The clarification conditions and the selection of the clarification agent are pivotal in eliminating the haze components from red grape juice (RGJ) while minimizing the loss of functional color components. In this context, we synthesized a water glass-based APTES functionalized magnesium silica aerogel (MSA-NH3) incorporating 61.44 molecules/nm2 of amine groups, resulting in a positively charged zeta potential value of 33.9 mV (pH 3.4) for clarification of RGJ by targeting negatively charged polyphenols. The optimum clarification conditions using MSA-NH3 were determined as 0.18 g MSA-NH3/L RGJ, 20 °C, and 60 min through the application of Box-Behnken design. Under these conditions, MSA-NH3 exhibited excellent adsorption of haze components (3.61 NTU), outperforming the commercial bentonite-gelatine combination (BGC) (5.45 NTU). Furthermore, it exhibited greater efficacy in preserving anthocyanins while adsorbing browning components. MSA-NH3 has a high potential to serve as a functional alternative clarification agent in the beverage industry due to its promising clarification performance.

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination.