shift from testing at centralized diagnostic laboratories to remote locations is being driven by the development of point-of-care (POC) instruments and represents a transformative moment in medicine. POC instruments address the need for rapid results that can inform faster therapeutic decisions and interventions. These instruments are especially valuable in the field, such as in an ambulance, or in remote and rural locations. The development of telehealth, enabled by advancements in digital technologies like smartphones and cloud computing, is also aiding in this evolution, allowing medical professionals to provide care remotely, potentially reducing healthcare costs and improving patient longevity. One notable POC device is the lateral flow immunoassay (LFIA), which played a major role in addressing the COVID-19 pandemic due to its ease of use, rapid analysis time, and low cost. However, LFIA tests exhibit relatively low analytical sensitivity and provide semi-quantitative information, indicating either a positive, negative, or inconclusive result, which can be attributed to its one-dimensional format. Immunoaffinity capillary electrophoresis (IACE), on the other hand, offers a two-dimensional format that includes an affinity-capture step of one or more matrix constituents followed by release and electrophoretic separation. The method provides greater analytical sensitivity, and quantitative information, thereby reducing the rate of false positives, false negatives, and inconclusive results. Combining LFIA and IACE technologies can thus provide an effective and economical solution for screening, confirming results, and monitoring patient progress, representing a key strategy in advancing diagnostics in healthcare.

Utilization of Dairy By-Products as a Source of Functional and Health Compounds-The Role of Ovine Colostrum and Milk Whey on Chronic Myeloid Leukemia Cells

Nowadays, the search for food products that promote consumers' health has gained interest, and dairy by-products, due to their biological quality, could have a prominent position among products with health benefits. However, little is known about their activity on cancer cells. This study aimed to provide evidence about the effect of ovine colostrum and milk whey on K562 cells, a model of the human chronic myeloid leukemia cell line. The exposure of K562 cells to a single administration of sheep by-products at different concentrations for three days and three treatments for three days was carried out. Using a flow cytometric approach, we found that CD235a expression remained stable in the cells exposed to ovine whey (milk and colostrum) at concentrations ranging from 1 ng/mL to 100 μg/mL, after three days from one or three administrations, respectively. A significant reduction in fluorescent cells was observed in the populations exposed to 1 mg/mL of both milk and colostrum at the same time points. In these conditions, the size and granularity of the leukemic cells also changed, with a substantial reduction in the number of actively dividing cells in the S phase of the cell cycle. This phenomenon was highlighted by the Annexin V/PI cytofluorimetric test, which is able to provide quantitative results regarding the population of cells in early or late apoptosis or necrotic cells after exposure to a single dose or three doses of colostrum or sheep whey for three days, respectively. This report showed that both colostrum and milk whey were able to modify the phenotypic profile and cell cycle of the K562 cell line, inducing apoptosis at the highest concentration.

Separation and characterization of bovine milk proteins by capillary electrophoresis-mass spectrometry

Protein profiling of major bovine milk proteins (i.e., whey and casein proteins) is of great interest in food science and technology. This complex set of protein proteoforms may vary with breed, genetics, lactation stage, health, and nutritional status of the animal. Current routine methods for bovine milk protein profiling at the intact level are typically based on capillary electrophoresis-ultraviolet, which does not allow confirming unequivocally the identity of the separated proteins. As an alternative, in this study, we describe for the first time a novel and simple capillary electrophoresis-mass spectrometry method in positive electrospray ionization mode. Under the optimized conditions, capillary electrophoresis-mass spectrometry allowed the separation and identification at the intact level of major bovine milk whey and casein proteins in less than 15 min. Furthermore, high-resolution mass spectrometry confirmed its importance in the reliable characterization of bovine milk protein proteoforms, especially those with slight molecular mass differences, such as β-casein A1 and A2, which are relevant to unequivocally identify milk with specific β-casein compositions (e.g., A2A2 milk, which is widely known as A2 milk). This differentiation was not possible by matrix-assisted laser desorption/ionization mass spectrometry, which provided rapidly and easily a rich but less accurate fingerprint of bovine milk proteins due to the lower mass resolution.

