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Registro Completo |
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Biblioteca(s): |
Embrapa Clima Temperado. |
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Data corrente: |
17/12/2021 |
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Data da última atualização: |
17/12/2021 |
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Tipo da produção científica: |
Nota Técnica/Nota Científica |
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Autoria: |
MORAES, D. P.; CHIM, J. F.; BARIN, J. S.; VIZZOTTO, M.; FARIAS, C. A. A.; BALLUS, C. A.; BARCIA, M. T. |
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Afiliação: |
DÉBORA P. MORAES; JOSIANE F. CHIM; JULIANO S. BARIN; MARCIA VIZZOTTO, CPACT; CARLA A.A. FARIAS; CRISTIANO A. BALLUS; MILENE T. BARCIA. |
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Título: |
Influence of the cultivar on the composition of blackberry (Rubus spp.) minerals. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Journal of Food Composition and Analysis, v. 100, 103913, July 2021. |
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ISSN: |
0889-1575 |
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DOI: |
doi.org/10.1016/j.jfca.2021.103913 |
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Idioma: |
Inglês |
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Conteúdo: |
The mineral profile of fruits is allied to their nutritional value, although studies on the influence of cultivars on the profile of these micronutrients in blackberries are scarce. Therefore, the present study aimed to verify if the different blackberry cultivars (?Tupy,? ?Guarani,? ?Xavante,? and ?BRS Xingu?) influence their mineral profiles. Fourteen minerals were evaluated (Al, Ba, Ca, Cu, Fe, Cr, K, Mg, Mn, Mo, Na, Ni, Sr, and Zn), and the macrominerals were: potassium (majority), calcium, magnesium, and sodium. The results showed that the type of cultivar influenced the profile of the microminerals. ?Guarani? presented high chromium and copper content, while ?BRS Xingu? presented higher manganese content than the other cultivars in both harvest periods (2016 and 2017) studied. Three of the blackberry cultivars studied can supply the lack of some microminerals, being ?Tupy? a source of copper and manganese, ?Guarani? a source of chromium and copper, and ?BRS Xingu? a source of manganese, chromium, and copper. |
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Thesagro: |
Amora; Amora Preta; Composição de Alimento; Mineral; Nutrição. |
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Thesaurus Nal: |
Rubus. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01850naa a2200289 a 4500
001 2138028
005 2021-12-17
008 2021 bl uuuu u00u1 u #d
022 $a0889-1575
024 7 $adoi.org/10.1016/j.jfca.2021.103913$2DOI
100 1 $aMORAES, D. P.
245 $aInfluence of the cultivar on the composition of blackberry (Rubus spp.) minerals.$h[electronic resource]
260 $c2021
520 $aThe mineral profile of fruits is allied to their nutritional value, although studies on the influence of cultivars on the profile of these micronutrients in blackberries are scarce. Therefore, the present study aimed to verify if the different blackberry cultivars (?Tupy,? ?Guarani,? ?Xavante,? and ?BRS Xingu?) influence their mineral profiles. Fourteen minerals were evaluated (Al, Ba, Ca, Cu, Fe, Cr, K, Mg, Mn, Mo, Na, Ni, Sr, and Zn), and the macrominerals were: potassium (majority), calcium, magnesium, and sodium. The results showed that the type of cultivar influenced the profile of the microminerals. ?Guarani? presented high chromium and copper content, while ?BRS Xingu? presented higher manganese content than the other cultivars in both harvest periods (2016 and 2017) studied. Three of the blackberry cultivars studied can supply the lack of some microminerals, being ?Tupy? a source of copper and manganese, ?Guarani? a source of chromium and copper, and ?BRS Xingu? a source of manganese, chromium, and copper.
650 $aRubus
650 $aAmora
650 $aAmora Preta
650 $aComposição de Alimento
650 $aMineral
650 $aNutrição
700 1 $aCHIM, J. F.
700 1 $aBARIN, J. S.
700 1 $aVIZZOTTO, M.
700 1 $aFARIAS, C. A. A.
700 1 $aBALLUS, C. A.
700 1 $aBARCIA, M. T.
773 $tJournal of Food Composition and Analysis$gv. 100, 103913, July 2021.
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Registro original: |
Embrapa Clima Temperado (CPACT) |
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
21/05/2019 |
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Data da última atualização: |
13/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SANTOS, I. G. dos; CRUZ, C. D.; NASCIMENTO, M.; FERREIRA, R. de P. |
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Afiliação: |
Iara Gonçalves dos Santos, UFV; Cosme Damião Cruz, UFV; Moysés Nascimento, UFV; REINALDO DE PAULA FERREIRA, CPPSE. |
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Título: |
Selection index as a priori information for using artificial neural networks to classify alfalfa genotypes. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 2, gmr18221, 2019. |
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DOI: |
doi.org/10.4238/gmr18221 |
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Idioma: |
Inglês |
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Conteúdo: |
The efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear, because the wrong classifications were placed in groups close to the correct ones. This ensured that the best genotypes did not run the risk of being discarded, since they would not classified in the group of bad genotypes. The ANNs that were developed have good potential for use in alfalfa breeding programs. MenosThe efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear,... Mostrar Tudo |
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Palavras-Chave: |
Computational intelligence; Tai index. |
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Thesagro: |
Medicago Sativa. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/197607/1/gmr18221-selection-index-priori-information-using.pdf
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Marc: |
LEADER 02470naa a2200205 a 4500
001 2109207
005 2023-03-13
008 2019 bl uuuu u00u1 u #d
024 7 $adoi.org/10.4238/gmr18221$2DOI
100 1 $aSANTOS, I. G. dos
245 $aSelection index as a priori information for using artificial neural networks to classify alfalfa genotypes.$h[electronic resource]
260 $c2019
520 $aThe efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection index is advantageous for selecting genotypes since an ANN has the capacity to classify genotypes in an automated way. We propose the use of ANNs for the selection of alfalfa genotypes, based on a selection index. Data were collected from 77 alfalfa genotypes evaluated based on nine traits from four cuttings. The traits were divided into forage yield and nutritive value groups. In order for the ANNs to learn the classification pattern, the Tai index was used, which allows secondary traits to be included in the index to improve the gains of the main traits. An index was established for each group of traits, and based on the index scores the genotypes were subdivided into four classes (optimal, good, medium, and bad). After testing different topologies, ANNs were established for each index, according to the apparent error rates. The chosen ANNs were efficient in classifying the genotypes since the highest apparent error rate reached 15%, meaning that the ANNs efficiently captured the data pattern. Considering the ANN classification for both groups of traits, there was a high degree of agreement with the classification obtained from the Tai index, as expected. Even in the cuttings where the ANNs presented the worst performance, their potential to classify alfalfa genotypes was clear, because the wrong classifications were placed in groups close to the correct ones. This ensured that the best genotypes did not run the risk of being discarded, since they would not classified in the group of bad genotypes. The ANNs that were developed have good potential for use in alfalfa breeding programs.
650 $aMedicago Sativa
653 $aComputational intelligence
653 $aTai index
700 1 $aCRUZ, C. D.
700 1 $aNASCIMENTO, M.
700 1 $aFERREIRA, R. de P.
773 $tGenetics and Molecular Research$gv. 18, n. 2, gmr18221, 2019.
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