Near Infrared Technology As a Robust and Environmental Friendly Approach To Biofuel Analysis: Rapid Biodiesel Classification and Quality Prediction

Agus Arip Munawar, Kiman Siregar, Agussabti Agussabti


Abstract. The use of ethanol and biodiesel, which are alternative fuels or biofuels, has increased in the last few years. Modern official standards list 25 parameters that must be determined to certify biodiesel quality. In order to determine biofuel quality, several methods were already widely used in which most of them were based on solvent extraction followed by other laboratory procedures. Yet, these methods are expensive, laborious and complicated processing for samples. Near infrared reflectance spectroscopy (NIRS) can be considered as a fast, pollution-free and non-destructive method in determining biofuel quality parameters. The objective of this study is to apply near infrared technology in classifying biodiesel based on KOH (0.3, 0.5 and 0.7) and to predict related biodiesel quality properties (water content, linolenic fatty acid, oleic acid,  and stearic acid) based on its infrared reflectance. Biodiesel infrared spectrum was acquired in wavelength range from 1000 to 2500 nm for different mentioned three KOH content. Principal component analysis (PCA) with non-iterative partial least square (NIPALS) was applied to analyze biodiesel spectral data. The result showed that two principal components (PC1=97% ad PC2 = 2%) based on infrared reflectance data were successfully able to recognize and classify biodiesel based on their used KOH. Moreover, the wavelength range of 1000 – 1140 were to be believed related to linolenic fatty acid whilst 1450 nm and 1930 nm were associated with water content. Stearic acid can be predicted in wavelength range of 1330 – 1380 nm and wavelength range of 1725 – 1790 nm were related to oleic acid of biodiesel. This may conclude that infrared technology was feasible to use as a rapid, effective and non-invasive method in biofuel classification and evaluation.


near infrared; biofuel; catalyst; classification; prediction

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