Escherichia coli and coliform are traditionally used as indicator organisms for fecal contamination in water. Due to persistence outbreak of E. coli, there is an urgent need to develop a method that could detect the bacteria in timely and accurate manners. The main limitation of standard and alternative methods is the time to obtain the result (18 – 48 ℎ). An analysis time exceeding one day is often too slow for authorities to take a rapid response in case of an outbreak. There are emerging analytical methods which are relatively faster, but most existing analytical methods have some technical limitations, such as the need for vacuum, the need for oven, and the size of the analytical instruments which are not practical for on-site applications. In this study, a method for rapid detection and identification of coliform and E. coli bacteria was developed. The method is a combination of enzymatic and analytical methods. The enzymatic method was built upon the modern taxonomy of coliform and E. coli, in which the presence/ absence of coliform and E. coli is characterized by the presence/absence of β- galactosidase and β-glucuronidase enzymes, respectively. As E. coli is also a type of coliform, the presence of E. coli is indicated by the presence of both enzymes. The analytical method employed the use of microAnalyzer™ (a miniaturized Gas Chromatography – Differential Mobility Spectrometry (GC-DMS) system), which is an advanced gas detector that requires a low power consumption, has a built-in GC system, compact, portable, and could be operated using ambient pressure. Differential mobility spectrometry (DMS) is an ambient pressure ion-separation technique that characterizes chemical substances using differences in the gas phase mobility of ions in alternating strong and weak electric fields that are generated using a high frequency asymmetric waveform. In this study, at first the performance of GC-DMS in the detection of volatile metabolite compounds released by E. coli was investigated through “finger-print” recognition analysis. Twelve compounds known to be metabolites of E. coli (2,5-dimethyltetrahydrofuran, dimethyl disulfide, 2-heptanone, 2,5-dimethylpyrazine, benzaldehyde, dimethyl trisulfide, 2- nonanone, nonanal, decanal, 2-undecanone, indole, and 2-tridecanone) were prepared from standard solutions and the headspace gases were analyzed by GC-DMS. It was found that the last three compounds (which have relatively low volatility) could not be detected by the GC-DMS. This study, however, revealed the effect of radio-frequency (RF) voltage on the peak separation and signal intensity: the higher the RF voltage, the better the separation among the peaks, but the poorer the signals intensity; 1200 V (corresponds to 24 kV/cm) was found to be an optimum RF voltage for the aforementioned compounds. As the type and composition of metabolites released by bacteria are determined by many factors (such as the type of growth medium, the temperature of growth, and cell age), the study was continued by the determination of suitable growth medium, i.e. a medium which could stimulate E. coli in producing either unique “finger-print” compounds or unique biomarker compounds, which could be detected by the miniaturized GC-DMS. Five media commonly used to grow E. coli were examined: Colilert-18®, glucose broth, M9-medium, tryptic soy broth (TSB) and tryptophan broth. It was found that unlike all other four media, Colilert-18® medium stimulated E. coli growth in a way that it produced a unique biomarker, namely o-nitrophenol (ONP), and this biomarker was detectable by the GC-DMS. The finding was confirmed by gas chromatography – mass spectrometry (GC-MS) analysis. Colilert-18® contains ortho-Nitrophenyl-β-galactoside (ONPG) and 4-methylumbelliferyl-β-D-glucoronide (MUG) substrates. In the presence of ONPG substrate, β- Galactosidase enzyme in E. coli and coliform is activated and helped the hydrolysis of ONPG into β-D-Galactose and onitrophenol. In standard Colilert-18® test, due to its appearance, o-nitrophenol (which is a yellow crystalline solid) is usually used as a chromogenic indicator which confirms the absence/ presence of coliform. In the presence of the MUG substrate, β-Glucuronidase enzyme in E. coli is supposed to be activated and helped the hydrolysis of MUG into β-DGlucuronate and methylumbelliferone. However, headspace analysis of E. coli metabolites by GC-DMS and GC-MS analysis performed in this work only detected and identified the presence of o-nitrophenol, not of methylumbelliferone, due to the poor volatility of methylumbelliferone. Therefore, up to this particular point, the developed method was able to detect and identify coliforms including E. coli, but not able to distinguish E. coli from other coliforms. To distinguish E. coli from non - E. coli, a