Detection of milk adulteration using coffee ring effect and convolutional neural network.

A low-cost and effective method is reported to identify water and synthetic milk adulteration of cow's milk using coffee ring patterns. The cow's milk samples were diluted with tap water (TW), distilled water (DW) and mineral water (MW) and drop cast onto glass slides to observe coffee ring patterns. The area of the ring, total particle area and average particle diameter were extracted from these patterns. For each ring, the ratio of total particle area versus total ring area was calculated. The area ratio, regardless of water adulterants, follows an exponential model with respect to average particle diameter. Unlike TW, the ratio for DW and MW adulterated milk are clustered and classified together with respect to the particle diameter. These results were independent of dilution level and are used for adulterant classification. The ring of milk adulterated using synthetic milk gave multiple concentric rings, flower-like structures, and oil globules throughout the dilution level. An Alexnet model was used to classify water and synthetic milk adulterants in authentic milk. The trained model could achieve 96.7% and 95.8% accuracy for binary and tertiary classification respectively. These results enable us to distinguish synthetic milk from pure milk and segregate DW and MW with respect to TW adulterated milk.

Novel and Sensitive Touchdown Polymerase Chain Reaction Assays for the Detection of Goat and Sheep Milk Adulteration with Cow Milk.

Milk is the most consumed liquid food in the world due to its high nutritional value and relatively low cost, characteristics that make it vulnerable to adulteration. One of the most common types of milk adulteration involves the undeclared addition of cow's milk to milk from other mammalian species, such as goats, sheep, buffalo or donkeys. The incidence of such adulteration not only causes a crisis in terms of commercial market and consumer uncertainty but also poses a risk to public health, as allergies can be triggered by proteins in undeclared cow's milk. In this study, a specific qualitative touchdown (TD) PCR method was developed to detect the undeclared addition of cow's milk in goat and sheep milk based on the discrimination of the peak areas of the melting curves after the modification of bovine-specific primers. The developed methodology has high specificity for the DNA templates of other species, such as buffalos and donkeys, and is able to identify the presence of cow's milk down to 1%. Repeatability was tested at low bovine concentrations of 5% and 1% and resulted in %RSD values of 1.53-2.04 for the goat-cow assay and 2.49-7.16 for the sheep-cow assay, respectively. The application of this method to commercial goat milk samples indicated a high percentage of noncompliance in terms of labeling (50%), while a comparison of the results to rapid immunochromatographic and ELISA kits validated the excellent sensitivity and applicability of the proposed PCR methodology that was able to trace more adulterated samples. The developed assays offer the advantage of multiple detection in a single run, resulting in a cost- and time-efficient method. Future studies will focus on the applicability of these assays in dairy products such as cheese and yogurt.

Perceptions of rural and urban residents in Borana pastoral and agro-pastoral areas in Ethiopia related to milk adulteration.

Milk is a nutritious food that plays a great role in the diets of a society largely dependent on livestock production. On the other hand, contaminants can enter milk naturally or intentionally, causing a negative impact on the health of consumers. Milk adulteration is a wide concern in the dairy industry in many countries, including Ethiopia, with a subsequent negative impact on its nutritive value and potentially affecting the health of consumers. This study was designed to assess the perceptions of rural and urban residents in Borana pastoral and agro-pastoral areas in Ethiopia related to milk adulteration. It was also aimed at identifying the potential reasons for milk adulteration in the area. A semi-structured questionnaire and focus group discussions (FGDs) were used to collect quantitative and qualitative data, respectively, focusing on the types of substances added to milk and the reasons for the addition of the substances. In rural and urban areas, 73.1% and 91.7% of respondents reported suspicion of the addition of nonmilk substances or milk of other animal species to cow's milk before selling, respectively. According to the qualitative data, most reported adulterants were water and 'pasta or rice water' (a murky fluid left after boiling rice or pasta). Respondents mentioned that they identify adulterated milk by observation or tasting. Economic gain was the primary perceived reason to adulterate the milk according to the study participants. The respondents had concerns about the quality and safety of milk associated with adulteration in the area. The weak enforcement of regulations related to milk quality and marketing as well as the inadequacy of capacity for the detection of adulteration were mentioned as gaps toward mitigating the problems. Awareness creation about the negative impacts of milk adulteration among the community supported by strategies for regulation, such as improving regular testing of milk and taking actions on adulterated milk, is recommended to tackle consumer concerns around milk adulteration in the area.

Highly sensitive biofunctionalized nanostructures for paper-based colorimetric sensing of hydrogen peroxide in raw milk.

