CURRENTLY, METHODS USED TO DIRECTLY MEASURE MICROBIAL CONTAMINATION ARE LABOR INTENSIVE AND TIME INSENSITIVE. THERE IS A NEED FOR A LOW COST, COMPACT FULLY AUTOMATED MICROBIAL DETECTION SYSTEM THAT CAN CAPTURE DYNAMIC MICROBIAL ACTIVITY IN VARIOUS FORMS OF WATERS. IN THIS STUDY, FLUORESCENCE SPECTROSCOPY IS USED IN COMBINATION WITH MACHINE LEARNING ALGORITHMS TO DETECT AND QUANTIFY A LARGE RANGE OF CONTAMINANTS IN WATER. THE GOAL OF THIS STUDY IS TO DEMONSTRATE THE MONITORING OF MICROBIOLOGY AGAINST REGULATORY REQUIREMENTS, AS WELL AS DETERMINING MAINTENANCE SCHEDULING THAT WILL BE DICTATED BY THE TYPES OF WATERS BEING MONITORED. TESTING WILL BE PERFORMED AT RECLAMATION'S WATER QUALITY IMPROVEMENT CENTER IN YUMA, AZ.