A simple and fast triplex-PCR for the identification of milk's animal origin in Halloumi cheese and yoghurt

In this research communication we describe a straightforward triplex-PCR protocol able to differentiate the origin of milk from three closely related species (goat, sheep and cow) in Halloumi, a cheese with Protected Designation of Origin (PDO), and yogurts. Halloumi must contain at least 51% sheep or goat milk, therefore, the fraudulent adulteration of this cheese with excess of cow milk must be routinely tested. The assay employs one universal forward primer and three species-specific reverse primers giving rise to 287 bp (cow), 313 bp (goat), and 336 bp (sheep) amplicons, under the same amplification conditions. This protocol, when used to test a small number of Cyprus commercial products, correctly detected mislabeling in Halloumi (2 out of 6 samples were adulterated) and yogurt brands (1 out of 4 was adulterated). The suggested protocol is a reliable tool for identifying the origin of milk in Halloumi cheeses and yogurts and can be used in any laboratory equipped with a thermocycler and an agarose gel electrophoresis apparatus.

Mid-Infrared (MIR) Spectroscopy for the quantitative detection of cow’s milk in buffalo milk

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R²cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R²val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow’s milk at no additional cost.

Detection of Ovine or Bovine Milk Components in Commercial Camel Milk Powder Using a PCR-Based Method

Food ingredient adulteration, especially the adulteration of milk and dairy products, is one of the important issues of food safety. The large price difference between camel milk powder, ovine, and bovine milk powder may be an incentive for the incorporation of ovine and bovine derived foods in camel milk products. This study evaluated the use of ordinary PCR and real-time PCR for the detection of camel milk powder adulteration based on the presence of ovine and bovine milk components. DNA was extracted from camel, ovine, and bovine milk powder using a deep-processed product column DNA extraction kit. The quality of the extracted DNA was detected by amplifying the target sequence from the mitochondrial Cytb gene, and the extracted DNA was used for the identification of milk powder based on PCR analysis. In addition, PCR-based methods (both ordinary PCR and real-time PCR) were used to detect laboratory adulteration models of milk powder using primers targeting mitochondrial genes. The results show that the ordinary PCR method had better sensitivity and could qualitatively detect ovine and bovine milk components in the range of 1% to 100% in camel milk powder. The commercial camel milk powder was used to verify the practicability of this method. The real-time PCR normalization system has a good exponential correlation (R² = 0.9822 and 0.9923) between ovine or bovine content and Ct ratio (specific/internal reference gene) and allows for the quantitative determination of ovine or bovine milk contents in adulterated camel milk powder samples. Accuracy was effectively validated using simulated adulterated samples, with recoveries ranging from 80% to 110% with a coefficient of variation of less than 7%, exhibiting sufficient parameters of trueness. The ordinary PCR qualitative detection and real-time PCR quantitative detection method established in this study proved to be a specific, sensitive, and effective technology, which is expected to be used for market detection.

Animal Species Authentication in Dairy Products

Milk is one of the most important nutritious foods, widely consumed worldwide, either in its natural form or via dairy products. Currently, several economic, health and ethical issues emphasize the need for a more frequent and rigorous quality control of dairy products and the importance of detecting adulterations in these products. For this reason, several conventional and advanced techniques have been proposed, aiming at detecting and quantifying eventual adulterations, preferentially in a rapid, cost-effective, easy to implement, sensitive and specific way. They have relied mostly on electrophoretic, chromatographic and immunoenzymatic techniques. More recently, mass spectrometry, spectroscopic methods (near infrared (NIR), mid infrared (MIR), nuclear magnetic resonance (NMR) and front face fluorescence coupled to chemometrics), DNA analysis (real-time PCR, high-resolution melting analysis, next generation sequencing and droplet digital PCR) and biosensors have been advanced as innovative tools for dairy product authentication. Milk substitution from high-valued species with lower-cost bovine milk is one of the most frequent adulteration practices. Therefore, this review intends to describe the most relevant developments regarding the current and advanced analytical methodologies applied to species authentication of milk and dairy products.