Hydrogen Peroxide (H2O2) is a highly hazardous, toxic, and carcinogenic chemical compound utilised in various industries-based applications. Despite strict restriction, they are deliberately added to food items such as milk as preservatives to increase its shelf life. Herein, we have formulated a green rapid colorimetric nanosensor for detection of H2O2 in milk using cotton leaves as both reducing and functionalizing agent for synthesis of silver nanoparticles (AgNPs). UV-Vis spectra exhibit a strong plasmonic peak at around 434 nm. X-Ray Diffraction (XRD) analysis was performed to determine the crystallinity of the nanoparticles. Field Emission Scanning Electron Microscope (FESEM) and Transmission Electron Microscope (TEM) characterizations revealed spherical morphology with size approximately âˆ¼16 nm. This functionalized nanoparticle could colorimetrically sense presence of H2O2 in milk samples both in liquid media and on paper substrates with Limit of Detection (LOD) of 8.46 ppm even in presence of other interfering substances in milk. This inexpensive route will pave the way for in depth research.

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.

Mitochondrial DNA D-Loop Amplification and Sequencing for Species Differentiation in Milk.

Adulteration of dairy products, mainly through the substitution of high-quality milk for lower-quality milk, results in the production of low-value products, raising health, social, and economic concerns. As such, the development of methods to ensure dairy products' safety and quality is of great concern for governments and consumers. Although several methods have been developed for species differentiation in dairy products, their application and the establishment of reliable molecular markers for authentication purposes still need to be improved. In this chapter, we describe a low-cost, sensitive, fast, and reliable PCR-based method for mitochondrial D-loop DNA amplification for efficient detection of cattle milk in binary mixtures with sheep milk, thereby allowing the authentication of processed dairy products.

Direct sandwich ELISA to detect the adulteration of human breast milk by cow milk.

The demand for commercially available human breast milk has significantly increased in recent years. For various reasons, a significant amount of commercially available human breast milk is being adulterated with other types of milk. This fraudulent practice poses a threat to consumers' health due to potential adulterants such as cow milk, which may put the infant at risk due to intolerance or allergy. A direct sandwich anti-bovine IgG ELISA has been developed for the sensitive and specific detection of cow milk in adulterated human breast milk. This assay uses polyclonal anti-bovine IgG antibody as a capture antibody and monoclonal anti-bovine IgG-alkaline phosphatase antibody as a detection antibody. Once optimized, the assay was found to be highly sensitive, and specific to bovine IgG. The assay had no significant cross-reaction with human breast milk, indicating that it was highly specific. The anti-bovine IgG ELISA was able to detect the presence of cow milk in adulterated human breast milk with a detection limit of 0.001% cow milk. The developed assay was highly reproducible (coefficient of variation <10%). The developed direct sandwich anti-bovine IgG ELISA is simple, reliable, and reproducible, making it an ideal test for this purpose.

Rapid identification of adulteration in raw bovine milk with soymilk by electronic nose and headspace-gas chromatography ion-mobility spectrometry.

The adulteration of soymilk (SM) into raw bovine milk (RM) to gain profit without declaration could cause a health risk. In this study, electronic nose (E-nose) and headspace-gas chromatography ion-mobility spectrometry (HS-GC-IMS) were applied to establish a rapid and effective method to identify adulteration in RM with SM. The obtained data from HS-GC-IMS and E-nose can distinguish the adulterated samples with SM by principal component analysis. Furthermore, a quantitative model of partial least squares was established. The detection limits of E-nose and HS-GC-IMS quantitative models were 1.53% and 1.43%, the root mean square errors of prediction were 0.7390 and 0.5621, the determination coefficients of prediction were 0.9940 and 0.9958, and the relative percentage difference were 10.02 and 13.27, respectively, indicating quantitative regression and good prediction performances of SM adulteration levels in RM were achieved. This research can provide scientific information on the rapid, non-destructive and effective adulteration detection for RM.

A hydrogen bonding based SERS method for direct label-free L-hydroxyproline detection.

The detection of non-protein nitrogen adulterants is a major challenge in dairy testing. As a marker molecule of animal hydrolyzed protein, the presence of non-edible L-hydroxyproline (L-Hyp) molecules can be used to identify low-quality milk containing components of animal hydrolyzed protein. However, it is still difficult to detect L-Hyp directly in milk. The Ag@COF-COOH substrate in this paper can be used to realize label-free L-Hyp sensitive detection based on the hydrogen bond transition mechanism. To explore the mechanism, the binding sites of hydrogen bond interaction have been verified experimentally and computationally, and the charge transfer process was also explained in terms of HOMO/LOMO energy level. In conclusion, the quantitative models for L-Hyp in an aqueous environment and in milk were developed. The limit of detection (LOD) of L-Hyp in an aqueous environment could reach 8.18 ng/mL, with R2 of 0.982. The linear range of quantitative detection in milk was 0.5-1000 µg/mL and the LOD was as low as 0.13 µg/mL. In this work, a hydrogen bond interaction based Surface-enhanced Raman spectroscopy (SERS) method for the label-free detection of L-Hyp was proposed, which complemented the application of SERS technology in the detection of dairy products.

