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Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties
Mangifera indica species presents a wide varietal diversity in terms of fruit size and morphology and also of physicochemical and organoleptic properties of the pulp. In Senegal, in addition to the well-known export varieties, such as ‘Kent’, local varieties have been little studied particularly dur...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740562/ https://www.ncbi.nlm.nih.gov/pubmed/36496567 http://dx.doi.org/10.3390/foods11233759 |
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author | Dieye, Mor Ndiaye, Nafissatou Diop Bassama, Joseph Mertz, Christian Bugaud, Christophe Diatta, Paterne Cissé, Mady |
author_facet | Dieye, Mor Ndiaye, Nafissatou Diop Bassama, Joseph Mertz, Christian Bugaud, Christophe Diatta, Paterne Cissé, Mady |
author_sort | Dieye, Mor |
collection | PubMed |
description | Mangifera indica species presents a wide varietal diversity in terms of fruit size and morphology and also of physicochemical and organoleptic properties of the pulp. In Senegal, in addition to the well-known export varieties, such as ‘Kent’, local varieties have been little studied particularly during ripening. This study aims to propose prediction models integrating variables deduced from varietal characteristics. Five mango varieties (‘Diourou’, ‘Papaye’, ‘Sierraleone’, ‘Boukodiekhal’ and ‘Sewe’) endemic to Senegal were characterized at harvest and followed during ripening storage. Caliber parameters were determined at green-mature stage as well as storage (25 °C) weight losses. Considering the ‘ripening storage time’ (R(ST)) variable as ripeness level index, intra-varietal prediction models were built by multi-linear regression (R(2) = 0.98) using pulp pH, soluble solid content (SSC) and Hue angle. In addition to these physicochemical parameters, variety-specific size, shape and weight loss parameters, were additional variables in multi-linear models (R(2) = 0.97) for multi-varietal prediction of R(ST). Results showed that storage time, which was the most influential factor on the pH, SSC and Hue, can be used as a response for varietal prediction of mango ripening. As a decision support tool, theses statistical models, validated on two seasons, will contribute to reduce post-harvest losses and enhance mango value chain through a better ripening process monitoring. |
format | Online Article Text |
id | pubmed-9740562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97405622022-12-11 Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties Dieye, Mor Ndiaye, Nafissatou Diop Bassama, Joseph Mertz, Christian Bugaud, Christophe Diatta, Paterne Cissé, Mady Foods Article Mangifera indica species presents a wide varietal diversity in terms of fruit size and morphology and also of physicochemical and organoleptic properties of the pulp. In Senegal, in addition to the well-known export varieties, such as ‘Kent’, local varieties have been little studied particularly during ripening. This study aims to propose prediction models integrating variables deduced from varietal characteristics. Five mango varieties (‘Diourou’, ‘Papaye’, ‘Sierraleone’, ‘Boukodiekhal’ and ‘Sewe’) endemic to Senegal were characterized at harvest and followed during ripening storage. Caliber parameters were determined at green-mature stage as well as storage (25 °C) weight losses. Considering the ‘ripening storage time’ (R(ST)) variable as ripeness level index, intra-varietal prediction models were built by multi-linear regression (R(2) = 0.98) using pulp pH, soluble solid content (SSC) and Hue angle. In addition to these physicochemical parameters, variety-specific size, shape and weight loss parameters, were additional variables in multi-linear models (R(2) = 0.97) for multi-varietal prediction of R(ST). Results showed that storage time, which was the most influential factor on the pH, SSC and Hue, can be used as a response for varietal prediction of mango ripening. As a decision support tool, theses statistical models, validated on two seasons, will contribute to reduce post-harvest losses and enhance mango value chain through a better ripening process monitoring. MDPI 2022-11-22 /pmc/articles/PMC9740562/ /pubmed/36496567 http://dx.doi.org/10.3390/foods11233759 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dieye, Mor Ndiaye, Nafissatou Diop Bassama, Joseph Mertz, Christian Bugaud, Christophe Diatta, Paterne Cissé, Mady Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties |
title | Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties |
title_full | Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties |
title_fullStr | Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties |
title_full_unstemmed | Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties |
title_short | Storage Time as an Index for Varietal Prediction of Mango Ripening: A Systemic Approach Validated on Five Senegalese Varieties |
title_sort | storage time as an index for varietal prediction of mango ripening: a systemic approach validated on five senegalese varieties |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740562/ https://www.ncbi.nlm.nih.gov/pubmed/36496567 http://dx.doi.org/10.3390/foods11233759 |
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