
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.
This lecture reviews food adulterant identification using spectroscopic techniques, including near-infrared, terahertz, and FTIR, highlighting non-destructive, low-cost detection in spices, milk, and fruits.
Develop an IoT smart gas sensor classifier system to detect papaya adulteration in pepper, using MQ sensors, Arduino data acquisition, and WiFi-enabled data transmission for real-time analysis.
Spectroscopic, E-nose sensor array system, MIP coated sensor mote and geo-tracing techniques were employed to determine the adulteration in pepper samples. Literature works focus on determining the Volatile Organic Compounds of pepper samples and the presence of other adulterants in pepper seeds. Moreover, these experimental methods are carried out in a lab environment; samples are analyzed in whole and powdered form, and it takes time delay to produce the results. Real-time data analysis enables the system to be more effective and predict the results on the field spot.
Design a low-cost, portable gas sensor array system for rapid data collection in the field.
Machine Learning algorithm-based adulteration detection mechanism is developed to handle the variation factor such as placing the sensor inside a measurement chamber, aged pepper sample, sensor placement during data collection and able to achieve an accuracy of 100% adulteration determination. The designed sensor system is IoT enabled to visualize and update the results on the webpage, which can be accessed by an authenticated end user anywhere.
Identify black pepper adulteration using mq gas sensors connected to an Arduino Uno, capturing volatile organic compounds and streaming data to Excel for machine learning across varying pepper concentrations.
Develop an IoT enabled sensor system to detect papaya seed adulteration in black pepper by sensing volatile organic compounds with mq3 and mq135 gas sensors, using machine learning for detection.
Explore how ultrasound waves enable cavitation and sonication to enhance drying, extraction, emulsification, freezing, and quality assessment, including adulteration detection in milk and oil grading.
Detect milk adulterants with a multispectral 7265X sensor (visible and near-infrared) at one-inch distance, processed via Arduino, decision tree/lda/svm ML, and neural networks in an IoT web app.
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