Nanosecond laser induced breakdown spectroscopy for biofouling analysis and classification of fouling constituents

Marine biofouling refers to the undesirable growth and adhesion of marine organisms such as barnacles, macroalgae and microbial slimes on immersed structures. Biofouling composition is determined by collecting samples from marine structures and vessels and analyzing them in laboratories. Alternatively, an approach of online sensing based on Laser Induced Breakdown Spectroscopy (LIBS), which consists of analysis of the spectral emission from laser-induced plasma, can be considered for the elemental composition of biofouling. The study of marine biofouling using LIBS has not been attempted previously. In the present work, a laboratory-scale LIBS technique is used to analyze biofouling samples and its constituent common water-borne algae and bacterial species. First, LIBS database is created under controlled conditions for the selected biofilm constituent algae (Nitzschia sigma and Chaetoslorenzianus) and bacterial (Pseudomonas aeruginosa, Bacillus subtilis, E.coli) species. LIBS spectra are also acquired for Biofilm samples grown on Fiber Reinforced Plastic (FRP) and Stainless steel (SS) 316 L substrates suspended at a depth of 1 m in the tropical Indian Ocean. The studies are carried out for fouling growth period of 5, 10, 15 and 20 days in the intertidal region at a distance of 480 m from the shore. The study shows that the LIBS technique combined with the Principal Component Analysis (PCA) can be used for acquiring the spectra of biofouling species, its rapid classification and studying the growth characteristics. The LIBS database would serve as a means for early identification of marine fouling bacteria and algae species. [Display omitted] •LIBS technique for studying marine biofouling is proposed and demonstrated.•Marine fouling bacteria and algae species grown in controlled condition are studied.•Growth of biofouling films on substrates suspended in the Indian Ocean is investigated.•LIBS combined with Principal Component Analysis (PCA) is used for classification.