standard Colilert-18® test which involved the viewing of the sample under a 365 nm -. fluorescent UV lamp was needed; the presence of E. coli was indicated by a blue fluorescence effect. The time to perform standard Colilert-18® test is usually between 18 and 24 ℎ, which is the main limitation of the method. To shorten the analysis time, E. coli DSM 30083 bacteria were grown in Colilert-18® under various incubation periods. It was found that the cleavage opening (the cleaving of ONPG by β-galactosidase enzyme) took approximately 2.5 ℎ, as indicated by the presence of o-nitrophenol which could be detected by the GC-DMS after E. coli was incubated for just 2.5 ℎ. This means, the analysis time is 7 to 9 times faster than the standard Colilert-18® test. This is because the GC-DMS could detect a very low amount of onitrophenol despite the subtle or insignificant color change of the chromogenic indicator in the sample. Signal peak of o-nitrophenol was detected by the GC-DMS at both positive and negative ion channels of the DMS detector. The signal appeared at a retention time of tr = 184.9 s and compensation voltages of Cv,1 = −2.82 V (in the positive mode) and Cv,2 = −4.09 V (in the negative mode). GC-DMS system has three-dimensional data; it consists of: (1) retention time and (2) compensation voltage(s) which are unique to compounds’ identity, and (3) signal intensity. Unlike similar spectrometry methods, the difference in retention times of compound signals in GC-DMS could be very small (a matter of seconds instead of minutes), and compounds could still be differentiated based on their unique compensation voltages. Overall, the work in this section showed that compared to the already shortened incubation period (2.5 ℎ), the GC-DMS retention time (184.9 ) is much shorter. Hence, the analysis time using GC-DMS does not affect much the overall analysis time, which is excellent. Detection limit of o-nitrophenol was determined by calibrating mass of o-nitrophenol standard against signal intensity using DIN 32645 method. Detection limits of 45.11 ng and 48.85 ng of o-nitrophenol were obtained for the positive and negative modes of the detector, respectively. As the amount of o-nitrophenol is a dependent variable (the amount of o-nitrophenol in the headspace depends on the concentration of E. coli in the samples), concentration of E. coli was calibrated against signal intensity. When E. coli was incubated for 2.5 ℎ, detection limits of initial concentration of E. coli (concentration level before E. coli was incubated) of 3.37 × 10^7 and 3.21 × 10^7 cells/ml were obtained for the positive and negative modes, respectively. As E. coli growth curve showed the increase of final concentration of E. coli with respect to incubation period, it is concluded that the limit of initial concentration could be decreased if the incubation period is increased. To investigate the performance of the developed method in the differentiation of E. coli from other E. coli and from other bacteria, 5 types of bacteria were grown in Colilert-18® for 3 ℎ and the metabolite gases were analyzed by GC-DMS. These bacteria were (1) E. coli DSM 30083, (2) E. coli DSM 1576, (3) E. coli RV, (4) K. pneumonia (a coliform bacteria), and P. aeruginosa (a non-coliform bacteria). Based on the presence/absence of ONP, E. coli and K. pneumoniae could be distinguished from P. aeruginosa. Based on the intensity of ONP and the final cell concentration, E. coli could be distinguished from K. pneumonia. The method, however, could not distinguish the difference of E. coli from one strain to another. To anticipate the effect of seasonal variation in practical application, e.g. change in water temperature, the effect of sample preheating temperature (i.e. incubation temperature) variation was investigated. It was found that the sample should be incubated at 36 ° to accommodate maximum cell growth and signal intensity. From the overall finding, an algorithm to detect and identify E. coli and coliform was presented. Overall, the developed method was significantly faster than existing methods, was able to differentiate target organisms from non-target organisms, and is potential for on-site application. The main limitation is the relatively high detection limit, which could be improved by improving the sample enrichment technique using membrane filtration technique, and by improving the sample extraction and introduction methods. Nevertheless, to the author’s knowledge, the method developed in this work is the first reported application of miniaturized (portable) GC-DMS technology for headspace analysis of volatile metabolite biomarkers in conjugation to enzymatic approach using a defined substrate media and the first one which is applied for the detection and identification of fecal contaminant in water samples.