桑蚕鲜茧干壳量的可见/近红外光谱无损检测

Nondestructive Detection of Dry Weight of Cocoons Layer of Mulberry Silkworm Fresh Cocoons Using Visible/Near Infrared Spectroscopy

摘要:

选择Savitzky—Golay平滑作为光谱数据的预处理方法，根据偏最小二乘模型的回归系数进行有效波长的选取，最终筛选出了桑蚕鲜茧干壳量指标在可见/近红外光谱谱区的7个有效波长，并结合多元线性回归建立干壳量的检测模型。该模型运算简单且检测精度较高，预测决定系数和剩余预测偏差分别为0.7587和2.0464，是应用可见/近红外光谱检测桑蚕鲜茧干壳量的理想模型。

Abstract:

Visible/near infrared (Vis—NIR) spectroscopy was investigated to determine the dry weight of the cocoons layer of mulberry silkworm fresh cocoons. Optimal partial least squares (PLS) models were developed with different preprocessing, and the data preprocessed by Savitzky—Golay (SG) smoothing was chosen for the effective wavelengths selection. The selection was operated based on regression coefficients in PLS models, and reduced the original 601 varieties into 7. Then multiple linear regression (MLR) was used for calibration and prediction based on the seven effective wavelengths, compared with the PLS model built on full-spectrum data. The results showed that MLR model was the optimum model for the dry weight of the cocoons layer detection in the process of production and marketing, because of its simple arithmetic and accurate detection. The correlation coefficient and residual predictive deviation were 0.7587 and 2.0464.