[Purity measurement of hybrid rice seed Yixiang 725 with visible-near infrared reflectance spectra].
A rapid and non-invasive method was put forward to measure the purity of hybrid rice seed by visible-near infrared reflectance spectra. Ninety hybrid rice seed samples (Yixiang 725) with the purity of 90%-99% were collected using a FieldSpec 3 visible-near infrared spectometer. All samples were divided randomly into two groups, one group with 75 samples used as calibrated set, and the other with 15 samples used as validated set. Based on the spectra in the range of 380-2 400 nm, the regression model was established using the PLS (partial least square), and different spectra pretreatment methods were compared. The study showed that spectra information can be extracted thoroughly by the pretreatment method of first derivative combined with standard normal variate, with the SEC (standard error of calibration) of 0.002 5, SEP (standard error of prediction) of 0.006 6, and determination coefficients of 0.988 4 (calibration set) and 0.922 7 (validation set) respectively. The spectra, which were pretreated with the method of first derivative combined standard normal variate, were analyzed by principal component analysis (PCA). The top 20 principal components, which were computed by PCA and accounted for 86.09% variation of the original spectral information, were used to build BP-ANN model for measuring the purity of hybrid rice seed as the new variables. The study showed that the SEC and SEP of BP-ANN model were 0.001 7 and 0.006 1, and the determination coefficients of that were 0.995 2 (calibration set) and 0.936 9 (validation set) respectively. Therefore, the predictive power of BP-ANN model was better than that of PLS model. Results indicated that it was feasible to measure the purity of the hybrid rice seed by visible-near reflectance spectra as a rapid and non-contact way, and PCA combined with BP-ANN was a preferred method.
Liang
Liang
L
School of Info-Physics and Geomatics Engineering, Central South University, Changsha 410083, China. liangliang198119@163.com
Yang
Min-Hua
MH
Liu
Zhi-Xiao
ZX
Xu
Hai-Wei
HW
Liu
Fu-Hui
FH
He
Qi-Zhuang
QZ
Luo
Yun-Fei
YF
D002678:Chimera; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D012275:Oryza; D025341:Principal Component Analysis; D012044:Regression Analysis; D012639:Seeds; D019265:Spectroscopy, Near-Infrared