1. Quantitative prediction of minced chicken gel strength under ultrasonic treatment by NIR spectroscopy coupled with nonlinear chemometric tools evaluated using APaRPs.
- Author
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Li H, Nunekpeku X, Zhang W, Adade SYS, Ahmad W, Sheng W, and Chen Q
- Subjects
- Animals, Ultrasonics, Chemometrics, Meat Products analysis, Support Vector Machine, Food Handling methods, Poultry Products analysis, Chickens, Spectroscopy, Near-Infrared methods, Gels chemistry
- Abstract
Achieving the ideal gel strength is essential for desired texture in minced chicken products. This study developed a rapid, non-destructive method using near-infrared (NIR) spectroscopy and nonlinear chemometric modeling to predict minced chicken gel strength under ultrasonic treatment. Initially, minced chicken samples were subjected to high-intensity ultrasound for 0-50 min. This was followed by heat-induced gelation. Gel strength was conventionally measured, and NIR spectra were collected. Nonlinearity between gel strength and spectral data was confirmed using augmented partial residual plots (APaRPs). Subsequently, nonlinear support vector machine (SVM) and extreme learning machine (ELM) models were developed using full NIR spectra and variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighting (CARS), and genetic algorithms (GA). GA proved most effective for enhancing model performance, achieving the highest predicted coefficient of determination (Rp
2 = 0.8772) with the ELM model, demonstrating potential for rapid, non-destructive prediction of minced chicken gel strength quality., Competing Interests: Declaration of competing interest The authors disclose no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)- Published
- 2025
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