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dc.contributor.authorALVES, J. S.
dc.contributor.authorPIRES, B. P. C.
dc.contributor.authorSANTOS, L. F.
dc.contributor.authorMELLO JÚNIOR, N. R. C. de
dc.contributor.authorMONTEIRO, S. R. S.
dc.contributor.authorKIDO, E. A.
dc.contributor.authorWALSH, K. B.
dc.contributor.authorFREITAS, S. T. de
dc.date.accessioned2025-01-02T16:47:07Z-
dc.date.available2025-01-02T16:47:07Z-
dc.date.created2025-01-02
dc.date.issued2024
dc.identifier.citationIn: CONGRESSO BRASILEIRO DE PROCESSAMENTO MÍNIMO E PÓS-COLHEITA DE FRUTAS, FLORES E HORTALIÇAS, 3., 2024, Piracicaba. Anais... Piracicaba: ESALQ/USP, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1171096-
dc.descriptionThe objective of this study was to develop a nondestructive method to predict and detect black flesh internal disorder in mango using visible/near-infrared (Vis-NIR) and machine learning. Vis-NIR spectra of healthy and disordered ‘Palmer’, ‘Keitt’ and ‘Tommy Atkins’ mangos were acquired in the wavelength range of 300 to 1100 nm using a portable spectrometer (F-750 Produce Quality Meter, Felix Instruments, WA, USA). Spectra were collected from the equatorial region of both sides of each fruit, with fruit at 24°C (±1 °C). Spectra were collected from 543 fruit that resulted in 1,086 spectra at harvest (healthy = 350; disordered = 736) and 1,051 after storage at 12 °C (±1 °C), when the fruit reached the ready-to-eat ripening stage (healthy = 349; disordered = 702). VIS-NIR spectra data were collected at 24 °C (±1 °C). Spectra data were subjected to the second derivative pre-processing method. Random Forest, Multilayer Perceptron, SMO, LibSVM and J48 algorithms were trialed using the WEKA 3.9 Software. The algorithms performances were evaluated using tenfold cross-validation and model performances were determined by the average accuracy, precision, recall, F-measure, receiver operating characteristics curve (ROC), and Kappa statistics. The models to predict black flesh at harvest showed an average accuracy ranging from 80 to 83.6%, ROC area from 0.80 to 0.91, Kappa from 0.59 to 0.63, precision, recall and f-measure from 0.82 to 0.84 across algorithms triled. The models developed to detect black flesh in ready-to-eat mango showed an average accuracy ranging from 73.2 to 77%, ROC area from 0.63 to 0.86, Kappa from 0.32 to 0.49, precision from 0.74 to 0.77, recall and f-measure from 0.73 to 0.77. The most accurate models to predict at harvest and detect in ready-to-eat mango the incidence of black flesh were developed with the Random Forest algorithm, reaching accuracy of 83.6% and 77%, respectively.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectPolpa preta
dc.subjectVis-NIR
dc.titleEarly detection of black flesh internal physiological disorder in mango by Vis-NIR spectra and machine learning.
dc.typeResumo em anais e proceedings
dc.subject.thesagroManga
dc.subject.thesagroPós-Colheita
dc.subject.thesagroDistúrbio Fisiológico
dc.subject.nalthesaurusMangoes
dc.subject.nalthesaurusPostharvest technology
dc.subject.nalthesaurusPostharvest physiology
dc.description.notesResumo 179.
riaa.ainfo.id1171096
riaa.ainfo.lastupdate2025-01-02
dc.contributor.institutionJASCIANE S. ALVES, UNIVERSIDADE FEDERAL DE PERNAMBUCO
dc.contributor.institutionBRUNA P. C. PIRES, UNIVERSIDADE FEDERAL DE PERNAMBUCOeng
dc.contributor.institutionLUANA F. SANTOS, UNIVERSIDADE FEDERAL DE PERNAMBUCOeng
dc.contributor.institutionNILO RICARDO CORRÊA DE MELLO JÚNIOR, STATE UNIVERSITY OF BAHIAeng
dc.contributor.institutionSANDY R. S. MONTEIRO, UNIVERSIDADE FEDERAL DE PERNAMBUCOeng
dc.contributor.institutionEDERSON A. KIDO, UNIVERSIDADE FEDERAL DE PERNAMBUCOeng
dc.contributor.institutionKERRY B. WALSH, CENTRAL QUEENSLAND UNIVERSITYeng
dc.contributor.institutionSERGIO TONETTO DE FREITAS, CPATSA.eng
Aparece en las colecciones:Resumo em anais de congresso (CPATSA)

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