The objectives of this study was to develop partial least square (PLS) models using NIR spectroscopy for the determination of SSC and firmness in intact low chilling 'Aurora-1' peach fruit, and verify the influence of maturity stage and harvest season on the models to be developed (robustness). FT-NIR spectra were obtained as log 1/R with fruit harvested in 2013 at 3 maturity stages and in 2014. The spectra were collected on the background and blush colour skin areas of the each fruit. Model performance was evaluated based on the values of root mean square error for prediction (RMSE P) and coefficient of determination (R P 2) obtained from validation fruit set (Kennard-Stone), and prediction fruit set (2014). PCA could not group the fruit based on blush and background skin colour, maturity stages, and harvest season. The model constructed using the external validation method obtained a RMSE VE of 1.08 % with 11 latent variables (LV S) and a R VE 2 of 0.59. The prediction set, independent data, resulting in a less accurate model (RMSE P 1.04 %, R p 2 0.45 and 11 LV S). The same trend happened for determining firmness with the external validation resulting in better model with RMSE VE 9.51 N and R VE 2 of 0.40 and the prediction set with RMSE P of 13.2 N, R P 2 0.40 with 7 LV S. The NIR spectroscopy showed to be a potential analytical method to determine SSC and firmness of intact low chilling 'Aurora 1' cultivar. However, it is necessary to optimize the models in other to reduce the prediction errors. 2015 Elsevier B.V. All rights reserved.

18Aguardente is a typical Brazilian spirit produced by the distillation of sugarcane most. The 19 valorisation of this spirit can be attributed to its notoriety, related to the production origin which can 20 influence its quality. Therefore, the objective of this study was to use NIR spectroscopy coupled with 21 discriminant analysis as a non-destructive method to attribute authenticity of aguardentes produced 22 in two geographic regions and to predict the ethanol content. Some chemometric methods were used 23 to discriminate sugarcane aguardente, namely partial least squares-linear discriminant analysis 24 (PLS-LDA), principal component analysis-linear discriminant analysis (PCA−LDA), and variable 2 selection techniques such as successive projection algorithm (SPA−LDA) and genetic algorithm 26 (GA−LDA). NIR spectra were collected using a FT−NIR spectrometer (4,000-10,000 cm -1 ) at a 27 spectral resolution of 16 cm -1 , 8 cm -1 interval, and 64 scans. PCA results were not effective to classify 28 the aguardente samples, but with PLS−DA, PCA−LDA, SPA−LDA, GA−LDA and LDA it was 29 possible to get 87.2% prediction accuracy. Better results were obtained using PLS−DA on raw 30 spectra and GA-LDA using only six wavelengths (namely, 1,025 nm, 1,181 nm, 1,596 nm, 1,610 nm, 31 1,653 nm, 2,125 nm) which gave relatively good accuracy rate (up to 87.2 %). NIR spectroscopy and 32 chemometrics can be used as a non-destructive method to attribute authenticity, and PLSR combined 33 with NIR was a good non-destructive method to predict ethanol content in sugarcane aguardente. 34 35 while the Minas Gerais State is the main alembic producer, both representing 25% of the Brazilian 46 market 3 . In the alembic production of sugarcane spirit the product is divided into three fractions: 47 head, heart, and tail, while in column distillation there is no such separation into fractions and that 48 explains to a range extent the chemical differences produced by the two processes 4 . 49

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