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Talanta. 2016 Aug 1;155:47-52. doi: 10.1016/j.talanta.2016.04.022. Epub 2016 Apr 12.

Quantitative determination based on the differences between spectra-temperature relationships.

Author information

1
State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China.
2
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China.
3
State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China; Guangdong Polytechnic Normal University, Guangdong 510665, China.
4
State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China. Electronic address: linling@tju.edu.cn.

Abstract

In the Near-infrared (NIR) spectral measurement it is not always possible to keep the experimental conditions constant. The fluctuations in external variables, such as temperature, will result in a nonlinear shift and a broadening of the spectral bands. In this study, the temperature-induced spectral variation coefficient (TSVC) was obtained by using loading space standardization (LSS). The relationship between TSVC and normalized squared temperature was quantitatively analyzed and applied to the quantitative determination of the compositions in mixtures. NIR spectra of peanut-soy-corn oil mixtures measured at seven temperatures were analyzed. It was found that, the relationship between TSVC and normalized squared temperature can be established by using LSS. Furthermore, the quantitative determination of the compositions in a mixture can be achieved by using the difference between the relationships, i.e., the slope of the relationship. The calibration curves between slope and composition volume are found to be reliable with the correlation coefficients (R(2)) as high as 0.9992. Quantitative determination by the calibration curves were also validated. Therefore, the method can be an effective tool for investigating the effect of temperature and quantitatively analysis.

KEYWORDS:

Loading space standardization; Near-infrared spectroscopy; Quantitative determination; Relationship between TSVC and normalized squared temperature; Temperature; Temperature-induced spectral variation

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