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Risqiwati D, Wibawa AD, Pane ES, Yuniarno EM, Islamiyah WR, Purnomo MH. Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation. Brain Inform 2024; 11:12. [PMID: 38740660 DOI: 10.1186/s40708-024-00225-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 04/17/2024] [Indexed: 05/16/2024] Open
Abstract
A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient's relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.
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Affiliation(s)
- Diah Risqiwati
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Departement of Informatics, Universitas Muhammadiyah Malang, Tlogomas, Malang, 65144, Indonesia
| | - Adhi Dharma Wibawa
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Medical Technology, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
| | - Evi Septiana Pane
- Industrial Training and Education of Surabaya, Ministry of Industry RI, Gayungan, Surabaya, 60235, Indonesia
| | - Eko Mulyanto Yuniarno
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
- Departement of Computer Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia
| | - Wardah Rahmatul Islamiyah
- Neurology Department, Faculty of Medicine, Universitas Airlangga, Gubeng, Surabaya, 60131, Indonesia
| | - Mauridhi Hery Purnomo
- Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia.
- Departement of Computer Engineering, Institut Teknologi Sepuluh Nopember, Keputih, Surabaya, 60111, Indonesia.
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Höppener EM, Shahmohammadi M(S, Parker LA, Henke S, Urbanus JH. Classification of (micro)plastics using cathodoluminescence and machine learning. Talanta 2023. [DOI: 10.1016/j.talanta.2022.123985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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3
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Cheng Z, Liang J, Hu Y, Lv Q, Li X, Shen X. Comprehensive evaluation of solvent in dispersive liquid-liquid microextraction for determination of itraconazole and hydroxy itraconazole by high performance liquid chromatography with fluorescence detection. ARAB J CHEM 2023. [DOI: 10.1016/j.arabjc.2023.104565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
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4
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A critical review of recent trends in sample classification using Laser-Induced Breakdown Spectroscopy (LIBS). Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Chen Y, Liu Z, Zhao X, Sun S, Li X, Xu C. Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model. APPLIED SPECTROSCOPY 2022; 76:1068-1079. [PMID: 35583031 DOI: 10.1177/00037028221104823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In order to extract useful information from X-ray fluorescence (XRF) spectra and establish a high-accuracy prediction model of soil heavy metal contents, a hybrid model combining a deep belief network (DBN) with a tree-based model was proposed. The DBN was first introduced into feature extraction of XRF spectral data, which can obtain deep layer features of spectra. Owing to the strong regression ability of the tree-based model, it can offset the deficiency of DBN in prediction ability so it was used for predicting heavy metal contents based on the extracted features. In order to further improve the performance of the model, the parameters of model can be optimized according to the prediction error, which was completed by sparrow search algorithm and the gird search. The hybrid model was applied to predict the contents of As and Pb based on spectral data of overlapping peaks. It can be obtained that R2 of As and Pb reached 0.9884 and 0.9358, the mean square error of As and Pb are as low as 0.0011 and 0.0058, which outperform other commonly used models. That proved the combination of DBN and tree-based model can obtain more accurate prediction results.
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Affiliation(s)
- Ying Chen
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, 530247Yanshan University, Qinhuangdao, China
| | - Zhengying Liu
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, 530247Yanshan University, Qinhuangdao, China
| | - Xueliang Zhao
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, 530247Yanshan University, Qinhuangdao, China
- Center for Hydrogeology and Environmental Geology, China Geological Survey, Geological Environment Monitoring Engineering Technology Innovation Center of The Ministry of Natural Resources, Baoding, China
| | - Shicheng Sun
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, 530247Yanshan University, Qinhuangdao, China
| | - Xiao Li
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, 530247Yanshan University, Qinhuangdao, China
| | - Chongxuan Xu
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, 530247Yanshan University, Qinhuangdao, China
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Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine). Food Chem 2022; 372:131158. [PMID: 34601421 DOI: 10.1016/j.foodchem.2021.131158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 08/08/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023]
Abstract
In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.
