A global shortfall in protein supply from capture fisheries has motivated the Malaysian government to revise its aquaculture strategy, focusing on three commodities: seaweed, fish and marine shrimp. However, the performance of the Malaysian aquaculture sector, particularly seaweed production, is poorly documented. This is the first empirical study to undertake a value chain analysis (VCA) of the Malaysian seaweed sector using stakeholder perceptions and secondary data that encompass members of seaweed farming cooperatives (the Semporna Area Farmers’ Association and the governments’ flagship Seaweed Cluster Project). Fieldwork was conducted between April and June 2015 among seaweed stakeholders involved in the value chain using a mixed methods approach—in-depth interviews with key informants, focus group discussions, household surveys, personal observation and secondary data. Qualitative and quantitative data were collected from both upstream (seaweed farming, marketing structure and the Malaysian Good Aquaculture Practices [MyGAP] certification programme) and downstream (seaweed processing) activities involving farmers, intermediaries/middlemen (buyers), processors and officials.Kappaphycusspp. was sold in two forms: (1) dried seaweed to be used as raw materials in carrageenan processing (approximately 90% of total harvest) and (2) fresh seaweed to be used as a source of seedlings (approximately 10% of total harvest). The value chain ended with the carrageenan form, which is exported to international markets. The price of dried seaweed varied according to a combination of seaweed quality, the strength of farmer’s relationships with intermediaries and processors and in response to demand from the carrageenan industry. The prices obtained by Malaysian farmers for dried seaweed and carrageenan remained low, US$ 0.60 and US$ 4.43 per kg, respectively, despite efforts by the government to enhance the value chain by imposing seaweed standards (via MyGAP) for farm management, dried seaweed and semi-refined carrageenan. The VCA was a useful tool to identify and map the market, with the results providing a better understanding of the seaweed sector, which could be helpful in supporting further aquaculture development in Malaysia.

The inherent source of gelatin used for commercial hard capsules causes a surging demand for vegetarian capsules. In this work, carrageenan is utilized in preparing hard capsules to meet consumer preferences. Hydroxypropyl methylcellulose (HPMC) was incorporated as a reinforcing agent to improve the low mechanical properties of hard capsules made of carrageenan. The HPMC concentration was manipulated from 0.2 to 1.0 w/v% in the carrageenan matrix. The increasing concentration of HPMC exerts significant effects on the tensile strength and elongation at break, with an improvement of 59.1% and 46.9%, respectively, at the optimized HPMC concentration of 0.8 w/v%. The loop strength of the capsule is also increased by 56.4% with decreasing moisture content. The downfield movement from around 3.20 ppm of the carrageenan proton to 3.33 ppm in the proton nuclear magnetic resonanance ( ¹H‐NMR) spectrum suggests the formation of intermolecular hydrogen bonding between carrageenan and HPMC, which correlates to the results of Fourier‐transform infrared spectroscopy (FTIR) and zeta potential. The glass transition temperature of the film was increased from 37.8 to 65.3°C, showing an upgrade in thermal stability. The film possesses a major mass loss with an activation energy of 64.7 kJ/mol with an increment of 43.4% compared to the control carrageenan. These findings support the conclusion that HPMC enhanced the mechanical properties and thermal stability of the carrageenan film, and the comprehensive analysis of the molecular interaction and decomposition kinetics subsequently may expand the application fields of the carrageenan‐HPMC hard capsule as an alternative to gelatin in the future.

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.