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Hyperspectral imaging combined with chemometric analysis for non-destructive prediction of mutton quality during refrigerated storage

Year: 2025

Type: article

Abstract: During refrigerated storage (4℃), mutton quality parameters (TVB-N, TVC, pH and the color L *-value) and freshness change significantly, serving as key indicators. Conventional assessment methods are typically destructive, time-consuming and lack spatial mapping capability. This study investigates hyperspectral imaging (HSI) combined with chemometrics for the rapid, non-destructive prediction of mutton quality parameters and visualization of their distribution maps. The results indicated that the CARS-PLSR model for TVB-N prediction (RPD = 3.6432), the RFLA-PLSR model for TVC prediction (RPD = 3.2150), the CARS-ELM model for pH prediction (RPD = 3.2451), and the CARS-ELM model for the color L *-value prediction (RPD = 3.3367) exhibited the best performance. Subsequently, the distributions maps of TVB-N, TVC, pH and the color L *-value in mutton were generated using their respective optimal prediction models were visualized. Among the tested classification approaches for freshness evaluation, Multinomial Logistic Regression (MLogR) discrimination models built using CARS exhibited higher accuracy (98.29 %) for the calibration set and highest discrimination accuracy (98.87 %) for the prediction set. These results confirm the potential of hyperspectral imaging as an effective tool for rapid and non-destructive assessment of mutton quality and freshness.

Source: Journal of Food Composition and Analysis

DOI: 10.1016/j.jfca.2025.108815

Authors: Xuan Dong, Zhi-Fu Xu, Sun-Hao Wu, Hao Xu, Bin Zhang, Huan Li

Institution: Bengbu University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,450