Although the refractive index itself does not provide information about the nature of
the contamination, it can provide an early warning of a quality incident. Moreover,
preliminary classification becomes possible when it is combined with one or more
other measurements such as electrical conductivity or UV absorption spectroscopy.
With refractive index being sensitive to all types of substances, both organic and
inorganic, and other technologies being primarily sensitive to either of these groups
of substances, a combination of sensors with intelligent data analysis software will
be able to classify the nature of the agent [34].
A number of technical solutions exist for the accurate tracking of refractive index
changes required to reach ppm and sub-ppm level sensitivity. These include the
Mach-Zehnder interferometer and the optical ring resonator. A crucial factor for
accurate refractive index measurement by these devices is the temperature compensation of the signal. Both have also been used in combination with surface coatings,
such as antibodies, to increase their sensitivity and make them selective to specific
molecules and even microorganisms [35].
5.4.4 Image Analysis
With the growing processing power and image analysis algorithms, various systems
have now been introduced that perform fully automated scanning the particles of
water samples. In such systems a set of optical properties of each particle is
measured. This then allows the classification of the individual particles. Applications
include counting and analysis of algae in surface water as well as monitoring the
total number of bacteria in drinking and wastewater. An approach used in commercially available products for water analysis is 3D scanning with a microscope,
collecting images at different depths in a sample and analysing the in-focus objects.
Another approach uses flow cytometry to analyse individual particles, collecting
scattering and fluorescence spectra of each particle that passes through a laser beam.
Both methodologies assess a range of optical parameters for each particle, such as
shape, size and optical density, in order to classify them. Although not capable of
monitoring hygienic parameters in drinking water, which require single cell detection and identification in large volumes, these systems provide automatic cultureand reagent-free analysis of sum microbiological parameters, e.g. total cell count
(TCC) and intact cell count (ICC), based on the characteristics of individual cells.
Products of this type have been arriving on the market over the last 5 years and are
used to monitor drinking water treatment processes and reservoirs, where an increase
in cell counts can be indicative for failure of the treatment or contamination of a
reservoir. They are also used to detect harmful algal blooms [36], for quality control
in aquaculture and to monitor ballast water to prevent dispersion of nonindigenous
organisms [37].
Spectroscopic Methods for Online Water Quality Monitoring
307
the contamination, it can provide an early warning of a quality incident. Moreover,
preliminary classification becomes possible when it is combined with one or more
other measurements such as electrical conductivity or UV absorption spectroscopy.
With refractive index being sensitive to all types of substances, both organic and
inorganic, and other technologies being primarily sensitive to either of these groups
of substances, a combination of sensors with intelligent data analysis software will
be able to classify the nature of the agent [34].
A number of technical solutions exist for the accurate tracking of refractive index
changes required to reach ppm and sub-ppm level sensitivity. These include the
Mach-Zehnder interferometer and the optical ring resonator. A crucial factor for
accurate refractive index measurement by these devices is the temperature compensation of the signal. Both have also been used in combination with surface coatings,
such as antibodies, to increase their sensitivity and make them selective to specific
molecules and even microorganisms [35].
5.4.4 Image Analysis
With the growing processing power and image analysis algorithms, various systems
have now been introduced that perform fully automated scanning the particles of
water samples. In such systems a set of optical properties of each particle is
measured. This then allows the classification of the individual particles. Applications
include counting and analysis of algae in surface water as well as monitoring the
total number of bacteria in drinking and wastewater. An approach used in commercially available products for water analysis is 3D scanning with a microscope,
collecting images at different depths in a sample and analysing the in-focus objects.
Another approach uses flow cytometry to analyse individual particles, collecting
scattering and fluorescence spectra of each particle that passes through a laser beam.
Both methodologies assess a range of optical parameters for each particle, such as
shape, size and optical density, in order to classify them. Although not capable of
monitoring hygienic parameters in drinking water, which require single cell detection and identification in large volumes, these systems provide automatic cultureand reagent-free analysis of sum microbiological parameters, e.g. total cell count
(TCC) and intact cell count (ICC), based on the characteristics of individual cells.
Products of this type have been arriving on the market over the last 5 years and are
used to monitor drinking water treatment processes and reservoirs, where an increase
in cell counts can be indicative for failure of the treatment or contamination of a
reservoir. They are also used to detect harmful algal blooms [36], for quality control
in aquaculture and to monitor ballast water to prevent dispersion of nonindigenous
organisms [37].
Spectroscopic Methods for Online Water Quality Monitoring
307