Enhancing the antioxidant properties of functional herbal beverages using Ultrasonic-Assisted extraction: Optimized formulation and synergistic combinations of taurine and vit. C.

Functional herbal beverages are gaining popularity in the beverage industry due to their natural antioxidants. However, the high concentration of antioxidants in these beverages can lead to increased toxicity, limiting their use. Moreover, the composition of tap water, including mineral salts and hydrogen carbonate ions, hampers the extraction process of polyphenolic compounds, thereby reducing the antioxidant properties. This study aims to address these challenges by enhancing antioxidant properties, reducing toxic effects, and improving the extraction process. Low-dose herbal extracts of green tea, rosemary, milk thistle, and sage were extracted using 100 ml of boiling water as a solvent, with ultrasonication employed for 20 min. Taurine, vit. C, and their combination were added to the extracts. The antioxidant properties, polyphenol, and flavonoid content were evaluated. The results demonstrated that the low-dose herbal tea combined with taurine and vit. C exhibited higher antioxidant activity compared to high-dose tea. Notably, the combination of taurine and vit. C showed the strongest synergistic effect. The addition of vit. C to these combinations eliminated any antagonism and resulted in a robust synergistic effect. The optimal conditions for enhancing antioxidant properties were determined as follows: an herbal type of 0.030 ≈ 0 (sage), vit. C concentration of 0.045 g/100 ml, and taurine concentration of 0.179 g/100 ml. The measured responses for reducing power, DPPH, and ABTS were 0.152 µg vit. C equivalent/ml, 67.778 %, and 87.630 %, respectively. This study provides valuable insights into optimizing the antioxidant properties of herbal beverages through the synergistic combinations of taurine and vit. C. By employing proper preparation techniques and including taurine and vit. C, the antioxidant capacity of these beverages can be significantly improved, potentially offering health benefits against degenerative diseases.

Semantic segmentation of microbial alterations based on SegFormer.

Introduction: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation. Results: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general. Discussion: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.