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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
08/06/2020 |
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Data da última atualização: |
08/06/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MOGOLLÓN, R.; CONTRERAS, C.; SILVA NETA, M. L. da; MARQUES, E. J. N.; ZOFFOLI, J. P.; FREITAS, S. T. de. |
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Afiliação: |
René Mogollón; Carolina Contreras; Magnólia Lourenço da Silva Neta; Emanuel José Nascimento Marques; Juan Pablo Zoffoli; SERGIO TONETTO DE FREITAS, CPATSA. |
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Título: |
Non-destructive prediction and detection of internal physiological disorders in 'Keitt' mango using a hand-held Vis-NIR spectrometer. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Postharvest Biology and Technology, v. 167, sept. 2020. |
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DOI: |
https://doi.org/10.1016/j.postharvbio.2020.111251 |
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Idioma: |
Inglês |
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Notas: |
Article 111251. |
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Conteúdo: |
Mango (Mangifera indica L.) is a major tropical fruit that can develop internal physiological disorders at late ripening stages. These include jelly seed characterized by a transparent and jelly tissue around the seed that eventually becomes a brown ring enclosing the seed, and black flesh characterized by a diffuse brown discoloration that covers the seed. Both disorders can result in high postharvest losses due to the fact that little information is available about mechanisms involved and efficient control approaches. The objective of this study was to establish the feasibility of using a visible and near-infrared (Vis-NIR) portable spectrometer for predicting at harvest and detecting mangoes with internal disorders, such as jelly seed and black flesh after storage. A total of 141 'Keitt' mangoes from two commercial harvests were measured spectrally between 400–1100 nm on two opposite cheeks, at harvest and after 30 d at 12 °C. Spectra data and the incidence of jelly seed and black flesh after storage were used to develop classification models using logistic, linear discriminative analyses (LDA), supporting vector machine, functional data and random forest modeling approaches. The results show that wavelengths between 550 and 650 nm can be used to predict at harvest and detect after storage, fruit with internal physiological disorders, such as jelly seed and black flesh. However, it was not possible to differentiate internal disorders from each other. The spectral data show that healthy fruit have higher reflectance intensity than jelly seed and black flesh ones, both at harvest and after storage. The best classification models were obtained with Logistic and LDA model development approaches. In the validation process for internal disorder prediction at harvest, the Logistic model showed accuracy of 65 %, sensitivity of 78 % and specificity of 49 %, whereas the LDA model showed accuracy of 63 %, sensitivity of 76 % and specificity of 46 %. In the validation process for detecting internal disorders after storage, the Logistic model showed accuracy of 71 %, sensitivity of 75 % and specificity of 67 %, whereas the LDA model showed accuracy of 76 %, sensitivity of 78 % and specificity of 73 %. In conclusion, Vis-NIR technology associated with Logistic and LDA modeling approaches can be used to predict at harvest and detect after storage the incidence of jelly seed and black flesh in mangoes. MenosMango (Mangifera indica L.) is a major tropical fruit that can develop internal physiological disorders at late ripening stages. These include jelly seed characterized by a transparent and jelly tissue around the seed that eventually becomes a brown ring enclosing the seed, and black flesh characterized by a diffuse brown discoloration that covers the seed. Both disorders can result in high postharvest losses due to the fact that little information is available about mechanisms involved and efficient control approaches. The objective of this study was to establish the feasibility of using a visible and near-infrared (Vis-NIR) portable spectrometer for predicting at harvest and detecting mangoes with internal disorders, such as jelly seed and black flesh after storage. A total of 141 'Keitt' mangoes from two commercial harvests were measured spectrally between 400–1100 nm on two opposite cheeks, at harvest and after 30 d at 12 °C. Spectra data and the incidence of jelly seed and black flesh after storage were used to develop classification models using logistic, linear discriminative analyses (LDA), supporting vector machine, functional data and random forest modeling approaches. The results show that wavelengths between 550 and 650 nm can be used to predict at harvest and detect after storage, fruit with internal physiological disorders, such as jelly seed and black flesh. However, it was not possible to differentiate internal disorders from each other. The spectral data sho... Mostrar Tudo |
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Palavras-Chave: |
Carne preta; Espectrômetro portátil; Espectroscopia no infravermelho próximo; Modelos de classificação; Semente de geléia. |
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Thesagro: |
Fisiologia Vegetal; Fruticultura; Geléia; Manga; Mangifera Indica; Pós-Colheita. |
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Thesaurus Nal: |
Mangoes. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 03540naa a2200349 a 4500
001 2123128
005 2020-06-08
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.postharvbio.2020.111251$2DOI
100 1 $aMOGOLLÓN, R.
