Section II: Chromatic Monitoring of Liquids and Biological Tissue

The evolution of chromatic monitoring for liquid and biological tissue monitoring is described. It shows how chromatic techniques evolved from a basic optical fibre system for monitoring petroleum fuels, to a personal computer (PC)-based desktop system using visual display unit (VDU) illumination for monitoring E. coli in urine, followed by a portable PC system for honey monitoring in remote geographical regions, to an adaptation based upon the use of a mobile phone for in vivo neonate jaundice monitoring, to the production of a compact, portable unit for transformer oil monitoring with remote wireless addressing.

General Overview of Liquid Chromatic Monitoring

C. R. Jones, A. T. Sufian and D. H. Smith


Chromatic optical monitoring of various conditions may be regarded as having focussed upon the use of two classes of systems - optical fibre and camera systems. Both systems have broadly similar structures (Figure 2.1), each of which involves an illumination source whose output is directed onto a sensor element where the light is modulated before transmission to a chromatic optical detector. The detector output is processed to produce chromatic parameters (e.g.. H. L, S; Chapter 1) which indicate the condition of the sample. Examples of optical fibre and camera monitoring units are shown in Figure 2.2a and b, which have been deployed for monitoring the condition of the liquid digestate at a biodigestion processing site Rallis et al. (2005). This indicates the different arrangements with each system - the optical fibre system has an optical probe which is immersed into the liquid, whilst the camera system is nonintrusive and addresses the liquid remotely.

A list of some parameters which have been addressed chromatically by optical fibre and camera systems is given in Figure 2.3. This indicates that both optical fibre and camera systems are capable of monitoring various liquids, the choice depending upon various factors such as access to a liquid/ tissue sample, portability or being site based, effective cost, flexibility and so on.

Camera-Based Systems Options

The camera-based system offers much flexibility in being adapted for various purposes and also in the capability of its operation to be automatically varied even during its use. As camera-based technology continues to evolve, the use of such systems for chromatic monitoring is also evolving. There is therefore a need to indicate the various parameters of such systems which may be varied and controlled. For such a purpose, a matrix of such parameters - Illumination source. Modulation (optical). Processing (e.g., camera setting), Sensor (detector) (IMPS)- is useful, an example of which is given in Figure 2.4.

Figure 2.4 shows a rectangle with each side representing a system property which can be varied - that is, optical source, camera detector, optical modulation and chromatic detection parameters. The optical source may be in the form of a visual display unit (VDU) whose chromatic output (e.g., purple, white, blue) can be software controlled to produce a range of different colours and which can be supplemented if needed by light-emitting diodes.

General structure of optical chromatic systems for liquid monitoring

FIGURE 2.1 General structure of optical chromatic systems for liquid monitoring.

Examples of two options for chromatic monitoring of liquids on industrial sites

FIGURE 2.2 Examples of two options for chromatic monitoring of liquids on industrial sites: (a) camera- based system: (b) optical fibre-based system.

Examples of condition monitored with chromatic optical fibres and electronic cameras

FIGURE 2.3 Examples of condition monitored with chromatic optical fibres and electronic cameras.

Permutations of adjustable operation parameters available for camera-based chromatic monitoring (IMPS)

FIGURE 2.4 Permutations of adjustable operation parameters available for camera-based chromatic monitoring (IMPS).

The spectral sensitivity of the camera may be varied via a number of settings (e.g., tungsten, daylight, fluorescent) available on the camera.

The optical modulation can be arranged through the choice of VDU and camera settings plus the use of external filters (e.g., polarisation, fluorescence, etc.).

The chromatic output sensitivity can be adjusted by varying the amplitude of each camera output channel, that is, R, G, B.

These features illustrate the flexibility of such a system to be tuned for particular monitoring requirements.

Additional Monitoring Options

A major aspect of the camera-based system is its production of a two-dimensional image which can be adapted for implementing additional features, as shown in Figure 2.5. This shows an image of skin tissue within a rectangular aperture window (WT) in a template around which there are a number of rectangular areas of different controlled colours. Two of these areas (rO, r2) represent chromatic signatures of the extreme conditions being monitored which can be used for normalising the chromaticity of the sample, WT. In addition, there are red-coloured dots at three of the template corners which are used for correcting the template orientation. Furthermore, there is a series of horizontal lines (top and bottom) for checking for the correct zoom level, along with a rectangular frame. These image features are all controllable in software via the camera.

