Hyperspectral imaing approach for contaminant detection in food: A review

Abstract

In recent years, growing health concerns have highlighted the urgent need for innovative detection methods in the field of food safety. Hyperspectral imaging (HSI) has emerged as a highly promising and widely recognized technology, offering non-destructive detection capabilities that are crucial for identifying food contamination. This paper explores the key characteristics of HSI, its significance in scientific research, and its technological advancements, particularly in its integration with machine learning techniques to enhance the accuracy and efficiency of contamination detection. It becomes evident that HSI not only has the potential to revolutionize food safety practices but also plays a critical role in improving decision-making processes through precise data analysis. The increasing accessibility and global application of HSI technology, combined with advanced data processing methods, position it as a vital tool in addressing the growing challenges related to food contamination. This offers significant benefits for both industry and public health.