Quality Monitoring of Biodiesel and Diesel/Biodiesel Blends: A Comparison between Benchtop FT-NIR versus a Portable Miniaturized NIR Spectroscopic Analysis

A methodology such as near-infrared (NIR) spectroscopy, which enables in situ and in real-time analysis, is crucial to perform quality control of biodiesel, since it is blended into diesel fuel and the presence of contaminants can hinder its performance. This work aimed to compare the performance of a benchtop Fourier Transform (FT) NIR spectrometer with a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) to detect and quantify contaminants in biodiesel and biodiesel in diesel. In general, good models based on principal component analysis-linear discriminant analysis (PCA-LDA) of FT-NIR spectra were obtained, predicting with high accuracies biodiesel contaminants and biodiesel in diesel (between 75% to 95%), as well as good partial least square (PLS) regression models to predict contaminants concentration in biodiesel and biodiesel concentration in diesel/biodiesel blends, with high coefficients of determination (between 0.83 and 0.99) and low prediction errors. The miniNIR prototype's PCA-LDA models enabled the prediction of target contaminants with good accuracies (between 66% and 86%), and a PLS model enabled the prediction of biodiesel concentration in diesel with a reasonable coefficient of determination (0.68), pointing to the device's potential for preliminary analysis of biodiesel which, associated with its potential low cost and portability, could increase biodiesel quality control.

Automated summary

This study compared the performance of a benchtop Fourier Transform (FT) NIR spectrometer and a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) in detecting and quantifying contaminants in biodiesel and biodiesel blends. The FT-NIR spectra-based PCA-LDA models accurately predicted the contaminants in biodiesel and biodiesel blends with high accuracies (75% to 95%), while the PLS regression models accurately predicted the concentration of contaminants in biodiesel and the concentration of biodiesel in diesel/biodiesel blends (coefficients of determination between 0.83 and 0.99, and low prediction errors). The miniNIR prototype's PCA-LDA models achieved good accuracies in predicting target contaminants (between 66% and 86%), and the PLS model reasonably predicted the biodiesel concentration in diesel (coefficient of determination of 0.68), indicating the device's potential for preliminary biodiesel analysis and potential cost and portability advantages for biodiesel quality control.