We're able to see a lot of things using computers that we can't see normally: yellow-green, UV light, X-rays, etc. What do we have that harnesses the ability to "see" what dogs smell (e.g., harnessing a dog's sense of smell to detect cancer and so forth)?
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1$\begingroup$ Welcome to SE Biology. Please re-read the Tour to see why your question is not appropriate here: "Focus on questions about an actual problem you have faced. Include details about what you have tried and exactly what you are trying to do. Ask about... general questions about biological concepts, questions about the biological mechanisms behind medical conditions, questions about techniques in a biological or biochemical laboratory" and "Avoid questions that are primarily opinion-based, or that are likely to generate discussion rather than answers." $\endgroup$– DavidCommented Aug 21, 2019 at 14:30
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4$\begingroup$ @David Is it truly opinion-based? I'm asking for actual methods of being able to perceive senses that are outside of our natural abilities in the same way that we perceive sound and sight when they are outside of our immediate knowledge. $\endgroup$– RoCoCommented Aug 21, 2019 at 14:33
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1$\begingroup$ RoCo I believe your question is a valid one and fits on our site. However, please indicate what previous research you've done (e.g., Google, Google Scholar, web of science, the library, etc.) and what you've already found out (or not found). Our goal is not to simply be an answer site, but rather a site that promotes self-learning with some expert help along the way :). Please take a moment to edit your post with this additional detail, and it will likely be received more positively by our community. Thanks! $\endgroup$– theforestecologist ♦Commented Aug 22, 2019 at 14:58
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1$\begingroup$ In addition to my original point and that made by @theforestecologist I would have voted to close your question on two other grounds. First it was too broad, it didn’t focus on one specific problem such as odour detection of a particular disease, and second it concerns the technology of odour detection devices, rather than biology. As they say, please read the label before use. $\endgroup$– DavidCommented Aug 22, 2019 at 22:34
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2 Answers
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NMR spectroscopy is a technology that is used to identify molecules. So-called "NMR spectra catalogs" document the spectra of various known compounds. Acquiring these spectra requires purified samples, expensive equipment, and time. In addition to making next-generations of this technology into something that would be practical for realtime "smelling", you'd have to match up catalog entries with what dogs actually respond to. That's going to take a fair bit of work.
There's some literature online about growing olfactory sensory neurons in a Petri dish (with difficulty). If we can learn how the signaling pathways work for "odorant detection" for specific odorants or "fragrances" that we already know dogs perceive, perhaps we could genetically engineer a cultured neuron cell to "light up" a fluorescent marker or issue some other signal that is perceptible to humans, whenever some dog-specific odor wafts in. I suspect this would be more realistic than "tabletop NMR" in our lifetime.
answered Aug 21, 2019 at 17:25
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Warwick University's e-nose technology has been around for quite a while: https://warwick.ac.uk/fac/sci/eng/research/impact/electronicnose/
As far as I understand their technology, they sample gas, and analyse it using a solid state CMOS device to generate a signature signal for particular compounds/mixtures. The devices have been used in a variety of industrial gas sensing applications.
There has been some progress in using e-noses in medical diagnosis, for instance this paper reports the use of e-noses to sample volatile organic compounds from urine in type 2 diabetes diagnosis: https://www.ncbi.nlm.nih.gov/pubmed/30513787
The difference from NMR is that e-noses don't generally try to identify specific molecules, but rather, to generate a signature for a particular organic compound or complex mixture, which can then be compared with other signatures from other samples using a machine-learning approach.
