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dc.contributor.authorSOUSA, M. B. e
dc.contributor.authorSAMPAIO FILHO, J. S.
dc.contributor.authorANDRADE, L. R. B. de
dc.contributor.authorOLIVEIRA, E. J. de
dc.date.accessioned2024-12-28T17:31:16Z-
dc.date.available2024-12-28T17:31:16Z-
dc.date.created2024-12-06
dc.date.issued2023
dc.identifier.citationFrontiers in Plant Science, Frontiers Media SA, v.14, n., p., 2023 1664-462X, 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1170102-
dc.descriptionCassava (Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F2 seeds were obtained from controlled crosses performed between 77 F1 genotypes (wild-type, Wx_). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.
dc.language.isoeng
dc.rightsopenAccess
dc.titleNear-infrared spectroscopy for early selection of waxy cassava clones via seed analysis.
dc.typeArtigo de periódico
dc.subject.thesagroMandioca
dc.subject.thesagroMelhoramento Vegetal
dc.subject.nalthesaurusCassava
dc.subject.nalthesaurusBreeding and Genetic Improvement
dc.subject.nalthesaurusAmylopectin
dc.subject.nalthesaurusAmylose
dc.subject.nalthesaurusManihot
riaa.ainfo.id1170102
riaa.ainfo.lastupdate2024-12-06
dc.identifier.doi10.3389/fpls.2023.1089759
dc.contributor.institutionMASSAINE BANDEIRA E SOUSA; JURACI SOUZA SAMPAIO FILHO, UNIVERSIDADE FEDERAL DO RECÔNCAVO DA BAHIA; LUCIANO ROGERIO BRAATZ DE ANDRADE; EDER JORGE DE OLIVEIRA, CNPCA.
Aparece en las colecciones:Artigo em periódico indexado (CNPMF)

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