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Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation.

Ting-Tiao Pan¹, Da-Wen Sun^1,2, Jun-Hu Cheng¹

¹College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering, South China Univ. of Technology, Guangzhou, 510641, China.

Comprehensive Reviews in Food Science and Food Safety

|January 6, 2021

Summary

This summary is machine-generated.

Hyperspectral imaging (HSI) combined with chemometrics offers a fast, non-destructive way to analyze meat quality. This review guides selecting the best algorithms for hyperspectral data analysis to improve meat product detection.

Keywords:

chemometric analysis hyperspectral imaging meat quality quantitative regression

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Area of Science:

Food Science
Analytical Chemistry
Spectroscopy

Background:

Hyperspectral imaging (HSI) and chemometrics are advanced techniques for analyzing food products.
Current chemometric methods for hyperspectral data analysis of meat products have limitations in meeting practical demands.
Multivariate data analysis is crucial for interpreting complex hyperspectral data.

Purpose of the Study:

To review and compare various regression algorithms used in hyperspectral data analysis for meat quality assessment.
To provide guidelines for selecting appropriate algorithms for modeling the relationship between meat quality attributes and hyperspectral data.
To highlight the advantages and limitations of different chemometric methods in HSI applications for food analysis.

Main Methods:

Review of widely used regression algorithms in chemometrics.
Analysis of the application of these algorithms to hyperspectral data from meat products.
Comparative discussion of algorithm performance, advantages, and limitations.

Main Results:

Different chemometric algorithms exhibit varying suitability for hyperspectral data analysis in meat quality assessment.
Understanding algorithm strengths and weaknesses is key to accurate modeling.
The review identifies areas where current methods fall short of practical requirements.

Conclusions:

Selecting the right algorithm is critical for effective hyperspectral imaging data analysis in the meat industry.
This review offers valuable insights for future research and development in HSI-based food quality detection.
Further advancements in chemometric methods are needed to fully leverage HSI technology for meat product analysis.