Fast and Accurate Discrimination of Brachiaria brizantha (A.Rich.) Stapf Seeds by Molecular Spectroscopy and Machine Learning
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Gianluigi Bacchetta | Gustavo Nicolodelli | Izabela C. Oliveira | Thiago Franca | Carla P. Morais | Bruno Marangoni | Debora M.B.P. Milori | Charline Z. Alves | Cicero Cena | G. Bacchetta | D. Milori | G. Nicolodelli | B. Marangoni | C. Cena | T. França | I. C. Oliveira | C. Morais
[1] G. Nicolodelli,et al. Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms. , 2020, Analytical methods : advancing methods and applications.
[2] S. Lima,et al. Discrimination of Transgenic and Conventional Soybean Seeds by Fourier Transform Infrared Photoacoustic Spectroscopy , 2008, Applied spectroscopy.
[3] C. Kiefer,et al. Evaluation of rice varieties using LIBS and FTIR techniques associated with PCA and machine learning algorithms. , 2020, Applied optics.
[4] Fengchang Wu,et al. Investigation of tissue level distribution of functional groups and associated trace metals in rice seeds (Oryza sativa L.) using FTIR and LA-ICP-MS , 2016 .
[5] I. Jolliffe. Discarding Variables in a Principal Component Analysis. Ii: Real Data , 1973 .
[6] M. Macedo,et al. BRS Paiaguás: A New Brachiaria ( Urochloa ) Cultivar for Tropical Pastures in Brazil , 2013 .
[7] Ariadne Morbeck Santos Oliveira,et al. Accelerated aging for evaluation of vigor in Brachiaria brizantha ‘Xaraés’ seeds , 2020, Journal of Seed Science.
[8] P. Manzari,et al. Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms. , 2020, Talanta.
[9] L. Buydens,et al. Estimating the number of components and detecting outliers using Angle Distribution of Loading Subspaces (ADLS) in PCA analysis. , 2018, Analytica chimica acta.
[10] L. Jank,et al. The value of improved pastures to Brazilian beef production , 2014, Crop and Pasture Science.
[11] C. Cavariani,et al. Vigor de sementes, população de plantas e desempenho agronômico de soja , 2017 .
[12] D. Milori,et al. Laser-Induced Breakdown Spectroscopy as a Powerful Tool for Distinguishing High- and Low-Vigor Soybean Seed Lots , 2020, Food Analytical Methods.
[13] O. Pereira,et al. MASSA DE FORRAGEM E CARACTERÍSTICAS ESTRUTURAIS E BROMATOLÓGICAS DE CULTIVARES DE Brachiaria E Panicum , 2016 .
[14] Dennis B. Egli,et al. Relationship of Seed Vigor to Crop Yield: A Review , 1991 .
[15] C. R. Storck,et al. Categorizing rice cultivars based on differences in chemical composition , 2005 .
[16] Eduardo Henrique Bevitori Kling de Moraes,et al. PRODUÇÃO DE BOVINOS DE CORTE NO SISTEMA DE PASTO-SUPLEMENTO NO PERÍODO SECO , 2014 .
[17] Julio Marcos Filho,et al. Seed vigor testing: an overview of the past, present and future perspective , 2015 .
[18] A. G. Oliveira,et al. Intraspecific differentiation of sandflies specimens by optical spectroscopy and multivariate analysis , 2021, Journal of biophotonics.
[19] P. D. Zimmer,et al. Composição química e mobilização de reservas em sementes de soja de alto e baixo vigor , 2010 .
[20] Yi Chen,et al. FT-IR and Raman spectroscopy data fusion with chemometrics for simultaneous determination of chemical quality indices of edible oils during thermal oxidation , 2020 .
[21] S. Mooney,et al. Brachiaria species influence nitrate transport in soil by modifying soil structure with their root system , 2020, Scientific Reports.
[22] V. Uarrota,et al. Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA) , 2020, Heliyon.