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Pubmed for Handhelds

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  • Title: Predicting cyanobacterial biovolumes from phycocyanin fluorescence using a handheld fluorometer in the field.
    Author: Thomson-Laing G, Puddick J, Wood SA.
    Journal: Harmful Algae; 2020 Jul; 97():101869. PubMed ID: 32732055.
    Abstract:
    Toxic cyanobacterial blooms are becoming more prevalent in freshwater systems, increasing the need for monitoring to protect human health. Phycocyanin fluorescence sensors have been developed as tools for providing fast and cost-effective proxy measurements for cyanobacterial biomass. However, poor precision and low sensitivity in many of the probe sensors assessed to-date has restricted their potential for practical application in cyanobacterial monitoring programmes. In the present study, the sensitivity and accuracy of a handheld fluorometer, the CyanoFluor, was assessed using 12 cyanobacterial strains and samples from four different lakes collected weekly for 12 weeks. After the initial measurements, the samples were lysed by sonication, which we hypothesised would reduce inter and intra-specific differences. The CyanoFluor displayed high sensitivity (limit of quantification = 3.5 µg L-1 of phycocyanin) and was able to detect cyanobacterial biovolumes to levels much lower than the threshold levels in current recreational guidelines worldwide. There were strong and significant phycocyanin to biovolume relationships (r2 ≥ 0.88, P < 0.05) for all 12 cyanobacterial cultures. Collectively, strong relationships between phycocyanin fluorescence and cyanobacterial biovolumes were also identified in environmental samples (r2 ≥ 0.78, P < 0.001), although weaker relationships were identified when lakes were analysed separately (r2 = 0.06 - 0.90). There were differences in phycocyanin per biovolume between both cultured strains and lakes, highlighting innate interspecific differences that exist between cyanobacterial species. Lysis of samples consistently reduced variability between technical replicates, in cyanobacteria cultures (up to 87% reduction in sample variability) and environmental samples (71 - 93% reduction), indicating that it would be a useful methodological step to improve the repeatability of results. When guideline thresholds (aligned with currently enforced risk assessment categories) were modelled based on the most successful linear regression model, 74% of samples were assigned to the correct risk category. The sensitivity of the CyanoFluor and accuracy of the phycocyanin threshold models, indicates high potential for this method to be integrated into cyanobacterial monitoring programmes.
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