Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration

Ong, Pauline; Jian, Jinbao; Li, Xiuhua; Yin, Jianghua; Ma, Guodong

doi:10.1016/j.saa.2023.123477

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Chen et al used the full visible NIR wavelength range of 400–2498 nm, combined with the modified partial least squares regression method, to accurately monitor the concentrations of total catechins and theanine during the tea fermentation process [ 21 ]. To quickly detect the content of theanine in oolong tea, Ong et al proposed a quantitative prediction method using near-infrared spectroscopy combined with a flower pollination algorithm and the Gaussian process regression (GPR) method and achieved good prediction results [ 22 ]. These studies have shown the excellent analytical performance of NIRS in predicting the main active components of tea, including catechins, caffeine, theaflavins, and chlorophyll.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

Liu,

Pan,

Liu

et al. 2024

Sensors

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As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

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Section: Introductionmentioning

confidence: 99%