Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Li, Yating; Sun, Jun; Wu, Xiaohong; Lu, Bing; Wu, Minmin; Dai, Chunxia

doi:10.1111/1750-3841.14706

Cited by 34 publications

(20 citation statements)

References 23 publications

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“…Moreover, compared with some mainstream methods of multivariate data analysis, including iteratively retaining informative variables, competitive adaptive reweighted sampling, and Monte Carlo uninformative variable elimination, VISSA provides the lowest degree of overfitting (Talebi et al., 2015), as well as better prediction ability for the calibration of NIR data (Deng et al., 2014). Besides, novel algorithms developed from VISSA, such as iVISSA, VISSA-iPLS, and VISSA-ABC-SVM, also showed better performance for the analysis of NIR data (Deng et al., 2015; Song et al., 2016; Li et al., 2019). In our study, the VISSA algorithm improved the performance of PLS-DA model by removing the uninformative variables and interfering variables using shrinkage of variable space.…”

Section: Discussionmentioning

confidence: 99%

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

et al. 2020

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Metabolomics is an emerging tool to uncover the complex pathogenesis of disease, as well as the multitargets of traditional Chinese medicines, with chemometric analysis being a key step. However, conventional algorithms are not suitable for directly analyzing data at all times. The variable iterative space shrinkage approach-partial least squares-discriminant analysis, a novel algorithm for data mining, was first explored to screen metabolic varieties to reveal the multitargets of Xuefu Zhuyu decoction (XFZY) against traumatic brain injury (TBI) by the 7th day. Rat plasma from Sham, Vehicle, and XFZY groups was used for gas chromatography/mass spectrometry-based metabolomics. This method showed an improved discrimination ability (area under the curve ¼ 93.64%). Threonine, trans-4-hydroxyproline, and creatinine were identified as the direct metabolic targets of XFZY against TBI. Five metabolic pathways affected by XFZY in TBI rats, were enriched using Metabolic Pathway Analysis web tool (i.e., phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; galactose metabolism; alanine, aspartate, and glutamate metabolism; and tryptophan metabolism). In conclusion, metabolomics coupled with variable iterative space shrinkage approach-partial least squares-discriminant analysis model may be a valuable tool for identifying the holistic molecular mechanisms involved in the effects of traditional Chinese medicine, such as XFZY.

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Section: Discussionmentioning

confidence: 99%

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

et al. 2020

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“…BOSS was a variable selection algorithm based on weighted bootstrap sampling (WBS), bootstrap sampling (BSS), and model population analysis (MPA) (Li, Sun, Wu, Lu, & Dai, 2019). WBS and BSS were used to select subsets of variables from the original variables and to build sub models.…”

Section: Methodsmentioning

confidence: 99%

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Sun

Zhang

Zhou

et al. 2021

J Food Process Engineering

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Accurate, rapid, and nondestructive identification of rice seed varieties has great significance for agriculture and food security, a method based on information fusion and artificial fish swarm algorithm (AFSA) combined with the hyperspectral imaging (HSI) of five kinds of rice seeds was proposed in this work. First, the spectral and image data were obtained from HSI, and the spectral data were preprocessed by detrending. Then, bootstrapping soft shrinkage (BOSS), variable iterative space shrinkage approach, successive projections algorithm, and principal component analysis were adopted to select feature variables from the spectral and image data. Next, the support vector machine (SVM) model was constructed based on the spectral and image feature variables. In order to further improve the classification accuracy of single feature model, the model based on fused feature was developed and it was finally optimized by AFSA. The research showed that the feature variables (114 spectral variables and 11 image variables) selected by BOSS were representative, and the model accuracy based on BOSS spectral and image features reached 91.48% and 70%, respectively. The performance of the SVM model based on fused feature was improved significantly, and the model accuracy reached 97.22%. After AFSA optimization, the model accuracy finally reached 99.44%. The above result confirmed that using AFSA to optimize the model based on fused feature could be a promising method to identify rice seed varieties. Practical application Rapid and accurate identification of rice seed varieties contributes to establishment of online rice seed identification system. HSI combined with information fusion and AFSA can overcome the disadvantage of low accuracy of traditional nondestructive testing methods. The method proposed in this article can be recommended to be widely popularized in farms, grain markets, and market regulators.

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“…Common kernel functions are radial basis function (RBF), linear, polynomial kernel, etc. RBF was selected to establish the classification model of grape varieties based on SVM in this article (Bonah et al., 2020; Li et al., 2019; Ye et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

Research on nondestructive identification of grape varieties based on EEMD‐DWT and hyperspectral image

Sun

Zhou

et al. 2021

Journal of Food Science

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Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved denoising algorithm based on ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) is proposed to couple with the hyperspectral image (HSI) of grape varieties in this study. First, the hyperspectral data of grape varieties are collected by using HSI instrument, and denoised by the proposed EEMD-DWT and other denoising algorithms. CARS-SPA (competitive adaptive reweighed sampling coupled with successive projections algorithm) is introduced to select the effective wavelengths and a discriminative model is constructed by using support vector machine (SVM). Finally, Monte Carlo experiments verified that EEMD-DWT was an effective and powerful spectra denoising method, and the SVM model constructed by combining with CARS-SPA had an excellent identification accuracy (99.3125%). The results suggested that the key wavelengths selected by using CARS-SPA and EEMD-DWT could be an alternative to the deal with HSI, and its potential to become a method for identifying grape varieties. Practical Application: Traditional grape varieties identification methods are destructive and time consuming. Therefore, HSI technology is applied to realize fast and nondestructive identification of grape varieties in this study. The research results indicate that it is feasible to combine HSI technology with machine learning algorithm to discriminate grape varieties. It is of great significance for grape grading and the promotion of excellent varieties, and also provides reference for grape industry producers to identify grape varieties quickly and accurately.

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Grade Identification of Tieguanyin Tea Using Fluorescence Hyperspectra and Different Statistical Algorithms

Cited by 34 publications

References 23 publications

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Research on nondestructive identification of grape varieties based on EEMD‐DWT and hyperspectral image

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