Saha Dhritiman, Manickavasagan Annamalai
School of Engineering, University of Guelph, N1G2W1, Canada.
Curr Res Food Sci. 2021 Feb 3;4:28-44. doi: 10.1016/j.crfs.2021.01.002. eCollection 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.
多年来，无损检测技术在监测食品质量方面变得越来越重要。高光谱成像是重要的无损质量检测技术之一，它能提供空间和光谱信息。机器学习技术在快速分析方面的进步以及更高的分类准确率提高了将该技术应用于食品领域的潜力。本文概述了不同机器学习技术在分析高光谱图像以确定食品质量方面的应用。它涵盖了高光谱成像的基本原理、每种机器学习技术的优点和局限性。机器学习技术能够对食品的高光谱图像进行快速分析且准确率高，从而建立强大的分类或回归模型。从高光谱数据中选择有效的波长至关重要，因为这能大大减少计算量和时间，从而扩大实时应用的范围。由于深度学习的特征学习特性，它是实时应用中最有前途和最强大的技术之一。然而，深度学习领域相对较新，需要进一步研究以充分利用它。同样，终身机器学习为实时高光谱成像应用铺平了道路，但需要进一步研究以纳入食品质量的季节性变化。此外，还讨论了高光谱图像分析中机器学习技术的研究差距和前景。