Further Options

A further option includes the system operation being controlled to selected extents via a personal computer (PC) connected to the system. If a small-volume unit is needed, a miniature VDU can be used for illumination and a Raspberry Pi unit used for operating and controlling the system with a capability of operating and controlling the system wirelessly from a remote location.

Illustrative Examples of Chromatic System Evolution

The various aspects indicated have evolved through the production of systems required by particular applications and which reflect the evolution path to date. An initial system was capable of monitoring

Typical camera image of a chromatically monitored sample (WT) including reference areas (rO. r2). spot orientation indicators and zoom correcting scale

FIGURE 2.5 Typical camera image of a chromatically monitored sample (WT) including reference areas (rO. r2). spot orientation indicators and zoom correcting scale.

Choice of operational components for optical chromatic monitoring,

FIGURE 2.6 Choice of operational components for optical chromatic monitoring, (a) Images (1. 2, 3. 4) of a liquid in a cuvette produced with different VDU illumination (e.g.. Chapters 4, 5. 7). (b) Choice of operating conditions (IMPS: Figure 2.4) [A = Processor response (R, G, В); В = Camera setting (e.g.. tungsten, daylight etc.); C = Form of optical modulation (transmission, reflection, fluorescence, polarisation); D = Source colour (purple, white, blue etc.) (e.g.. Chapter 7)]. (c) Three test samples [(i), (ii), (iii)] viewed through a central window in a peripheral. Reference template (e.g., Chapter 6).

petroleum fuel at fuel stations using optical fibre-based probing (Chapter 3). A PC-based system for monitoring E. coli in urine samples provides a first insight into a camera-based system (Chapter 4). Such a portable PC system was adapted for monitoring honey samples in remote geographical areas, including the use of fluorescence (Chapter 5). The approach was further adapted for the in vivo monitoring of jaundice in the tissue of new'born babies using a mobile phone system (Chapter 6). A small portable system for the remote monitoring of high-voltage transformer oils was produced using a Raspberry Pi-based monitoring unit (Chapter 7).


It has been explained how new forms of liquid and tissue monitoring based upon various manifestations of chromatic monitoring have evolved. The coloured image on the book cover illustrates examples of various options for chromatically addressing a variety of applications. Various sections of the image are indicated in the greyscale Figure 2.6.


Rallis. I.. Deakin, A.. Spencer. J.W.. and Jones. G.R. (2005) Novel sensing techniques for industrial scale biodigesters. Proc. of SPIE I7tli Int. Conference on Optical Fibre Sensing. 5855. 110.

Optical Chromaticity for Petroleum Discrimination

J. W. Spencer


There have been a number of attempts (Workman, 1996) to develop an optical system for distinguishing between different fuels purchased from fuel forecourts. Such a system w'ould assist in preventing cross-contamination between diesel and petrol during delivery, ensure that a particular brand of fuel, at a higher price, is dispensed into customers’ tanks and also stop contaminated fuel from being dispensed into a tank. The complexity of the fuel mix arising from the use of additives (~a few percent) such as cleaning agents makes the discrimination between different fuels difficult.

This provides a challenge for the application of liquid chromatic monitoring for distinguishing between different fuel types and brands and also for the fuel processing refinery. It would lead to the chromatic approach having the potential for producing sensors and sensor systems that could be used in fuel forecourts to monitor fuel quality in real time.

Chromatic Monitoring System

A chromatic monitoring system for addressing samples of petroleum has been developed based upon polychromatic optical fibre transmission of light. Light from a white light emitting diode (LED) was transmitted through an optical fibre bundle to a remote probe which addressed a petroleum sample before returning the petroleum-modulated polychromatic light to a miniature optical spectrometer (Figure 3.1a). The spectrum produced by the spectrometer was addressed with three chromatic processors to yield outputs of R, G, В (Chapter 1) from which values of various chromatic parameters (x, y, z, L. H, S; Chapter 1) were produced. Figure 3.1b shows an image of the optical fibre probe, whilst Figure 3.1c shows the deployment of such probes on board a petroleum monitoring vehicle (courtesy of Fairbanks) for visiting various fuel filling stations for on-site fuel testing and on a fuel tank filler inlet (Figure 3.Id).

