摘要
对采摘于一枣园的180个壶瓶枣样本,随机分成150个样本校正集和30个样本预测集。用FieldSpec3光谱仪采集光谱,并进行多元散射校正(MSC)预处理,之后分别利用连续投影算法(SPA)和逐步回归法(SRA)提取特征波长,并结合光谱理论分析确定,再分别基于偏最小二乘法(PLS)和最小二乘-支持向量机(LS-SVM)建立壶瓶枣可溶性固形物含量预测的简化模型和全波段模型。结果表明,全波段PLS模型预测的相关系数和预测均方根误差分别为0.887 4和1.088 9,预测效果最好;建立的MSC-SPA-PLS模型预测的相关系数和均方根误差分别为0.799 0和1.407 8,建立的MSC-SRA-PLS模型预测的相关系数和均方根误差分别为0.822 4和1.3851,与全波段的MSC-PLS相比,精度均降低;建立的MSC-SPA-LS-SVM模型预测的相关系数和均方根误差分别为0.796 3和1.145 8,与全波段的MSC-LS-SVM相比,精度提高;建立的MSC-SRA-LS-SVM模型预测精度很低,不适用。
Totally 180 samples coming from one orchard were divided into calibration set with 150 samples and prediction set with 30 samples. Field Spec3 spectrometer was used for collecting spectra data of 180 fresh jujube samples separately. Then successive projection algorithm and stepwise regression analysis combined with spectral theory were used to process the spectral data after MSC pretreatment. Characteristic wavelengths of 150 samples in calibration set were selected by using SPA and SRA, and the partial least square （PLS） and LS - SVM methods were used to establish models of the fresh jujube soluble solids with the whole spectrum and characteristic wavelengths selected by using SPA and SRA. At last, the models of MSC - PLS model, MSC - LS - SVM model, the MSC - SPA - PLS model, the MSC - SPA- LS- SVM model, the MSC- SRA- PLS model and the MSC- SRA- LS- SVM model were used to predict the soluble solids of 30 samples in the prediction set. The results showed that the correlation coefficient and the root mean square error of prediction of MSC - PLS model for full band are 0. 887 4 and 1. 088 9 and that is the best. The correlation coefficient and the root mean square error of prediction of MSC - SPA - PLS model and MSC - SRA - PLS model are 0. 799 0, 1. 407 8 and 0. 822 4, 1. 385 1, and they are less precise than the MSC - PLS model. The correlation coefficient and the root mean square error of prediction of MSC SPA - LS - SVM model are 0. 796 3 and 1. 145 8, and that is more precise than the MSC - LS - SVM model. The precision of MSC - SRA - LS - SVM model is very low and is not suitable.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2012年第3期108-112,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
高等学校博士学科点专项科研基金资助项目(20101403110003)
关键词
鲜枣
可溶性固形物
可见/近红外光谱
无损检测
Fresh jujube, Soluble solids, NIR spectroscopy, Nondestructive detection
