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

Zhe Li1, Mei Zhou2, Yongshun Luo3

  • 1State 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.

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Summary
This summary is machine-generated.

Temperature fluctuations affect Near-infrared (NIR) spectral measurements. This study introduces a method using temperature-induced spectral variation coefficient (TSVC) to accurately quantify mixture compositions despite temperature changes.

Keywords:
Loading space standardizationNear-infrared spectroscopyQuantitative determinationRelationship between TSVC and normalized squared temperatureTemperatureTemperature-induced spectral variation

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Near-infrared (NIR) spectral measurements are sensitive to experimental conditions like temperature.
  • Temperature fluctuations cause nonlinear shifts and broadening of spectral bands, complicating analysis.
  • Accurate quantitative analysis requires accounting for temperature-induced spectral variations.

Purpose of the Study:

  • To develop a method for quantifying mixture compositions using NIR spectroscopy under varying temperatures.
  • To investigate the relationship between temperature variations and spectral changes.
  • To establish a reliable calibration model for quantitative analysis of oil mixtures.

Main Methods:

  • Utilized loading space standardization (LSS) to obtain the temperature-induced spectral variation coefficient (TSVC).
  • Analyzed NIR spectra of peanut-soy-corn oil mixtures across seven different temperatures.
  • Quantitatively analyzed the relationship between TSVC and normalized squared temperature.

Main Results:

  • Established a quantifiable relationship between TSVC and normalized squared temperature using LSS.
  • Demonstrated that the slope of this relationship can be used for quantitative determination of mixture compositions.
  • Achieved highly reliable calibration curves (R(2) up to 0.9992) for composition analysis.

Conclusions:

  • The developed method effectively addresses temperature-induced spectral variations in NIR analysis.
  • The slope of the TSVC-temperature relationship provides a robust parameter for quantitative composition determination.
  • This approach offers an effective tool for investigating temperature effects and performing quantitative analysis in complex mixtures.