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| 3. |  | SILVA, S. R.; SANTOS, H. P. dos; LOLLATO, R. P.; SANTI, A.; FONTANELI, R. S. Long-term effects of tillage systems on liming efficiency, soil chemical and physical properties, and wheat yield in southern Brazil. Soil Research, v. 60, n. 5-6, p. 497-510, 2022.| Biblioteca(s): Embrapa Trigo. |
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| 6. | | GAZZIERO, D. L. P.; RIBEIRO, R. B.; LOLLATO, R. P.; VOLL, E.; BRIGHENTI, A. M. Utilização de diclosulam no programa de controle de plantas daninhas em soja resistente ao glyphosate. In: CONGRESSO BRASILEIRO DE SOJA, 4., 2006, Londrina. Resumos... Londrina: Embrapa Soja, 2006. p. 158. Organizado por Odilon Ferreira Saraiva, Simone Ery Grosskopf.| Biblioteca(s): Embrapa Soja. |
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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
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
26/10/2018 |
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Data da última atualização: |
02/12/2019 |
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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: |
VASCONCELOS, O. C. de M.; DUARTE, D.; SILVA, J. de C.; MESA, N. F. O.; MEDERIOS, B. J. T.; FREITAS, S. T. de. |
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Afiliação: |
OSVALDO CAMPELO DE MELLO VASCONCELOS, Universidade Estadual de Campinas; DIRLIANE DUARTE, IFSertão, Petrolina PE; JOSÉ DE CASTRO SILVA, UNIVASF; NELSON FELIPE OLIVEROS MESA, Universidade Estadual de Campinas; BARBARA JANET TERUEL MEDEROS, Universidade Estadual de Campinas; SERGIO TONETTO DE FREITAS, CPATSA. |
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Título: |
Modeling Tommy Atkins mango cooling time based on fruit physicochemical quality. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Scientia Horticulturae, v. 244, p. 413-420, jan. 2019. |
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DOI: |
10.1016/j.scienta.2018.09.06 |
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Idioma: |
Inglês |
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Conteúdo: |
Mango physicochemical quality can potentially affect fruit cooling time. In that case, an efficient cooling time should take into account fruit quality parameters. The objective of this study was to develop models that can be used to predict Tommy Atkins mango cooling time based on fruit physicochemical quality. The prediction models were developed using 150 fruit harvested at maturity stages ranging from 2 to 4. Two thermocouples were fixed in each fruit, one immediately below the epidermis and the other close to the endocarp. From the total number of fruit, 83 were subjected to hydrothermal treatment and 67 were maintained at room temperature (25 °C). The hydrothermal treatment was applied by keeping the mangoes in water bath at 46 °C for 75 min. This treatment is commercially used as a phytosanitary method for mangoes exported to North America and Asian countries. After hydrothermal treatment, treated and non-treated fruit were kept in a cold room to determine the cooling time of the flesh immediately below the epidermis and close to the endocarp, untilreaching 12 °C. After reaching storage temperature, each fruit was evaluated for physicochemical characteristics, which were used to obtain the cooling time prediction models. Quality attributes presenting the highest to the lowest influence on fruit cooling time were fruit weight, flesh and skin color (Chroma, Hue angle and Lightness), soluble solids, dry matter, fruit diameter, fruit length, flesh thickness and seed thickness. Cooling time prediction models were generated with these variables by multiple linear regression (MLR) and exhibited high prediction accuracy for fruit without hydrothermal treatment (R²SEC= 88% RMSEC 42.1% R²SEP= 85.5%RMSEP = 52.2%) and with hydrothermal treatment (R²SEC= 72.2% RMSEC 42.4% R²SEP= 73.6% RMSEP = 47.2%). According to the results, the models developed based on physicochemical parameters canpredict with relatively high accuracy the cooling time of‘ Tommy Atkins mangoes. These models can be used in the mango industry to determine the most effective cooling time required to maintain fruit quality. MenosMango physicochemical quality can potentially affect fruit cooling time. In that case, an efficient cooling time should take into account fruit quality parameters. The objective of this study was