Determination of sodium fatty acid in soap Formulation Using Fourier Transform Infrared (FTIR) spectroscopy and multivariate calibrations.

Fourier Transform Infrared (FTIR) spectroscopy using an attenuated total reflectance (ATR) accessory has been investigated as a method for the determination of sodium-fatty acid (sodium-FA) in soap formulations. Multivariate calibrations namely partial least squares regression (PLS) and principle component regression (PCR) were developed for the prediction of sodium-FA using spectral ranges on the basis of relevant IR absorption bands related to sodium-FA. The sodium-FA content in soap formulations was predicted accurately at wavenumbers of 1,570–1,550 cm−1, which is specific for RCOO− Na+ vibration. The PLS method was found to be a consistently better predictor when both PLS and principal component regression (PCR) analyses were used for quantification of sodium-FA. Furthermore, FTIR spectroscopy can be an alternative technique to American oil Chemist Society methods which use a titrimetric technique because FTIR offers rapid, easy sample preparation and is friendly to the environment

Use of the SAW sensor electronic nose for detecting the adulteration of virgin coconut oil with RBD palm kernel olein.

An electronic nose (zNose™) was applied to the detection of adulteration of virgin coconut oil. The system, which is based on a surface acoustic wave sensor was used to generate a pattern of volatile compounds present in the samples. Virgin coconut oil was mixed with refined, bleached and deodorized palm kernel olein at a level of adulteration from 1 to 20% (wt/wt). Adulterant peaks were identified from the chromatogram profile and fitted to a curve using linear regression. The best relationship (R 2 = 0.91) was obtained between the peak tentatively identified as methyl dodecanoate and the percentage of palm kernel olein added. Pearson’s correlation coefficients (r) of 0.92 and 0.89 were obtained between adulterant peak methyl dodecanoate and of the iodine and peroxide values, respectively. Principal component analysis (PCA) was used to differentiate between pure and adulterated samples. The PCA provided good differentiation of samples with 74% of the variation accounted for by PC 1 and 17% accounted for by PC 2. Pure samples formed a separate cluster from all of the adulterated samples

Application of FTIR spectroscopy for the determination of virgin coconut oil in binary mixtures with olive oil and palm oil.

Rapid Fourier transform infrared (FTIR) spectroscopy combined with attenuated total reflectance (ATR) was applied for quantitative analysis of virgin coconut oil (VCO) in binary mixtures with olive oil (OO) and palm oil (PO). The spectral bands correlated with VCO, OO, PO; blends of VCO and OO; VCO and PO were scanned, interpreted, and identified. Two multivariate calibration methods, partial least square (PLS) and principal component regression (PCR), were used to construct the calibration models that correlate between actual and FTIR-predicted values of VCO contents in the mixtures at the FTIR spectral frequencies of 1,120–1,105 and 965–960 cm−1. The calibration models obtained were cross validated using the “leave one out” method. PLS at these frequencies showed the best calibration model, in terms of the highest coefficient of determination (R 2) and the lowest of root mean standard error of calibration (RMSEC) with R 2 = 0.9992 and RMSEC = 0.756, respectively, for VCO in mixture with OO. Meanwhile, the R 2 and RMSEC values obtained for VCO in mixture with PO were 0.9996 and 0.494, respectively. In general, FTIR spectroscopy serves as a suitable technique for determination of VCO in mixture with the other oils

Fourier-transform infrared spectra combined with chemometrics and fatty acid composition for analysis of pumpkin seed oil blended into olive oil

Fourier-transform infrared spectra in combination with chemometrics and fatty acid composition were used to analyze pumpkin seed oil blended into extra virgin olive oil. Two multivariate calibrations, namely, partial least square and principle component regression were evaluated for quantification of pumpkin seed oil in extra virgin olive oil. The combined frequency regions of 3020–2995 and 1070–900 cm−1 were preferred for such quantification using partial least square model with coefficient of determination (R2) obtained of >0.99. The errors in partial least square calibration and validation models were of 0.166 and 1.32% (vol/vol), respectively. Discriminant analysis can successfully classify extra virgin olive oil and extra virgin olive oil blended with pumpkin seed oil. The change of oleic and linoleic acids due to the addition of pumpkin seed oil into extra virgin olive oil can be used for the identification of blending practice of pumpkin seed oil into extra virgin olive oil. The level of oleic acid was decreased with the increasing concentration of pumpkin seed oil with R2 of 0.946. In addition, the level of linoleic acid was increased with the increasing level of pumpkin seed oil addition with R2 of 0.978

Authentication analysis of butter from beef fat using Fourier Transform Infrared (FTIR) spectroscopy coupled with chemometrics

The use of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric techniques to differentiate butter from beef fat (BF) was investigated. The spectral bands associated with butter, BF, and their mixtures were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure butter and BF. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the selected fingerprint regions of 1500–1000 cm-1, with the values of coefficient of determination (R2 ) and root mean square error of calibration (RMSEC) are 0.999 and 0.89% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with BF. Using 6 principal components, root mean square error of prediction (RMSEP) is 2.42% (v/v). These results proved that FTIR spectroscopy in combination with multivariate calibration can be used for the detection and quantification of BF in butter formulation for authentication use