摘要
应用可见/近红外光谱技术、小波变换（WT）和连续投影算法（SPA）,对火龙果总酸含量（TA）进行精确、快速的无损检测,为火龙果内部品质无损检测提供科学依据。利用Maya2000光纤光谱仪采集380~1 099nm范围的火龙果漫反射光谱数据,通过WT消噪、SPA优选波长和偏最小二乘回归（PLSR）分析方法,建立了火龙果总酸的定量预测模型。试验结果表明：经过WT消噪联合SPA优选波长压缩光谱变量后建立的WT-SPA-PLSR模型,预测精度都高于全谱PLSR模型。由全部样本的原始光谱变量作为输入变量建立PLSR模型的预测相关系数（R_p）为0.851 394,预测均方根误差（RMSEP）为0.086 848;全部样本的原始光谱数据使用dbN（N=2,3,…,10）小波进行分解消噪,其中消噪效果最优的是db4小波2层分解（db4-2）,WT-PLSR模型的Rp为0.915 635,RMSEP为0.066 752,小波变换消噪后的光谱预测模型精度明显提高;原始光谱经过db10-3小波消噪联合SPA算法,从570个光谱变量中优选出530,545,604,626,648,676,685,695,730,897,972,1 016nm共12个变量作为输入变量,建立WT-SPA-PLSR预测模型,模型的RP为0.882 83,RMSEP为0.077 39。SPA算法适合火龙果TA模型的光谱变量选择,能够有效提取与总酸相关度高的波长变量,增加了预测模型的精度和稳定性。研究结果表明小波变换技术联合连续投影算法的漫反射近红外光谱无损检测火龙果总酸含量具有可行性。
The objective of present study was to find out an accurate,rapid and nondestructive method to detect total acid content（TA）of pitaya with visible/near-infrared spectrometry,wavelet transform（WT）and successive projections algorithm（SPA）,which will provide scientific basis for non-destructive measurement of pitaya.Maya2000 fiber-optic spectrumeter was used to collect spectral data of pitaya on the wavelength in the range of 380~1 099nm;and then with the methods of WT denosing pretreatment,SPA and partial least squares regression（PLSR）quantitative forecasting model of TA of pitaya was established.The result showed that the precision of WT-SPA-PLSR model,which combine the WT with SPA,was better than that of PLSR model based on the whole wave variables.The relation coefficient of the PLSR model（R_p）that predicted TA based on the original spectrum of all samples as the input variables was 0.851 394 and RMSEP was 0.086 848.The original spectrum variable of the all samples were processed by using wavelet function dbN（N=2,3,…,10）for wavelet decomposition and de-noising.The optimal results of noise reduction were decomposed in level 2 using wavelet function db4（db4-2）.The Rpof WT-PLSR model was 0.915 635 and RMSEP was 0.066 752.The prediction of model using wavelet transform de-noising was improved significantly.After the original spectrum processed by db10-3and SPA,12 preferred variables were selected from 570 spectrum variables,such as 530,545,604,626,648,676,685,695,730,897,972,1 016 nm spectrum variables.The WT-SPA-PLSR model based on these 12 variables as input variables was established.Rpof the WT-SPA-PLSR prediction model was 0.882 83 and RMSEP was 0.077 39.SPA algorithm was suitable for the selection of spectrum variables which could effectively obtain the spectrum variables which were strong correlation with TA and increase the accuracy and stability of the prediction model.The results indicated that the nondestructive detection for TA of pitaya based on the diffuse reflectance visible/near-infrared spectrometry,WT and SPA was feasible.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2016年第5期1345-1351,共7页
Spectroscopy and Spectral Analysis
基金
国家现代农业产业技术体系建设专项项目(CARS-27)
教育部高等学校博士学科点专项科研基金项目(20124404120006)资助
