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| 43. |  | MARQUES, E. J. N.; FREITAS, S. T. de; FIGUEIREDO NETO, A.; CAVALCANTE, I. H. L. Espectroscopia na região do infravermelho próximo (NIR): técnica analítica não destrutiva para determinação da qualidade de manga. In: FIGUEIREDO NETO, A.; ALMEIDA, F. de A. C.; CAVALCANTE, I. H. L. Manga: maturação, colheita e conservação. Petrolina: Univasf, 2017. cap. 3, p. 63-87.| Biblioteca(s): Embrapa Semiárido. |
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| 52. | | MOREIRA, R.; VIEIRA, D.; BIASOTO, A. C. T.; FREITAS, S. T. de; SILVA, F.; SASSI, K. Perfil sensorial do consumidor de uva-passa na região de João Pessoa - PB. In: CONGRESSO INTERNACIONAL DE GASTRONOMIA E CIÊNCIA DE ALIMENTOS, 2., 2016, Fortaleza. Gastronomia: da tradição à inovação - anais. Fortaleza: UFC, 2016. p. 481-482.| Biblioteca(s): Embrapa Semiárido. |
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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. |
Registro Completo
|
Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
08/04/2021 |
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Data da última atualização: |
08/04/2021 |
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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: |
MOGOLLON, M. R.; CONTRERAS, C.; FREITAS, S. T. de; ZOFFOLI, J. P. |
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Afiliação: |
MIGUEL RENE MOGOLLON; CAROLINA CONTRERAS; SERGIO TONETTO DE FREITAS, CPATSA; JUAN PABLO ZOFFOLI. |
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Título: |
NIR spectral models for early detection of bitter pit in asymptomatic Fuji apples. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Scientia Horticulturae, v. 280, jan. 2021. |
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Idioma: |
Inglês |
|
Conteúdo: |
Bitter pit (BP) is a physiological disorder that develops in apples, mainly during storage. This study sought to develop NIR spectral models for prediction of future BP incidence and severity in ?Fuji? apples using spectral data collected at harvest and during storage. Partial Least Square classification models obtained from spectra reflectance between 950 and 1200 nm were compared, starting at harvest, at 10 days postharvest and every 20 days thereafter over 110 days at 0 ◦C in relation to BP severity (number of pits per fruit) after 150 days at 0 ◦C. The models used data from a total of 3000 fruit, collected over two seasons (2018 and 2019) from two orchards. All models were evaluated for Accuracy, Sensitivity, Specificity, Positive Predicted Value (PPV) and Negative Predicted Value (NPV). In the validation dataset, Accuracy, Specificity and NPV values varied between 60 and 80 % and were independent of the time of evaluation during storage. Sensitivity and PPV values did not exceed 60 % in the same dataset. Here, BP incidences in fruit with severities of <8 pits per fruit, achieved accuracies and NPVs between 60 and 70 % in the calibration and validation datasets using spectral data collected at harvest. For comparison, the detection of high BP severities (8?9 pits per fruit), these same metrics achieved between 80 and 90 % using spectral data collected during the first 10 days of storage. |
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Palavras-Chave: |
Avaliação não destrutiva; Desordem fisiológica; Modelo de classificação; Próximo ao infravermelho. |
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Thesagro: |
Distúrbio Fisiológico; Maçã; Pós-Colheita. |
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Thesaurus NAL: |
Apples; Bitter pit; Postharvest physiology; Postharvest technology. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02283naa a2200289 a 4500
001 2131128
005 2021-04-08
008 2021 bl uuuu u00u1 u #d
100 1 $aMOGOLLON, M. R.
245 $aNIR spectral models for early detection of bitter pit in asymptomatic Fuji apples.$h[electronic resource]
260 $c2021
520 $aBitter pit (BP) is a physiological disorder that develops in apples, mainly during storage. This study sought to develop NIR spectral models for prediction of future BP incidence and severity in ?Fuji? apples using spectral data collected at harvest and during storage. Partial Least Square classification models obtained from spectra reflectance between 950 and 1200 nm were compared, starting at harvest, at 10 days postharvest and every 20 days thereafter over 110 days at 0 ◦C in relation to BP severity (number of pits per fruit) after 150 days at 0 ◦C. The models used data from a total of 3000 fruit, collected over two seasons (2018 and 2019) from two orchards. All models were evaluated for Accuracy, Sensitivity, Specificity, Positive Predicted Value (PPV) and Negative Predicted Value (NPV). In the validation dataset, Accuracy, Specificity and NPV values varied between 60 and 80 % and were independent of the time of evaluation during storage. Sensitivity and PPV values did not exceed 60 % in the same dataset. Here, BP incidences in fruit with severities of <8 pits per fruit, achieved accuracies and NPVs between 60 and 70 % in the calibration and validation datasets using spectral data collected at harvest. For comparison, the detection of high BP severities (8?9 pits per fruit), these same metrics achieved between 80 and 90 % using spectral data collected during the first 10 days of storage.
650 $aApples
650 $aBitter pit
650 $aPostharvest physiology
650 $aPostharvest technology
650 $aDistúrbio Fisiológico
650 $aMaçã
650 $aPós-Colheita
653 $aAvaliação não destrutiva
653 $aDesordem fisiológica
653 $aModelo de classificação
653 $aPróximo ao infravermelho
700 1 $aCONTRERAS, C.
700 1 $aFREITAS, S. T. de
700 1 $aZOFFOLI, J. P.
773 $tScientia Horticulturae$gv. 280, jan. 2021.
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