Abstract
Free amino acids are an important indicator of the freshness of yellow tea. This study investigated a novel procedure for predicting the free amino acid (FAA) concentration of yellow tea. It was developed based on the combined spectral and textural features from hyperspectral images. For the purposes of exploration and comparison, hyperspectral images of yellow tea (150 samples) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing. To reduce the dimension of spectral data, five feature wavelengths were extracted using the successive projections algorithm (SPA). Five textural features (angular second moment, entropy, contrast, correlation, and homogeneity) were extracted as textural variables from the characteristic grayscale images of the five characteristic wavelengths using the gray-level co-occurrence matrix (GLCM). The FAA content prediction model with different variables was established by a genetic algorithm-support vector regression (GA-SVR) algorithm. The results showed that better prediction results were obtained by combining the feature wavelengths and textural variables. Compared with other data, this prediction result was still very satisfactory in the GA-SVR model, indicating that data fusion was an effective way to enhance hyperspectral imaging ability for the determination of free amino acid values in yellow tea.
Highlights
Tea is one of the world’s three most popular drinks.[1]
Five typical yellow tea samples were purchased from the local market in Anhui, China, and were treated as experimental materials in this work, including Pingyang huangtang (PY), Mogan huangya (MG), Huoshan huangya (HS), Mengding huangya (MD), and Junshan
Data fusion and prediction model yinzhen (JS). Their places of production were as follows: PY was from Pingyang of Zhejiang Province; MG was produced in Deqing, Zhejiang Province; HS was produced in Huoshan, Anhui Province; MD was produced in Mingshan, Sichuan Province; and JS was produced in Yueyang, Hunan Province
Summary
Tea is one of the world’s three most popular drinks.[1]. As important chemical components of tea, amino acids determine the taste and quality of the tea[2,3,4] and provide many health benefits as necessary human nutrients.[5,6,7] Many studies have focused on the analysis of amino acids in red tea, black tea or green tea.[8,9,10] There is very little research on yellow tea, a traditional Chinese tea that many people like to drink.[11]. In this study, we focus on building prediction models based on hyperspectral images to predict the amount FAA in yellow tea
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