Faculty of Humanities and Sciences

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    PublicationOpen Access
    Impact of geographical variation on nutritional and antioxidant properties of Basella alba L. from Sri Lanka
    (BioMed Central Ltd, 2025-01) Dahanayaka, L.W; Mapa, M.M. S T; Kadigamuwa, C.C; Udayanga, D
    Background: Basella alba L. (Malabar spinach) is a widely consumed leafy vegetable, well known for its nutritional and therapeutic properties. These properties arise from the availability of essential nutrients, phytochemicals, and antioxidant potential, which may vary depending on environmental factors induced by the geographical location. In this study our aim is to investigate the correlation between the geographical location and proximate composition, phytochemical content, and antioxidant activity of B. alba harvested from fifteen locations in Sri Lanka. Results: According to the statistical analysis by ANOVA and Tukey test, the results of proximate analysis confirmed that samples from different locations showed statistically significant variance in nutritional content. Furthermore, phytochemical content and antioxidant potential varied showing a significant difference between locations in total chlorophyll (27.53 to 6.69 µg/g dry weight), carotene (4.54 to 1.15 µg/g dry weight), total flavonoid content (10.54 to 3.94 mg/g dry weight in Quercetin equivalents), total phenolic content (8.33 to 0.46 mg/g dry weight in gallic acid equivalents), 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (38.03–11.4% inhibition), and ferric ion-reducing antioxidant power (1.23 to 3.76 mg/g dry weight in ascorbic acid equivalents) (p < 0.05). The Pearson correlation showed a strong positive correlation between total phenolic content and antioxidant activity. Principal component analysis indicates the role of antioxidant activity and chlorophyll content in location differentiation, forming distinct clusters. Cluster analysis categorized samples into four groups, linking biochemical traits to agro-climatic zones. The principal component analysis and cluster analysis showed a close relationship between some locations due to their high antioxidant and phytochemical accumulation. Conclusion: This study exhibits the importance of geographical location on the phytochemical profile and antioxidant properties of B. alba. These findings can be used to refine optimal cultivation sites for B. alba to enhance the efficacy of its nutraceutical and pharmaceutical potential.
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    PublicationOpen Access
    Impact of geographical variation on nutritional and antioxidant properties of Basella alba L. from Sri Lanka
    (BioMed Central Ltd, 2025-01-27) Dahanayaka, L.W; Mapa, M. M. S. T.; Kadigamuwa, C.C; Udayanga, D
    Background Basella alba L. (Malabar spinach) is a widely consumed leafy vegetable, well known for its nutritional and therapeutic properties. These properties arise from the availability of essential nutrients, phytochemicals, and antioxidant potential, which may vary depending on environmental factors induced by the geographical location. In this study our aim is to investigate the correlation between the geographical location and proximate composition, phytochemical content, and antioxidant activity of B. alba harvested from fifteen locations in Sri Lanka. Results According to the statistical analysis by ANOVA and Tukey test, the results of proximate analysis confirmed that samples from different locations showed statistically significant variance in nutritional content. Furthermore, phytochemical content and antioxidant potential varied showing a significant difference between locations in total chlorophyll (27.53 to 6.69 µg/g dry weight), carotene (4.54 to 1.15 µg/g dry weight), total flavonoid content (10.54 to 3.94 mg/g dry weight in Quercetin equivalents), total phenolic content (8.33 to 0.46 mg/g dry weight in gallic acid equivalents), 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (38.03–11.4% inhibition), and ferric ion-reducing antioxidant power (1.23 to 3.76 mg/g dry weight in ascorbic acid equivalents) (p < 0.05). The Pearson correlation showed a strong positive correlation between total phenolic content and antioxidant activity. Principal component analysis indicates the role of antioxidant activity and chlorophyll content in location differentiation, forming distinct clusters. Cluster analysis categorized samples into four groups, linking biochemical traits to agro-climatic zones. The principal component analysis and cluster analysis showed a close relationship between some locations due to their high antioxidant and phytochemical accumulation. Conclusion This study exhibits the importance of geographical location on the phytochemical profile and antioxidant properties of B. alba. These findings can be used to refine optimal cultivation sites for B. alba to enhance the efficacy of its nutraceutical and pharmaceutical potential.
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    PublicationOpen Access
    A Comprehensive Investigation of Microplastic Contamination and Polymer Toxicity in Farmed Shrimps; L. vannamei and P. monodon
    (Springer Nature, 2025-02-20) Jayaweera, Y.U; Hennayaka, H.M.A.I; Herath, H.M.L.P.B; Gajanayake Mudalige, P.K; Mahagamage, M.G.Y.L; Rodrigo, U.D; Manatunga, D.C
    Microplastic (MP) pollution poses a significant threat to marine ecosystems, seafood safety, and human health. This study investigates the accumulation of microplastics in two commercially important shrimp species, Litopenaeus vannamei (L. vannamei) and Penaeus monodon (P. monodon), sourced from cluster farming sites in Puttalam, Sri Lanka. Shrimp exoskeletons and edible soft tissues underwent rigorous microplastic analysis, including density separation, alkali digestion, stereo microscopy, and Raman spectroscopy. The results revealed high microplastic contamination, with L. vannamei containing an average of 4.99 ± 1.81 MP particles/g and P. monodon containing 1.87 ± 0.55 MP particles/g. Microplastic sizes varied, with L. vannamei predominantly contaminated with 100–250 µm particles and P. monodon with 500 µm—1000 µm particles. Fiber morphotypes were prevalent in L. vannamei, while blue-colored microplastics were dominant in P. monodon. These comprised polystyrene (PS), nylon 6,6, and polyethylene (PE) which were identified by Raman spectroscopy. Additionally, the study investigated the acute toxicity effects of microplastic polymer combinations using a zebrafish embryo model (FET236 assay). Zebrafish embryos exposed to polyethylene-nylon 6,6 combinations exhibited significant adverse effects on hatching, survival, and heart function at lower concentrations, while polyethylene terephthalate-polystyrene combinations showed no considerable effects. These findings underscore the urgent need for monitoring and managing microplastic contamination in shrimp farming areas. Future research should focus on elucidating the ecological impacts and human health risks associated with microplastic exposure.
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    PublicationEmbargo
    Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications
    (Springer Nature Link, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, C
    Au nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.