Issue 36, 2021

Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat

Abstract

Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900–1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with Rc of 0.8716, RMSEC of 0.0793 g/100 g, RPDc of 2.398, and Rp of 0.8121 with RMSEP of 0.1481 g/100 g and RPDp of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.

Graphical abstract: Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat

Article information

Article type
Paper
Submitted
04 May 2021
Accepted
11 Aug 2021
First published
12 Aug 2021

Anal. Methods, 2021,13, 4157-4168

Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat

Y. Wang, C. Wang, F. Dong and S. Wang, Anal. Methods, 2021, 13, 4157 DOI: 10.1039/D1AY00757B

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