Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of sam- ples followed by laboratory cumbersome and time-consuming measurements. Visible and near-infrared di...Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of sam- ples followed by laboratory cumbersome and time-consuming measurements. Visible and near-infrared diffuse reflectance spectroscopy （VNIR-DRS） provides a rapid and inexpensive tool to predict various soil properties simultaneously. This study evaluated the suitability of VNIR-DRS for predicting soil properties, including organic matter （OM）, pH, and heavy metals （Cu, Pb, Zn, Cd, and Fe）, using a total of 254 samples collected in soil profiles near a large copper smelter in China. Partial least square regression （PLSR） with cross-validation was used to relate soil property data to the reflectance spectral data by applying different preprocessing strategies. The performance of VNIR-DRS calibration models was evaluated using the coefficient of determination in cross-validation （R^2cv） and the ratio of standard deviation to the root mean standard error of cross-validation （SD/RMSEcv）. The models provided fairly accurate predictions for OM and Fe （R2v 〉 0.80, SD/RMSEcv 〉 2.00）, less accurate but acceptable for screening purposes for pH, Cu, Pb, and Cd （0.50 〈 Rcv 〈 0.80, 1.40 〈 SD/RMSEcv 〈 2.00）, and poor accuracy for Zn （R2v 〈 0.50, SD/RMSEcv 〈 1.40）. Because soil properties in conta- minated areas generally show large variation, a comparative large number of calibrating samples, which are variable enough and uniformly distributed, are necessary to create more accurate and robust VNIR-DRS calibration models. This study indicated that VNIR-DRS technique combined with continuously enriched soil spectral library could be a nondestructive alternative for soil environment monitoring.展开更多
Nondestructive determination the internal quality of thick-skin fruits has always been a challenge.In order to investigate the prediction ability of full transmittance mode on the soluble solid content(SSC)in thick-sk...Nondestructive determination the internal quality of thick-skin fruits has always been a challenge.In order to investigate the prediction ability of full transmittance mode on the soluble solid content(SSC)in thick-skin fruits,the full transmittance spectra of citrus were collected using a visible/near infrared(Vis/NIR)portable spectrograph(550–1100 nm).Three obvious absorption peakswere found at 710,810 and 915 nmin the original spectra curve.Four spectral preprocessing methods including Smoothing,multiplicative scatter correction(MSC),standard normal variate(SNV)and first derivativewere employed to improve the quality of the original spectra.Subsequently,the effective wavelengths of SSC were selected from the original and pretreated spectra with the algorithms of successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)and genetic algorithm(GA).Finally,the prediction models of SSC were established based on the full wavelengths and effectivewavelengths.Results showed that SPA performed the best performance on eliminating the useless information variable and optimizing the number of effective variables.The optimal predictionmodel was established based on 10 characteristic variables selected from the spectra pretreated by SNV with the algorithmof SPA,with the correlation coefficient,root mean square error,and residual predictive deviation for prediction set being 0.9165,0.5684°Brix and 2.5120,respectively.Overall,the full transmittance mode was feasible to predict the internal quality of thick-skin fruits,like citrus.Additionally,the combination of spectral preprocessing with a variable selection algorithmwas effective for developing the reliable predictionmodel.The conclusions of this study also provide an alternative method for fast and real-time detection of the internal quality of thick-skin fruits using Vis/NIR spectroscopy.展开更多
基金Supported by the National Natural Science Foundation of China (Nos. 40801081 and 40271104)the open fund from the Key Laboratory of Virtual Geographic Environment of the Ministry of Education,China (No. NS207002)
文摘Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of sam- ples followed by laboratory cumbersome and time-consuming measurements. Visible and near-infrared diffuse reflectance spectroscopy （VNIR-DRS） provides a rapid and inexpensive tool to predict various soil properties simultaneously. This study evaluated the suitability of VNIR-DRS for predicting soil properties, including organic matter （OM）, pH, and heavy metals （Cu, Pb, Zn, Cd, and Fe）, using a total of 254 samples collected in soil profiles near a large copper smelter in China. Partial least square regression （PLSR） with cross-validation was used to relate soil property data to the reflectance spectral data by applying different preprocessing strategies. The performance of VNIR-DRS calibration models was evaluated using the coefficient of determination in cross-validation （R^2cv） and the ratio of standard deviation to the root mean standard error of cross-validation （SD/RMSEcv）. The models provided fairly accurate predictions for OM and Fe （R2v 〉 0.80, SD/RMSEcv 〉 2.00）, less accurate but acceptable for screening purposes for pH, Cu, Pb, and Cd （0.50 〈 Rcv 〈 0.80, 1.40 〈 SD/RMSEcv 〈 2.00）, and poor accuracy for Zn （R2v 〈 0.50, SD/RMSEcv 〈 1.40）. Because soil properties in conta- minated areas generally show large variation, a comparative large number of calibrating samples, which are variable enough and uniformly distributed, are necessary to create more accurate and robust VNIR-DRS calibration models. This study indicated that VNIR-DRS technique combined with continuously enriched soil spectral library could be a nondestructive alternative for soil environment monitoring.
基金This study was supported by National Key Research and Development Program(2016YFD0200104)Beijing Talents Foundation(2018000021223ZK06)National Natural Science Foundation of China(Grant No.31671927).
文摘Nondestructive determination the internal quality of thick-skin fruits has always been a challenge.In order to investigate the prediction ability of full transmittance mode on the soluble solid content(SSC)in thick-skin fruits,the full transmittance spectra of citrus were collected using a visible/near infrared(Vis/NIR)portable spectrograph(550–1100 nm).Three obvious absorption peakswere found at 710,810 and 915 nmin the original spectra curve.Four spectral preprocessing methods including Smoothing,multiplicative scatter correction(MSC),standard normal variate(SNV)and first derivativewere employed to improve the quality of the original spectra.Subsequently,the effective wavelengths of SSC were selected from the original and pretreated spectra with the algorithms of successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)and genetic algorithm(GA).Finally,the prediction models of SSC were established based on the full wavelengths and effectivewavelengths.Results showed that SPA performed the best performance on eliminating the useless information variable and optimizing the number of effective variables.The optimal predictionmodel was established based on 10 characteristic variables selected from the spectra pretreated by SNV with the algorithmof SPA,with the correlation coefficient,root mean square error,and residual predictive deviation for prediction set being 0.9165,0.5684°Brix and 2.5120,respectively.Overall,the full transmittance mode was feasible to predict the internal quality of thick-skin fruits,like citrus.Additionally,the combination of spectral preprocessing with a variable selection algorithmwas effective for developing the reliable predictionmodel.The conclusions of this study also provide an alternative method for fast and real-time detection of the internal quality of thick-skin fruits using Vis/NIR spectroscopy.
