高光谱成像
偏最小二乘回归
内容(测量理论)
残余物
校准
多光谱图像
生物系统
数学
计算机科学
人工智能
统计
生物
算法
数学分析
作者
Xuan Chu,Rui Li,Hongyu Wei,Hongli Liu,Yinghui Mu,Hongzhe Jiang,Zhiyu Ma
标识
DOI:10.1016/j.infrared.2022.104098
摘要
This study demonstrated the capability of Vis/NIR hyperspectral imaging (400–1000 nm) in determining total flavonoid (TF) and total polysaccharide (TP) content in Anoectochilus formosanus that were cultivated in different light quality environments. We extracted spectra from the whole leaf (the control) and four zones (leaf tip, margin, middle and base) respectively. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were used for building prediction models using the full spectral range. PLSR models outperformed SVMR models with determination of the coefficients of calibration (R2c) and validation (R2v) being over 0.9982 and the residual predictive deviations (RPD) being over 23.21. Comparing to the model for the control, the models for the four zones achieved similar performance; the model for the middle zone achieved slightly better performance than models for the other three zones (R2v = 0.9974, RMSEV = 0.14, RPD = 18.04 for TF; and R2v = 0.9982, RMSEV = 0.20, RPD = 23.56 for TP). We further selected effective wavelengths through the correlation coefficients between single wavelength, dual-wavelength combinations and measured content of TF and TP to simplify prediction models. New PLSR models were built based on the effective wavelengths with R2v, RMSEV and RPD of 0.8990, 0.85 and 3.07 for TF, and 0.8842, 1.71 and 2.79 for TP. Obtained results showed that hyperspectral imaging is promising for rapid and accurate determination of TF content and TP content in A. formosanus based on the PLSR models and the identified effective wavelengths.
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