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Assessment of wine quality traits can be costly, time-consuming, and usually undertaken with complex chemical analysis and sensory evaluation in specialized laboratories. This study aimed to use novel digital technologies based on near-infrared (NIR) spectroscopy and a low-cost electronic nose (e-nose) integrated with machine learning modeling to assess wine quality traits and provenance in a vertical vintage of Shiraz wines. Results showed highly accurate machine learning (ML) models for the classification of wine vintages for Model 1 (NIR; 98.3%) and Model 2 (e-nose; 99.5%), and prediction of (i) intensity of sensory descriptors (NIR Model 3; R = 0.97), (ii) peak area of volatile aromatic compounds (NIR Model 4; R = 0.96), (iii) physicochemical parameters (NIR Model 5; R = 0.94), (iv) intensity of sensory descriptors (e-nose Model 6; R = 0.97), (v) peak area of volatile aromatic compounds (e-nose Model 7; R = 0.96), and (vi) physicochemical parameters (e-nose Model 8; R = 0.93). Winemakers may use these models to assess vintages and maintain high-quality wines associated with specific vineyards and regions or for a distinctive wine variety. Models could be developed further by including data from different vineyards, regions, wine variety, seasonality, other production, and winemaking techniques. • Novel digital technologies as rapid and low-cost methods to assess wine quality. • Correlations found between weather data, aromatic volatiles and sensory descriptors. • NIR and e-nose data used to classify wine vintage using machine learning. • Accurate ML models using NIR and e-nose inputs to predict wine quality traits. [ABSTRACT FROM AUTHOR]

[Display omitted] • An overview of the main physicochemical quality and sensory attributes associated with consumer preferences for rice is provided. • The advances in new and emerging technologies, including four key sub-disciplines of artificial intelligence (e.g., digital sensors, computer vision, machine learning, and biometrics) for rice quality research, have been discussed. • Integrating these technologies may offer the rice industry to assess rice quality and consumer preferences using low-cost, rapid, and non-destructive techniques compared to traditional methods. The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches. [ABSTRACT FROM AUTHOR]