近红外高光谱成像技术结合偏最小二乘-判别分析所建模型快速鉴定核桃仁的品质  

Rapid Identification of Quality of Walnut Kernel by Model Established by Near Infrared Hyperspectral Imaging Technology with Partial Least Square Discriminant Analysis

作　　者：丁坤 项安[1] DING Kun;XIANG An(School of Electronics and Information Engineering,Tongji University,Shanghai 201800,China)

机构地区：[1]同济大学电子与信息工程学院,上海201800

出　　处：《理化检验（化学分册）》2023年第7期844-848,共5页Physical Testing and Chemical Analysis(Part B:Chemical Analysis)

摘　　要：将近红外高光谱成像技术与偏最小二乘-判别分析(PLS-DA)结合,建立了快速无损鉴定核桃仁品质的分类模型。在900~1 700 nm全波长范围内,采集不同品质核桃仁的光谱数据,以平均光谱作为原始光谱,以标准正态变量对原始光谱数据进行预处理,采用主成分分析对原始光谱数据降维,提取到970,1 151,1 210,1 215,1 256,1 309,1 340,1 379,1 389,1 404,1 460 nm等11个特征波长。基于全光谱和特征波长,分别建立两种PLS-DA分类模型。结果表明:全光谱条件下所建模型在校准集和验证集上的预测正确率最高,可达100%;特征波长条件下所建模型在相同数据集上的分类正确率略有下降,为99.3%;两种模型在测试集上的预测正确率均为100%。The model for rapid nondestructive identification of quality of walnut kernel was established by near infrared hyperspectral imaging technology combined with partial least square discriminant analysis(PLS-DA).The spectral data of walnut kernels with different quality were collected in the full wavelength range of 900-1 700 nm,and the average spectra were used as the original spectra.The original spectral data was pretreated by standard normal variate.The dimension of the original spectral data was reduced by principal component analysis,and 11 characteristic wavelengths were extracted,including 970,1 151,1 210,1 215,1 256,1 309,1 340,1 379,1 389,1 404,1 460 nm.Two PLS-DA classification models were established based on full spectra and characteristic wavelengths.The results showed that the prediction accuracy of model under full spectra condition on the calibration set and verification set was the highest,reaching 100%,while that of model under characteristic wavelengths condition on the same data sets decreased slightly,reaching 99.3%.The prediction accuracy of the both models on the testing set was 100%.

关 键 词：近红外高光谱成像技术 偏最小二乘-判别分析(PLS-DA) 核桃仁 品质 

分 类 号：O657.3[理学—分析化学]