2014Science and Technology of Food IndustryRequires access

Adulteration detection of corn oil in olive oil based on near-infrared spectroscopy

Gong Dong-ju

Open publisher page 1 citations

Abstract

A rapid method was developed for quantification detection of adulteration of corn oil in olive oil based on near-infrared spectroscopy(NIRS). A total of 191 olive oil samples and mixed oil samples(olive oil and corn oil) were analyzed by NIRS,respectively. After first derivative and smooth processing,principal component analysis(PCA) was used to reduce the dimensionality of the spectral data. Through the loading graph of the first three principal components,characteristic wave band(7520~6927cm-1,6270~5440cm-1and 4970~4400cm-1, respectively) with max-relativity was determined. Predictive models of corn oil content were established in three bands using the partial least squares(PLS) method,respectively. The results showed that the model with best accuracy and stability were in 6270~5440cm-1 band and 7 factors. In the range of 0.5% to 100%,the R2 of calibration samples and test sample were 0.9999,and the standard deviation between 0.126~0.139. The results indicated that it would be feasible to destructive analysis the content of corn oil in olive oil by NIRS,and better prediction results could be obtained in 6270~5440cm-1 band.

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What this paper is about

A rapid method was developed for quantification detection of adulteration of corn oil in olive oil based on near-infrared spectroscopy(NIRS). A total of 191 olive oil samples and mixed oil samples(olive oil and corn oil) were analyzed by NIRS,respectively. After first derivative and smooth processing,principal component analysis(PCA) was used to reduce the dimensionality of the spectral data. Through the loading graph of the first three principal components,characteristic wave band(7520~6927cm-1,6270~5440cm-1and 4970~4400cm-1, respectively) with max-relativity was determined. Predictive models of corn oil content were established in three bands using the partial least squares(PLS) method,respectively. The results showed that the model with best accuracy and stability were in 6270~5440cm-1 band and 7 factors. In the range of 0.5% to 100%,the R2 of calibration samples and test sample were 0.9999,and the standard deviation between 0.126~0.139. The results indicated that it would be feasible to destructive analysis the content of corn oil in olive oil by NIRS,and better prediction results could be obtained in 6270~5440cm-1 band.

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Available abstract

A rapid method was developed for quantification detection of adulteration of corn oil in olive oil based on near-infrared spectroscopy(NIRS). A total of 191 olive oil samples and mixed oil samples(olive oil and corn oil) were analyzed by NIRS,respectively. After first derivative and smooth processing,principal component analysis(PCA) was used to reduce the dimensionality of the spectral data. Through the loading graph of the first three principal components,characteristic wave band(7520~6927cm-1,6270~5440cm-1and 4970~4400cm-1, respectively) with max-relativity was determined. Predictive models of corn oil content were established in three bands using the partial least squares(PLS) method,respectively. The results showed that the model with best accuracy and stability were in 6270~5440cm-1 band and 7 factors. In the range of 0.5% to 100%,the R2 of calibration samples and test sample were 0.9999,and the standard deviation between 0.126~0.139. The results indicated that it would be feasible to destructive analysis the content of corn oil in olive oil by NIRS,and better prediction results could be obtained in 6270~5440cm-1 band.

Key concepts: Olive oil, Partial least squares regression, Principal component analysis, Corn oil, Chemistry, Calibration curve, Chromatography, Food science

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