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Vodka constitutes a significant sector of Vietnam’s alcohol industry, including both domestic and imported varieties. However, this diversity faces challenges from illegal imports and adulterated products, threatening consumer health and brand integrity. This study employs Fourier transform infrared spectroscopy (FTIR) and inductively coupled plasma mass spectrometry (ICP-MS) to analyze 300 vodka samples from five brands collected across Hanoi. Significant variations were found in elemental compositions, with sodium concentrations ranging from 205.67 μg/L to 1269.24 μg/L and magnesium levels from 65.57 μg/L to 1453.34 μg/L. Principal Component Analysis (PCA) of the FTIR and ICP-MS data effectively differentiated the samples, with the first two principal components explaining 84.78% and 73.02% of the total variance, respectively. The PCA plots revealed distinct chemical profiles, notably isolating Rocket Vodka. These findings enhance food safety enforcement, protect consumer rights, and preserve brand reputations. The study underscores the importance of advanced analytical tools in combating beverage adulteration, ensuring public health, and maintaining market integrity, offering a replicable model for similar research in other regions.

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma–mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands’ identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.