Screening of adulteration in packaging biocomposites by infrared spectroscopy and chemometrics

Abstract

Fourier transform infrared spectroscopy coupled with chemometrics was employed to detect packaging polylactic acid-based biocomposite samples adulterated with polypropylene (PP) 30–45% and linear low-density polyethylene 2–10%. Principal component analysis, soft independent modeling of class analogy (SIMCA) and partial least square discriminate analysis (PLS-DA) chemometric techniques were utilized to classify samples in different classes. Totally, 362 samples were modeled in three different classes (two adulterated and one non-adulterated). The obtained results revealed that PLS-DA is the most suitable chemometric approach for prediction of probable adulteration in biocomposite samples with reliable specificity and selectivity. It could provide 99% correct class prediction rate between non-adulterated biocomposite samples and adulterated ones, while SIMCA methods provided 73.33% prediction accuracy in classification.