A single-copy specific primer was designed based on beef and duck samples and through drop digital polymerase chain reaction (ddPCR) for the quantitative analysis. Results revealed that the primers had no specific amplification with sheep, chicken, pork, or other species. Both the relationships between meat weight and DNA weight and between DNA weight and DNA copy number (C) were nearly linear within the dynamic range. To calculate the original meat weight from the DNA copy number, the DNA weight was used as the intermediate value to establish the following formulae: Mbeef = 0.058C − 1.86; Mduck = 0.0268C − 7.78. To achieve a good quantitative analysis, all species used in the experiment were made of lean meat. The accuracy of the method was verified by artificial adulteration of different proportions. Testing of the commercial samples indicated that adulteration is present in the market. The established digital PCR method provided an effective tool for monitoring the adulterated meat products and reducing the adulteration in the market.
To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN), and a sparrow search algorithm–backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.
Acetic acid bacteria (AAB) can spoil food. as a core subgroup of AAB is usually isolated from yogurt. should be timely and effectively detected to prevent yogurt contamination. The present study focused on to establish an assay that can be performed to detect AAB in yogurt. LAMP, PCR, and real-time PCR were applied and compared for detecting from pure culture and artificially contaminated yogurt samples. In pure culture, LAMP showed the highest detection sensitivity with 10 CFU/mL. For yogurt samples, the sensitivity limit of LAMP was 10 CFU/mL, which was lower than that of real-time PCR (10 CFU/mL). The results indicated that these methods could be quickly and efficiently applied to detect . As LAMP technology has low cost and high detection efficiency, it can potentially be applied for detecting in production and quality control programs of yogurt.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.