Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning
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Abstract
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Graph of accuracy of CNN when the test set is perturbed at various28wavenumbers. Sharp decreases can be seen with perturbation at bands associated with (i) adenine, (ii) thymine, (iii)29aryl, (iv) phosphate, and (v) carboxyl.
this is a nice way to identify which peaks contribute to the separability of the species, especially when it is not clear by eye. As an alternative, it may be possible to simply do an ANOVA across the species at every datapoint.
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Error bars indicate standard deviation of 1599 cm-1 peak, which is representative of8standard deviation for all peaks.
i understand what this is showing but it seems like an unconventional way to show this kind of data. The shading colors make it seem like there is some kind of continuous concentration sweep from 0 to 150.
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We preferentially enhance Raman signal from bacteria in19wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the20bacterial surface
will this result in false positives due to other benign bacteria that may be present in the wastewarer that is still negatively charged or other non-bacterial cells?
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