Use of MALDI-TOF MS technology to evaluate adulteration of small ruminant milk with raw bovine milk

Detection of adulteration of small ruminant milk is very important for health and commercial reasons. New analytical and cost-effective methods need to be developed to detect new adulteration practices. In this work, we aimed to explore the ability of the MALDI-TOF mass spectrometry to detect bovine milk in caprine and ovine milk using samples from 18 dairy farms. Different levels of adulteration (0.5, 1, 5, 10, 20, 40, 60, and 80%) were analyzed during the lactation period of goat and sheep (in May, from 60 to 90 d in milk, and in August, from 150 to 180 d in milk). Two different ranges of peptide-protein spectra (500-4,000 Da; 4-20 kDa) were used to establish a calibration model for predicting the concentration of adulterant using partial least squares and generalized linear model with lasso regularization. The low molecular weight part of the spectra together with the generalized linear model with lasso regularization regression model appeared to have greater potential for our aim of detection of adulteration of small ruminants' milk. The subsequent prediction model was able to predict the concentration of bovine milk in caprine milk with a root mean square error of 11.4 and 17.0% in ovine milk. The results offer compelling evidence that MALDI-TOF can detect the adulteration of small ruminants' milk. However, the method is severely limited by (1) the complexity of the milk proteome resulting from the adulteration technique, (2) the potential degradation of thermolabile proteins, and (3) the genetic variability of tested samples. Additionally, the root mean square error of prediction based only on one individual sample adulteration series can drop down to 6.34% for quantification of adulterated caprine milk and 6.28% for adulterated ovine milk for the full set of concentrations or down to 2.33 and 4.00%, respectively, if we restrict only to low concentrations of adulteration (0, 0.5, 1, 5, 10%).

Bioimpedance sensor to detect water content in milk based on van Der Pauw method

Milk fraud poses serious problems for the dairy industry and consumers' health. The main aim of this study was to reveal the effect of water added to milk by measuring its electrical impedance spectrum. The required sensor was designed based on the van der Pauw method to measure the electrical conductivity of milk at the frequency band of 10 Hz to 5 MHz. The bioimpedance spectrum of the milk of five different cows showed that the electrical impedance spectrum has a high potential for detecting water added to the milk (P < 0.01). The area under the Nyquist curve was introduced as a suitable index to detect water‐added milk. In addition, the characteristic frequency of the bioimpedance spectrum was used as an important index to differentiate water‐added milk from waterless milk. An electrical model was introduced to interpret the amount of water added to the milk using the characteristic frequency. Results showed that it is possible to detect raw milk from boiled milk by measuring its electrical impedance.

Authenticity assessment of dairy products by capillary electrophoresis

Milk and derivatives are a very important part in the diet of the world population. Products from goat, buffalo, and sheep species have a greater economic value than the cow ones, therefore, authenticity frauds by improperly adding cow's milk occur frequently: dairy products are among the seven more attractive foods for adulteration. Milk from each of the above-cited animal species has its own definite profile of whey proteins (variants of α-lactalbumin and β-lactoglobulin) and its definite profile of caseins (variants of α_S1 -, α_S2 -, β-, and κ-casein). Such proteins can be usefully exploited as markers of authenticity by using capillary electrophoresis which is the technique of choice for the analysis of proteins. Due to the multiple adjustable parameters that are unknown to other analytical techniques, capillary electrophoresis is able to detect frauds in milk mixtures and cheese with little use of solvents, fast analysis time, and ease of operation. This makes it attractive and competitive for routine checks that are very important to fight the adulteration market. Advantages and limitations are discussed.

