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

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach

Background

Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains.

Methods

This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study.

Results

Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and ‎‎1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively.

Conclusions

As a proof of concept, NIRS technology coupled with appropriate ‎multivariate classification models enables fast detection of citric acid-adulterated ‎lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.

The pathogenic potential and genetic attributes of Escherichia coli in milk from dairy cows with subclinical mastitis

The centrality of milk and dairy products to the human diet allows potential pathogens to pose a threat to human health. Pathogenic Escherichia coli is a zoonotic foodborne pathogen with many virulence genes which cause variations in its pathogenicity. The current study aimed to investigate the pathogenic potential of E. coli from milk of dairy cows with subclinical mastitis and evaluate the genetic relatedness to E. coli from human sources. The majority of the E. coli isolates belonged to the A (55.0%) and B2 (22.5%) phylogenetic groups and the most prevalent virulence genes were colV (90.0%), fyuA (75.0%) and vat (42.5%). Mice injected with G4-BD23 (P < 0.05) and G5-BD3 had lower survival rates than controls and visible pathological changes to lung and kidney. Nineteen MLST types were identified in 40 dairy E. coli isolates and three STs (ST10, ST48 and ST942) were shared with those from human sources. Some dairy E. coli isolates were phylogenetically related to human E. coli isolates indicating pathogenic potential.

Evaporation-Based Low-Cost Method for the Detection of Adulterant in Milk

Adulteration of milk poses a severe health hazard, and it is crucial to develop adulterant-detection techniques that are scalable and easy to use. Water and urea are two of the most common adulterants in commercial milk. Detection of these adulterants is both challenging and costly in urban and rural areas. Here we report on an evaporation-based low-cost technique for the detection of added water and urea in milk. The evaporative deposition is shown to be affected by the presence of adulterants in milk. We observe a specific pattern formation of nonvolatile milk solids deposited at the end of the evaporation of a droplet of unadulterated milk. These patterns alter with the addition of water and urea. The evaporative deposits are dependent on the concentrations of water and urea added. The sensitivity of detection of urea in milk improves with the dilution of milk with water. We show that our method can be used to detect a urea concentration as low as 0.4% in milk. Based on the detection level of urea, we present a regime map that shows the concentration of urea that can be detected at different extents of dilution of milk.

Which Company Characteristics Make a Food Business at Risk for Food Fraud?

Fraud can happen to any food business, but some sectors show more historical evidence of food fraud than others. This may be due to particular company characteristics that affect a company’s level of vulnerability. In the current study, we examined the relevance of the industry segment, business size, and location of food businesses on their food fraud vulnerabilities. Over 8000 food fraud vulnerability self-assessments conducted by food businesses active in 20 industry segments located in five continents were collected and the data analyzed. Results revealed that a company’s industry segment (chain and tier) affects its fraud vulnerability greatly and to a larger extent than the size of the business. The effect of industry segment on fraud vulnerability appears fairly similar across continents, whereas the effect of business size exhibits large geographical variation. The results demonstrate that those involved in animal product supply chains and end of chain nodes (catering, retail) are most vulnerable, and so are larger businesses, and businesses located in Africa and Asia. Current results imply that company characteristics are important determinants of the level of fraud vulnerability, and they may be used reversely in the future, i.e., as predictors of vulnerability.

Multiple authentications of high-value milk by centrifugal microfluidic chip-based real-time fluorescent LAMP

Adulteration of food ingredients, particularly replacement of high-value milk with low-cost milk, affects food safety. For rapid and accurate identification of the possible adulterating milk species in an unknown sample, a centrifugal microfluidic chip-based real-time fluorescent multiplex loop-mediated isothermal amplification (LAMP) assay was developed to simultaneously detect milk from cow, camel, horse, goat, and yak. Using precoated primers in different reaction wells, the centrifugal microfluidic chip markedly simplified the detection process and reduced false-positive results. The entire amplification was completed within 90 min with a genomic detection limit of 0.05 ng/µL in cow, camel, horse, and goat milk and 0.005 ng/µL in yak milk. Using simulated adulterated samples for validation, the detection limit for adulterated milk samples was 2.5%, satisfying authentication requirements, as the proportion of adulterated milk higher than 10% affects economic interests. Therefore, this simple, centrifugal, microfluidic chip-based multiplex real-time fluorescent LAMP assay can simultaneously detect common milk species in commercial products to enable accurate labeling.