The Role of Vibrational Spectroscopy and Machine Learning in Seaweed Quality Control

In a recent study published in the International Journal of Food Science, a research team led by Daniel Cozzolino from the University of Queensland's Centre for Nutrition and Food Sciences (CNAFS) and Queensland Alliance for Agriculture and Food Innovation (QAAFI) investigated how vibrational spectroscopy techniques are helping to improve seaweed quality and traceability to help ensure its safety for human consumption (1). This study gets to the heart of a growing trend in the food and beverage industry, which is to look for sustainable food sources and ensure their quality (1).

Sustainable food sources, such as seaweed, are being investigated more closely because of their nutritional properties. Seaweed is filled with nutrients, vitamins, and proteins that are considered good for human health (2). Its nutrients and functional properties have also made seaweed a valuable resource in other industries such as the medical field and in cosmetics (2). Scientists have been investigating new ways to evaluate seaweed for its nutritional content, ensuring that consumers are getting high-quality seaweed in the marketplace.

Seaweed in shallow ocean water. Generated by AI. | Image Credit: © Richard Miller - stock.adobe.com

In this study, Cozzolino and his team looked at how vibrational spectroscopy techniques and machine learning (ML) has been used to address these challenges. Vibrational spectroscopy has been used in various applications to study biological assays and conduct chemical analysis (3). Including techniques such as near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and Raman spectroscopy, vibrational spectroscopy is particularly useful for analyzing the molecular composition of food items to ensure it has not been tampered with (1,3). Vibrational spectroscopy techniques can conduct analysis quickly without requiring much expertise, and that is why it is valuable in the food and medical industry (4).

The study highlights the ability of IR spectroscopy to predict seaweed's chemical composition and biomass production under varying environmental conditions. Cozzolino and his team emphasize the integration of ML and chemometric techniques. These advanced computational tools enhance data interpretation, enabling precise and efficient decision-making. ML algorithms can process complex spectral data, uncover patterns, and predict outcomes with remarkable accuracy (1).

There are several benefits to combining ML with vibrational spectroscopy. To start, it allows quality control methods to be more dynamic, which can help producers identify hazards and contaminants more quickly (1). Using both techniques together also contributes to current sustainability efforts. As the authors write in their study, vibrational spectroscopy improves resource efficiency by enabling precise measurements of biomass yield and chemical composition (1). These insights allow producers to maximize output while minimizing waste, aligning with global efforts to reduce the environmental impact of food production.

Moreover, the research emphasizes the importance of traceability in the seaweed supply chain. Vibrational spectroscopy can verify the origin and authenticity of seaweed products, addressing growing consumer demands for transparency and ethical sourcing (1). By integrating these tools, the industry can establish robust systems for monitoring and managing risks, further enhancing consumer confidence (1).

Cozzolino and his team also state in their article that the continued development of vibrational spectroscopy technologies should focus on miniaturizing equipment, improving accessibility, and integrating these tools into routine industrial processes (1). These innovations promise to provide the seaweed industry with objective and efficient methods for quality assessment, ensuring the safety and sustainability of its products (1). Other studies have also highlighted how spectroscopy is helping detect heavy metals in seaweed as well, showcasing the versatility of spectroscopy techniques in this space (5).

The review article also highlights the potential of these technologies to contribute to the broader goals of food security and environmental conservation. By supporting sustainable seaweed farming practices, vibrational spectroscopy and ML can help meet the rising demand for nutritious, eco-friendly food sources (1).

As the industry continues to evolve, these advanced tools will play a crucial role in shaping a future where seaweed remains a cornerstone of sustainable nutrition and resource efficiency.

References

1. Power, A.; Chapman, J.; Hoffman, L.; Cozzolino, D. Shining Light on Seaweed—The Utilization of Vibrational Spectroscopy and Machine Learning in the Seaweed Industry. Int. J. Food Sci. Technol. 2025, vvaf012. DOI: 10.1093/ijfood/vvaf012
2. Chen, Z.; Zhang, L.; Wen, Y.; Shan, S.; Zhao, C. Seaweed as a Sustainable Future Food Source. Int. J. Food Sci. Technol. 2024, 59 (3), 1237–1247. DOI: 10.1111/ijfs.16910
3. Baiz, C. R.; Blasiak, B.; Bredenbeck, J.; et al. Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction. Chem. Rev. 2020, 120 (15), 7152–7218. DOI: 10.1021/acs.chemrev.9b00813
4. Pahlow, S.; Weber, K.; Popp, J.; et al. Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review. Appl. Spectrosc. 2018, 72 (1 Suppl.), 52–84. DOI: 10.1177/0003702818791939
5. Acevedo, A. Determining Heavy Metals in Seaweed: An Interview with Kacie Ho of the University of Hawaii. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/determining-heavy-metals-in-seaweed-an-interview-with-kacie-ho-of-the-university-of-hawaii (accessed 2025-01-17).