Machine learning with RFID can validate Food adultery .....!!!

If the food items are not safe to eat, then it should be rejected by someone before using it. But it is not possible manually to test every prepared food as well bottled food even when a threat like the recent baby food scare. Recently MIT researchers discovered a way to check many food items instantly, non invasively and from a distance - using the popular RFID tags.

RFID is the radio frequency Identification mechanism, uses the tiny antena embedded in a sticker or level that is activated and powered by radio waves at a very specific frequency. When a transceiver sends out a 950Mhz signal, the RFID tag wakes up and re-transmits a slightly different signal identifying itself. Products that announce themselves? Convenient for doing inventory!

What the researchers found was that this return signal, outside the actual information-bearing part, can be affected by the actual contents of the product, since the radio waves have to pass through them. Consequently, a jar full of pasta sauce and one full of olives would produce different signal profiles - as would an untouched jar of baby food compared with one contaminated with melamine.

The problem is that these differences can be very minor and it’s not like they’ve been documented anywhere - this is the first time anyone’s tried this. So naturally, the team turned to machine learning. They trained up a model that can tell with confidence what a signal profile corresponds to, with the minor variations that come from, say, slight differences in orientation or glass width.

Right now the system, which they call RFIQ, can tell the difference between pure and melamine-contaminated baby formula, and between various adulterations of pure ethyl alcohol. That’s pretty much everything on my shopping list so I’m set, but obviously the team would like to have it apply to many more products. Now that the method has been shown to work, that’s the plan.

The task will only get harder, as things like environmental variables (shelves) and other wireless interference add to the problem. But machine learning algorithms are good at plucking signal out of the noise, so with luck the technique will work without too much trouble.