Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Adulteration detection in food items using sensor system &ML
Rating: 4.5 out of 5(5 ratings)
372 students

Adulteration detection in food items using sensor system &ML

Introduction food adulteration detection
Created bySowmya N
Last updated 5/2025
English

What you'll learn

  • knowledge about embedded systems and ML integration
  • 6 months
  • real time constraints
  • Completion of embedding hardware with the software modules

Course content

9 sections9 lectures41m total length
  • Introduction to food adulteration detection2:47

    Black pepper is an expensive commodity with a high risk of adulteration. Ground papaya seed is the main adulterant in pepper because it cannot be discriminated visually. There are few destructive methods. Since pepper is costlier, non-destructive method of adulteration is must but it is challenging one. The existing non-destructive method uses costlier equipment, bulky, involve laboratory-based testing, time consuming in the process. To overcome the above issues, this article presents the development of Non-destructive E- nose gas sensor for pepper adulteration detection. This system determines the VOC in a controlled environment. The proposed system utilizes MQ2 and MQ3 gas sensor arrays to identify Volatile Organic Compounds present in pepper seeds to discriminate adulterant and non-adulterant sample. The sensor data are utilized to perform the qualitative analysis to determine the adulteration using a support vector machine learning algorithm. The proposed sensor system with Support Vector Machine learning algorithm outperforms in comparison with existing methods with 100% classification accuracy. Conclusion: The developed gas sensor system is connected to the internet via the IoT application model to show results on the web pages and enables access by the authenticated user from anywhere. Client server model with MQTT protocol is used for developing IoT application.

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, Physical

ii, Chemical/ biochemical

iii, 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 law

A= ɛcl

Where,

A= Absorbance

Ɛ = molar coefficient

C= concentration

l= path length

Who this course is for:

  • beginners of Data science