Palmer' mango cultivar is a late season variety which is greatly accepted by European consumers. However, it is common to get reports of fruit quality problems, mainly due to maturity. Thus, the objective of this study was to develop calibration models for soluble solids content (SSC) and dry matter (DM) of 'Palmer' mangoes using portable (VIS-NIR) spectrometer. Interactance spectra were obtained with a portable F-750 spectrometer in the wavelength range of 306-1140 nm, 8 nm spectrum resolution, and 4 scans averaged per spectra. Spectra were used to develop SSC and DM models using partial least square regression (PLSR) with full cross validation. The best SSC calibration model was developed using spectra pre-processed with standard normal variate (SNV), first derivative of Savitzky-Golay and window of 699-999 nm. It was observed a RMSE CV of 1.39%, with a R CV 2 of 0.87, and RPD of 2.77. Better results were observed for the DM calibration model which was built with raw spectra using the window of 699-981 nm (RMSE CV of 8.81 g kg −1 , R CV 2 of 0.84, and RPD of 2.51). Poor calibration models were obtained for firmness. The results indicated that portable VIS-NIR spectrometer can be used as a non-destructive technique to assess SSC and DM content for 'Palmer' mangoes. It is necessary to incorporate more sources of variation, to reduce RMSE values and improve robustness, especially for fruit SSC and DM prediction.

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.

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Guava is a climacteric fruit, extremely perishable, that has a short shelf life. Controlled atmosphere (CA) and cold storage can be used to extend guava shelf life. However, few studies have been conducted to evaluate the effects of CA storage on guavas, especially with high levels of carbon dioxide (CO 2). Therefore the objective of this study was to evaluate the quality changes of 'Pedro Sato' guava fruit during CA storage with atmospheres containing low concentrations of O 2 and increasing levels of CO 2. For that, 'Pedro Santo' guavas were stored at 12.2 • C for up to 28 days in atmospheres with low oxygen (O 2) concentration (5 kPa) and increasing level of CO 2 (1, 5, 10, 15 or 20 kPa CO 2) in order to evaluate the fruit quality changes. It was possible to identify the relationships among quality variables in response to atmosphere composition and storage duration which were related to modifications due to ripening (ratio SS/TA, TSS, RS, TA), ripening and early CO 2 injury modifications (pH, SS, chromaticity), and CO 2 injury modifications (hue angle, firmness, soluble pectin). A clear CO 2 injury occurred in fruit stored in 5 kPa O 2 + 15 kPa CO 2 and 5 kPa O 2 + 20 kPa CO 2 after 28 days at 12.2 • C, with increasing pH values and soluble pectin content, which were inversely related to fruit firmness. 'Pedro Sato' guavas should be stored in atmospheres with 5 kPa O 2 and no more than 5 kPa CO 2 in order to prevent CO 2 damage. Further investigation is necessary to identify the metabolites responsible for the onset of responses to high CO 2 in guava fruit.

No abstract

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

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near-infrared (FT-NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA-LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (Rp (2) ) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, Rp (2) = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT-NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.

As the verification of authenticity of agricultural foods has become a potential application of spectroscopic methods, this study aimed to use near-infrared (NIR) spectroscopy associated with linear discriminant analysis (LDA) to discriminate intact fruit of the species Euterpe oleracea Mart. (açaí) and Euterpe edulis Mart. (juçara). One hundred sixty-eight açaí fruits from 17 genotypes and 200 fruits from 20 juçara genotypes were investigated and two groups were used for discriminating the species: calibration (294 fruits) and prediction (74 fruits). L*, a*, b*-PCA, and NIR-PCA of spectra pretreated with multiplicative scatter correction (MSC) showed the best results. The L*, a*, b*-LDA model resulted in 96.3% correct classification and 93.2% prediction accuracy of the external validation group. NIR spectra pretreated with MSC had 98% correct classification and 97.3% prediction accuracy. NIR spectroscopy associated with LDA is a reliable method for the discrimination of intact açaí and juçara fruit. PRACTICAL APPLICATIONSRecently, there has been an increasing interest by food industry and producers to assure consumers the authenticity of their products. Many food properties are related to individual compounds and their active chemical ingredients, such as essential oils, terpenoids, flavonoids, phenolic compounds, amino acids and organic acids. The Euterpe genus has many species of economic interest and E. oleracea (açaí or assai), E. edulis (juçara) and E. precatoria are among the most important species in the agribusiness sector. As the fruit of these species are morphologically similar, it is important to develop instrumental methods to sort these fruit at the convey belt in order to ensure authenticity and near-infrared (NIR) spectroscopy can be used for that. Based on our results, the NIR spectroscopy and chemometrics provide a useful approach for authenticating fruit from Euterpe genus and can be used by the food industry to identify adulteration.