机器学习在生鲜农产品质量与安全快速无损智能检测中的应用与展望

Non-destructive Intelligent Testing of the Quality and Safety of Fresh Agricultural Products Based on Machine Learning: Principles, Challenges, and Applications

摘要:

随着现代食品工业的发展和全球化进程的推进，食品质量与安全日益成为公众关注的焦点。果蔬、肉品、水产等生鲜农产品采后易腐败、难保存、损失大，实现对生鲜农产品质量与安全的实时、准确且快速的检测对食品工业的发展和消费者权益的保障具有重要的科学意义。传统的检测方法大多存在着检测要求条件较高、检测周期长、检测会对样本造成破坏等问题，无法在食品工业中实现大规模运用。随着人工智能和计算机技术的快速发展，具有强大预测能力和高准确性的机器学习模型已逐渐应用于生鲜农产品质量与安全的检测中，有效克服了新兴无损检测技术检测过程中数据冗余且无法大量处理的缺点。该文介绍了机器学习及深度学习算法基本理论方法，分析了无损检测技术融合机器学习在生鲜农产品质量与安全检测中的应用与挑战，为未来食品品质与安全的精准实时监控提供了重要理论支撑与方法指导。

Abstract:

With the development of the modern food industry and the advancement of globalization, food quality and safety have emerged as major concerns for the public. In the context of expanding global trade and supply chains, fresh agricultural products, including fruits and vegetables, meat, and aquatic products, encounter considerable challenges related to perishability and the preservation. It is imperative to develop accurate and rapid quality and safety testing to improve monitoring and provide consumer protection. Traditional detection methods, such as gas chromatography, high-performance liquid chromatography, and polymerase chain reaction, are often limited by stringent detection requirements, long detection cycles, and potential sample damage, hindering their large-scale application in the food industry. With the rapid development of artificial intelligence and computer technology, machine learning models with strong predictive abilities and high accuracy have been widely used in quality and safety inspections of fresh agricultural products, effectively addressing the limitations related to data redundancy and the processing of large datasets. This article provides a systematic overview of machine learning technology and deep learning algorithms and analyzes their applications in quality and safety inspections of fresh agricultural products. It also introduces the principles of several emerging non-destructive testing technologies, and discusses current research progress and potential application prospects for machine learning and non-destructive testing technologies in the food industry.