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Evaluation of benzene residue in edible oils using Fourier transform infrared (FTIR) spectroscopy

  • Joshi, Ritu (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Lohumi, Santosh (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Joshi, Rahul (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Lee, Jayoung (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Lee, Hoonsoo (Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University) ;
  • Mo, Changyeun (Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University)
  • Received : 2018.11.13
  • Accepted : 2019.04.08
  • Published : 2019.06.01

Abstract

The use of food grade hexane (FGH) for edible oil extraction is responsible for the presence of benzene in the crude oil. Benzene is a Group 1 carcinogen and could pose a serious threat to the health of consumer. However, its detection still depends on classical methods using chromatography which requires a rapid non-destructive detection method. Hence, the aim of this study was to investigate the feasibility of using Fourier transform infrared (FTIR) spectroscopy combined with multivariate analysis to detect and quantify the benzene residue in edible oil (sesame and cottonseed oil). Oil samples were adulterated with varying quantities of benzene, and their FTIR spectra were acquired with an attenuated total reflectance (ATR) method. Optimal variables for a partial least-squares regression (PLSR) model were selected using the variable importance in projection (VIP) and the selectivity ratio (SR) methods. The developed PLS models with whole variables and the VIP- and SR-selected variables were validated against an independent data set which resulted in $R^2$ values of 0.95, 0.96, and 0.95 and standard error of prediction (SEP) values of 38.5, 33.7, and 41.7 mg/L, respectively. The proposed technique of FTIR combined with multivariate analysis and variable selection methods can detect benzene residuals in edible oils with the advantages of being fast and simple and thus, can replace the conventional methods used for the same purpose.

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Fig. 1. Raw Fourier transform infrared (FTIR) spectra for pure sesame and cottonseed oil.

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Fig. 2. Principal component analysis (PCA) score plot for sesame oil (a) and cottonseed oil (b) after applying multiplicative scatter correction (MSC) preprocessing.

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Fig. 3. First three principal components loading plots from principal component analysis (PCA) for benzene-adulterated sesame oil (a), (b), (c) and the spectrum of pure benzene with variable importance in projection (VIP) and selectivity ratio (SR) for selected variables (d).

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Fig. 4. Regression plot of the actual versus calculated percentages of benzene in the validation set of the whole spectral region (a), VIP (b) and SR (c) variable selection methods. VIP, variable importance in projection; SR, selectivity ratio.

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Fig. 5. Beta coefcient plot from the PLSR (a), Residual plot for whole variables, PLS-VIP and PLS-SR method (b). PLSR, partial least squares regression; VIP, variable importance in projection; SR, selectivity ratio.

Table 1. Prediction results by partial least squares regression (PLSR), variable importance in projection (VIP), and selectivity ratio (SR) variable-selection methods for detecting pure and benzene-adulterated edible oils.

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Acknowledgement

Supported by : Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries(IPET)

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