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《Chinese Journal of Analytical Chemistry》 2017-11
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Rapid Quantitative Detection and Model Optimization of Trans Fatty Acids in Edible Vegetable Oils by Near Infrared Spectroscopy

MO Xin-Xin;SUN Tong;LIU Mu-Hua;YE Zhen-Nan;Key Laboratory of Jiangxi University for Optics-Electronics Application of Biomaterials,College of engineering,Jiangxi Agricultural University;Comprehensive Technology Center,Jiangxi Entry-Exit Inspection and Quarantine Bureau;  
Near infrared spectroscopy( NIR) was used to detect trans fatty acids( TFA) in edible vegetable oils quantitatively. And prediction model of TFA was optimized through band selection,pretreatment method,variable selection and modeling method. NIR spectra of 98 edible vegetable oil samples were collected in spectral range of 4000-10000 cm$1 using an Antaris II Fourier transform near infrared spectrometer,and the true content of TFA was measured by gas chromatography. First,optimization of waveband and pretreatment method was conducted on original spectra. On this basis,competitive adaptive reweighted sampling( CARS)was used to select important variables that related to TFA. Finally,the prediction models of TFA content in edible vegetable oils were established using principal component regression( PCR),partial least square(PLS) and least square support vector machine(LS-SVM). The results indicated that NIR spectroscopy was feasible for detecting TFA content in edible vegetable oils,R2 of the best prediction model after optimized in calibration and prediction sets were 0.992 and 0.989,and root mean square error of calibration( RMSEC) and root mean square error of prediction( RMSEP) were 0. 071% and 0. 075%,respectively. Only 26 variables were used in the best prediction model,accounting for 0.854% of the whole waveband variables. In addition,compared with the full waveband PLS prediction model,the R2 in prediction set increased from 0.904 to 0.989,and RMSEP decreased from 0. 230% to 0. 075%. It shows that model optimization is very necessary,CARS method can select important variables related to TFA effectively and immensely reduce the number of modeling variables,so it can simplify the prediction model,and greatly improve the accuracy and stability of prediction model.
【Fund】: 国家自然科学基金(No.31401278);; 江西省自然科学基金(No.20151BAB204025);; 江西省教育厅科学研究基金(No.GJJ13254)项目资助~~
【CateGory Index】: O657.33;TS227
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