Chemometrics approach for the screening of potential adulterations in pomegranate sour by infrared spectroscopy and conventional methods

This study aims to generate predictive models based on FT-MIR and FT-NIR spectroscopy to characterize unique compositions of pomegranate sour, allowing for rapid authentication and detection of adulteration with ingredients. Labeled 100% pomegranate sour ( n = 27 ) and pomegranate sauce ( n = 19 ) samples were purchased from local grocery stores in Türkiye. A benchtop FT-MIR spectrometer and a portable FT-NIR sensor were used to collect the spectra. Samples were characterized by their sugar and organic acid content using HPLC, soluble solid content using refractometry, and total phenolic content by Folin–Ciocalteu methods. The results showed that 33% of the pomegranate sour samples (9 out of 27) exhibited unusual sugar and organic acid profiles. Furthermore, all adulterated samples were identified by spectral data combined with soft independent modeling of class analogy and partial least squares-discriminant analysis. Moreover, partial least squares regression was used to predict all quality parameters of pomegranate sour, and it showed excellent predictions ( r val > 0.93).