2024Journal articleFree to read

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

Qinghai He, Zhiyuan Liu, Xiaoli Li, Yong He, Zhi Lin

Agriculture

Abstract

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the Rp2 values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control.

Abstract text comes from PubMed, OpenAlex or Europe PMC and belongs to the authors and publisher.

Study type

Chemical / analytical

Measuring what is in matcha, or how to detect it.

Assigned automatically from the title, abstract and subject terms.

Topics

Processing, milling and quality Authenticity and analytical methods Metabolism and weight

Matcha is a main subject here (named in the title).

Keywords and subject terms

matcha tea processing chlorophyll a chlorophyll b carotenoids hyperspectral imaging non-destructive testing partial least squares regression real-time process monitoring tea quality control

Cite this study

Qinghai He, Zhiyuan Liu, Xiaoli Li, Yong He, Zhi Lin. (2024). Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology. Agriculture. https://doi.org/10.3390/agriculture14112033

DOI: 10.3390/agriculture14112033 · Cited by 4 (OpenAlex)

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