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

Rapid authentication of characteristic milk powders by recombinase polymerase amplification assays

The authentication of dairy species has great significance for food safety. This study focused on a more rapid method for identifying major dairy species, and specific recombinase polymerase amplification (RPA)-based assays for cattle, goat, sheep, camel and donkey were developed. Through the developed RPA-based assays, goats and sheep could be simultaneously identified and bovine families could be differentiated. The performances of the RPA assays were validated using 37 milk powder samples, of which 16.2% (6/37) were suspected of being adulterated and 24.3% (9/37) were potentially at risk of being wrongly identified as adulteration. The effectiveness of the developed assays for crude DNA detection was also validated by a rapid nucleic acid extraction kit, and results showed that the presence of large amounts of protein and fat did not affect the qualitative results. Therefore, these assays could combine with the rapid nucleic acids extraction methods for being used in field detection.

An instrument-free, integrated micro-platform for rapid and multiplexed detection of dairy adulteration in resource-limited environments

In dairy industry, expensive yak's milk, camel's milk, and other specialty dairy products are often adulterated with low-cost cow's milk, goat's milk and so on. Currently, the detection of specialty dairy products typically requires laboratory settings and relies on skilled operators. Therefore, there is an urgent need to develop a multi-detection technology and on-site rapid detection technique to enhance the efficiency and accuracy of the detection of specialty dairy products. In this study, we introduced a fully integrated and portable microfluidic detection platform called Sector Self-Driving Microfluidics (SDM), designed to simultaneously detect eight common species-specific components in milk. SDM integrated nucleic acid extraction, purification, loop-mediated isothermal amplification (LAMP), and lateral flow strip (LFS) detection functions into a closed microfluidic system, enabling contamination-free visual detection. The SDM platform used a constant-temperature heating plate, powered by a mobile battery, eliminated the need for additional power support. The SDM platform achieved nucleic acid enrichment and transfer through magnetic force and liquid flow driven by capillary forces, operating without external pumps. The standalone SDM platform could detect dairy components with as low as 1% content within 1 h. Validation with 35 commercially available samples demonstrated 100% specificity and accuracy compared to the gold standard real-time PCR. The SDM platform provided the dairy industry with an efficient, convenient, and accurate detection tool, enabling rapid on-site testing at production facilities or sales points. This facilitated real-time monitoring of quality issues during the production process, quickly identifying potential risks and preventing substandard products from entering the market.

Identification of Milk Adulteration in Camel Milk Using FT-Mid-Infrared Spectroscopy and Machine Learning Models

Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10-90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.