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.
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