Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

Abstract

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.

Integration of Unmanned Aerial Vehicle Spectral and Textural Features for Accurate Above-Ground Biomass Estimation in Cotton

“…Among the models, the MSR-ANN-AGB model achieved the highest accuracy, with a test set R 2 of 0.89, RMSE of 0.20 kg•m −2 , MAE of 0.14 kg•m −2 , and nRMSE of 0.33. Notably, the cotton AGB model constructed based on the MSR feature selection algorithm selected numerous features (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34), leading to higher accuracy compared to other feature selection algorithms. However, the optimal modeling strategy was observed in the RfF-ANN-AGB model, which employed a smaller number of features.…”

“…Principal component analysis (PCA) excels in selecting the most important variables for known data but struggles with unknown and nonlinear data [20]. On the other hand, techniques like Multivariate Stepwise Regression (MSR) [21] and ReliefF (RfF) [22] can address various issues such as determining the importance of the information contained in features, the weights of each feature concerning the target trait, and the feature's divergence [23]. However, given the abundance of remote sensing features available for selection [24], the choice of feature selection algorithm for dimensionality reduction is particularly important.…”