Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review - Saha, Dhritiman; Manickavasagan, Annamalai (Current research in food science, 2021)
Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed. [Display omitted] •Artificial neural network has been intensively used for Hyperspectral image (HSI) analysis.•Support vector machines and random forest techniques are gaining momentum for HSI analysis.•Deep learning applications has potential for implementation in real time HSI analysis.•Lifelong machine learning needs further research to incorporate the seasonal variations in food quality.