We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Detection of microplastics in sea salt using hyperspectral imaging and machine learning methods: Pollution control in the Mediterranean sea as a case study
Summary
Hyperspectral imaging combined with machine learning was used to detect and classify microplastics in Mediterranean sea salt samples, demonstrating a rapid, non-destructive analytical approach with potential for routine quality control in the food industry.
Microplastics represent 80% of the marine waste, becoming one of the main problems worldwide today, one of the reasons they have been categorised as the 10th greatest threat in the World Economic Forum's Global Risks Report 2024. To address this issue, many recognised organisations have developed action plans for monitorization, mitigation and prevention of microplastic contamination. This includes the development of analytical methods for the detection, characterisation and quantification of these contaminants. In this regard, this work presents a novel approach for the direct detection and analytical evaluation of microplastics in sea salt sampled from solar sea saltworks. These factories act as a natural 'pre-concentrator' of solid pollutants, and sea salt is thus a good indicator of their presence in the marine environment. The developed methodology is based on the application of hyperspectral imaging a non-destructive/non-invasive analytical technique, in combination with machine learning methods, to detect five of the most common microplastics (PE, PET, PS, PP, PVC) in natural sea salt samples collected directly from a solar saltworks located on the Mediterranean coast of southern Spain. For this purpose, some key features were assessed to develop the methodology, including sample bank generation, particle size determination, imaging conditions, and others. Finally, once the HSI analyses were performed directly on the solid salt samples, partial least square-discriminant analysis was applied to develop a classification model capable of identifying salt-containing pixels and thus detecting µP pollution.
Sign in to start a discussion.
More Papers Like This
Hyperspectral Imaging and Data Analysis for Detecting and Determining Plastic Contamination in Seawater Filtrates
Researchers tested whether hyperspectral imaging combined with multivariate data analysis could detect and identify plastic particles on filters from seawater samples, finding the method could locate plastic contamination and distinguish polymer types. This approach could offer a faster and more automated alternative to manual microscopy for environmental microplastic monitoring.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging
Scientists used hyperspectral imaging combined with machine learning to distinguish between microplastic particles and microalgae in seawater samples. Developing reliable automated methods for identifying microplastics in complex environmental samples is critical for accurate contamination monitoring.
In‐Situ Detection of Microplastic Particles on Food Using Hyperspectral Imaging With One‐Dimensional Convolutional Neural Network and Artificial Neural Network
Scientists trained AI models on hyperspectral camera images to detect microplastic particles directly on the surface of raw seafood (tilapia) without needing to isolate or remove the particles first, achieving a 96.3% detection score for 600-micron particles. Current food safety testing for microplastics requires laborious physical separation, making routine screening impractical; this approach could enable rapid, non-destructive screening in food processing facilities. The method represents a practical step toward monitoring microplastic contamination in seafood before it reaches consumers.
Spectrometric Detection Of Microplastics In The Environment: A Novel Approach Using Hyperspectral Imaging System
This study developed a novel spectrometric approach to detect microplastics in environmental samples, combining spectral analysis with machine learning classification. The method enabled rapid, accurate identification of multiple polymer types without extensive sample preparation.