Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

dc.contributorUniversidade de São Paulo (USP)
dc.contributorCentral Queensland University
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.creatorCunha Júnior, Luis Carlos
dc.creatorNardini, Viviani
dc.creatorKhatiwada, Bed P.
dc.creatorAlmeida Teixeira, Gustavo Henrique de [UNESP]
dc.creatorWalsh, Kerry B.
dc.date2015-10-22T06:42:01Z
dc.date2015-10-22T06:42:01Z
dc.date2015-04-01
dc.date.accessioned2023-09-12T07:02:09Z
dc.date.available2023-09-12T07:02:09Z
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0956713514005672
dc.identifierFood Control. Oxford: Elsevier Sci Ltd, v. 50, p. 630-636, 2015.
dc.identifier0956-7135
dc.identifierhttp://hdl.handle.net/11449/129745
dc.identifier10.1016/j.foodcont.2014.09.046
dc.identifierWOS:000347581100087
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8779075
dc.descriptionThe processing of acai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruit requires water addition for adequate pericarp extraction. Currently, the amount of added water is based on fruit moisture content as estimated using a convection oven method. In this study, diffuse reflectance FTNIR spectra (1000-2500 nm, 64 scans and spectral resolution of 8 cm(-1)) of intact gal and jucara fruit were used to discriminate fruit batches based on the dry matter (DM) content using mature fruit collected over two years. Spectra were collected of similar to 25 fruits per batch, placed on a 90 mm diameter glass dish in a single layer. The calibration set contained of 371 lots, while the prediction set consisted of 132 lots (of different locations, times). Spectra were subject to several pre-processing methods and models were developed using Partial Least Squares Regression (PLSR), Partial Least Squares-Discriminant Analysis (PLS-DA) and Principal Component Analysis Discriminant Analysis (PCA-DA). A PLSR model constructed using the wavelength range of 1382-1682 nm and full multiplicative scatter correction achieved a root mean square error for prediction on DM of 5.25% w/w with a ratio of the standard deviation of DM set to the bias corrected RMSEP of 1.5 on the test set. A PCA-DA model based on the same wavelength of region outperformed the PLS-DA method to segregate the test population into categories of high (>32 %DM) and low DM (<32% DM) with 74% accuracy achieved. The PCA-DA technique is recommended to the processing industry as a non-destructive and rapid method for optimisation of water added during processing using batch assess of fruit from incoming lots of fruits. (C) 2014 Elsevier Ltd. All rights reserved.
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionBEPE scholarship
dc.descriptionUniversidade de São Paulo (USP), Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP), Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Av. do Café s/n – Campus Universitário da USP, Ribeirão Preto, São Paulo CEP: 14.040-903, Brazil
dc.descriptionPlant Sciences Group, Building 7, Central Queensland University, Rockhampton 4702, Queensland, Australia
dc.descriptionUniversidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias de Jaboticabal, Vila Industrial, Jaboticabal, Preto, São Paulo CEP: 14.884-900, Brazil
dc.descriptionFAPESP: 2008/51408-1
dc.descriptionFAPESP: 2011/19669-2
dc.descriptionBEPE scholarship: 2013/0.6089-3
dc.format630-636
dc.languageeng
dc.publisherElsevier B.V.
dc.relationFood Control
dc.relation3.667
dc.relation1,502
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectAçai
dc.subjectJucara
dc.subjectReflectance near infrared spectroscopy
dc.subjectPartial Least Squares Regression
dc.subjectPartial least squares-discriminant analysis
dc.subjectPrincipal component analysis discriminant analysis
dc.titleClassification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy
dc.typeArtigo


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