Evaluation of metal content in tea samples commercialized in sachets using multivariate data analysis techniques

An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples

Principal component analysis (PCA) is currently one of the most used multivariate data analysis techniques for evaluating information from food analysis. In this review, a brief introduction to the theoretical principles that underlie PCA will be given, in addition to presenting the most commonly used computer programs. An example from the literature was discussed to illustrate the use of this chemometric tool and interpretation of graphs and parameters obtained. A list of recently published articles will also be presented, in order to show the applicability and potential of the technique in the food analysis field.

Chemical Analysis of Various Tea Samples Concerning Volatile Compounds, Fatty Acids, Minerals and Assessment of Their Thermal Behavior

Tea is the most consumed drink worldwide due to its pleasant taste and various beneficial effects on human health. This paper assesses the physicochemical analysis of different varieties of tea (leaves, flowers, and instant) after prior drying and fine grinding. The thermal decomposition behavior of the tea components shows that the tea has three stages of decomposition, depending on temperature. The first stage was attributed to the volatilization of water, while the second stage involved the degradation of volatiles, polyphenols, and fatty acids. The degradation of cellulose, hemicellulose, and lignin content occurs at the highest temperature of 400 °C in the third stage. A total of 66 volatile compounds, divided into eight classes, were identified in the tea samples. The volatile compounds were classified into nine odor classes: floral, fruity, green, sweet, chemical, woody, citrus, roasted, and alcohol. In all flower and leaf tea samples, monounsaturated (MUFAs), polyunsaturated (PUFAs), and saturated fatty acids (SFAs) were identified. A high content of omega-6 was quantified in acacia, Saint John's Wort, rose, and yarrow, while omega-3 was found in mint, Saint John's Wort, green, blueberry, and lavender samples. The flower and leaf tea samples studied could be a good dietary source of polyphenolic compounds, essential elements. In instant tea samples, a low quantity of polyphenols and major elements were identified. The physicochemical analysis demonstrated that both flower and leaf teas have high-quality properties when compared to instant tea.

Authenticating teas using multielement signatures, strontium isotope ratios, and volatile compound profiling

High value food products are subject to adulterations and frauds. This study aimed to combine, in our knowledge for the first time, inorganic chemical tracers (multi-elements and Sr isotopy) with volatile organic compound (VOCs) to discriminate the geographic origin, the varieties and transformation processes to authenticate 26 tea samples. By measuring Sr isotope ratio using the multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), 6 out of 11 regions were successfully discriminated. The combination with the ICP-MS inorganic pattern allowed to discriminate 4 more regions with a significance level of 0.05. VOCs fingerprints, obtained with selected ion flow tube mass spectrometer (SIFT-MS), were not correlated with origin but with the cultivar and transformation processes. Green, oolong, and dark teas were clearly differentiated, with hexanal and hexanol contributing to the discrimination of oxidation levels. With this multi-instrumental approach, it is possible to certify the geographical origin and the tea conformity.

Unsupervised pattern-recognition and radiological risk assessment applied to the evaluation of behavior of rare earth elements, Th, and U in monazite sand

The Brazilian coast is rich in monazite which is found in beach sand deposits. In this study, the composition of the monazite sands from beaches of State of Espírito Santo, Brazil, was investigated. The concentrations of rare earth elements (REEs), Th, and U were determined by inductively coupled plasma mass spectrometry (ICP-MS). In the studied region, the mean concentration of investigated elements increased in the following order: Tm < Yb < Ho < Lu < Eu < Er < Tb < Dy < U < Y < Th < Gd < Sm < Pr < Nd < La < Ce. The sampling sites were classified into three clusters and discriminated by the concentrations of REEs, Th, and U found. In general, the radiological risk indices were higher than the established limits, and the risk of developing cancer was estimated to be higher than the world average.

Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil

Fish are important sources of protein, making them very significant in the human diet. Although the consumption of this food is beneficial for health, it is essential that the product does not contain inorganic components above the limits recommended by the current legislation. Therefore, a method for determination of elements in fish (Mugil cephalus) samples was optimized. A simplex centroid mixture design with restriction was applied for optimization of the acid digestion of samples in an open system under reflux in order to evaluate the best ratio between the reagents HNO₃, H₂O₂ and H₂O. The results indicated that more intense analyte signals were obtained when a mixture containing 3.6 mL of HNO₃ (65% v/v), 0.4 mL of H₂O₂ (30% v/v) and 6.0 mL of H₂O was used. The accuracy of the method was assessed with a CRM of oyster tissue (NIST 1566b). The method presented relative standard deviations (RSDs) of 3.54%; 3.82%; 4.81% and 3.50% for Zn, Fe, Cu and S, respectively. The detection limits were 0.002 mg kg^-1 for Cu and Zn and 0.02 mg kg^-1 for Fe and S. The proposed method was applied for the determination of Zn, Fe, Cu and S in fish samples. A Kohonen Self-Organizing Map (KSOM) with K-means implementation was applied to better delimit the boundary between groups and the spatial and temporal influence on how concentrations of the chemical elements were perceived. To verify the separation, the Davies-Bouldin and Silhouette indices were used, obtaining 0.5374 and 0.8541, respectively, indicating satisfactory separation.