RESUMEN
As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.
Asunto(s)
Algoritmos , Catequina , Espectroscopía Infrarroja Corta , Té , Catequina/análisis , Catequina/química , Catequina/análogos & derivados , Espectroscopía Infrarroja Corta/métodos , Té/química , Análisis de los Mínimos Cuadrados , Aprendizaje AutomáticoRESUMEN
The leaves of the Camellia sinensis plant are used to produce many different types of teas, and green tea is particularly noted for its health promoting properties which are attributed to high concentrations of phenolic compounds known as catechins. Green tea is available in forms such as bagged, gunpowder (rolled leaf), and matcha, a fine powder. This study evaluated the phenolic content, catechin composition, caffeine concentration, and antioxidant properties of fifteen commercially-available green teas with an emphasis on identifying differences between ceremonial and culinary matcha. Gunpowder and bagged green teas had higher total phenolic contents and greater CUPRAC (cupric ion reducing antioxidant capacity) and ORAC (oxygen radical absorbance capacity) antioxidant capacities than matcha teas. Among matcha samples, less expensive culinary teas had greater total phenolic contents and higher antioxidant capacities than ceremonial products. Hierarchical cluster analysis was used to determine similarities and differences in catechin composition and revealed that bagged and gunpowder green teas had the highest levels of the stereoisomers epi/catechin gallate, which correlated with their strong antioxidant properties. Bagged and gunpowder teas were the lowest cost products in this study, indicating that they are an economical choice for consumers who want a green tea beverage with high catechin content, although they generally had lower caffeine levels than matcha.