Best of the Week: Microplastics in U.S. Seafood, Tea Classification, Artificial Intelligence

This week, Spectroscopy published various articles that covered many topics in analytical spectroscopy. This week’s articles touch upon several important application areas such as environmental analysis, food and beverage analysis. These pieces also feature discussions of data analysis using artificial intelligence (AI). Several key techniques are highlighted, including near-infrared spectroscopy (NIR) and laser ablation inductively coupled plasma–mass spectrometry (LA-ICP-MS). Happy reading!

Quantifying Microplastics and Anthropogenic Particles in Marine and Aquatic Environments

Microplastics and anthropogenic particles are two of the most common types of contaminants being routinely found in marine ecosystems, where they pose significant threats to ecosystems and human health (1). Anthropogenic particles are human-made materials released into the environment through activities such as industrial processes, urban development, and the combustion of fossil fuels (1). These particles encompass a wide range of materials, including microplastics, metal particulates, soot, and construction debris, and they also pose a direct threat to the environment, including in marine and aquatic ecosystems (1).

In this video interview, Spectroscopy recently sits down with Elise Granek, Susanne Brander, and Summer Traylor to discuss their recent study quantifying microplastics (MPs) and anthropogenic particles (APs) in the edible tissues of black rockfish, lingcod, Chinook salmon, Pacific herring, Pacific lamprey, and pink shrimp, and how their findings are helping to influence technological advancements and policy measures that can help reduce microfiber pollution in these ecosystems (1).

Measuring Freshness of Salmon with Handheld Spectroscopic Instruments

Recently, researchers from Queen’s University, the University of North Dakota, and SafetySpect Incorporated collaborated to explore integrating multiple data modalities to improve produce freshness assessment. Their study extended previous work by incorporating visible and near-infrared (vis-NIR) absorbance data to enhance spectral feature selection and classification accuracy (2). In this Q&A interview, corresponding author Mike Hardy discussed how combining spectroscopy with statistical and machine learning approaches can create more robust models for food analysis, addressing limitations in current spectroscopic techniques (2).

NIR Spectroscopy with AI Proves to be a Powerful Combination for Tea Classification

Researchers at Nankai University have developed a novel tea classification method combining near-infrared spectroscopy (NIRS) and a fine-tuned 1DResNet model (3). Published in Infrared Physics & Technology, their approach significantly outperforms traditional machine learning techniques, improving classification accuracy by over 4% (3). By leveraging transfer learning, the model enhances spectral data analysis, offering a cost-effective, non-destructive solution for the tea industry (3). This advancement ensures better consumer protection and market integrity by addressing issues of tea mislabeling and classification inaccuracies.

Analysis of Deposition Patterns and Influencing Factors of Lithium and Transition Metals Deposited on Lithium-Ion Battery Graphitic Anodes by LA-ICP-MS

Understanding aging mechanisms in lithium-ion batteries (LIBs) is crucial for improving performance and longevity. A key factor is the solid electrolyte interphase (SEI), which protects battery components (4). However, transition metal dissolution (TMD) from the cathode can degrade the SEI, leading to lithium loss and reduced efficiency (4). Researchers explored TMD mechanisms in nickel-cobalt-manganese (NCM) cathodes, including oxygen vacancies, particle cracking, and phase transitions (4). Using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), the authors of this study visualized how metal deposition on the anode correlates with lithium passivation, providing insights for enhancing battery stability and safety.

The Marriage of Near-Infrared Spectroscopy with AI: The Small-Sample Breakthrough

Researchers at Fujian Agriculture and Forestry University have developed a convolutional neural network (CNN)-based self-supervised learning (SSL) framework to enhance near-infrared (NIR) spectroscopy analysis. This method reduces reliance on large databases and expert-driven preprocessing by automating feature extraction (5). Published in Analytical Methods, the study demonstrates how SSL can improve spectral analysis, making NIR spectroscopy more efficient and accessible, even with small data sets, while maintaining its non-destructive and rapid analytical capabilities (5).

References

1. Wetzel, W. Quantifying Microplastics and Anthropogenic Particles in Marine and Aquatic Environments. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/quantifying-microplastics-and-anthropogenic-particles-in-marine-and-aquatic-environments (accessed 2025-01-29).
2. Chasse, J. Measuring Freshness of Salmon with Handheld Spectroscopic Instruments. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/measuring-freshness-of-salmon-with-handheld-spectroscopic-instruments (accessed 2025-01-29).
3. Workman, Jr., J. NIR Spectroscopy with AI Proves to be a Powerful Combination for Tea Classification. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/nir-spectroscopy-with-ai-proves-to-be-a-powerful-combination-for-tea-classification (accessed 2025-01-29).
4. Harte, P.; Winter, M.; Wiemers-Meyer, S.; Nowak, S. Analysis of Deposition Patterns and Influencing Factors of Lithium and Transition Metals Deposited on Lithium-Ion Battery Graphitic Anodes by LA-ICP-MS. Spectroscopy 2025, 40 (1), 30–33. Available at: https://www.spectroscopyonline.com/view/analysis-of-deposition-patterns-and-influencing-factors-of-lithium-and-transition-metals-deposited-on-lithium-ion-battery-graphitic-anodes-by-la-icp-ms
5. Workman, Jr., J. The Marriage of Near-Infrared Spectroscopy with AI: The Small-Sample Breakthrough. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/the-marriage-of-near-infrared-spectroscopy-with-ai-the-small-sample-breakthrough (accessed 2025-01-29).