Arieff Salleh Rosman

Fourier transform infrared spectroscopy combined with multivariate calibrations for the authentication of avocado oil.

Avocado oil is one of the functional oils having high quality and high price in the market. This oil shows many benefits for the human health and is applied in many cosmetic products. The authentication of avocado oil becomes very important due to the possible adulteration of avocado oil with other lower priced oils, such as palm oil and canola oil. In this study, Fourier transform infrared spectroscopy using attenuated total reflectance in combination with chemometrics techniques of partial least squares and principal component regression is implemented to construct the quantification and classification models of palm oil and canola oil in avocado oil. Partial least squares at the wavenumbers region of 1260–900 cm–1 revealed the best calibration models, having the highest coefficient of determination (R2 = 0.999) and the lowest root mean square error of calibration, 0.80%, and comparatively low root mean square error of prediction, 0.79%, for analysis of avocado oil in the mixture with palm oil. Meanwhile, the highest R2, root mean square error of calibration, and root mean square error of prediction values obtained for avocado oil in the mixture with canola oil at frequency region of 3025–2850 and 1260–900 cm–1 were 0.9995, 0.83, and 0.64%, respectively.

Use of Fourier Transform Infrared Spectroscopy in Combination with Partial Least Square for Authentication of Black Seed Oil

Fourier transform infrared spectroscopy in combination with multivariate calibration of partial least square is intended for quantitative analysis of black seed oil in binary mixture with sunflower oil and walnut oil, as well as in ternary mixture with sunflower oil and walnut oil. The spectra of black seed oil, sunflower oil, walnut oil, and their mixture with certain concentration were scanned using attenuated total reflectance at mid infrared region of 4000–650 cm−1. For quantitatve analysis, Fourier transform infrared spectral treatment (normal or derivatives) with the highest values of coefficient of determination (R2) and the lowest values of root mean square error of calibration was selected as optimal calibration model. Partial least square at whole mid infrared region of 4000–650 cm−1 is well suited for quantitative analysis of black seed oil either in binary mixture or ternary mixture with walnut oil and sunflower oil. Furthermore, using absorbancies at frequency region of 3009–721 cm−1, principal component analysis is succesfully used for classification of black seed oil and that mixed with sunflower oil and walnut oil. The developed method is rapid, no sample preparation needed, and is not involving the use of chemical reagents and solvents. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ljfp.

Detection of butter adulteration with lard by employing 1 H-NMR spectroscopy and multivariate data analysis

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

Application of chemometrics in combination with Fourier Transform Mid Infrared spectroscopy for authentication of avocado oil

An UV spectrophotometric area under curve method was developed for the estimation of Levofloxacin Hemihydrate in its mono component tablets. The spectrophotometric method for estimation employed Area under curve method for analysis using 0.1M Sodium Hydroxide as solvent for the drug Levofloxacin Hemihydrate at the wavelength range of 285-295nm. Levofloxacin Hemihydrate obeys Beer’s law in concentration range 10-50µg/ml. The recovery studies ascertained accuracy of the proposed method and the result validated according to ICH guideline. Results of analysis have been valid statistically by recovery studies. The method was successfully for evaluation of Levofloxacin Hemihydrate in tablet dosage form without the interference of common excipients.