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논문 기본 정보

자료유형
학술저널
저자정보
Ritu Joshi (Chungnam National University) Byoung-Kwan Cho (Chungnam National University) Santosh Lohumi (Chungnam National University) Rahul Joshi (Chungnam National University) Jayoung Lee (Chungnam National University) Hoonsoo Lee (Chungbuk National University) Changyeun Mo (Kangwon National University)
저널정보
충남대학교 농업과학연구소 Korean Journal of Agricultural Science Korean Journal of Agricultural Science Vol.46 No.2
발행연도
2019.6
수록면
257 - 271 (15page)

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초록· 키워드

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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 R2 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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Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2019-520-000726307