Abstract
Data detailed on Machine Learning have been presented. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Temperature fluctuations at different stages of the supply chain increase the frozen-thawed cycle of perishable foods, potentially leading to quality and safety issues. For raw edible salmon in particular, it is not possible to ignore the issue of adulteration when frozen-thawed flesh is sold as fresh flesh.” Financial support for this research came from Horizontal Project of Lenovo Joyvio Group Professor Workstation. Our news editors obtained a quote from the research from China Agricultural University, “It is a challenge to achieve realtime detection of frozen-thawed salmon adulteration in fresh salmon. Existing impedance change ratio (Q-value) and PCA models cannot accurately authenticate frozen-thawed cycle adulterated salmon. In this paper, a flexible bioimpedance based non-destructive detection system was designed to authenticate adulterated salmon by online monitoring of changes in bioimpedance signals, ambient temperature, and relative humidity. The system provided a high level of monitoring accuracy and stability. Furthermore, an improved machine learning classification model based on principal component analysis - Bayesian optimization algorithm - support vector machine (PCA-BOA-SVM) was developed to effectively identify frozen-thawed adulterated salmon. The optimised model performance enhanced with prediction accuracy, precision, recall and F1 score of 0.9683, 0.9708, 0.9683 and 0.9679, respectively.”
NETL
NSTL
Elsevier