Phytoplankton monitoring by flow cytometry

The application of flow cytometry to the monitoring of phytoplanktonis demonstrated. A comparison is made with conventional approachesto phytoplankton monitoring: light microscopy for the determinationof species abundance, and chlorophyll a determination and insitu chlorophyll a measurement by fluorescence for the determinationof the biomass. Flow cytometric measurements correlate wellwith these conventional types of measurements, as has been shownby comparing a full year of monitoring data obtained at a fixedmonitoring location 10 km off the Dutch coast. Flow cytometrybridges the gap between labour-intensive, but highly informative,microscopic observations and simple biomass measurements withless information content: via flow cytometry optical data areobtained at high speed for individual particles, which can betranslated into biomass information. On the basis of the flowcytometric measurements, rough discrimination of phytoplanktonspecies groups is possible, particularly for the abundant species.Of crucial importance is careful calibration of the flow cytometer,to ensure quantitative and comparable measurements over a longperiod of time. Calibration and quality assurance aspects arecovered in detail. ³Present address: Akzo Nobel Central Research Laboratories Arnhem,Department CRL, PO Box 9300, NL-6800 SB Arnhem, The Netherlands     

Photochemical tuning of light emission in a conjugated polymer containing norbornadiene units in the main chain.

A conjugated alternating copolymer containing norbornadiene and bis(ethynylene)phenylene units was prepared by the Cassar-Heck-Sonogashira cross-coupling reaction. Its electroluminescence was tested in a device, and its fluorescence colour could be tuned by light-induced norbornadiene-quadricyclane isomerization.     

Automatic identification of algae: neural network analysis of flow cytometric data

The performance of an artificial neural network for automaticidentification of phytoplankton was investigated with data fromalgal laboratory cultures, analysed on the Optical PlanktonAnalyser (OPA), a flow cytometer especially developed for theanalysis of phytoplankton. Data from monocultures of eight algalspecies were used to train a neural network. The performanceof the trained network was tested with OPA data from mixturesof laboratory cultures. The network could distinguish Cyanobacteriafrom other algae with 99% accuracy. The identification of specieswas performed with less accuracy, but was generally >90%.This indicates that a neural network under supervised learningcan be used for automatic identification of species in relativelycomplex mixtures. Incorporation of such a system may also increasethe operational size range of a flow cytometer. The combinationof the OPA and neural network data analysis offers the elementsto build an operational automatic algal identification system.     

A moorable,automated plankton sampler: comments on Lewis & Heckl (1991)

Comments on a recently described moorable, automated plankton sampler are given, mainly because it was designed to capture large zooplankton. However, the need for automatic devices for sampling phyto- and zooplankton is stressed. A new design for such a device is presented. A preliminary test was made using standard continuous-flow (auto-analyser) equipment, a cultured flagellate and formalin as a fixative.     

Corrected fluorescence excitation and emission spectra of phytoplankton: toward a more uniform approach to fluorescence measurements

Techniques for correction of fluorescence emission and excitationspectra of phytoplankton are described, which can be appliedin any commercially available spectrophotometer. The correctionof the emission spectrum is based on the measurement of a calibratedlight source. The excitation spectra are corrected by meansof a quantum counter solution that measures the spectral intensityof the excitation system and separate correction for wavelength-dependenteffects of the excitation optics. The correction proceduresgive technically corrected spectra, i.e. spectra that are freefrom wavelength dependent bias, but do not give absolute intensityvalues. Spectra that have been properly corrected for instrumentalwavelength dependencies are suitable for intercomparison, bothintra- and interlaboratory. Another application is the derivationof spectral data that will be obtained by other techniques thatmake use of fluorescence measurements, such as flow cytometry,remote sensing and in situ instruments. A necessary conditionis that the spectral response functions of these instrumentsmust be known. ¹Present address: AKZO, Arla-CRL, PO Box 9300, NL-6800 SB Arnhem,The Netherlands