Article

Study on structural analysis and physicochemical properties of meat using hyperspectral image analysis

Ji Yoon Cha1, Yea-Ji Kim1, Jeong-Heon Kim1, Min Kyung Park1, Sameool Jung2, Yun-Sang Choi1,*
Author Information & Copyright
1Research Group of Food Processing, Wanju-Gun 55365, Korea.
2Division of Animal and Dairy Science, Daejeon 34134, Korea.
*Corresponding Author: Yun-Sang Choi, Research Group of Food Processing, Wanju-Gun 55365, Korea, Republic of. Phone: +821047135623. E-mail: kcys0517@kfri.re.kr.

© Copyright 2025 Korean Society for Food Science of Animal Resources. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Dec 11, 2024; Revised: Jan 01, 2025; Accepted: Jan 20, 2025

Published Online: Feb 10, 2025

Abstract

The objective of this study was to evaluate the physicochemical and structural properties of pork (loin, tenderloin, neck, and belly) and beef (loin, tenderloin, round, and brisket) using hyperspectral imaging and food scanner analyses. Color, pH, and water-holding capacity (WHC) were analyzed, revealing that pork belly showed the highest lightness (69.90), while beef round exhibited the lowest (35.54). Redness and yellowness varied significantly among cuts, influenced by intramuscular fat and chemical composition. The pH ranged from 5.66 to 6.23, with pork neck having the highest pH and WHC, confirming the correlation between pH and WHC. Food scanner analysis quantified fat, moisture, protein, ash, and collagen content, showing that pork belly had the highest fat content, whereas beef loin had the highest collagen content. Hyperspectral imaging analysis extracted mean reflectance spectra, identifying key wavelengths such as 430 nm, 541 nm, 574 nm, and 980 nm related to heme, water, and oxidation states. Pork showed higher reflectance than beef, highlighting chemical composition differences. Partial least squares regression (PLSR) models predicted fat, moisture, and protein content, with protein prediction demonstrating moderate accuracy (R² = 0.411). However, limited data posed challenges to model generalization. These findings suggest hyperspectral imaging as a promising tool for comprehensive meat quality assessment.

Keywords: pork; beef; hyperspectral imaging; food scanner; PLSR