http://irgu.unigoa.ac.in/drs/handle/unigoa/18 2026-10-26T14:58:35Z 2026-10-26T14:58:35Z Palkar, Rutuja Satish http://irgu.unigoa.ac.in/drs/handle/unigoa/8118 2026-10-16T10:52:59Z 2021-11-01T00:00:00Z

Documentation, Biochemical Analysis, Bioprospecting and Molecular Phylogeny of Garcinia Species from the Western Ghats of India Palkar, Rutuja Satish

2021-11-01T00:00:00Z Shirodker, Amisha Gokuldas http://irgu.unigoa.ac.in/drs/handle/unigoa/8084 2026-10-15T07:40:38Z 2026-03-01T00:00:00Z

Studies on Arbuscular Mycorrhizal (AM) Fungi in Wild and Cultivated Tubers, found in Goa. Shirodker, Amisha Gokuldas

2026-03-01T00:00:00Z Panchbhai, K.G. Lanjewar, M. Naik, A.V. http://irgu.unigoa.ac.in/drs/handle/unigoa/7945 2026-08-19T08:56:40Z 2025-01-01T00:00:00Z

Modified MobileNet with leaky ReLU and LSTM with balancing technique to classify the soil types Panchbhai, K.G.; Lanjewar, M.; Naik, A.V. Accurate soil type classification is essential for sustainable agriculture, environmental monitoring, and effective land management. Due to the inherent variability in soil types and the prevalent class imbalance in soil imaging datasets, reliable classification remains a complex challenge. This study combined four unique soil image datasets, yielding 11 distinct soil type classes. The MobileNet model was modified to enhance classification with essential adjustments: replacing ReLU with Leaky ReLU, incorporating an average pooling layer (APL), and integrating a Long Short-Term Memory (LSTM) Network. Since class imbalance was evident in the extracted features, the Synthetic Minority Oversampling Technique (SMOTE) was employed to balanced these features. The MobileNet-LSTM-SMOTE approach demonstrated strong performance, achieving a Kappa score of 98.6 percent and a 5-fold average Kappa score of 97.9 percent. Additionally, testing on an independent dataset confirmed the model's robustness and generalizability. We also conducted a comprehensive performance comparison, including statistical analysis, against five established models-MobileNet, Xception, ResNet50V2, VGG-19, and DenseNet121. The results underscore the efficacy of proposed method in overcoming the challenges of class imbalance and feature variability, offering a reliable solution for soil type classification.

2025-01-01T00:00:00Z Naik, A.V. Velip, P.M. Amonkar, S.G. http://irgu.unigoa.ac.in/drs/handle/unigoa/7894 2026-06-30T06:39:25Z 2026-01-01T00:00:00Z

Microencapsulation-mediated modulation of phytochemical stability and antioxidant performance in Annona muricata L. leaf extracts under controlled drying and extraction conditions Naik, A.V.; Velip, P.M.; Amonkar, S.G. Annona muricata L. (soursop) is a pharmacologically important tropical plant known for its rich profile of bioactive compounds and therapeutic potential. The leaves of A. muricata have gained significant attention for their medicinal value in functional foods, plant-based formulations, and phytopharmaceutical applications, mainly because of their high concentration of secondary metabolites, particularly acetogenins. The present study aimed to optimize the extraction and microencapsulation of A. muricata leaf extract to improve the stability of its bioactive constituents. Among the drying methods evaluated, shade drying was the most effective. It preserved an optimal moisture content of 67.92 plus-minus 0.065 percent while maintaining phytochemical integrity and minimizing thermal degradation. Extraction using 100 percent methanol (T2) gave the highest total extract yield, total phenolic content (1.10 plus-minus 0.007nmg GAE/g), and acetogenin concentration. This extract also showed the best antioxidant activity, with the lowest IC sub(50) value of 116 Mu g/mL in the DPPH radical scavenging assay. Microencapsulation using sodium alginate, inulin, and gum arabic produced stable emulsions with particle sizes ranging from 250 plus-minus 0.005 nm to 6943.47 plus-minus 0.003 nm. The highest emulsion stability index (99.33 plus-minus 0.1 percent at 24 h) was recorded for formulations combining gum arabic and sodium alginate with distilled water extract. Overall, microencapsulation is an effective strategy to control release kinetics, improve therapeutic efficacy, and reduce risks associated with rapid degradation or uncontrolled release of bioactive compounds. These findings indicate that the selected drying, solvent extraction, and microencapsulation approaches support the potential of A. muricata leaves as a resource for scalable nutraceutical and functional food applications.

2026-01-01T00:00:00Z