Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology.

ABSTRACT: To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths

SUBMITTER: Gu Y 

PROVIDER: S-EPMC11507928 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature