Red wine grapes have an important impact on the economy of many regions, both for wine quality and for their richness in phenolic compounds, which have many health benefits. Climate has been changing substantially in the last years, which affects greatly grape polyphenolic composition and wine quality. In this review, we will unveil the importance of climate in grape development, both physically and chemically, the different methodologies used to evaluate grape quality, the interesting new approaches using NIR spectroscopy, and the functional properties of grapes and red wine, due to their high phenolic content. Climate has an impact in the development of phenolic compounds in grapes, namely in the anthocyanins biosynthesis. The phenolic chemical composition changes during maturation, therefore, it is essential to keep on track the accumulation of these key compounds. This information is crucial to help producers choose the best harvest date since specific compounds like polyphenols are responsible for the color, taste, and mouthfeel of wines, which directly affects wine quality. The usage of different methodologies to assess quality parameters in grapes and wine, can be used to provide essential information to create the chemical profile of each variety to develop calibration methods. NIR spectroscopy seems to be a reliable method to be used in vineyards during grape maturation to provide real time information on quality parameters to producers since many reliable calibration models have been developed over time.

Red wine grapes require a constant follow-up through analytical chemistry to assure the greatest wine quality. Wet chemical procedures are time-consuming and produce residues that are hard to eliminate. NIR (near infrared radiation) spectroscopy has been referred as an accurate, rapid, and cost-efficient technique to evaluate quality in many fruit species, both in field and in industry. The main objective of this study was to develop predictive models using NIR spectroscopy to quantify important quality attributes in wine grapes. Soluble solids content (SSC), titratable acidity (TA), total phenolic content, total flavonoids, total anthocyanins, and total tannins were quantified in four red wine grape varieties, ‘Aragonês’, ‘Trincadeira’, ‘Touriga Nacional’, and ‘Syrah’. Samples were collected during 2017 and 2018 along véraison. Prediction models were developed using a near-infrared portable device (Brimrose, Luminar 5030), and spectra were collected from entire grapes under near field conditions. Models were built using a partial least square regression (PLSR) algorithm and SSC, TA, total anthocyanins, and total tannins exhibited a determination coefficient of 0.89, 0.90, 0.87, and 0.88, respectively. The Residual Prediction Deviation (RPD) values of these models were higher than 2.3. The prediction models for SSC, TA, total anthocyanins, and total tannins have considerable potential to quantify these attributes in wine grapes. Total flavonoids and total phenolic content were predicted with a slightly lower capacity, with R² = 0.72 and 0.71, respectively, and both with a RPD of 1.6, indicating a very low to borderline potential for quantitative predictions in flavonoids and phenols models.

“Persian” walnut (Juglans Regia L.) is one of the most consumed tree nuts in the world. It is rich in several bioactive compounds, with polyunsaturated and monounsaturated fatty acids (PUFA and MUFA) appearing at high concentrations. Walnut consumption protects against cardiovascular, carcinogenic, and neurological disorders. The fatty acid profile has usually been determined by gas chromatography, a reliable and robust tool, but also complex, polluting, and time consuming. In this study, near infrared hyperspectral imaging has been used for the screening of total fat, MUFA, PUFA, saturated, and individual fatty acids in walnuts. Five different walnuts varieties have been considered and modified partial least square (MPLS) regressions have been performed. The SEs of prediction (SEP) in external validation (ranged from 2.12% for PUFA to 13.08% for MUFA) suggest that hyperspectral imaging can be a reliable tool for controlling these parameters in a simple, non-destructive and environmentally friendly way.