2021Egyptian Journal of ChemistryOpen access

Study of Oleaster Oil’s Falsification by ATR-FTIR and Chemometrics Tools

Moulouda El mouftari, F.Z. Mahjoubi, Fouzia Kzaiber, Wafa Terouzı, Gomaa A. M. Ali, Said Souhassou, Abdelkhalek Oussama

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Abstract

This study aims to create a model of oleaster oil simply and reliably to detect adulteration, which presents a large danger that attacks the food sector and human health. For this reason, a study to detect the falsification of oleaster oil was carried out by Fourier-Transform Infrared Spectroscopy FTIR and chemometric method. The experimental samples are shared into two sets, 32 Training set, 8 Test set (4 calibration samples opposite one for validation), and a falsification interval of 1.5-40%. The treatment of infrared spectral results has been done by chemometrics techniques utilizing Partial Least Squares regression or Projection to Latent Structures (PLSR) and Principal Component Regression (PCR). The results show that the perfect falsification model of oleaster oil by olive-oil and soybean oil is illustrated in the spectral region 3050-2700 cm-1, with R2 of 0.999 from PLSR and PCR to soybean-oil, concerning olive-oil shows also the better results for the PLSR technical with R2 of 0.995. The spectral and chemometrics results revealed an effective model that can detect adulteration whatever the type of adulterant used in this study (olive oil and soybean oil) with a percentage of adulteration ranging from 1.5% to 40%.

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This study aims to create a model of oleaster oil simply and reliably to detect adulteration, which presents a large danger that attacks the food sector and human health. For this reason, a study to detect the falsification of oleaster oil was carried out by Fourier-Transform Infrared Spectroscopy FTIR and chemometric method. The experimental samples are shared into two sets, 32 Training set, 8 Test set (4 calibration samples opposite one for validation), and a falsification interval of 1.5-40%. The treatment of infrared spectral results has been done by chemometrics techniques utilizing Partial Least Squares regression or Projection to Latent Structures (PLSR) and Principal Component Regression (PCR). The results show that the perfect falsification model of oleaster oil by olive-oil and soybean oil is illustrated in the spectral region 3050-2700 cm-1, with R2 of 0.999 from PLSR and PCR to soybean-oil, concerning olive-oil shows also the better results for the PLSR technical with R2 of 0.995. The spectral and chemometrics results revealed an effective model that can detect adulteration whatever the type of adulterant used in this study (olive oil and soybean oil) with a percentage of adulteration ranging from 1.5% to 40%.

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Available abstract

This study aims to create a model of oleaster oil simply and reliably to detect adulteration, which presents a large danger that attacks the food sector and human health. For this reason, a study to detect the falsification of oleaster oil was carried out by Fourier-Transform Infrared Spectroscopy FTIR and chemometric method. The experimental samples are shared into two sets, 32 Training set, 8 Test set (4 calibration samples opposite one for validation), and a falsification interval of 1.5-40%. The treatment of infrared spectral results has been done by chemometrics techniques utilizing Partial Least Squares regression or Projection to Latent Structures (PLSR) and Principal Component Regression (PCR). The results show that the perfect falsification model of oleaster oil by olive-oil and soybean oil is illustrated in the spectral region 3050-2700 cm-1, with R2 of 0.999 from PLSR and PCR to soybean-oil, concerning olive-oil shows also the better results for the PLSR technical with R2 of 0.995. The spectral and chemometrics results revealed an effective model that can detect adulteration whatever the type of adulterant used in this study (olive oil and soybean oil) with a percentage of adulteration ranging from 1.5% to 40%.

Key concepts: Chemometrics, Partial least squares regression, Adulterant, Fourier transform infrared spectroscopy, Olive oil, Principal component regression, Soybean oil, Biological system

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