245 $aNon-destructive prediction and detection of internal physiological disorders in 'Keitt' mango using a hand-held Vis-NIR spectrometer.$h[electronic resource]
260 $c2020
500 $aArticle 111251.
520 $aMango (Mangifera indica L.) is a major tropical fruit that can develop internal physiological disorders at late ripening stages. These include jelly seed characterized by a transparent and jelly tissue around the seed that eventually becomes a brown ring enclosing the seed, and black flesh characterized by a diffuse brown discoloration that covers the seed. Both disorders can result in high postharvest losses due to the fact that little information is available about mechanisms involved and efficient control approaches. The objective of this study was to establish the feasibility of using a visible and near-infrared (Vis-NIR) portable spectrometer for predicting at harvest and detecting mangoes with internal disorders, such as jelly seed and black flesh after storage. A total of 141 'Keitt' mangoes from two commercial harvests were measured spectrally between 400–1100 nm on two opposite cheeks, at harvest and after 30 d at 12 °C. Spectra data and the incidence of jelly seed and black flesh after storage were used to develop classification models using logistic, linear discriminative analyses (LDA), supporting vector machine, functional data and random forest modeling approaches. The results show that wavelengths between 550 and 650 nm can be used to predict at harvest and detect after storage, fruit with internal physiological disorders, such as jelly seed and black flesh. However, it was not possible to differentiate internal disorders from each other. The spectral data show that healthy fruit have higher reflectance intensity than jelly seed and black flesh ones, both at harvest and after storage. The best classification models were obtained with Logistic and LDA model development approaches. In the validation process for internal disorder prediction at harvest, the Logistic model showed accuracy of 65 %, sensitivity of 78 % and specificity of 49 %, whereas the LDA model showed accuracy of 63 %, sensitivity of 76 % and specificity of 46 %. In the validation process for detecting internal disorders after storage, the Logistic model showed accuracy of 71 %, sensitivity of 75 % and specificity of 67 %, whereas the LDA model showed accuracy of 76 %, sensitivity of 78 % and specificity of 73 %. In conclusion, Vis-NIR technology associated with Logistic and LDA modeling approaches can be used to predict at harvest and detect after storage the incidence of jelly seed and black flesh in mangoes.
650 $aMangoes
650 $aFisiologia Vegetal
650 $aFruticultura
650 $aGeléia
650 $aManga
650 $aMangifera Indica
650 $aPós-Colheita
653 $aCarne preta
653 $aEspectrômetro portátil
653 $aEspectroscopia no infravermelho próximo
653 $aModelos de classificação
653 $aSemente de geléia
700 1 $aCONTRERAS, C.
700 1 $aSILVA NETA, M. L. da
700 1 $aMARQUES, E. J. N.
700 1 $aZOFFOLI, J. P.
700 1 $aFREITAS, S. T. de
773 $tPostharvest Biology and Technology$gv. 167, sept. 2020.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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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: |
17/02/2017 |
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Data da última atualização: |
22/02/2017 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
OLIVEIRA, C. R. de; CORREA, D. S.; CRESTANA, S. |
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Afiliação: |
CAUE RIBEIRO DE OLIVEIRA, CNPDIA; DANIEL SOUZA CORREA, CNPDIA; SILVIO CRESTANA, CNPDIA. |
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Título: |
A nanotecnologia e suas aplicações na pecuária de leite. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: VILELA, D.; FERREIRA, R. de P.; FERNANDES, E. N.; JUNTOLLI, F. B. (Ed.). Pecuária de leite no brasil: cenários e avanços tecnológicos. Brasília, DF: Embrapa, 2016. p. 375-382. |
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Idioma: |
Português |
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Palavras-Chave: |
Bioteconologia; Ciência cognitiva; Nanotecnologia; Robótica. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00660naa a2200181 a 4500
001 2064667
005 2017-02-22
008 2016 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, C. R. de
245 $aA nanotecnologia e suas aplicações na pecuária de leite.$h[electronic resource]
260 $c2016
653 $aBioteconologia
653 $aCiência cognitiva
653 $aNanotecnologia
653 $aRobótica
700 1 $aCORREA, D. S.
700 1 $aCRESTANA, S.
773 $tIn: VILELA, D.; FERREIRA, R. de P.; FERNANDES, E. N.; JUNTOLLI, F. B. (Ed.). Pecuária de leite no brasil: cenários e avanços tecnológicos. Brasília, DF: Embrapa, 2016. p. 375-382.
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