Current Applications of Hyperspectral Imaging Techniques Combined with Chemometrics for Food Analysis

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ID: 332449
2026
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Abstract
Abstract With the growing demand for high-quality and safe food products, innovations in food detection methods are becoming increasingly important. Hyperspectral imaging (HSI) technique combined with chemometrics, has emerged as an efficient, non-destructive, and high-precision approach, offering innovative solutions for food quality and safety analysis in the food sector. HSI technology enables the simultaneous acquisition of spectral and spatial information from food samples. Chemometrics facilitates the extraction of valuable insights from large HSI datasets, allowing for rapid and accurate prediction and classification of food quality parameters. The integration of these two technologies (HSI-chemometrics) has evolved into a promising analytical tool with potential applications across the entire food industry chain. However, its widespread adoption is still constrained by challenges related to model robustness, standardization, and real-world implementation. This paper summarizes recent research progress on the application of HSI-chemometrics in food analysis, focusing on areas such as quantitative analysis of constituents, quality assessment, detection of defects and contaminants, and authenticity verification. Additionally, we summarize current methods for model construction and validation, discuss issues related to model generalization capabilities, and analyze the primary challenges hindering the technology's wider adoption. The goal is to provide a balanced perspective and and technical support for the broader adoption of HSI in food analysis, ultimately contributing to food safety assurance and sustainable development goals.
Reference Key
openalex_W7222249278 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lili Guo, Huiwen Yu, Teng Wang, Mourad Kharbach
Journal journal of aoac international
Year 2026
DOI
10.1093/jaoacint/qsag094
URL
https://doi.org/10.1093/jaoacint/qsag094
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