A comprehensive approach for milk adulteration detection using inherent bio-physical properties as ‘Universal Markers’: Towards a miniaturized adulteration detection platform

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.

Riboflavin as an internal marker for spoilage and adulteration detection in milk

Milk is a common consumable in daily life due to its nutritional values. Ensuring milk's integrity and authenticity is a prime task for researchers and food industries by providing solutions to prevent spoilage and adulteration. We present a robust and reliable optical method to ensure milk quality through its constituent riboflavin as an internal biomarker. Riboflavin is a widely present constituent in several food matrices. This research demonstrates the characteristic fluorescence of riboflavin for checking spoilage and urea adulteration in real-time. The proposed method can even detect and quantify high urea adulteration levels up to 80 mM (i.e., eight times permissible standard value) with a LOD value of 9.3 mM. The linearity (0-80 mM) and high R2 value (0.98, 0.93) of riboflavin's fluorescence in pure and milk solutions, respectively present this strategy closely associated with fate of milk samples in terms of spoilage and adulteration. Thus, this optical method of riboflavin biosensing in real-time is intuitive and conclusive for determining milk quality.

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.

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.

SERS detection of sodium thiocyanate and benzoic acid preservatives in liquid milk using cysteamine functionalized core-shelled nanoparticles

A cysteamine functionalized core shelled nanoparticles (Au@Ag-CysNPs) was presented for simultaneous and rapid detection of sodium thiocyanate (STC) and benzoic acid (BA) preservatives in liquid milk using surface-enhanced Raman spectroscopy (SERS) technique. A spectrum covering 350-2350 cm^-1 region was selected to detect STC with concentrations ranging from 0.5 to 10 mg/L and BA with concentrations ranging from 15 to 240 mg/L in milk samples. Characterization of nanoparticles using high-resolution TEM confirmed that the successful synthesis of Au@AgNPs with core (gold) size of 28 nm and shell (silver) thickness of about 5 nm was grafted with 120 μL of 0.1 nM cysteamine hydrochloride. Results showed that Au@Ag-CysNPs could be used to detect STC up to 0.03 mg/L with a limit of quantification (LOQ) of 0.039 mg/L and a coefficient of determination (R²) of 0.9833 in the milk sample. For detecting BA, it could be screened up to 9.8 mg/L with LOQ of 10.2 mg/L and R² of 0.9903. The proposed substrate was also highly sensitive and the employed method involved only minor sample pretreatment steps. It is thus hoped that the new substrate could be used in the screening of prohibited chemicals in complex food matrices in future studies.