The aggregate reports from a small collection of such assorted sensors, each with its own response to a great many odors, can be extracted by a number of mathematical weighting procedures, made verifiable mainly by their agreement. This wide class of unusual computer programs has a long history, dating back 30 years to a kind of computer called the perceptron. Such programs march to no firm clockbeat, carry out no precise logical chains, tolerate substantial error and indecision, and learn best from patterns shown to them as examples. That is why specific chemical sensors have proved useful for security and in industry. But untiring, automated new electronic noses take a minute or two to judge complexity and can work on-line at quality control 24 hours a day.
Neural nets perform best when the task is to assess complex odors that defy description.
For the practical cases above, using a dozen or two varied sensors each, some 30 training inputs have been enough to ensure that the next try of the silicon gourmet will agree nine times out of 10 with the judgment of the carbonaceous gourmets on the panel. That is no less consistent than decisions by different expert juries. Now for a closer examination of your own sense of smell. Take a good whiff. Your nose samples a small teacup volume of air, along with any odorous contaminants.