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Optimization of Characteristic Wavelength Variables of Near Infrared Spectroscopy for Detecting Edible Oil Acid Value

WANG Liqi, LIU Yanan, ZHANG Qing, CUI Yue, GE Huifang, YU Dianyu   

  1. 1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China;
    2. School of Food Science and Technology, Northeast Agricultural University, Harbin 150030, China
  • Online:2016-08-25 Published:2016-08-30
  • Contact: YU Dianyu

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Abstract:

With the goal of achieving rapid detection of soybean oil acid value by using near-infrared (NIR) spectroscopy,
this study optimized the selection of the characteristic wavelength variables by combined use of interval partial least square
(iPLS), genetic algorithm (GA) and successive projection algorithm (SPA). A total of 100 soybean oil samples with different
acid values were collected, their NIR transmittance spectra in the range of 4 000–12 000 cm-1 were acquired. Firstly, the
characteristic wavebands of 4 540–5 346 cm-1and 6 807–7 004 cm-1 were extracted from the original spectra by iPLS, with a
determination coefficient (R2) and root mean square error of prediction (RMSEP) of 0.978 9 and 0.064 3, respectively. Then,
the characteristic wavelength variables closely related to oil acid value were selected by GA and SPA from the previously
selected bands, respectively. It was shown that the PLS calibration model established using 12 variables consisting of the top
6 characteristic wavelengths from optimal selection results of each of the two algorithms was optimum, with R2 and RMSEP
of 0.985 9 and 0.045 1, respectively. The research indicated that selection of the characteristic wavelength variables by
iPLS-GA-SPA in NIR analysis for oil acid value could effectively remove redundant information, and decrease the
complexity of the model. This paper can offer important reference for rapid and non-destructive detection of oil acid value.

Key words: oil acid value, near infrared spectroscopy (NIR), characteristic wavelength variables, interval partial least square (iPLS), genetic algorithm (GA), successive projection algorithm (SPA)

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