A New Acoustic-Based Approach for Assessing Induced Adulteration in Bovine Milk

Milk is an important dietary requirement for many populations due to its high nutritional value. However, increased demand has also made it prone to fraudulent activity. In this sense, scientists have sought to develop simple, low-cost, and portable techniques to achieve quality control of milk in industry and farms as well. This work proposes a new instrumentation system based on acoustic propagation and advanced signal processing techniques to identify milk adulteration by industrial contaminants. A pair of transmitter-receiver low-cost piezoelectric transducers, configured in a pitch-catch mode, propagated acoustic waves in the bovine milk samples contaminated with 0.5% of sodium bicarbonate, urea, and hydrogen peroxide. Signal processing approaches such as chromatic technique and statistical indexes like the correlation coefficient, Euclidian norm and cross-correlation square difference were applied to identify the contaminants. According to the presented results, CCSD and RMSD metrics presented more effectiveness to perform the identification of milk contaminants. However, CCSD was 2.28 × 10⁵ more sensitivity to distinguish adulteration in relation to RMSD. For chromatic clustering technique, the major selectivity was observed between the contamination performed by sodium bicarbonate and urea. Therefore, results indicate that the proposed approach can be an effective and quick alternative to assess the milk condition and classify its contaminants.

Discrimination of Milk from Different Animal Species by a Foodomics Approach Based on High-Resolution Mass Spectrometry

An untargeted foodomics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole orbitrap and chemometrics was used to observe subtle differences in the molecule profiles of raw milk from different animal species (cow milk, goat milk, and water buffalo milk), which could prevent the fraud activities in the dairy industry. In data-dependent acquisition (DIA), spectra for all precursor ions facilitated the comprehensive identification of unknown compounds in untargeted foodomics. Chemometrics techniques were used to analyze large amounts of complex data to observe the separation of different sample groups and find the potential markers of sample groups. Finally, five markers were putatively identified by the potential marker identification workflow. The quantification results showed that β-carotene was found only in cow milk; ergocalciferol was found only in water buffalo milk; and the contents of nonanoic acid, decanoic acid, and octanoic acid were higher in goat milk than those in cow milk and water buffalo milk. The quantification of β-carotene enabled the detection of cow milk with a sensitivity threshold of 5% (w/w). This work provided an efficient approach for the discrimination of cow milk, goat milk, and water buffalo milk. Compared with proteomics and genomics, the simpler analytical procedures, lower costs, and higher speed of this work make it of great benefit for routine operations.

Technical note: Capillary electrophoresis as a rapid test for the quantification of immunoglobulin G in serum of newborn lambs

Finding a rapid and simple method of serum IgG determination in lambs is essential for monitoring failure of passive transfer of immunity. The aim of this study was to assess the ability of capillary electrophoresis (CE), an instrument mainly used in blood serum protein analysis, to estimate IgG content in serum of newborn lambs through determination of only total Ig percentage by comparing the results with those obtained with radial immunodiffusion (RID), the reference method for serum IgG quantification. Serum samples were collected at 24 h after birth from 40 Sarda lambs. The IgG concentration measured by RID and serum total Ig concentration measured by CE were (mean ± standard deviation) 29.8 ± 16.1 g/L and 37.8 ± 15.0%, respectively. Data provided by RID and CE analysis showed a polynomial trend (RID = 0.02CE² - 0.04CE + 4.13; coefficient of determination, R² = 0.96), displaying a strong relationship between these 2 methods. Applying the polynomial equation, the IgG values were predicted. Predicted IgG values were highly correlated (r = 0.98) and related (R² = 0.96) to IgG values obtained by RID assay. These data were subjected to Bland-Altman analysis, revealing a high level of agreement between CE and RID methods with a bias that was not different from 0 (-0.04 g/L) and agreement limits of -6.38 g/L (low) and +6.30 g/L (high). In addition, the linear regression analysis between differences (dependent variable) and average of IgG concentration by CE and RID (independent variable) did not show proportional bias (R² = 0.01). In conclusion, CE is a reliable instrument for a lamb health monitoring program, where Bland-Altman analysis also confirmed that the CE method can be a suitable alternative to RID.