Simultaneous detection for adulterations of maltodextrin, sodium carbonate, and whey in raw milk using Raman spectroscopy and chemometrics.

To achieve rapid on-site identification of raw milk adulteration and simultaneously quantify the levels of various adulterants, we combined Raman spectroscopy with chemometrics to detect 3 of the most common adulterants. Raw milk was artificially adulterated with maltodextrin (0.5-15.0%; wt/wt), sodium carbonate (10-100 mg/kg), or whey (1.0-20.0%; wt/wt). Partial least square discriminant analysis (PLS-DA) classification and a partial least square (PLS) regression model were established using Raman spectra of 144 samples, among which 108 samples were used for training and 36 were used for validation. A model with excellent performance was obtained by spectral preprocessing with first derivative, and variable selection optimization with variable importance in the projection. The classification accuracy of the PLS-DA model was 95.83% for maltodextrin, 100% for sodium carbonate, 95.84% for whey, and 92.25% for pure raw milk. The PLS model had a detection limit of 1.46% for maltodextrin, 4.38 mg/kg for sodium carbonate, and 2.64% for whey. These results suggested that Raman spectroscopy combined with PLS-DA and PLS model can rapidly and efficiently detect adulterants of maltodextrin, sodium carbonate, and whey in raw milk.

Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics.

This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R2) was 0.9889, and the root means square error (RMSE) was 3.28 × 10-4, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.

Identifying adulteration of raw bovine milk with urea through electrochemical impedance spectroscopy coupled with chemometric techniques.

This study aimed to evaluate the applicability of electrochemical impedance spectroscopy to identify raw bovine milk adulteration with urea. Three batches of raw milk adulterated with urea were studied. Hierarchical clustering indicated that the samples could be split in three groups corresponding to low adulteration (less than 7 wt%), medium adulteration (between 8 and 16 wt%) and high adulteration (over than 16 wt%). A linear discriminant analysis was performed resulting in 90% of accuracy in classifying between groups. Besides, a partial least squares model containing three directions provided good accuracy in quantitatively predicting the urea mass fraction added to raw bovine milk. Finally, calculations using an approximated electric circuit model suggested the formation of urea aggregates that hinder charge transportation within the milk thus diminishing the solution conductivity. Results indicate that electrochemical impedance spectroscopy can be a useful, low cost and rapid tool to identify milk adulteration with urea.

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.

Synthesis of silver nanoparticles (AgNPs) using culinary banana peel extract for the detection of melamine in milk.

Melamine adulteration in milk is a serious health concern for the consumers. A reliable and sensitive technique using silver nanoparticle (AgNPs) was developed for the detection of melamine in milk sample. The AgNPs was synthesized using culinary banana peel extract (BPE) where pH, temperature, the amount of concentration of BPE and concentration of AgNO3 was standardized. The effect of the parameters used for the synthesis of AgNPs was analyzed by observing the colour of reaction mixture and surface plasmon resonance. The AgNPs synthesized under optimum conditions were characterized by SEM-EDX, TEM and FTIR. FTIR studies reveal the effective conjugation between AgNPs and bioactive components of BPE and formation of spherical and regular shaped AgNPs were confirmed by TEM images. Presence of Ag as a dominating metal in AgNPs confirmed the formation of AgNPs. The level of melamine above 0.5 mg/L in milk could easily be detected through the interference synthesis of AgNPs.

Application of genetic algorithm and multivariate methods for the detection and measurement of milk-surfactant adulteration by attenuated total reflection and near-infrared spectroscopy.

BACKGROUND: The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg-1 in adulterated samples. RESULTS: Prominent peaks in the spectral range of 5500-6400 cm-1 , 1160-1260 cm-1 and 1049-1080 cm-1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R2 value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test. CONCLUSION: The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg-1 in milk. © 2020 Society of Chemical Industry.

Identification of milk adulteration by a sensor array based on cationic polymer induced aggregation of a perylene probe.