to develop models that can be used to predict Tommy Atkins mango cooling time based on fruit physicochemical quality. The prediction models were developed using 150 fruit harvested at maturity stages ranging from 2 to 4. Two thermocouples were fixed in each fruit, one immediately below the epidermis and the other close to the endocarp. From the total number of fruit, 83 were subjected to hydrothermal treatment and 67 were maintained at room temperature (25 °C). The hydrothermal treatment was applied by keeping the mangoes in water bath at 46 °C for 75 min. This treatment is commercially used as a phytosanitary method for mangoes exported to North America and Asian countries. After hydrothermal treatment, treated and non-treated fruit were kept in a cold room to determine the cooling time of the flesh immediately below the epidermis and close to the endocarp, untilreaching 12 °C. After reaching storage temperature, each fruit was evaluated for physicochemical characteristics, which were used to obtain the cooling time prediction models. Quality attributes presenting the highest to the lowest influence on fruit cooling time were fruit weight, flesh and skin color (Chroma, Hue angle and Lightness), soluble solids, dry matter, fruit diameter, fruit length, flesh thickness and seed thick... Mostrar Tudo |
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Palavras-Chave: |
Modelos de previsão. |
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Thesagro: |
Fruta Tropical; Manga; Mangifera Indica; Resfriamento; Tratamento Hidrotérmico. |
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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 02947naa a2200277 a 4500
001 2098311
005 2019-12-02
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1016/j.scienta.2018.09.06$2DOI
100 1 $aVASCONCELOS, O. C. de M.
245 $aModeling Tommy Atkins mango cooling time based on fruit physicochemical quality.$h[electronic resource]
260 $c2019
520 $aMango physicochemical quality can potentially affect fruit cooling time. In that case, an efficient cooling time should take into account fruit quality parameters. The objective of this study was to develop models that can be used to predict Tommy Atkins mango cooling time based on fruit physicochemical quality. The prediction models were developed using 150 fruit harvested at maturity stages ranging from 2 to 4. Two thermocouples were fixed in each fruit, one immediately below the epidermis and the other close to the endocarp. From the total number of fruit, 83 were subjected to hydrothermal treatment and 67 were maintained at room temperature (25 °C). The hydrothermal treatment was applied by keeping the mangoes in water bath at 46 °C for 75 min. This treatment is commercially used as a phytosanitary method for mangoes exported to North America and Asian countries. After hydrothermal treatment, treated and non-treated fruit were kept in a cold room to determine the cooling time of the flesh immediately below the epidermis and close to the endocarp, untilreaching 12 °C. After reaching storage temperature, each fruit was evaluated for physicochemical characteristics, which were used to obtain the cooling time prediction models. Quality attributes presenting the highest to the lowest influence on fruit cooling time were fruit weight, flesh and skin color (Chroma, Hue angle and Lightness), soluble solids, dry matter, fruit diameter, fruit length, flesh thickness and seed thickness. Cooling time prediction models were generated with these variables by multiple linear regression (MLR) and exhibited high prediction accuracy for fruit without hydrothermal treatment (R²SEC= 88% RMSEC 42.1% R²SEP= 85.5%RMSEP = 52.2%) and with hydrothermal treatment (R²SEC= 72.2% RMSEC 42.4% R²SEP= 73.6% RMSEP = 47.2%). According to the results, the models developed based on physicochemical parameters canpredict with relatively high accuracy the cooling time of‘ Tommy Atkins mangoes. These models can be used in the mango industry to determine the most effective cooling time required to maintain fruit quality.
650 $aMangoes
650 $aFruta Tropical
650 $aManga
650 $aMangifera Indica
650 $aResfriamento
650 $aTratamento Hidrotérmico
653 $aModelos de previsão
700 1 $aDUARTE, D.
700 1 $aSILVA, J. de C.
700 1 $aMESA, N. F. O.
700 1 $aMEDERIOS, B. J. T.
700 1 $aFREITAS, S. T. de
773 $tScientia Horticulturae$gv. 244, p. 413-420, jan. 2019.
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