近红外反射光谱技术预测花生种子含水量  

Prediction of Moisture Content in Single Peanut Seed by Near Infrared Reflectance Spectroscopy

作　　者：杨传得[1] 于洪涛[2] 关淑艳[1] 王传堂[2] 张建成[2] 唐月异[2] 王秀贞[2] 吴琪[2] 

机构地区：[1]吉林农业大学,吉林长春130118 [2]山东省花生研究所,山东青岛266100

出　　处：《花生学报》2012年第1期6-9,20,共5页Journal of Peanut Science

基　　金：国家花生产业技术体系(CARS-14);山东省科技发展计划项目(2009GG10009008);青岛市科技发展计划项目(09-1-3-67-jch,10-3-3-30-nsh)

摘　　要：选取116份大花生为实验材料,应用近红外反射光谱技术,结合偏最小二乘法,采用交叉检验建立了大花生含水量的近红外模型。优化结果表明,原始光谱不经过预处理,光谱范围为4597.7～11988cm-1,维数12,此时建立的模型校正结果最佳。模型决定系数(R2)为93.62,根均方差(RMSECV)为1.17。用该模型对20个未参与建模的实验材料进行预测,偏差为-1.781～1.902,相对误差为0.122%～2.855%。结果表明含水量模型具有很好的预测准确性,可用于鲜食花生种子水分含量快速检测。A near infrared reflectance spectroscopy calibration model for prediction of moisture content in individual single peanut seed was developed using 116 peanut seed samples, PLS algorithms and cross validation. The best pretreatment method was no treatment, the optimal wavenumber was 4597.7-11988 cm-1, and rank was 12. R2 and RMSECV was 93.62 and 1.17, respectively. The model was further tested with 20 external peanut seed samples, and it was found that the error was --1. 781-1. 902, and relative error was 0. 122%-2. 855~/60, demonstrating the robustness of the model. The NIRS model can be used for rapid analysis of moisture content in freshly harvested peanut seeds.

关 键 词：花生 含水量 近红外反射光谱 

分 类 号：S565.2[农业科学—作物学] TN219[电子电信—物理电子学]