Rapid prediction of yellow tea free amino acids with hyperspectral images

PLoS One. 2019 Feb 20;14(2):e0210084. doi: 10.1371/journal.pone.0210084. eCollection 2019.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amino Acids / analysis*
  • Equipment Design
  • Optical Imaging / instrumentation
  • Optical Imaging / methods
  • Principal Component Analysis
  • Spectrophotometry, Infrared / instrumentation
  • Spectrophotometry, Infrared / methods*
  • Support Vector Machine
  • Tea / chemistry*

Substances

  • Amino Acids
  • Tea

Grants and funding

Funded by Natural Science Foundation of Anhui Province (1808085MF195 to BY), http://www.ahkjt.gov.cn/; the Natural Science Research Project of Anhui Province (KJ2016A837 to BY), http://www.ahedu.gov.cn/; the Open Fund of the Key Laboratory of Technology Integration and Application in Agricultural Internet of Things, Ministry of Agriculture (2016KL02 to BY), http://www.ahau.edu.cn/; and the National Key R&D Program (2016YFD0300608 to BY), http://www.most.gov.cn/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.