Adulteration Detection In Food Items Using Sensor System &ml
 

knowledge about embedded systems and ML integration
6 months
real time constraints
Completion of embedding hardware with the software modules
Requirements
basic ML algorithms execution
Description
Adulteration defined in three ways:Common process of adding low quality substances in the food for gaining extra profit.Some harmful chemical substances are added as adulterant to the food substances to make it sustain for long period and for its freshness.Removing some valuable ingredient from the food substances.Impact:Severe harmful effects to human beings like food poison, risk of cancer, allergic reactions.Types of Adulteration:ØIncidental Adulteration- un intentional contamination of food ingredients by chemical, physical or biological agents like contaminated water and soil, larva, pest intrusion.ØIntentional Adulteration - deliberate contamination of food with a harmful substances like Urea in milk, muds in rice, colors in juices, ripening mangoes, adding chalk powder on turmeric, starch on curry powder, blending papaya seeds on black pepper, etc.There are many other techniques are available for the food adulteration identification.i, Physicalii, Chemical/ biochemicaliii, Molecular•Physical analysis involves visual structural evaluation(texture, solubility, bulk density)•Molecular analysis involves Plant DNA,RNA protein extraction based evaluation.•Chemical and biochemical detection involves the spectroscopic, chromatographic, electophoresis analysis.•Though many adulterant identification are present , spectroscopic method provides better accuracy , non- destructive ,low cost and real time analysis can be made.•Spectroscopy defines an interaction between light and matter and process of getting the spectrum.•It can also be defined as the study of emission, absorbance and transmittance of light and radiation by the compound of molecules (matter).•The transmittance / absorbance of spectrum is based on the principle of Beer -Lambert's lawA= ɛclWhere,A= AbsorbanceƐ = molar coefficientC= concentrationl= path length
Who this course is for
beginners of Data science