Today’s modern food processing industries are heavily reliant upon water as both an ingredient and as an integral part of their preparation and processing functions. While in some instances the water used is further processed and treated by the food manufacturer, in many cases this supply of water is obtained from local municipal sources and under goes no further monitoring or processing beyond what is done by the local utility that supplies it to the end users. Unfortunately, in most cases little or no monitoring of the quality of this water by the water provider occurs beyond the water treatment plant. The vast labyrinth of pipes known as the distribution system that delivers the water is, for the most part, unmonitored.
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It is well known and widely accepted in the water supply industry that the distribution system is not secure. Due to the potential for backflow events, cross connections, corroding pipes, groundwater infiltration and biofilm events the water contained in the distribution system is not immune to becoming recontaminated after leaving the treatment plant. An added danger is the potential for deliberate contamination due to terrorist activities known as backflow attacks (see side bar). Much attention has recently been placed on the vulnerability of the U.S. drinking water supplies to assault by terrorists. The fact that our water supply systems, as they are currently configured, are vulnerable to attack has been widely recognized. (Hickman, 1999; Hoffbuhr, 2002; OSTP, 2003; Kroll, 2006) While most supply sources are limited in their vulnerability due to the massive volumes of water involved, the distribution system remains a vulnerable and tempting target as was clearly stated in a recent GAO report to Congress that listed the vulnerability of the distribution system to attack as the largest security risk to water supplies. (GAO, 2003) Terrorists could compromise a system through an assault anywhere in the distribution system through the introduction of any one of a large number of possible threat agents through a backflow attack. Such attacks are not just theoretical; they have already been attempted. Here’s a few examples:
- May 1983 – Israel uncovers Arab plot to poison Galilee water with “an unidentified powder.”
- February 2002 – Al Qaeda arrested with plans to attack U.S. embassy water in Rome with a cyanide-based compound.
- April 2003 – Jordan foils Iraqi plot to poison drinking water supplies from Zarqa feeding U.S. military bases along the Eastern desert.
- December 2002 – Al Qaeda operatives arrested with plans to attack water networks surrounding the Eiffel Tower neighborhoods, Paris.
- September 2003 – FBI bulletin warns of Al Qaeda plans found in Afghanistan to poison U.S. food and water supplies.
A system to effectively detect such incursions into the distribution system, whether they are accidental or terror related, would be a valuable tool to enhance the safety and quality of any products manufactured or processed with the water. The large diversity of potential agents that could find their way into the distribution system precludes monitoring for them on an individual basis. One method that has been developed and has found success in such an endeavor is the concept of using bulk parameter monitoring techniques along with advanced chemometrics.
A Real Time Monitoring System
One such system makes use of five common bulk parameters that are monitored simultaneously in real time. The parameters that are monitored are pH, conductivity, total organic carbon (TOC), turbidity and residual chlorine. When measured in real time, these parameters can show a lot of variability for a given water supply (see Figure. 2). That is why a baseline estimator that is sensitive to small perturbations and yet is resilient enough to not be constantly alarming due to normal fluctuations is required for such a system to function properly.
In the system as it is designed, the signals from all of the instruments are processed from five separate parameter measures into a single value in an event monitor computer system that contains the algorithm. The signal then goes through the crucial proprietary baseline estimator. A deviation of the signal from the estimated baseline is then derived. Then a gain matrix is applied that weights the various parameters based on experimental data for a wide variety of probable threat agents. The magnitude of the deviation signal is then compared to a preset threshold level that is set by the operator. If the signal exceeds the threshold, the trigger is activated. Figure 3 shows the same data from Figure 2 processed through the algorithm.