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| 3. |  | CASTRO, J. V. de. Maturacao controlada de frutas Campinas: ITAL, 1992 p.93-102 Manual tecnico, 9 In: BLEINROTH, E. W.; SIGRIST, J. M. M.; ARDITO, E. de F. G.; CASTRO, J. V. de; SPAGNOL, W. A.; NEVES FILHO, L. de C. Tecnologia de pos-colheita de frutas tropicais. Campinas. ITAL, 1992.| Biblioteca(s): Embrapa Mandioca e Fruticultura. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
07/06/2021 |
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Data da última atualização: |
10/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BORBA, K. R.; OLDONI, F. C. A.; MONARETTO, T.; COLNAGO, L. A.; FERREIRA, M. D. |
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Afiliação: |
LUIZ ALBERTO COLNAGO, CNPDIA; MARCOS DAVID FERREIRA, CNPDIA. |
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Título: |
Selection of industrial tomatoes using TD-NMR data and computational classification methods. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Microchemical Journal, v. 164, a. 106048, 2021. |
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ISSN: |
0026-265X |
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DOI: |
https://doi.org/10.1016/j.microc.2021.106048 |
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Idioma: |
Inglês |
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Conteúdo: |
Tomato processing chain has a world economic relevance for the food industry and the agribusiness, providing ready-to-eat products and raw material for other production chains. The product quality is depending on control of some fruit attributes, such as color, soluble solids content (SSC), and defects. The aim of this study was to develop accurate and nondestructive classification models according to the tomato maturation stage, SSC, and presence of defects using Time-Domain Nuclear Magnetic Resonance (TD-NMR) associated with computational classification methods. Each class showed different decay times. Green tomatoes showed a shorter decay signal than red tomatoes, mainly due to the relaxation signal being related to the water mobility in different vegetable tissue compartments. Classification models resulted in great accuracy performances, the best accuracy for each classification were: maturity index: 97% (SVM); SSC: 100% (SVM and kNN); presence of defects: 90% (PLS-DA). These results show that CPMG decays associated with computational methods can be used in the tomato processing industry to classify tomato samples. These classification models showed the potential of TD-NMR technique in a high-throughput screening application before the processing |
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Palavras-Chave: |
Machine learning; NMR; Relaxation time; Tomato processing. |
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Categoria do assunto: |
-- |
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Marc: |
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022 $a0026-265X
024 7 $ahttps://doi.org/10.1016/j.microc.2021.106048$2DOI
100 1 $aBORBA, K. R.
245 $aSelection of industrial tomatoes using TD-NMR data and computational classification methods.$h[electronic resource]
260 $c2021
520 $aTomato processing chain has a world economic relevance for the food industry and the agribusiness, providing ready-to-eat products and raw material for other production chains. The product quality is depending on control of some fruit attributes, such as color, soluble solids content (SSC), and defects. The aim of this study was to develop accurate and nondestructive classification models according to the tomato maturation stage, SSC, and presence of defects using Time-Domain Nuclear Magnetic Resonance (TD-NMR) associated with computational classification methods. Each class showed different decay times. Green tomatoes showed a shorter decay signal than red tomatoes, mainly due to the relaxation signal being related to the water mobility in different vegetable tissue compartments. Classification models resulted in great accuracy performances, the best accuracy for each classification were: maturity index: 97% (SVM); SSC: 100% (SVM and kNN); presence of defects: 90% (PLS-DA). These results show that CPMG decays associated with computational methods can be used in the tomato processing industry to classify tomato samples. These classification models showed the potential of TD-NMR technique in a high-throughput screening application before the processing
653 $aMachine learning
653 $aNMR
653 $aRelaxation time
653 $aTomato processing
700 1 $aOLDONI, F. C. A.
700 1 $aMONARETTO, T.
700 1 $aCOLNAGO, L. A.
700 1 $aFERREIRA, M. D.
773 $tMicrochemical Journal$gv. 164, a. 106048, 2021.
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