# Non-Destructive Sensing of Tea Pigments in Black Tea Rolling Process

## TL;DR

This paper presents a non-destructive method to sense tea pigments during the rolling process of black tea using electrical characteristics and advanced modeling techniques.

## Contribution

The study introduces a novel non-destructive sensing approach using electrical parameters and machine learning for real-time tea pigment monitoring during rolling.

## Key findings

- The Smooth-VCPA-IRIV-SVR model achieved high accuracy in predicting theaflavins, thearubigins, and theabrownins with correlation coefficients over 0.99.
- Relative prediction deviation (RPD) values exceeded 6.5, indicating strong model reliability for pigment content detection.
- The method enables rapid and non-destructive monitoring of tea pigments during the rolling process.

## Abstract

Rolling is a critical step in the processing of black tea, marking the beginning of fermentation. At this stage, the formation of tea pigments causes significant changes in the color of the processed leaves, laying the essential groundwork for the development of color and flavor quality components in subsequent fermentation processes. However, the rapid and non-destructive sensing of tea pigments during black tea rolling remains challenging. This study focused on black tea products undergoing rolling as its research subject, utilizing electrical characteristic detection technology to collect time-series electrical parameters of rolling leaves at various testing frequencies. The original electrical parameters were preprocessed using multiplicative scatter correction (MSC), min-max normalization (Min-Max), and smoothing (Smooth). Various selection methods, including the competitive adaptive reweighting algorithm (CARS), uninformative variable elimination (UVE), and the variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV), were employed to identify electrical parameters relevant to the targeted attributes. Quantitative prediction models for the content of tea pigments were established using partial least squares regression (PLSR) and support vector machine regression (SVR). The results demonstrated that the Smooth-VCPA-IRIV-SVR model exhibited superior performance in predicting the contents of theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs). Correlation coefficients of prediction (Rp) all exceeded 0.99, and Relative prediction deviation (RPD) values were all above 6.5, indicating that the model enables rapid and non-destructive detection of tea pigment content during black tea rolling. These findings provide preliminary technical support and reference for the digital production of black tea.