A novel fluorescence sensor array based on cationic polymer induced self-assembly of a perylene probe is developed. Cationic polymer induced aggregation of the carboxyl modified negatively charged perylene probe, and resulted in large quenching of monomer emission and generation of excimer emission. Upon the addition of negatively charged protein, monomer fluorescence restored with a decrease in excimer fluorescence. Based on these observations, we developed a six-channel sensor array to discriminate five main proteins in milk. In addition, we successfully identified pure milk out of different drinks using the developed sensor array since different drinks contained distinct species and contents of proteins. Furthermore, the sensor array exhibited excellent performance to discriminate milk adulterated by different concentrations of adulterants with 100% accuracy of cross validation. The analysis results also presented excellent linear correlation of adulterants contents and thus the developed sensor array shows great potential for quantitative detection of milk adulteration.

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples.

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Opportunities for fraudsters: When would profitable milk adulterations go unnoticed by common, standardized FTIR measurements?

Milk is regarded as one of the top food products susceptible to adulteration where its valuable components are specifically identified as high-risk indicators for milk fraud. The current study explores the impact of common milk adulterants on the apparent compositional parameters of milk from the Dutch market as measured by standardized Fourier transform infrared (FTIR) spectroscopy. More precisely, it examines the detectability of these adulterants at various concentration levels using the compositional parameters individually, in a univariate manner, and together in a multivariate approach. In this study we used measured boundaries but also more practical variance-adjusted boundaries to set thresholds for detection of adulteration. The potential economic impact of these adulterations under a milk payment scheme is also evaluated. Twenty-four substances were used to produce various categories of milk adulterations, each at four concentration levels. These substances comprised five protein-rich adulterants, five nitrogen-based adulterants, seven carbohydrate-based adulterants, six preservatives and water, resulting in a set of 360 samples to be analysed. The results showed that the addition of protein-rich adulterants, as well as dicyandiamide and melamine, increased the apparent protein content, while the addition of carbohydrate-based adulterants, whey protein isolate, and skimmed milk powder, increased the apparent lactose content. When considering the compositional parameters univariately, especially protein- and nitrogen-based adulterants did not raise a flag of unusual apparent concentrations at lower concentration levels. Addition of preservatives also went unnoticed. The multivariate approach did not improve the level of detection. Regarding the potential profit of milk adulteration, whey protein and corn starch seem particularly interesting. Combining the artificial inflation of valuable components, the resulting potential profit, and the gaps in detection, it appears that the whey protein isolates deserve particular attention when thinking like a criminal.

Rapid detection and quantification of sucrose adulteration in cow milk using Attenuated total reflectance-Fourier transform infrared spectroscopy coupled with multivariate analysis.

Adulteration of milk to gain economic benefit has become a common practice in recent years. Sucrose is illegally added in milk to reconstitute its compositional requirement by improving the total solid contents. The present study is aimed to use FTIR spectroscopy in combination with multivariate chemometric modelling for the differentiation and quantification of sucrose in cow milk. Pure milk and adulterated milk spectra (0.5-7.5% w/v) were observed in the spectral region 4000-400 cm-1. Principal component analysis (PCA) was used for the discrimination of pure milk and adulterated milk. Soft independent modelling of class analogy (SIMCA) was able to classify test samples with a classification efficiency of 100%. Partial least square regression (PLS-R) and principle component regression (PCR) models were established for normal spectra, 1st derivative and 2nd derivative for the quantification of sucrose in milk. PLS-R model (normal spectra) in the combined wavenumber range of 1070-980 cm-1 showed the best prediction based on parameters like coefficient of determination (R2) (Cal: 0.996; Val: 0.993), RMSE (Cal: 0.15% w/v; Val: 0.20% w/v), RE% (Cal: 4.9% w/v; Val: 5.1% w/v) and RPD (13.40). This method has a detection level of 0.5% w/v sucrose adulteration.

Prevalence of Milk Fraud in the Chinese Market and its Relationship with Fraud Vulnerabilities in the Chain.

This study aimed to assess the prevalence of ultra-high-temperature (UHT) processed milk samples suspected of being adulterated on the Chinese market and, subsequently, relate their geographical origin to the earlier determined fraud vulnerability. A total of 52 UHT milk samples purchased from the Chinese market were measured to detect possible anomalies. The milk compositional features were determined by standardized Fourier transform-infrared spectroscopy, and the detection limits for common milk adulterations were investigated. The results showed that twelve of the analysed milk samples (23%) were suspected of having quality or fraud-related issues, while one sample of these was highly suspected of being adulterated (diluted with water). Proportionally, more suspected samples were determined among milks produced in the Central-Northern and Eastern areas of China than in those from the North-Western and North-Eastern areas, while those from the South were in between. Combining the earlier collected results on fraud vulnerability in the Chinese milk chains, it appears that increased fraud prevalence relates to poorer business relationships and lack of adequate managerial controls. Since very few opportunities and motivations differ consistently across high and low-prevalence areas, primarily the improvement of control measures can help to mitigate food fraud in the Chinese milk supply chains.