Optimal combination of band-pass filters for theanine content prediction using near-infrared spectroscopy

Pauline Ong, Suming Chen, Chao Yin Tsai, Yung Kun Chuang

Research output: Contribution to journalArticlepeer-review

Abstract

The commonly used spectral variable selection methods in near-infrared (NIR) spectroscopy were more theoretical and difficult to put into practice, due to a large number of optical filters with extremely narrow bandwidth at the desired wavelength was required for the spectral acquisition. In this study, a method of optimally selecting a set of the band-pass filter (BPF) to reduce the dimensionality of the spectral data was proposed and subsequently applied to the determination of theanine content in oolong tea. By utilizing 4 BPFs, the developed multiple linear regression, support vector regression and Gaussian process regression models produced R-squared values of 0.7971, 0.9036 and 0.9080, respectively, for prediction, indicating the beneficial potential of the proposed method for accurate prediction of the analytes with the lower cost of spectral acquisition in real practice.

Original languageEnglish
Article number103701
JournalInfrared Physics and Technology
Volume115
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Band-pass filter
  • Gaussian process regression
  • Multiple linear regression
  • Near-infrared spectroscopy
  • Support vector machine regression
  • Tea
  • Theanine

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics

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