Comparison of GC and DSC monitoring the adulteration of camellia oil with selected vegetable oils View Full Text


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Article Info

DATE

2016-12

AUTHORS

Ruifen Li, Jiaoli Huang, Li Huang, Jianwen Teng, Ning Xia, Baoyao Wei, Mouming Zhao

ABSTRACT

This paper compared the potential application of gas chromatograph (GC) and differential scanning calorimetry (DSC) to verify adulteration of camellia oil (CMO) with sesame oil (SSO), sunflower oil (SFO), peanut oil (PNO), corn oil (CO) and canola oil (CNO) which are cheaper oils mixed as adulterants with CMO. DSC offers unique thermal profiling for each oil. A combination of analysis of FAs and fingerprint were applied for GC to detect the adulteration. According to a similarity calculation (with a standard below 0.9989) of included angle, the detection limit of sesame oil, sunflower oil and corn oil was 10 %, peanut oil 20 %, and rapeseed oil 30 %; for DSC, similarly CMOs had the unique fingerprint according to their DSC peak shape and thermodynamic parameters, and an adulteration contents of 5 % could be detected qualitatively. Satisfactory results were achieved from stepwise multiple linear regression analysis (SMLR) for the data of Ton, Toff, Tpeak, △H and peak height (H) of DSC to quantitatively predict the other five oils adulteration in CMO with R2 to 0.999. The average error obtained from the error analysis corresponding to SMLR was 1.2620 %. The preliminary results presented in this study suggest that DSC analysis is an attractive tool in detecting SSO, SFO, PNO, CO and CNO adulteration in CMO. More... »

PAGES

1735-1746

References to SciGraph publications

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    34 schema:description This paper compared the potential application of gas chromatograph (GC) and differential scanning calorimetry (DSC) to verify adulteration of camellia oil (CMO) with sesame oil (SSO), sunflower oil (SFO), peanut oil (PNO), corn oil (CO) and canola oil (CNO) which are cheaper oils mixed as adulterants with CMO. DSC offers unique thermal profiling for each oil. A combination of analysis of FAs and fingerprint were applied for GC to detect the adulteration. According to a similarity calculation (with a standard below 0.9989) of included angle, the detection limit of sesame oil, sunflower oil and corn oil was 10 %, peanut oil 20 %, and rapeseed oil 30 %; for DSC, similarly CMOs had the unique fingerprint according to their DSC peak shape and thermodynamic parameters, and an adulteration contents of 5 % could be detected qualitatively. Satisfactory results were achieved from stepwise multiple linear regression analysis (SMLR) for the data of Ton, Toff, Tpeak, △H and peak height (H) of DSC to quantitatively predict the other five oils adulteration in CMO with R2 to 0.999. The average error obtained from the error analysis corresponding to SMLR was 1.2620 %. The preliminary results presented in this study suggest that DSC analysis is an attractive tool in detecting SSO, SFO, PNO, CO and CNO adulteration in CMO.
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