Abstract
Total spectrofluorimetry associated with Principal Component Analysis (PCA) was used to discriminate samples of vegetable oil and animal fat. In addition, a multivariate calibration model was developed that combines spectroflurimetry with Partial Least Squares (PLS) for prediction of concentration of animal fat in mixture with vegetable oil. The multivariate calibration model had an R2 value of 0.98098, which indicates the accuracy of the model. This method has potential application in the control of quality of raw material for production of biodiesel. The control of the concentration of animal fat is important because animal fat is more susceptible to oxidation than vegetable oil. Furthermore, high concentrations of animal fats may increase electricity costs for biodiesel production due to the high melting points of saturated fats that solidify at room temperature and cause the fouling and clogging of pipes.
Highlights
The quality of raw material used is critical to the quality of industrially produced biodiesel [1], mainly in terms of the yield and the production cost, as raw material contributes 70% - 95% of the total cost
Due to the nearly complete absence of antioxidants, animal fats [4] are more susceptible to oxidation than vegetable oils, beef fat possesses a high concentration of saturated fats while monounsaturated and polyunsaturated fatty acids predominate in vegetable oils
In addition to the problems associated with low oxidation stability, the use of high concentrations of animal fats may increase electricity costs for biodiesel production due to the high melting points of saturated fats that solidify at room temperature and cause the fouling and clogging of pipes
Summary
The quality of raw material used is critical to the quality of industrially produced biodiesel [1], mainly in terms of the yield and the production cost, as raw material contributes 70% - 95% of the total cost. PCA enables an enhanced statistical vision of the data set through the reduction of the number of variables to a few principal components that are responsible for explaining a high proportion of the total variation associated with the original set This method allows for the observation of the interrelationship among the samples, i.e., how they are similar based on the variables used in the study. The present study proposes a simple, efficient, rapid and low-cost analysis method for quantification of animal fat in vegetable oil This method has potential applications for quality control in the food, cosmetics and pharmaceutical industries, as well as for the characterization of raw materials used for biodiesel production. The proposed method consists of comparing the spectrum of an unknown mixture of fats in vegetable oil with the spectra of previously analyzed standards by applying one multivariate calibration techniques, such as PLS
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