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(Re) thinking Towards a Sustainable Analytical Chemistry: Part I: Inorganic Elemental Sample Treatment, Part II: Alternative Solvents and Extraction Techniques. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Li X, Yao Y, Chen M, Ding H, Liang C, Lv L, Zhao H, Zhou G, Luo Z, Li Y, Zhang H. Comprehensive evaluation integrating omics strategy and machine learning algorithms for consistency of calculus bovis from different sources. Talanta 2022; 237:122873. [PMID: 34736706 DOI: 10.1016/j.talanta.2021.122873] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/31/2021] [Accepted: 09/09/2021] [Indexed: 01/20/2023]
Abstract
In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.
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Affiliation(s)
- Xinyue Li
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China; Key Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Yaqi Yao
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Meiling Chen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Haoran Ding
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Chenrui Liang
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Ling Lv
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China; Key Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Huan Zhao
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Guanru Zhou
- Wuhan Jianmin Dapeng Pharmaceutical Co., Ltd, Wuhan, Hubei Province, 430000, PR China.
| | - Zhanglong Luo
- Wuhan Jianmin Dapeng Pharmaceutical Co., Ltd, Wuhan, Hubei Province, 430000, PR China.
| | - Yubo Li
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
| | - Han Zhang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China; Key Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, PR China.
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Higuera JM, Silva ABS, Henrique W, Esteves SN, Barioni W, Donati GL, Nogueira ARA. Effect of Genetic Crossing and Nutritional Management on the Mineral Composition of Carcass, Blood, Leather, and Viscera of Sheep. Biol Trace Elem Res 2021; 199:4133-4144. [PMID: 33389621 DOI: 10.1007/s12011-020-02543-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/09/2020] [Indexed: 10/22/2022]
Abstract
The effect of genetic crossing and nutritional management on weight gain and the concentration of minerals and trace elements in the carcass, blood, leather, and viscera of sheep were evaluated. Several statistical strategies were used to evaluate the different elemental composition characteristics of pure breed animals, i.e., White Dorper (ODO), Ile de France (OIF), Texel (OTX), and Santa Inês (OSI), as well as their crossbreeds 1/2 White Dorper and 1/2 Santa Inês (ODS), 1/2 Ile de France, and 1/2 Santa Inês (OIS), 1/2 Texel × 1/2 Santa Inês (OTS). Three different diets were evaluated AL (ad libitum), R75, and R63 (75 and 63 g of dry matter/kg of the animal metabolic weight, respectively). Levels of Ca, Cu, Fe, K, Mg, Mn, P, S, and Zn were determined by inductively coupled plasma optical emission spectrometry (ICP OES). The concentration of inorganic elements in the different body components was not affected by the diet (P > 0.05), and OTX and OTS were the breeds with the highest weight gain. Random forest importance models demonstrated that Zn in the carcass, K, Ca, and Zn in blood, and K in leather are most associated with separating the different evaluated sheep's breeds. Crossbreeding the native Santa Inês breed with sheep of exotic breeds produces animals well adapted to confinement.
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Affiliation(s)
- Julymar M Higuera
- Embrapa Pecuária Sudeste, P.O. Box 339, São Carlos, SP, 13560-970, Brazil
- Group for Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, P.O. Box 676, São Carlos, SP, 13565-905, Brazil
| | - Ana Beatriz S Silva
- Embrapa Pecuária Sudeste, P.O. Box 339, São Carlos, SP, 13560-970, Brazil
- Group for Applied Instrumental Analysis, Department of Chemistry, Federal University of São Carlos, P.O. Box 676, São Carlos, SP, 13565-905, Brazil
| | - Wignez Henrique
- Agência Paulista de Tecnologia dos Agronegócios, UPD de São José do Rio Preto, São José do Rio Preto, SP, Brazil
| | - Sergio N Esteves
- Embrapa Pecuária Sudeste, P.O. Box 339, São Carlos, SP, 13560-970, Brazil
| | - Waldomiro Barioni
- Embrapa Pecuária Sudeste, P.O. Box 339, São Carlos, SP, 13560-970, Brazil
| | - George L Donati
- Department of Chemistry, Wake Forest University, Box 7486, Salem Hall, Winston-Salem, NC, 27109, USA
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Shugar AN, Drake BL, Kelley G. Rapid identification of wood species using XRF and neural network machine learning. Sci Rep 2021; 11:17533. [PMID: 34475421 PMCID: PMC8413463 DOI: 10.1038/s41598-021-96850-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/13/2021] [Indexed: 11/09/2022] Open
Abstract
An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.
