Non-destructive monitoring the freshness of sea bass fillets using Raman spectroscopy with orthogonal signal correction and multivariate analysis

Yan, Wenqian; Yao, Jiayi; Yue, Zilin; Lin, Hong; Wang, Lei; Wang, Kaiqiang; Li, Jinjie

doi:10.1016/j.microc.2023.108859

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…In recent years, a variety of nondestructive methods have been widely developed and shown increasing attention for qualitative and quantitative analysis of food products (Lin et al., 2023). These nondestructive methods include spectroscopic techniques such as near‐infrared (NIR) spectroscopy, Raman spectroscopy, and laser‐induced breakdown spectroscopy (LIBS), as well as non‐spectroscopic techniques such as machine vision (MV), electronic nose (E‐nose), and nuclear magnetic resonance (NMR) (Wang, chen et al., 2022; Yan et al., 2023b; Zhou et al., 2023). Compared to traditional techniques, the most satisfying aspect of these nondestructive detection techniques is that they eliminate the need for tedious physical and chemical pretreatment of samples, avoid the need for professionally trained detection personnel, reduce environmental pollution, and reduce damage to laboratory personnel.…”

Section: Introductionmentioning

confidence: 99%

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

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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.

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