Academic Journal
NIRS features and multi-model optimization fusion enabled comprehensive method for quantitative and qualitative assessment of lamb meat quality.
| Τίτλος: | NIRS features and multi-model optimization fusion enabled comprehensive method for quantitative and qualitative assessment of lamb meat quality. |
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| Συγγραφείς: | Liu J; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Zhang B; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Li X; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China., Teng L; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Wang Z; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Bao J; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Wang Y; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Liu P; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Xie X; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Li J; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Ma X; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China., Zhang M; College of Engineering, China Agricultural University, Beijing 100083, China. Electronic address: zhmystic@126.com., Luo H; State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China. Electronic address: luohailing@cau.edu.cn. |
| Πηγή: | Food research international (Ottawa, Ont.) [Food Res Int] 2026 Sep 30; Vol. 240, pp. 119670. Date of Electronic Publication: 2026 Jun 06. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science Country of Publication: Canada NLM ID: 9210143 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7145 (Electronic) Linking ISSN: 09639969 NLM ISO Abbreviation: Food Res Int Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Ottawa, Ontario, Canada : Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science, c1992- |
| Ιατρικοί όροι (MeSH): | Red Meat*/analysis , Red Meat*/standards , Food Quality*, Spectroscopy, Near-Infrared/methods ; Animals ; Support Vector Machine ; Convolutional Neural Networks ; Algorithms ; Sheep ; Least-Squares Analysis ; Soft Computing ; Neural Networks, Computer |
| Περίληψη: | To address the limitations in near-infrared spectroscopy (NIRS) for lamb meat quality (LMQ) assessment, specifically incomplete index coverage, constrained model accuracy, and the lack of a multi-index-based evaluation method, this study presents a comprehensive method for quantitative and qualitative assessment of LMQ. By combining NIRS features with multi-model optimization and fusion, the proposed method enables precise LMQ control, supports targeted marketing, and promotes maximized economic value. Quantitative models for key quality indices (KQIs) were constructed using a multi-model optimization fusion algorithm based on a convolutional neural network, the Rime-ice-based optimizer, and least squares support vector machine (CNN-RIME-LSSVM). Building on this, this study constructed a KQIs-based LMQ grading method using a multi-method approach integrating Analytic Hierarchy Process (AHP), Entropy Weighting Method (EWM), membership function, Ward's hierarchical clustering, and K-means. The grading information obtained from this grading method is associated with NIRS features to develop a qualitative model for quality grades. This qualitative model was developed using a composite algorithm, namely the Generalized Regression Neural Network optimized by the Caterpillar Fungus Optimizer (CFO-GRNN), thereby achieving rapid and intelligent grading of LMQ. Both the quantitative model (with R2P = 0.9792-0.9932 and RMSEP = 0.0155-0.0283) and the qualitative model (with Accuracy = 99.75% and Macro F1-score = 99.84%) demonstrated excellent performance. SHapley Additive exPlanations (SHAP) explains the prediction behavior of the quantitative and qualitative models, enhancing transparency and interpretability of the decision-making mechanism. This study provides a new approach for on-line quality detection and intelligent grading of LMQ. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Caterpillar fungus optimizer; Convolutional neural network; Lamb meat; Near-infrared spectroscopy; Quality grading; Quantitative detection |
| Entry Date(s): | Date Created: 20260706 Date Completed: 20260707 Latest Revision: 20260706 |
| Update Code: | 20260708 |
| DOI: | 10.1016/j.foodres.2026.119670 |
| PMID: | 42409496 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-7145 |
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| DOI: | 10.1016/j.foodres.2026.119670 |