A simple and efficient method has been developed for the simultaneous determination of eight flavonoids (orientin, hyperoside, rutin, myricetin, luteolin, quercetin, kaempferol, and apigenin) in Sonchus arvensis by high-performance liquid chromatography diode array detector (HPLC-DAD). This method was utilized to differentiate S. arvensis samples based on the plant parts (leaves, stems, and roots) and the plant’s geographical origin. The chromatographic separation was carried out on a reverse-phase C18 column by eluting at a flow rate of 1 mL/min using a gradient with methanol and 0.2% aqueous formic acid. In the optimum conditions, the developed method’s system suitability has met the criteria of good separation. The calibration curve shows a linear relationship between the peak area and analyte concentration with a correlation coefficient (r²) > 0.9990. The ranges for the analytes’ limits of detection and quantitation were 0.006–0.015 and 0.020–0.052 µg/mL, respectively. Intra-day and inter-day precision expressed in terms of RSD values were <2%, and the accuracy range based on recovery was 97–105%. The stability of all analytes within 48 h was about 2%. By combining HPLC-DAD fingerprint analysis with chemometrics, the developed method can classify S. arvensis samples based on the plant parts and geographical origin.

There is a difference in the selling price for cassava, taro, and wheat flour, with taro flour having a higher price. It could be a reason for adulterating the taro flour from the other two flours and reducing quality. This study aims to distinguish the three types of flour using the near-infrared (NIR) spectra combined with chemometrics. The NIR spectra of all samples were measured at a wavelength of 1000-2500 nm. The multivariate analysis used was principal component analysis (PCA), and PCA followed with discriminant analysis (DA). The preliminary process of the signal using area normalization was carried out before the multivariate analysis. The PCA results showed that most of the samples were grouped in their respective groups except for two samples, namely 1 sample of taro flour and 1 sample of cassava flour. Meanwhile, the PCA-DA results using seven main components showed that the three samples were grouped well. DA validation was carried out using the cross-validation method, showing that the samples could be identified into their respective groups. Therefore, a combination of NIR spectrum and chemometric analysis can be used to differentiate cassava, taro, and wheat flour