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Affiliation(s)
- Aaron N Shugar
- Garman Art Conservation Department, Buffalo State College - SUNY, Buffalo, NY, USA.
| | - B Lee Drake
- Department of Anthropology, University of New Mexico, Albuquerque, NM, USA
| | - Greg Kelley
- Collections, Curatorial and Conservation Branch, Indigenous Affairs and Cultural Heritage Directorate, Parks Canada, Government of Canada, Ottawa, ON, Canada
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PLS-DA and infrared spectroscopy based rapid and non-destructive discrimination of black ball and gel pen inks for forensic application. FORENSIC SCIENCE INTERNATIONAL: REPORTS 2021. [DOI: 10.1016/j.fsir.2020.100162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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12
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Beattie JR, Esmonde-White FWL. Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra. APPLIED SPECTROSCOPY 2021; 75:361-375. [PMID: 33393349 DOI: 10.1177/0003702820987847] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.
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Chen T, Zhang T, Li H. Applications of laser-induced breakdown spectroscopy (LIBS) combined with machine learning in geochemical and environmental resources exploration. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.116113] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Ribeiro MCS, Senesi GS, Cabral JS, Cena C, Marangoni BS, Kiefer C, Nicolodelli G. Evaluation of rice varieties using LIBS and FTIR techniques associated with PCA and machine learning algorithms. APPLIED OPTICS 2020; 59:10043-10048. [PMID: 33175777 DOI: 10.1364/ao.409029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/13/2020] [Indexed: 06/11/2023]
Abstract
Laser-induced breakdown spectroscopy (LIBS) for atomic multi-elementary analyses, and Fourier transform infrared spectroscopy (FTIR) for molecular identification, are often suggested as the most versatile spectroscopic techniques. The present work aimed to evaluate the performance of both techniques, LIBS and FTIR, combined with principal component analysis (PCA) and machine learning (ML) algorithms in the detection of the composition analysis and differentiation of four different types of rice, white, brown, black, and red. The two techniques were primarily used to obtain the elemental and molecular qualitative characterization of rice samples. Then, LIBS and FTIR data sets were subjected to PCA and supervised ML analysis to investigate which main chemical features were responsible for nutritional differences for the white (milled) and colored rice samples. In particular, PCA data analysis suggested that protein, fatty acids, and magnesium were the highest contributors to the sample's differentiation. The ML analysis based on this information yielded a 100% level of accuracy, sensitivity, and specificity on sample classification. In conclusion, LIBS and FTIR coupled with multivariate analysis were confirmed as promising tools alternative to traditional analytical techniques for composition analysis and differentiation when subtle chemical variations were observed.
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Larios G, Nicolodelli G, Ribeiro M, Canassa T, Reis AR, Oliveira SL, Alves CZ, Marangoni BS, Cena C. Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:4303-4309. [PMID: 32857095 DOI: 10.1039/d0ay01238f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented. Batches with high and low vigor soybean seeds were analyzed. Support vector machine (SVM), K-nearest neighbors (KNN), and discriminant analysis were applied to the raw spectral and reduced-dimensionality data from PCA (principal component analysis). Proteins, fatty acids, and amides were identified as the main molecules responsible for the discrimination of the batches. The cross-validation tests pointed out that high vigor soybean seeds were successfully discriminated from low vigor ones with an accuracy of 100%. These findings indicate FTIR spectroscopy associated with multivariate analysis as a new alternative approach to discriminate seed vigor.
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Affiliation(s)
- Gustavo Larios
- UFMS - Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
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Laser-Induced Breakdown Spectroscopy as a Powerful Tool for Distinguishing High- and Low-Vigor Soybean Seed Lots. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01790-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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