利用特征波段提取及结合机器学习对小米淀粉的高光谱检测研究
Hyperspectral Detection Methods of Starch of Millet by Feature Bands Extraction Combined with Machine Learning Algorithm
王国梁 1赵媛 2刘敏 2郭二虎 1王瑞 1范惠萍 1李瑜辉 1张艾英1
作者信息
- 1. 山西农业大学谷子研究所,长治 046000
- 2. 山西农业大学农学院,太谷 030801
- 折叠
摘要
运用高光谱检测技术实现小米淀粉的快速检测在小米定级、定价及降低加工成本中具有重要意义.本研究基于高光谱检测技术,采用化学计量学及机器学习相关知识对小米直链、支链淀粉含量进行检测,并提出特征波段提取联用预处理方法及Logistic结合COOT(coot optimization algorithm)优化算法.结果表明采用特征波段提取联用算法建立的PLSR(partial least squares regression)模型能够在减少波段冗余情况下不影响模型预测精度,其中直链淀粉较好模型为 MSC(multiplicative scatter correction)-RF(random frog)-IRIV(iteratively retains informative variables)-PLSR,支链淀粉较好模型为 MSC-CARS(competitive adaptive re-weighted sampling)-IRIV-PLSR.为了进一步提高模型预测精度,基于最佳预处理算法结合Logistic-COOT建立BP(back propagation)预测模型能够较好地预测小米直链、支链淀粉的含量,模型评价直链、支链淀粉相关系数(correlation coefficient,R)、均方根误差(root mean squared error,RMSE)、相对分析误差(relative percent deviation,RPD)分别为0.74、1.19、1.51和0.72、5.25、1.40,研究可为小米其他营养成分的高光谱检测及产品分类、定级等提供理论参考.
Abstract
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.
关键词
小米淀粉/高光谱检测/特征波段提取联用/机器学习Key words
millet starch/hyperspectral/feature bands extraction sequential combination/machine learning algo-rithm引用本文复制引用
基金项目
杂粮种质资源创新与分子育种国家实验室项目(202204010910001-13)
国家现代农业产业技术体系建设专项(2023CYJSTX04-04)
出版年
2024
NETL
NSTL
万方数据