Artificial Intelligence based techniques for authenticity of food products in Food fraud

Abstract

Food fraud is a widespread issue affecting almost all food commodities, leading to significant economic losses, public health risks, and violations of quality and consumer rights. Traditional detection methods are time-consuming, labor-intensive, and require costly equipment. With increasing competition in the food industry, there is a growing demand for faster, more efficient detection methods. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for predictive analysis in food fraud detection. These technologies allow for rapid analysis, aiding legal investigations ensuring food safety, authenticity, and traceability. Electronic nose (E-nose) systems, which identify organic compounds based on their unique aromas, are evaluated with chemometric methods to verify authenticity and help prevent fraudulent practices.