Multivariate Analysis in Tandem with Chemometric Tools for Food Identification and Quality Control

Dear Colleagues,

Multivariate analysis integrated with advanced chemometric methods has markedly driven advancements in food identification and quality control. Cutting-edge technologies such as artificial intelligence (AI), machine learning, and deep learning excel at efficiently handling high-dimensional non-linear datasets, enabling accurate pattern recognition, real-time monitoring, and predictive analytics. By establishing comprehensive databases covering food molecular profiles, processing parameters, and quality indicators, as well as by leveraging machine learning and deep learning algorithms, systematic research into food authenticity verification and quality prediction can be seamlessly conducted. AI-powered solutions deliver unprecedented levels of accuracy and efficiency for such endeavors, while the synergy of these multidisciplinary approaches further accelerates the development of intelligent, non-destructive food detection systems.

The present Special Issue aims to showcase original research in the following areas:

1. Intelligent food identification;
2. Food authenticity prediction;
3. Prediction of risk molecules during food processing;
4. Migration and transformation patterns of food risk factors;
5. Intelligent control and assessment of food quality.

Dr. Yutang Wang
Guest Editor

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