Real time monitoring of the combined effect of chlorophyll content and light filtering packaging on virgin olive oil photo-stability using mesh cell-FTIR spectroscopy

The presence of lard (LD) in cosmetics products is a serious matter for certain religion, like Islam. The Muslim community is not allowed to use cosmetics products containing pig derivatives such as LD. Therefore, analysis of LD in cosmetics products is highly needed. The present study highlighted the employment of Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of multivariate calibration and principle component analysis (PCA) for quantitative analysis and classification of LD in the binary mixture with extra virgin olive oil (EVOO) as oil base in cream formulations for halal authentication. The lipid component in cream was extracted using liquid-liquid extraction using hexane as extracting solvent, and the lipid obtained was subjected to FTIR spectra measurement, using horizontal attenuated total reflectance as sampling technique. The result showed that FTIR spectroscopy in combination with partial least squares can be used to quantify the levels of LD in the mixture with EVOO in cosmetics creams using the combined frequency regions of 1785-702 cm-1 and 3020-2808 cm-1. PCA using absorbance intensities at 1200 – 1000 cm-1 as variables has been successfully used for the classification of cream with and without LD in the formulation. The developed method is rapid and not involving the excessive sample preparation.

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Real time continuous oxygen concentration monitoring system during malaxation for the production of Virgin Olive Oil

Grasas y Aceites ◽

10.3989/gya.058012 ◽

2012 ◽

Vol 63 (4) ◽

pp. 475-483 ◽

Cited By ~ 14

Author(s):

M. Vallone ◽

G. Aiello ◽

P. Catania ◽

M. Enea ◽

G. La Scalia ◽

...

Keyword(s):

Olive Oil ◽

Real Time ◽

Monitoring System ◽

Oxygen Concentration ◽

Virgin Olive Oil

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Evaluation of Extra Virgin Olive Oil Adulteration with Edible Oils using ATR-FTIR Spectroscopy

Malaysian Journal of Applied Sciences ◽

10.37231/myjas.2020.5.1.231 ◽

2020 ◽

Vol 5 (1) ◽

pp. 35-44

Author(s):

Nuraznee Mashodi ◽

Nurul Yani Rahim ◽

Norhayati Muhammad ◽

Saliza Asman

Keyword(s):

Linear Regression ◽

Olive Oil ◽

Ftir Spectroscopy ◽

Corn Oil ◽

Edible Oils ◽

Limit Of Detection ◽

Linear Regression Analysis ◽

Virgin Olive Oil ◽

Extra Virgin Olive Oil ◽

Regression Graph

Extra virgin olive oil (EVOO) is categorized as expensive oil due to high-quality nutritional value. Unfortunately, EVOO is easily adulterated with other low-quality edible oils. Therefore, this study was done to differentiate and analyze the adulteration of EVOO with other edible oils using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The study was used several edible oils included canola oil, corn oil, sunflower oil, and soybean oil as an adulterant for EVOO. The adulterant EVOO samples were prepared by mixing with dissimilar concentrations of the solely edible oils (20 %, 40 %, 60 % and 80 % (v/v)). The main functional groups of EVOO and other edible oils are O-H, C-H, C=C and C=O groups were assigned around 3500 cm-1, 2925 cm-1, 3006 cm-1 and 1745 cm-1 wavenumbers, respectively. From the comparison of EVOO and other adulterant edibles oil spectra, it showed that the EVOO has the lowest absorbance intensity at around 3006 cm-1 represented double bond which is closely related to the composition of oil sample. The adulteration of EVOO was evaluated by analysing the changes in the absorbance based on the linear regression analysis graph of the bands at 3006 and 2925 cm-1 and the limit of detection (LOD) was measured. The graph of A3008/A2925 with good relative coefficients (R2) and lower LOD is more favourable than the linear regression graph of A3006 versus percentage of edible oils added in EVOO. This study showed that ATR-FTIR spectroscopy is a convenient tool for analysing the adulteration of EVOO.

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A predictive artificial neural network model as a simulator of the extra virgin olive oil elaboration process

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033517725639 ◽

2017 ◽

Vol 25 (4) ◽

pp. 278-285 ◽

Cited By ~ 2

Author(s):

Estrella Funes ◽

Yosra Allouche ◽

Gabriel Beltrán ◽

M Paz Aguliera ◽

Antonio Jiménez

Keyword(s):

Olive Oil ◽

Real Time ◽

Near Infrared ◽

Virgin Olive Oil ◽

Extra Virgin Olive Oil ◽

Prediction Errors ◽

Process Variables ◽

Virtual Plant ◽

Infrared Spectral ◽

Artificial Neural

Nine neural models were created to predict the characteristics of the extra virgin olive oil developed as a quality objective and by-products. These models are designed with the help of data of process variables from physical sensors such as temperature, flows, current intensity, etc. and physicochemical ones like the near infrared spectrum of the olive mass. The results obtained for the extractability of the process (fatty content and moisture) were highly significant correlations (r2≥0.90) and with similar prediction errors (root mean of squared error of prediction) relative to other analysis techniques which measure the by-product directly. For prediction the models gave correlations above 0.94, with the exception of ultraviolet absorption coefficients (0.72–0.84), with small prediction errors and the quality indicator relative error range with values above the optimal 10. The set of developed artificial neural networks models constitute the basis of the global ‘simulator’ tool of the extra virgin olive oil process. This simulator can perform a predictive optimization of the process to pre-adjust the process variables according to the goals marked in productivity or quality, from an near infrared spectral database or by real-time scanning. This simulator could be integrated into a control system that performs the function of a ‘virtual plant’ that allows the said system to adjust in real time the appropriate variables to meet the objectives.

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