Advances in Machine Learning and Deep Learning Algorithm-Assisted Hyperspectral Imaging for Food Quality and Safety Assessment

Food quality and safety have become increasingly critical public health concerns, while traditional detection approaches are constrained by laborious sample preparation, lengthy analysis times, and destructive procedures. Hyperspectral imaging (HSI) has emerged as a rapid, non-destructive method that synergistically fuses spatial and spectral information for comprehensive food quality and safety assessment. However, the high dimensionality and nonlinear features of HSI data pose substantial challenges for traditional chemometric models. This review provides a comprehensive overview of recent advances in machine learning (ML) and deep learning (DL) algorithm-assisted HSI food quality and safety detection. We first introduce the fundamentals of HSI technology and its data processing workflow. We then present a comprehensive and non-mathematical introduction to typical ML and DL algorithms, highlighting their respective strengths and trade-offs. Then, we summarize typical applications across various areas, such as fruit and vegetable quality assessment, meat quality evaluation, egg and tea quality detection, moisture content quantification, variety and origin identification, adulteration and additive detection, heavy metal contamination assessment, mold detection, and plant growth monitoring. Finally, we discuss current challenges, such as data bottlenecks, limited model interpretability and generalizability, and barriers to practical applications, and outline future directions toward intelligent, systematic, and practical HSI systems for food quality and safety detection.