Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/36385
metadata.artigo.dc.title: Longitudinal data assessment of global stability index in kale leaves
metadata.artigo.dc.creator: Azevedo, Alcinei Mistico
Andrade Júnior, Valter Carvalho de
Pedrosa, Carlos Enrrik
Valadares, Nermy Ribeiro
Ferreira, Marcos Aurélio Miranda
Cecon, Paulo Roberto
metadata.artigo.dc.subject: Brassica oleracea var. acephala DC
Post-harvest preservation
Repeated measure-ments
Multivariate statistics
Curve grouping
metadata.artigo.dc.publisher: Universidade de São Paulo (USP), Escola Superior de Agricultura "Luiz de Queiroz (ESALQ)
metadata.artigo.dc.date.issued: Feb-2016
metadata.artigo.dc.identifier.citation: AZEVEDO, A. M. et al. Longitudinal data assessment of global stability index in kale leaves. Scientia Agricola, Piracicaba, v. 73, n. 1, p. 79-84, Jan./Feb. 2016. DOI: 10.1590/0103-9016-2014-0334.
metadata.artigo.dc.description.abstract: Kale plants are usually sold “in natura” in street markets and malls. Kale leaves can have their appearance compromised by dehydration and discoloration due to increased post-harvest time exposure. We aimed to analyze the Global Stability Index (GSI) in kale accessions by means of repeated measurement analysis and curve grouping as a complementary form of superior sample identification with regard to post-harvest preservation. Thirty kale accessions were evaluated using a randomized block design with four blocks and five plants per plot. Two commercial leaves per plant were collected, and kept on workbenches in the shade at a temperature of 18 ± 1 °C. Subsequently, the degrees of discoloration and dehydration, total chlorophyll content, and accumulated fresh mass loss were evaluated over a 15-day period. From these data, the GSI was calculated for each day of evaluation. In addition, using mixed models, thirteen co-variance structures were tested. For graphical analysis, thirteen linear and non-linear models were assessed followed by curve grouping using multivariate analysis. The GSI was efficient for differentiating accessions, which became an important tool in post-harvest studies. GSI values were not equally correlated, therefore the use of mixed models became an important approach. The unstructured matrix was the best fit to model the dependence of error. The Melow I model was the best fit for studying the GSI. The accessions UFVJM-10, UFLA-1, COM-1, UFVJM-32, COM-3, UFVJM-8, UFVJM-36 and UFVJM-24, belonging to 3 and 5 clusters, are recommended for crop cultivation and as parental material in breeding programs.
metadata.artigo.dc.identifier.uri: http://repositorio.ufla.br/jspui/handle/1/36385
metadata.artigo.dc.language: en_US
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