Digital depiction of the quality of Dianhong black tea based on pocket-sized near infrared spectroscopy

Abstract

This paper investigates the feasibility of combining portable near-infrared (NIR) spectroscopy with chemometric algorithms to improve the quality prediction performance of Dianhong black tea. This study presented a pocket-sized NIR device (defined as NIR-S-R2) for the simultaneous identification of multiple categories of Dianhong black tea. Firstly, the spectral information of 700 multi-category Dianhong tea samples was sensed by the NIR-S-R2 system and high-quality analytical spectra of the samples were obtained by comparing multiple spectral pre-processing algorithms. Then, based on the best pre-processed spectral data, four characteristic wavelength evolution algorithms, namely particle swarm optimization, ant colony optimization, simulated annealing, and grey wolf optimization (GWO), were employed to obtain the characteristic spectral variables. Finally, extreme learning machine, partial least squares discriminant analysis, and support vector machine (SVM) methods were adopted to develop recognition models for seven categories of Dianhong black tea based on the selected features. The results showed that the GWO-SVM model had the best predictive capability, with a prediction accuracy of 91.42%. At suitable spectral wavelengths, the NIR-S-R2 system combined with chemometrics offers good discriminatory accuracy and potential for the application of extended detection.