Sabaragamuwa University of Sri Lanka

The growing demand for minimally processed, safe, and nutritionally superior foods has accelerated the adoption of non-thermal food processing technologies. While these technologies effectively inactivate spoilage and pathogenic microorganisms with minimal heat damage, their successful industrial implementation requires precise, process-specific, and real-time monitoring and control systems to ensure consistent food quality and safety. This review comprehensively examines recent advances in non-thermal food processing techniques with the integration of physical, chemical, optical, spectroscopic, and biological sensor systems for real-time process monitoring and control. Particular emphasis is placed on sensor-based approaches including temperature, pressure, flow, acoustic, electrical, chemical, optical, spectroscopic, and biosensor technologies that enable non-destructive, inline or online assessment of key quality attributes such as microbial inactivation, physicochemical stability, oxidation status, and structural integrity. The role of advanced spectroscopic tools such as near-infrared spectroscopy, Raman spectroscopy, fluorescence techniques, and hyperspectral imaging, in evaluating biochemical changes during non-thermal treatments is highlighted. Furthermore, emerging trends involving nano-biosensors, artificial intelligence, machine learning, big data analytics, and Internet of Things (IoT)-enabled platforms are discussed as critical enablers of intelligent, Industry 4.0 driven, closed-loop control systems. Overall, this review underscores the synergistic potential of combining non-thermal processing technologies with advanced sensor integration to enhance food safety, quality consistency, sustainability, and process efficiency in next generation food manufacturing systems.

The protection of crops from pests is essential for sustainable agriculture and global food security. Traditional pest identification techniques that are based on visual examination are time-consuming and prone to errors. Recent developments in the field of artificial intelligence (AI) and specifically deep learning (DL) enabled pests to be detected accurately and automatically. This review systematically examines DL-based approaches, including Convolutional Neural Networks (CNNs), Transformer models, ensemble methods, and Graph Neural Networks (GNNs), for pest classification, with an emphasis on benchmark datasets, model architectures, and evaluation metrics. Transformer models, such as GNViT, achieved 99.52% accuracy and a 90.9% F1-score on the IP102 dataset, which is approximately 10% higher than the CNNs. The Vision Transformer (ViT) model achieved 96.7% accuracy on PlantVillage. The ensemble model, like GAEnsemble, achieved excellent accuracies of 98.81% and 95.16% on D0 and SCD, respectively. CNN models had relatively lower performance on the IP102, and the GNNs showed poor performance (below 60%). This paper discusses prospective methodological enhancements, current limitations, and future prospects for developing scalable, understandable, and multi-domain pest classification systems.

This study highlights the diversity and ecological importance of freshwater endophytic fungi in Ratnapura district, Sabaragamuwa Province, Sri Lanka. Freshwater plant samples were collected from Chandrika tank (Embilipitiya), a seasonal pond at Haughton Estate (Kalawana), and Thiniyamita stream (Wewelkandura). Six endophytic species were isolated associated with different freshwater plants. These endophytes comprised of four Colletotrichum species C. hydei sp. nov. (RUFCC24116) and C. chlorophyti (RUFCC24117) from Vallisneria americana; C. chlorophyti (RUFCC241117) and C. vittalense (RUFCC241112) from Nymphaea pubescens; C. truncatum (RUFCC241119) from Lagenandra ovata); Daldinia eschscholtzii (RUFCC24114) from Hydrilla verticillata; and Nigrospora lacticolonia (RUFCC24115) from Persicaria glabra. Multi-locus phylogenetic analyses were used for species delimitation, based on ITS, GAPDH, ACT, CHS-1, and tub2 gene regions for Colletotrichum species; ITS, LSU, and rpb2 for Daldinia eschscholtzii; and ITS and tef1-alpha for Nigrospora lacticolonia. These results provide important insights into fungal host range and biodiversity at the global level.

Purpose This study aims to examine how travel influencers’ congruence with endorsed travel brands and alignment with their followers affect travel purchase behavior, addressing a critical gap in tourism marketing literature. Design/methodology/approach A novel conceptual framework grounded in the source credibility model and the persuasion knowledge model is proposed. A quantitative cross-sectional survey was conducted among 374 Sri Lankan travelers who actively engage with travel-related content on Instagram. The hypotheses were tested using partial least squares structural equation modeling. Findings Travel influencers who are perceived as trustworthy, attractive and congruent with both the endorsed travel brand and their audience enhance perceived authenticity. This perceived authenticity positively influences attitudes toward the travel brand, which in turn drives travel purchase behavior. Originality/value This study frames perceived influencer authenticity as a process-based mediator critical to the recognition of native advertising. Leveraging Sri Lanka’s emerging tourism context as a boundary condition, it demonstrates how authenticity mediates influencer–brand and influencer–follower congruence and provides actionable insights for designing credible, trust-enhancing and effective tourism promotion strategies.

Background and Objectives: Cervical intervertebral disc degeneration (CIDD) frequently leads to neck pain, yet its exact cause remains unclear. While inflammation is associated with degenerative disc disease, the precise function of cytokines in CIDD remains a mystery. The aim was to investigate the role of serum cytokines (interleukin [IL]-1 alpha, IL-6, and tumor necrosis factor [TNF]-alpha) as biomarkers in patients with chronic neck pain having CIDD. Subject and Methods: The case-control study was carried out among 88 participants. Forty-four patients with chronic neck pain (pain lasting >3 months), exhibiting moderate or severe CIDD in lateral X-ray of the cervical spine, and age, sex, and body mass index matched 44 controls, were included in the study. Levels of IL-1, IL-6, and TNF-alpha in serum were measured using enzyme-linked immunosorbent assay. Pain intensity was assessed utilizing a Visual Analog Scale. Results: Out of 44 patients, 14 had severe, whereas 30 had moderate CIDD. The mean intensity of pain in patients was 72.25 +/- 11.18. Serum levels of IL-1 (patients - 7.32 pg/mL, control - 2.98 pg/mL, P = 0.021), IL-6 (patients - 8.59 pg/mL, control - 2.98 pg/mL, P = 0.023), and TNF-alpha (patients - 19.29 pg/mL, control - 7.44 pg/mL, P = 0.011) were significantly elevated in patients compared to the controls. The pain intensity showed a positive correlation with the severity of CIDD (P = 0.045) and serum levels of IL-6 (P = 0.034) and TNF-alpha (P = 0.041). Conclusions: The serum levels of IL-1 alpha, IL-6, and TNF-alpha cytokines are significantly elevated in patients with moderate or severe CIDD. There is a significant positive association between serum IL-6 and TNF-alpha, with the intensity of pain. Assessing the cytokine profiles of patients could provide valuable insights into managing CIDD and predicting their outcomes.