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.

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Macadamia nut industry is increasingly gaining more space in the food market and the success of the industry and the quality are largely due to the selection of cultivars through macadamia nut breeding programs. Thus, the objective of this study was to investigate the feasibility NIRS coupled to chemometric classification methods, to build a rapid and non-invasive analytical procedure to classify different macadamia cultivars based on intact nuts. Intact nuts of five different macadamia cultivars (HAES 246, IAC 4-20, IAC 2-23, IAC 5-10, and IAC 8-17) were harvested in 2017. Two NIR reflectance spectra were collected per nut, and the mean spectra were used to chemometrics analysis. Principal component analysis-linear discriminant analysis (PCA-LDA) and genetic algorithm-linear discriminant analysis (GA-LDA) were used to develop the classifications models. The GA-LDA approach resulted in accuracy higher than 94.44%, with spectra preprocessed with Savitzky-Golay smoothing. Thus, this approach can be implemented in the macadamia industry, allowing the selection of cultivars based on intact nuts. However, it is recommended that more experimentation to include more data variability in order to increase the classification accuracy to 100%.

The objective of this study was to use dry matter (DM) calibration models to sort ‘Palmer’ mangoes prior cold storage and to evaluate the physiological and chemical changes during the storage period. PLS model developed with fruit from 2015/2016 season was not adequate to predict DM content in fruit from 2016/2017 (not adjusted R2). Therefore, VIS‐NIR spectra from 2016/2017 season were incorporated into data set and a new model was developed (RMSEcv of 10.5 g.kg−1, RnormalP2 of 0.75). With the new model, ‘Palmer’ mangoes were sorted into two maturity stages (150 g.kg−1 and 110 g.kg−1) which resulted in quality differences mainly in relation to DM and SSC. Portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on DM content and this classification affect fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1). Practical applications Although results can be found regarding the use of portable NIR spectrometers to estimate maturity in mango fruit, there are no studies stating the use of this method to sort fruit prior cold storage. Our results highlight that portable VIS‐NIR spectrometer can be used to sort fruit according to maturity stages based on dry matter (DM) content and this classification affects fruit quality during cold storage as fruit with higher DM (150 g.kg−1) presented better quality than fruit with lower DM (110 g.kg−1) at the end of the storage period.

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