Detection technologies, and machine learning in food: Recent advances and future trends

Abstract

The combination of food detection technology and machine learning can effectively enhance the efficiency and accuracy of food quality detection. Promoting the healthy development of the food industry is one of the keys to ensuring its sound progress and holds crucial significance for its advancement. The paper begins by introducing various food detection technologies, including spectroscopy, chromatography, mass spectrometry, odor sensors, and biosensors. It then delves into data preprocessing, feature extraction, and model algorithms within the realm of machine learning. Subsequently, we examine the progress made in applying machine learning-assisted detection technologies in the food sector. The synergy between food inspection technology and machine learning not only facilitates automated and intelligent inspection processes but also adeptly manages and analyzes vast amounts of data generated from diverse inspection instruments. Leveraging the robust modeling capabilities inherent to machine learning—particularly when addressing complex high-dimensional datasets—the food industry can more precisely identify potential quality concerns and safety risks. Looking ahead, emphasis should be placed on developing portable detection devices while enhancing deep learning interpretability and promoting model fusion establishment. Concurrently, ethical considerations and data privacy issues must be addressed as we strive to integrate food inspection technology with machine learning effectively. In conclusion, the integration of food inspection technology with machine learning is anticipated to significantly enhance technological innovation within the industry and bolster the capacity for monitoring food quality and safety.