摘要
为快速、准确检测小白杏的成熟度,该研究选择七成熟、八成熟、九成熟、十成熟的小白杏样本各120个,采用近红外高光谱成像系统采集样本的高光谱数据,进行去除噪声和剔除界外样本处理。然后使用均值中心化(mean centering,MC)、Savitzky-Golay卷积求导法(Savitzky-Golay derivative,S-G)、多元散射校正(multiplicative scatter correction,MSC)、标准正态变量变换(standard normal variate transformation,SNV)、归一化法5种方法分别对全波段和特征波段光谱进行预处理,采用光谱-理化值共生距离算法(sample set partitioning based on joint x-y distance,SPXY)、K-S法(Kennard-stone,K-S)、双向算法(Duplex)、交叉验证法、随机法将样本划分为校正集和验证集。最后用极限学习机(extreme learning machine,ELM)、支持向量机(support vector machine,SVM)、偏最小二乘法(partial least squares,PLS)、K最邻近法(K-nearest neighbor,KNN)、贝叶斯判别法建立不同的分类判别模型,比较各模型的识别率。结果表明,对小白杏成熟度定性判别模型,有以下最优组合:全波段+MSC+SPXY/Duplex/K-S/交叉验证/随机法+ELM/PLS/SVM/KNN、全波段+S-G/MSC/归一化/SNV+随机法+贝叶斯、全波段+S-G+SPXY/Duplex/K-S/交叉验证/随机法+ELM/PLS/SVM/KNN、全波段+归一化+SPXY/Duplex/K-S/交叉验证/随机法+PLS、特征波段+MSC+SPXY/Duplex/K-S/交叉验证/随机法+ELM/PLS/SVM/KNN/贝叶斯、特征波段+归一化+SPXY/Duplex/K-S/交叉验证/随机法+PLS。
To achieve rapid and accurate identification of different maturity of the little white apricot,firstly,120 samples of the little white apricot with four mature stages were selected respectively,and the hyperspectral data of samples were collected by the near-infrared hyperspectral imaging system.Five different pre-processing methods(MC,S-G,MSC,SNV,Normalization)pre-processed the full-wave and characteristic bands after eliminating the noise and out-of-bounds samples.Then five different sample set division methods(SPXY,K-S,Duplex,Cross-Validation,Randomization)divided the samples into correction and validation sets.Finally,five different classification and discrimination models(ELM,SVM,PLS,KNN,Bayes)were established.We compared the accuracy of each model,and the best maturity discrimination models were obtained as follows.Full-wave band+MSC+SPXY/Duplex/K-S/Cross-Validation/Randomization+ELM/PLS/KNN/SVM;full-wave band+S-G/MSC/SNV/Normalization+Randomization+Bayes;full-wave band+S-G+SPXY/Duplex/K-S/Cross-Validation/Randomization+ELM/PLS/KNN/SVM;full-wave band+Normalization+SPXY/Duplex/K-S/Cross-Validation/Randomization+PLS;characteristic bands+MSC+SPXY/Duplex/K-S/Cross-Validation/Randomization+ELM/PLS/KNN/Bayes/SVM;characteristic bands+Normalization+SPXY/Duplex/K-S/Cross-Validation/Randomization+PLS.
作者
刘金秀
贺小伟
罗华平
徐嘉翊
楚合营
申丽丽
LIU Jin-xiu;HE Xiao-wei;LUO Hua-ping;XU Jia-yi;CHU He-ying;SHEN Li-li(College of Mechanical and Electrical Engineering,Tarim University,Alar 843300,Xinjiang,China;Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region,Alar 843300,Xinjiang,China)
出处
《食品研究与开发》
CAS
北大核心
2022年第15期158-165,共8页
Food Research and Development
基金
国家自然科学基金项目(11964030、11464039)
塔里木大学校长基金青年项目(TDZKQN201810)。
关键词
高光谱
小白杏
成熟度
判别
模型
hyperspectral imaging
little white apricot
maturity
discrimination
model
