Characteristic wavelengths selection of rice spectrum based on adaptive sliding window permutation entropy

• Autor(a) principal: YANG,Sen
• Data de Publicação: 2022
• Outros Autores: ZHANG,Houqing, FAN,Wenmin
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Food Science and Technology (Campinas)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101173
• topic: rice varieties classification; infrared spectrum; characteristic wavelengths selection; adaptive sliding window permutation entropy

Abstract Due to the redundancy of rice spectral wavelengths and the strong correlation between adjacent wavelengths, the modeling classification accuracy based on traditional characteristic wavelengths selection methods is insufficient. Thus, a rice spectral characteristic wavelengths selection method based on adaptive sliding window permutation entropy (ASW-PE) was proposed in this paper. Firstly, the ASW-PE algorithm is constructed by combining the adaptive sliding window (ASW) method and permutation entropy (PE) method. Then, for the spectral data of rice varieties WC, XS, YS and YG, based on ASW-PE, sliding window permutation entropy (SW-PE), analysis of variance (ANOVA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) to carry out the characteristic wavelengths selection experiment, and evaluated the computational efficiency of the five algorithms from the perspective of time complexity. Finally, a partial least squares (PLS) rice varieties classification model was established based on the spectral characteristic wavelengths selected by the above algorithms, and the characteristic selection performance of the five algorithms was evaluated with the classification accuracy. Experimental results show that the ASW-PE algorithm has a speed advantage in selecting characteristic wavelengths for large sample spectral data. Compared with SW-PE, ANOVA, CARS and SPA algorithms, the accuracy of modeling classification based on ASW-PE method is improved by 5.6%, 22.6%, 8.6% and 15.2%, respectively.