Work

Comprehensive non-destructive assessment of beef quality: Focus on textural feature analysis and tenderness using AI and hyperspectral imaging

Year: 2025

Type: article

Abstract: This study developed a non-destructive method to predict beef tenderness using hyperspectral imaging (HSI), near-infrared (NIR) spectroscopy, and machine learning (ML). A total of 159 beef loin samples were analyzed to quantify moisture and collagen content using partial least squares regression ( R 2 = 0.973 and 0.953, respectively). Texture features were extracted from HSI-based chemical maps using gray-level co-occurrence matrix (GLCM) analysis. These features, combined with chemical indicators, were used to classify tenderness into three categories based on Warner-Bratzler Shear Force. The Random Forest model achieved the highest accuracy (F1-score = 1.00), followed by SVM (97 %). The proposed method offers accurate and efficient tenderness assessment suitable for large-scale industrial application.

Source: LWT

DOI: 10.1016/j.lwt.2025.118322

Authors: Je Hyeon Lee, Jia Yu, Xiangzi Li, Dae Jong Kim, Sungkwon Park

Institutions: Sejong University, Yanbian University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,240