Deep learning hyperspectral imaging: a rapid and reliable alternative to conventional techniques in the testing of food quality and safety

Naillah Gul, Khalid Muzaffar, Syed Zubair, Ahmad Shah, Assif Assad, H. Makroo, B.N.Dar
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Abstract

Food quality and safety are a great public concern; outbreaks of food-borne illnesses can lead to different health problems. Consequently, rapid and non-destructive artificial intelligence approaches are required for sensing the safety situation of foods. As a promising technology, deep learning for hyperspectral imaging (HSI) has the potential for rapid food safety and quality evaluation and control. Spectral signatures of food substances are sensitive to water content variation, the extent of hydrogen bonding, geographical origin, harvesting time and the variety of food under study. Deep learning models have shown great potential in addressing the challenge of sensitivity of spectral signatures of food substances. After discussing the basics of HSI, this review provides a detailed study of various deep-learning algorithms that have been put to use via HSI in the determination of sensory and physicochemical properties, adulteration and microbiological contamination of food products. The existing literature includes HSI for evaluating quality attributes and safety of different food categories like fruits, vegetables, cereals, milk and meat. This paper presents a practical framework for deep learning-based food quality assessment using hyperspectral imagery. We demonstrate its versatility across diverse food quality domains and provide a concise step-by-step guide for researchers. It has been predicted that deep learning for HSI can be considered a reliable alternative technique to conventional methods in realising rapid and accurate inspection, for testing food quality and safety.
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深度学习高光谱成像:食品质量与安全检测中传统技术的快速可靠替代品
食品质量和安全是公众极为关注的问题;食源性疾病的爆发会导致不同的健康问题。因此,需要快速、非破坏性的人工智能方法来感知食品的安全状况。高光谱成像(HSI)深度学习是一项前景广阔的技术,具有快速评估和控制食品安全与质量的潜力。食品物质的光谱特征对含水量变化、氢键程度、地理来源、收获时间和所研究食品的种类都很敏感。深度学习模型在应对食品物质光谱特征敏感性这一挑战方面显示出巨大潜力。在讨论了 HSI 的基本原理后,本综述详细研究了各种深度学习算法,这些算法已通过 HSI 用于确定食品的感官和理化特性、掺假和微生物污染。现有文献包括用于评估水果、蔬菜、谷物、牛奶和肉类等不同食品类别的质量属性和安全性的 HSI。本文介绍了一个基于深度学习的实用框架,利用高光谱图像进行食品质量评估。我们展示了该框架在不同食品质量领域的通用性,并为研究人员提供了简明的分步指南。据预测,用于高光谱成像的深度学习可被视为传统方法的可靠替代技术,可实现快速、准确的检测,以检验食品质量和安全。
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