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

The 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.

Keywords

Açai, Jucara, Reflectance near infrared spectroscopy, Partial Least Squares Regression, Partial least squares-discriminant analysis, Principal component analysis discriminant analysis