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In this study, four textural classes of soil (Clay, Clay Loam, Loam, and Loamy Sand) were used to investigate the dielectric properties of soils, using a vector network analyser with an open-ended coaxial probe kit at room temperature (25 ± 2°C) in the 700–7000 MHz microwave frequency range. Four levels of soil moisture content (oven dry, 33% field capacity, 66% field capacity and 100% field capacity) were maintained to perform the experiment with three replication and three observation each. The results showed that, with increasing soil moisture, from oven dry conditions to 100% field capacity, both the real (Dielectric Constant) and imaginary (Loss Factor) components of the dielectric properties increased; however, the responses were not linear. The dielectric properties of oven dry soils were very low compared with the soils with higher moisture content. Therefore, soil moisture was the major contributor to the dielectric behaviour of soil. The dielectric properties of sandy soil were much lower than the other soils; however, the dielectric loss factor of the Dookie clay soil was higher compare with the other soils. Models were developed to explain the dielectric properties of soils as a function of frequency and moisture content. The goodness of fit (r2) for these models varies between 0.952 for the Dookie Sandy Soil to 0.997 for the Dookie Loam Soil, suggesting that these models were adequate to describe the dielectric properties of these soils over the range of frequencies and moisture contents assessed in this study. Another model was developed to estimate the expected penetration depth of electromagnetic waves in these soils, based on the model of the dielectric properties. It was clear that penetration decreases with both frequency and moisture content. Low frequencies penetrate further into the soils than higher frequencies. Similarly, dry soils allow further penetration than moist soils. [ABSTRACT FROM AUTHOR]

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]