Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat.
Anal Methods
; 13(36): 4157-4168, 2021 09 23.
Article
em En
| MEDLINE
| ID: mdl-34554149
ABSTRACT
Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C180) 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 C180 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C180 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Carne Vermelha
/
Imageamento Hiperespectral
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Anal Methods
Ano de publicação:
2021
Tipo de documento:
Article