Hyperspectral imaging and data analysis for detecting and determining plastic contamination in seawater filtrates

Sammanfattning

One possible way of monitoring plastic particles in sea water is by imaging spectroscopic measurements on filtrates. The idea is that filters from seawater sampling can be imaged in many wavelengths and that a multivariate data analysis can give information on (1) spatial location of plastic material on the filter and (2) composition of the plastic materials. This paper reports on simulated samples, with spiked reference plastic particles and real seawater filtrates containing microplastic pollutants. These real samples were previously identified through visual examination in a microscope. The samples were imaged using three different imaging systems. The different wavelength ranges were 375-970 nm, 960-1662 nm and 1000-2500 nm. Data files from all three imaging systems were analysed by hyperspectral image analysis. The method using the wavelength span 1000-2500 nm was shown to be the most applicable to this specific type of samples and gave a 100% particle recognition on reference plastic, above 300 mu m, and an 84% pixel recognition on household polyethylene plastic. When applied to environmental samples the technique showed an increase in identified particles compared with visual investigations. These initial tests indicate a potential underestimation of microplastics in environmental samples. This is the first study to demonstrate that hyperspectral imaging techniques can be used to study microplastics down to 300 mu m, which is a common size limit used in microplastic surveys.

Nyckelord

visualisation of multivariate results; interactive visual data handling; plastic identification; visual spectroscopy; near infrared spectroscopy; microplastics

Publicerad i

Journal of Near Infrared Spectroscopy
2016, volym: 24, nummer: 2, sidor: 141-149
Utgivare: N I R PUBLICATIONS

SLU författare

• Geladi, Paul

  • Institutionen för skogens biomaterial och teknologi, Sveriges lantbruksuniversitet
    
  • Corpus Data & Image Analysis

UKÄ forskningsämne

Annan kemi

Publikationens identifierare

• DOI: https://doi.org/10.1255/jnirs.1212

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/77718