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Citation: CHEN Wan-Chao, TAO Xin, FAN Chang-Chun, ZHANG Fei-Yu, DU Yi-Ping. Robust Near Infrared Spectroscopy Model for Determination of Chloropropanol Concentration with Sampling Error Profile Analysis[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(2): 315-321. doi: 10.19756/j.issn.0253-3820.181658 shu

Robust Near Infrared Spectroscopy Model for Determination of Chloropropanol Concentration with Sampling Error Profile Analysis

1.
Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China;
2.
Department of Quality Assurance, Jiangsu Yangnong Chemical Group Co., LTD, Yangzhou 225009, China

Received Date: 18 September 2018
Revised Date: 27 November 2018

Fund Project: This work was supported bythe Science and Technology Commission of Shanghai Municipality (No 17142201100).

The analytical near infrared (NIR) spectroscopy model of 1,3-dichloro-2-propanol in the production process of 1,3-dichloro-2-propanol was established with sampling error profile analysis (SEPA) method. In SEPA, with a number of randomly sampling (1000 sampling in this work), 1000 sub-models were built and a series of cross validation errors under several PLS factors were obtained. With the errors, an error profile was plotted and some statistic parameters, including median, standard deviation, skewness and kurtosis were calculated. Condition optimization, modeling and evaluation of the model were carried out by comprehensively investigating the statistic performances. It was found that four spectral pretreatment methods showed good performance, and they were used to the following process of wavenumber selection to further improve the model with SEPA. The optimization results showed that the method was 1^st derivative coupled with standard normal variate, the wavenumber region selected here was 6931-6017 cm^-1, and five PLS factors were employed. Under the optimal conditions, the root mean squared error of calibration, the cross validation and the prediction were 0.881%, 1.282% and 1.167%, respectively. The selected wavenumber region was explainable, and the built model was reasonable and believable in view of statistics, which indicated that SEPA could supply a useful and robust NIR model.
- Sampling error profile analysis,
- Near infrared ray,Modeling,Robust,
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