The fast detection strategy allows the early detection of cyanobacteria blooms and related cyanotoxins in the water samples, either in that organic matrix, such as shell fish and other fish products. Cyanobacteria blooms are due to the overgrowth of cyanobacteria, which can live in any kind of environments. And they've emerged as an environmental problem across the globe within the last 15 years.
As the number of harmful cyanobacteria blooms has increased during the last several years, the need for the early detection, which is key for addressing bloom and toxic spread has become more urgent. Our first direction strategy combine remote and proximal sensing technical technology with the laboratory chemical and bioinformatic analysis in a unique integrated the workflow. The whole process is safe.
Appropriate safety measures are taken to prevent aerosol inhalation and skin contact during sampling and lab analysis. For data retrieval, first locate the target area on a global world map and retrieve the data from various public and private remote sensing data sets for the collection date. After retrieval, process the raw data, calculate the multi-spectral indexes, and classify the resulting information.
Then define the sampling sites on the generated thematic map. For sample collection, transport the equipment to the selected sampling site in the mobile lab and plan the drone flight path for performing a macro area survey. At the site, use several drones equipped with different payloads to perform flight missions.
And use the footage acquired by the drone to validate the bloom presence and extension and to identify precise sampling points. At the identified sampling point, put on the appropriate personal protective equipment and collect three 500 milliliter water samples from each site. Measure several environmental parameters such as the air and water temperature and the site pH and salinity.
Then store the collected samples in the mobile lab for transport to the university lab. Prepare slides and screen the samples with the mobile lab microscope equipped with a digital camera to allow microscopic taxonomic analysis and identification of the species present within the samples on the basis of their blue-green color, cell shape, and size pellets. Once the species of cyanobacteria collected within the samples has been identified, at the university lab, centrifuge the samples and transfer each supernatant to a new container without disturbing the sample pellets.
Add 500 milliliters of butanol to each sample supernatant and transfer each solution to be extracted into a separatory funnel. After shaking and placing the funnels upright in individual ring clamps, allow the aqueous phases to drain into individual Erlenmeyer flasks. After repeating the layer separation three times, concentrate the organic phases under vacuum and weigh them.
For sample extraction using organic solvents, add 50 milliliters of fresh methanol to each sample pellet and sonicate the samples in an ice bath. After five minutes, add 50 milliliters of fresh methanol to each sample and gently shake the flask before filtering the solutions through individual pieces of filter paper and collecting the filtrates in round bottom flasks. After filtering each sample two more times, as just demonstrated, analyze the sample extracts by liquid chromatography and high resolution tandem mass spectrometry according to standard protocols.
Then generate a molecular network using the global natural product social platform and use the appropriate tools to analyze the resulting network and tandem mass spectrometry data to identify any toxins determined to be present within the collected samples. The proposed strategy was validated by the results obtained in the coastal monitoring program, active on the Campania region in Southern Italy from 2015 to 2021. A visual workflow that links the techniques with the produced results was generated.
During the subsequent monitoring campaigns, each step was optimized with the aim of fast detection. Optimization of the remote sensing workflow permitted a reduction in the number of platforms and missions while improving the generated product level. For example, this fast detection strategy facilitates a transition from the use of several different aerial platforms to satellite and drone platforms only.
And from the use of several different multi-spectral specialized indexes to the more informative chlorophyll a and normalized difference vegetation index thematic maps. In parallel, the workflow was reduced from requiring a 16 S metogenomic analysis to using microscopic observation only for determining the cyanobacterial community. And the new chemical workflow utilizes LCMS-based molecular networking for a quick and accurate cyanotoxin detection.
This strategy allows the study of cyanobacteria as bioindicator of pollution, particularly within areas in which the presence of the bloom is related to eutrophication processes and used by anthropogenic pressure. This multi-disciplinary strategy requires the combination integration of different techniques, technology and expertise in a unique workflow for its successful implementation. The fast detection strategy is useful to prevent health community problems due to harmful cyanobacteria blooms and to monitor large areas in short time.
