Rapid determination of lamb meat freshness using the hyperspectral imaging combined with symmetric stacking ensemble algorithm

Freshness is a key indicator in determining the quality of lamb meat. This study explores the feasibility of a rapid detection method for assessing lamb meat freshness through hyperspectral imaging. The variations in total volatile basic nitrogen (TVB-N), total viable count (TVC), pH, and lightness...

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Bibliographic Details
Published in:Meat science Vol. 228; p. 109892
Main Authors: Zhang, Jue, Tian, Haiqing, Gong, Maoguo, Zhang, Lina, Zhao, Kai, Yu, Yang, Zhao, Hongyu, Zhang, Xinzhuang
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.10.2025
Subjects:
Algorithms
Animals
biomarkers
Food Quality
Food Storage
Freshness
Hydrogen-Ion Concentration
Hyperspectral imaging (HSI)
Hyperspectral Imaging - methods
Lamb meat
meat science
Nitrogen - analysis
Nondestructive determination
plate count
rapid methods
Red Meat - analysis
Red Meat - microbiology
Sheep
Sheep, Domestic
Stacking ensemble learning
storage time
Support Vector Machine
temporal variation
total volatile basic nitrogen
wavelengths
Freshness
Lamb meat
Nondestructive determination
Hyperspectral imaging (HSI)
Stacking ensemble learning
ISSN:0309-1740, 1873-4138, 1873-4138
Online Access:Get full text
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Summary:Freshness is a key indicator in determining the quality of lamb meat. This study explores the feasibility of a rapid detection method for assessing lamb meat freshness through hyperspectral imaging. The variations in total volatile basic nitrogen (TVB-N), total viable count (TVC), pH, and lightness (L*) of lamb samples were analyzed over 12 days. Variable combination population analysis (VCPA) was employed to enhance data reliability and reduce dimensionality, while a symmetric stacking ensemble learning (SSEL) network was developed to predict both the freshness indices and the storage duration of lamb meat. Consequently, the feature wavelengths for each freshness index were identified. In particular, the spectral peak in the 620–630 nm range emerged as a crucial biomarker wavelength for evaluating lamb meat freshness during storage. The results demonstrate that the SSEL network outperforms the optimal traditional model for each indicator. Specifically, the SVM-stacking model exhibits outstanding performance for TVB-N (Rp2 = 0.93, RMSEP = 2.28), while the random forest (RF) stacking model excels in predicting TVC (Rp2 = 0.91, RMSEP = 0.84), pH (Rp2 = 0.89, RMSEP = 0.19) and L* (Rp2 = 0.88, RMSEP = 1.83). In addition, the SVM-stacking model also surpassed traditional approaches in predicting the storage duration of lamb meat, with Rp2 and RMSEP values of 0.93 and 2.28, respectively. The proposed methodology enables rapid freshness evaluation and captures temporal variability while offering insights into the molecular mechanisms behind spectral variations. This research lays a foundation for the accurate detection of meat product quality.
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ISSN:0309-1740
1873-4138
1873-4138
DOI:10.1016/j.meatsci.2025.109892