Quantitative duplex real-time polymerase chain reaction assay with TaqMan probe detects and quantifies Crocodylus porosus in food chain and traditional medicines

Combination of Computational Techniques to Obtain High-Quality Gelatin-Base Gels from Chicken Feet

With the increasing global population, it has become necessary to explore new alternative food sources to meet the increasing demand. However, these alternatives sources should not only be nutritive and suitable for large scale production at low cost, but also present good sensory characteristics. Therefore, this situation has influenced some industries to develop new food sources with competitive advantages, which require continuous innovation by generating and utilising new technologies and tools to create opportunities for new products, services, and industrial processes. Thus, this study aimed to optimise the production of gelatin-base gels from chicken feet by response surface methodology (RSM) and facilitate its sensorial classification by Kohonen’s self-organising maps (SOM). Herein, a 2² experimental design was developed by varying sugar and powdered collagen contents to obtain grape flavoured gelatin from chicken feet. The colour, flavour, aroma, and texture attributes of gelatines were evaluated by consumers according to a hedonic scale of 1–9 points. Least squares method was used to develop models relating the gelatin attributes with the sugar content and collagen mass, and their sensorial qualities were analysed and classified using the SOM algorithm. Results showed that all gelatin samples had an average above six hedonic points, implying that they had good consumer acceptance and can be marketed. Furthermore, gelatin D, with 3.65–3.80% (w/w) powdered collagen and 26.5–28.6% (w/w) sugar, was determined as the best. Thus, the SOM algorithm proved to be a useful computational tool for comparing sensory samples and identifying the best gelatin product.

Comparative review and the recent progress in detection technologies of meat product adulteration

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.