Automatic determination of optimal spectral peaks for classification of Chinese tea leaves using laser-induced breakdown spectroscopy

Zhang, Hongyang; Zhu, Qihong; Huang, Min; Guo, Ya

doi:10.25165/j.ijabe.20181103.3482

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…UVE (Zhang, Zhu, Huang, & Guo, 2018) is a variable selection method based on stability analysis of the regression coefficient in the PLSR model. The goal of UVE is to eliminate variables that have no more modeling information than noise, and modeling by using the variables selected by UVE can avoid overfitting of the model and improve the prediction ability of the model (Ye, Wang, & Min, 2008).…”

Section: Selection Methods Of Characteristic Wavelengthsmentioning

confidence: 99%

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

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Hyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content in Flos Lonicerae. The least squares support vector machine (LS‐SVM) and partial least squares regression (PLSR) models were developed on the basis of full wavelengths data and characteristic wavelengths data chosen by six wavelengths selection ways. The results clarified that standard normal variable (SNV) was the optimal pretreatment method, and the nonlinear LS‐SVM model based on the characteristic wavelengths chosen by the combination (CARS‐SPA) of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) yielded the best prediction performance for the total phenolic content. The overall results demonstrated that the proposed CARS‐SPA was a good method for selecting characteristic wavelengths to enhance prediction performance of HSI, and nonlinear LS‐SVM was more appropriate than linear PLSR for the prediction of total phenolic content in Flos Lonicerae. Practical applications Flos Lonicerae is a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand for Flos Lonicerae is increasing. Therefore, monitoring the quality of Flos Lonicerae is very important to consumers and industry of Flos Lonicerae. The phenolic compounds content in Flos Lonicerae is one of the key internal quality factors. The traditional methods (such as spectrophotometric techniques or high‐performance liquid chromatography) for detecting total phenolic content are time‐consuming, labor‐consuming, and destructive. Hyperspectral imaging (HSI) is a nondestructive and reliable method for quality inspection. The results showed that SNV was determined as the best method for pretreatment, and the nonlinear CARS‐SPA‐LS‐SVM model was developed for total phenolic content detection with high precision. It could be verified that HSI technology is effective and promising for rapid determination of total phenolic content and quality control of Flos Lonicerae as well as other agricultural products.

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Section: Selection Methods Of Characteristic Wavelengthsmentioning

confidence: 99%

Total phenolic content prediction inFlos Loniceraeusing hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

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“…To observe the elements, the resulting spectra are compared to an established database, such as those from the National Institute of Standards and Technology (Wang et al, 2016). Aside from metal elements such as Ca, Fe, Al, Mn, Mg, K, and Si, molecular spectra of CN and C 2 can be observed from the LIBS spectra generated from tea leaves (Wang et al, 2016;Zhang et al, 2018a).…”

Section: Multielemental Determination Techniquesmentioning

confidence: 99%

“…Similarly, PLS-DA of either 98 or 261 LIBS spectral peaks representing C, Fe, Mg, Mn, Al, and Ca clearly separated six cultivars each with 100 tea samples. All of the samples were acquired from the same research institute (Zhang et al, 2018a).…”

Section: Cultivarsmentioning

confidence: 99%

Application of elemental fingerprinting for the authentication of tea: A review

Hastuti¹,

Rohman²

2022

J Appl Pharm Sci

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Tea is one of the most popular beverages in the world. Some tea products are highly sought after and have higher commercial value. As a result, the authentication of tea is of importance and is frequently addressed in current literature. Multielemental determination with the aid of multivariate data analysis has been successfully performed to determine the authenticity of tea. We conducted a literature review on existing methods that applied elemental fingerprinting for tea authentication. We systematically searched the electronic database Web of Science for literature over the last two decades. The elemental determinations for tea authentication were performed using inductively coupled plasma-mass spectrometry, inductively coupled plasma-optical emission spectroscopy, atomic absorption spectroscopy, X-ray fluorescence, and/or laser-induced breakdown spectroscopy. Principal component analysis, linear discriminant analysis, and artificial neural network were among the most frequently used multivariate data analysis techniques. The elemental fingerprinting technique appears to be a promising source of information for categorizing tea based on geographical origins, harvesting time, cultivars, and types.

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“…Gondal et al 180 studied by LIBS the composition of tea samples, nding a good agreement with ICP-MS measurements. Also Zhang et al 181 performed LIBS analysis on Chinese tea leaves, with the purpose of optimizing the optimal spectral lines for classication and quality control. Se et al 182 used chemometric analysis for the determination of Ca, Mg and Na content in honey.…”

Section: Food and Feed Industrymentioning

confidence: 99%