Background:Manga nese(Mn)is an essential microelement in cotton seeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reage...Background:Manga nese(Mn)is an essential microelement in cotton seeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reagent-free analytical way is demanded to substitute the traditional analytical method.Results:The Mn content in cottonseed meal was investigated by near-infrared spectroscopy(NIRS)and chemometrics techniques.Standard normal variate(SNV)combined with first derivatives(FD)was the optimal spectra pre-treatment method.Monte Carlo uninformative variable elimination(MCUVE)and successive projections algorithm method(SPA)were employed to extract the informative variables from the full NIR spectra.The lin ear and non linear calibration models for cott on seed Mn content were developed.Finally,the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM,with root mean squares error of prediction(RMSEP)of 1.994 6,coefficient of determination(R^2)of 0.949 3,and the residual predictive deviation(RPD)of 4.370 5,respectively.Conclusions:The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal,which can be used as an alter native way to substitute for traditional analytical method.展开更多
基金funded by The National Key Technology R&D program of China(2016YFD0101404)China Agriculture Research System(CARS-18-25)Jiangsu Collaborative Innovation Center for Modern Crop Production
文摘Background:Manga nese(Mn)is an essential microelement in cotton seeds,which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures.Therefore a rapid,low-cost,and reagent-free analytical way is demanded to substitute the traditional analytical method.Results:The Mn content in cottonseed meal was investigated by near-infrared spectroscopy(NIRS)and chemometrics techniques.Standard normal variate(SNV)combined with first derivatives(FD)was the optimal spectra pre-treatment method.Monte Carlo uninformative variable elimination(MCUVE)and successive projections algorithm method(SPA)were employed to extract the informative variables from the full NIR spectra.The lin ear and non linear calibration models for cott on seed Mn content were developed.Finally,the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM,with root mean squares error of prediction(RMSEP)of 1.994 6,coefficient of determination(R^2)of 0.949 3,and the residual predictive deviation(RPD)of 4.370 5,respectively.Conclusions:The MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal,which can be used as an alter native way to substitute for traditional analytical method.
