基于近红外光谱技术结合波长优选分析桑叶品质(2)

and R2 were 0.945, 0.863 and 0.841, respectively, and Bias was 0.32g/100g. The results of the UVE of the spectrum of the mulberry leaf powder MicroNIR1700 were the best. The RMSEC and R2 of the calibration set were 0.58g/100g and 0.933, respectively, and the RMSECV and R2 were 0.82g/100g and 0.867, respectively. The RMSEP and R2 of he predicted set were 0.93g/100g and 0.864, respectively, and Bias was 0.02 g / 100g. UVE screening of fresh mulberry leaves and mulberry leaves crude protein DLP NIRScan Nano spectral wavelength of the best modeling effect, fresh mulberry leaf DLP NIRScan Nano spectral correction set RMSEC and R2 were 0.50g/100g and 0.950, cross validation RMSECV and R2 were 0.92g/100g and 0.831, respectively, and the RMSEP and R2 of the predicted set were 1.15g/100g and 0.804, Bias were -0.03g/100 g, the RMSEC, RMSECV of the mulberry leaf DLP NIRScan Nano spectrum RMSEP were 0.62g/100g, 0.93g/100g and 1.01g/100g, R2 , R2 and R2 were 0.923, 0.826 and 0.824, respectively, and Bias was 0.46g/100g.The results showed that the UVE screening results of the fresh mulberry leaf soluble protein MicroNIR1700 and DLP NIRScan Nano were the best. The RMSEC, RMSECV and RMSEP of the MicroNIR1700 spectrum were 1.72%, 2.38% and 2.39%,C , RCV and RP were 0.823, 0.670 and 0.742, respectively, and Bias was 1.36%. The RMSEC, RMSECV and RMSEP of the DLP NIRScan Nano spectrum were 1.70%, 2.50% and 1.80%, R2 C, R2 and R2 were 0.813, 0.608 and 0.821, respectively, and Bias was 0.57%.

Randomfrog preferred mulberry leaf powder MicroNIR1700 spectral data modeling the best. The mean square error of calibration set, cross validation and prediction set were 1.41%, 2.34% and 2.14%, respectively, and the correlation coefficients were 0.849, 0.608 and 0.735 respectively, and Bias was -0.63%. The best results of the UVE of the DLP NIRScan Nano spectrum of the mulberry leaf powder

were the best. The RMSEC and R2 respectively. The RMSECV and R2 respectively. The RMSEP and R 2 of the calibration set were 1.14% and 0.921, of the cross validation were 2.13% and 0.732, of the prediction set were 2.58% and 0.560 respectively, and Bias was -1.18%. The results show that the moisture content of mulberry leaves and the crude protein can be detected by the near infrared spectroscopy combined with wavelength optimization. At the same time, it is necessary to continue to study how to establish a prediction model for the detection of soluble sugar in mulberry leaves.

Key words: Mulberry leaf; Near Infrared Reflectance Spectroscopy; Partial Least Square Method; Wavelength selection.

第一章绪论 1

1.1引言 1

1.2桑叶品质评价的指标 2

1.2.1粗蛋白 2

1.2.2可溶性糖 2

1.2.3含水率 3

1.3近红外光谱分析技术 3

1.3.1近红外光谱简介及发展历程 3

1.3.2近红外光谱分析技术的理论基础 4

1.3.3近红外光谱分析技术的特点 4

1.3.4近红外光谱分析技术的基本流程 5

1.3.5近红外光谱分析技术的应用 5

1.4光谱数据处理及建模 6

1.4.1偏最小二乘法基于近红外光谱技术结合波长优选分析桑叶品质(2):http://www.youerw.com/shengwu/lunwen_203692.html