The superluminescent diode has been fabricated by applying an AR coating to the output facet of the semiconductor laser for the purpose of eliminating or suitably reducing the optical feedback. An exact method for mea...The superluminescent diode has been fabricated by applying an AR coating to the output facet of the semiconductor laser for the purpose of eliminating or suitably reducing the optical feedback. An exact method for measuring the modal reflectivity of the antireflection coating to a laser diode is described. It is based on measurements of the spectrum modulation depth of the resulting superluminescent diode output spectrum at arbitrary injection current, and modal reflectivity of less than 3 × 10-4 is obtained.展开更多
Fourier transform infrared attenuated total reflectance （FTIR-ATR） spectroscopy was employed to characterize rapeseed oils. The spectral features of rapeseed oils were first investigated. Spectral data was processed...Fourier transform infrared attenuated total reflectance （FTIR-ATR） spectroscopy was employed to characterize rapeseed oils. The spectral features of rapeseed oils were first investigated. Spectral data was processed using principal component analysis （PCA） and linear discriminant analysis （LDA） to discriminate the oils from three cultivars of rapeseeds. As a result, 100% discrimination accuracy was obtained by LDA. Furthermore, the applicability of FTIR-ATR spectroscopy to characterize the changes of rapeseed oils caused by thermal treatment was studied. The rapeseed oil at 60 ℃ was regularly subjected to spectral measurement, and the spectral changes induced by thermal treatment were analyzed and discussed. This study had demonstrated the good performance of FTIR-ATR spectroscopy in characterizing rapeseed oils.展开更多
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflec...Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.展开更多
文摘The superluminescent diode has been fabricated by applying an AR coating to the output facet of the semiconductor laser for the purpose of eliminating or suitably reducing the optical feedback. An exact method for measuring the modal reflectivity of the antireflection coating to a laser diode is described. It is based on measurements of the spectrum modulation depth of the resulting superluminescent diode output spectrum at arbitrary injection current, and modal reflectivity of less than 3 × 10-4 is obtained.
文摘Fourier transform infrared attenuated total reflectance （FTIR-ATR） spectroscopy was employed to characterize rapeseed oils. The spectral features of rapeseed oils were first investigated. Spectral data was processed using principal component analysis （PCA） and linear discriminant analysis （LDA） to discriminate the oils from three cultivars of rapeseeds. As a result, 100% discrimination accuracy was obtained by LDA. Furthermore, the applicability of FTIR-ATR spectroscopy to characterize the changes of rapeseed oils caused by thermal treatment was studied. The rapeseed oil at 60 ℃ was regularly subjected to spectral measurement, and the spectral changes induced by thermal treatment were analyzed and discussed. This study had demonstrated the good performance of FTIR-ATR spectroscopy in characterizing rapeseed oils.
基金supported by the National Basic Research Program (973) of China (No.2010CB126200)China Postdoctoral Science Foundation Project (No.20090451437)
文摘Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions： healthy panicles, empty panicles caused by Nilaparvata lugens St^l, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis （PCA） was performed to obtain the principal component spectra （PCS） of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification （SVC） was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the in- dependent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
