State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China.
College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China.
Compr Rev Food Sci Food Saf. 2024 Jan;23(1):e13301. doi: 10.1111/1541-4337.13301.
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
近年来，食品行业越来越关注快速和非破坏性分析方法的发展。然而，单一的非破坏性检测技术只能提供对被检测样品有限的物理或化学信息。为了克服这一限制，将光谱学与数据融合策略相结合已经成为一种很有前途的方法。本综述深入探讨了食品分析领域中低水平、中水平和高水平数据融合策略的基本原理和优点。总结了各种数据融合技术，包括光谱到光谱、光谱到机器视觉、光谱到电子鼻和光谱到核磁共振。此外，本文还概述了光谱数据融合技术（SDFTs）在食品安全分析中分类、掺假、质量评估和污染物检测方面的最新应用。还讨论了 SDFTs 在实际应用中存在的挑战和未来展望。尽管存在技术复杂性，但在线数据融合平台的不断发展和基于智能手机的多传感器融合检测技术的出现，为 SDFTs 的实际应用提供了良好的前景，为食品分析领域的定性和定量分析提供了强大的能力。
