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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Vydáno v:	Meat science Ročník 228; s. 109892
Hlavní autoři:	Zhang, Jue, Tian, Haiqing, Gong, Maoguo, Zhang, Lina, Zhao, Kai, Yu, Yang, Zhao, Hongyu, Zhang, Xinzhuang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 01.10.2025
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0309-1740 1873-4138 1873-4138
DOI:	10.1016/j.meatsci.2025.109892