Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

Abstract

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Keywords:

• Nondestructive technologies
• food quality
• machine learning
• deep learning
• artificial intelligence

Acknowledgements

The authors are grateful to the National Natural Science Foundation of China (3217161084) for its support. This research was also supported by the Guangzhou Key Laboratory for Intelligent Sensing and Quality Control of Agricultural Products (202102100009), Laboratory of Lingnan Modern Agriculture Project (NZ2021035), the Guangdong Provincial Science and Technology Plan Projects (2020A1414010160), the Guangdong Basic and Applied Basic Research Foundation (2022A1515012489 and 2020A1515010936), the Contemporary International Collaborative Research Centre of Guangdong Province on Food Innovative Processing and Intelligent Control (2019A050519001), and the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2022KJ101).

Disclosure statement

All authors declare that they have no conflicts of interest.

Data availability

Data are available within the article or its supplementary materials.