Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).

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.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization.

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Highly sensitive and rapid determination of sunset yellow in drinks using a low-cost carbon material-based electrochemical sensor.

Starting from simple graphite flakes, an electrochemical sensor for sunset yellow monitoring is developed by using a very simple and effective strategy. The direct electrochemical reduction of a suspension of exfoliated graphene oxide (GO) onto a glassy carbon electrode (GCE) surface leads to the electrodeposition of electrochemically reduced oxide at the surface, obtaining GCE/ERGO-modified electrodes. They are characterized by cyclic voltammetry (CV) measurements and field emission scanning electron spectroscopy (FE-SEM). The GCE/ERGO electrode has a high electrochemically active surface allowing efficient adsorption of SY. Using differential pulse voltammetry (DPV) technique with only 2 min accumulation, the GCE/ERGO sensor exhibits good performance to SY detection with a good linear calibration for concentration range varying 50-1000 nM (R2 = 0.996) and limit of detection (LOD) estimated to 19.2 nM (equivalent to 8.9 µg L-1). The developed sensor possesses a very high sensitivity of 9 µA/µM while fabricated with only one component. This electrochemical sensor also displays a good reliability with RSD value of 2.13% (n = 7) and excellent reusability (signal response change < 3.5% after 6 measuring/cleaning cycles). The GCE/ERGO demonstrates a successful practical application for determination of sunset yellow in commercial soft drinks. Graphical abstract.