Multivariate statistical approach in food and pharmaceutical quality control

Abstract

IR spectra contain chemical information of matter and can be acquired from raw/untreated samples. The spectra are, however, complicated to interpret and could not be used directly for both qualitative and quantitative purposes. In this research a statistical approach namely, multivariate data analysis (MVDA) or chemometrics was employed for mining information related to chemical compositions from spectroscopic data. Two examples are used to illustrate the potential of this approach, one is edible oil (using benchtop FT-IR), and pharmaceuticals (using handheld NIR). Olive oil was differentiated from adulterants (sesame, sunflower, palm oil) in PCA, and the content of olive oil was successfully determined by the PLS model the error of olive oil content < 5%. Norfloxacin content in lab-scale powder formulation yield the auspicious results with the error < 6%. The results proved the developed techniques are promising for rapid analysis at significantly lower costs.