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Bacteria identification can be a time-consuming process. Machine learning algorithms that use deep convolutional neural networks (CNNs) provide a promising alternative. Here, we present a deep learning based approach paired with Raman spectroscopy to rapidly and accurately detect the identity of a bacteria class. We propose a simple 4-layer CNN architecture and use a 30-class bacteria isolate dataset for training and testing. We achieve an identification accuracy of around 86% with identification speeds close to real-time. This optical/biological detection method is promising for applications in the detection of microbes in liquid biopsies and concentrated environmental liquid samples, where fast and accurate detection is crucial. This study uses a recently published dataset of Raman spectra from bacteria samples and an improved CNN model built with TensorFlow. Results show improved identification accuracy and reduced network complexity. 

This is a proof-of-concept study for the development of a field-deployable and low-cost PCR thermocycler (FLC-PCR) to perform Polymerase Chain Reaction (PCR) for the rapid detection of environmental E. coli. Four efficient (77.1 W) peltier modules are used as the central temperature control unit. One 250 W silicone heating pad is used for the heating lid. The PID (proportional-integral-derivative) control algorithm for the thermocycles is implemented by a low-cost 8-bit, 16 MHz microcontroller (ATMEGA328P-PU). ybbW and uidA genes from specific E. coli colonies were used as amplicons for the PCR reactions that were carried out by a commercial PCR machine (Bio-Rad) and our FLC-PCR thermocycler. The heating and cooling speeds averaged 1.11 ± 0.33°C/s which is on a par with the commercial bench-top PCR thermocycler and the efficiency of the heating lid outperformed the Bio-Rad PCR thermocycler. The overall cost of the system is lower than $200 which is more than ten times lower than commercially available units. The heating block can be customized to accommodate different PCR tubes and even microfluidic chambers. An 8000 W portable power generator will be used as the power supply for field studies.