Chromatic Analysis of Petroleum Spectra

Figure 3.2 shows examples of the complex spectra of several diesel and petrol fuels overlaid on each other. These spectra indicate the high degree of similarity which exists between the spectra (300-1755 nm) of many different fuels. However, close inspection shows some possible differences

Optical fibre-based chromatic system for fuel monitoring,

FIGURE 3.1 Optical fibre-based chromatic system for fuel monitoring, (a) Schematic of the optical fibre system, (b) Photograph of an optical fibre probe, (c) Photograph of a vehicle for monitoring at fuel-filling stations, (d) Optical fibre probe attached onto a fuel tank.

in the wavelength range 1138—1258 nm. Thus, chromatic processors (also shown in Figure 3.2) may be effectively deployed in this range to extract values of chromatic parameters for distinguishing the various fuel samples.

The outputs from the RGB filters were processed to yield values of the chromatic parameters H. S, L using the relationships given in Chapter 1.

Chromatic Maps of Various Fuels

Examples of H-S and H-L chromatic maps for 13 diesel and petrol samples (Fairbanks) are shown in Figure 3.3a and b. These results show that in both H-S and H-L maps, the diesel and petrol samples form two clear clusters, mainly governed by the H values.

Ultimate and premium samples of petrol obtained from retailers (some were well-known worldwide brands and others were local supermarket brands available only in the United Kingdom) have also been chromatically analysed. More than one spectrum was obtained for each sample type to reduce the noise. The fuels collected from these forecourts were processed at four different plants from the United Kingdom. The HS and HL values were processed from the R, G, В data for the same spectral range (1138 and 1258 nm). The HS values obtained show a separation of the two types

Overlaid optical spectra of various diesel and petrol fuel samples and the spectral range addressed by three chromatic processors

FIGURE 3.2 Overlaid optical spectra of various diesel and petrol fuel samples and the spectral range addressed by three chromatic processors.

of petrol (ultimate and premium) on an expanded chromatic map (Figure 3.4). Sample-to-sample variation was also observed.

Figure 3.4 also shows that a third cluster of samples is located between the two main clusters. These were premium fuels from one particular refinery (Lindsey).

Further chromatic data evaluation involved taking an average HS value for the various samples of each fuel type and presenting it as a single data point on an expanded H-S map (Figure 3.5). This reduced the number of data points in the figure to allow further observations.

It shows that for ultimate fuels, there is a good discrimination between the manufacturers of each product, although the spread for one manufacturer is more notable than others (i.e., Total). For premium fuels, there is a greater variation in the HS values associated with branded fuel.

Chromatic H, S. L polar maps for diesel and petrol samples, (a) H-S map. (b) H-L map

FIGURE 3.3 Chromatic H, S. L polar maps for diesel and petrol samples, (a) H-S map. (b) H-L map.


The results of the investigation show the extent to which chromatic monitoring has the capability of distinguishing different fuel samples with similar complex optical spectra. It also demonstrates how optical chromaticity may be conveniently deployed using readily available cost-effective

Expanded scale HS values for premium and ultimate fuel samples

FIGURE 3.4 Expanded scale HS values for premium and ultimate fuel samples.

HS values for distinguishing fuel samples from different manufacturers

FIGURE 3.5 HS values for distinguishing fuel samples from different manufacturers.

instrument components. It has also been shown that diesel and petroleum fuels can be chromatically distinguished (Figure 3.3).


Fairbanks Environmental Ltd. are acknowledged for providing access to many fuel samples.


Workman. J. (1996) Review: A brief review of near infrared in petroleum product analysis. J. Near Infrared Spectrosc. 4, 69-74.

Л PC-Based Chromatic

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