Hyperspectral Detection Methods of Starch of Millet by Feature Bands Extraction Combined with Machine Learning Algorithm
The rapid detection of millet starch by hyperspectral technology is of great significance in millet grad-ing,pricing and reducing processing costs.In this paper,based on hyperspectral detection technology,the content of amylose and amylopectin in millet was detected by using chemometrics and machine learning algorithm,and the pre-treatment methods feature bands extraction sequential combination and Logistic combined with coot optimization algo-rithm optimization algorithm were proposed.The results indicated that partial least squares regression model estab-lished by feature bands extraction sequential combination can reduce the bands redundancy without affecting the pre-diction accuracy of the model,the better prediction model for amylose was MSC-RF-IRIV-PLSR,and the better prediction model for amylopectin was MSC-CARS-IRIV-PLSR.In order to further improve the accuracy of the model prediction,BP model based on the best pretreatment method combined with Logistic-COOT could predict the content of amylose and amylopectin in millet,R(correlation coefficient),RMSE(root mean squared error)and RPD(relative percent deviation)of amylose & amylopectin were 0.74,1.19,1.51;0.72,5.25,1.40,respectively.This study can provide a reference for hyperspectral in other nutritional components of millet and product classification or grading.