Electronic nose dataset for pork adulteration in beef

Area of Science:

• Food Science
• Analytical Chemistry
• Sensor Technology

Background:

• Food adulteration, particularly of meat products, is a significant concern due to economic and religious factors.
• Accurate detection methods are crucial for ensuring food integrity and authenticity.
• Electronic nose (e-nose) technology offers a promising approach for non-destructive analysis of food products.

Purpose of the Study:

• To create and present a comprehensive dataset on pork adulteration in beef using an electronic nose.
• To establish the detection limit of pork adulteration in beef mixtures.
• To facilitate further research in meat authentication and food safety.

Main Methods:

• Preparation of seven distinct beef-pork mixture ratios (0%, 10%, 25%, 50%, 75%, 90%, 100% pork).
• Data acquisition using an electronic nose (e-nose) system over 120-second intervals for each sample.
• Development of a dataset suitable for machine learning and signal processing applications.

Main Results:

• The electronic nose system successfully differentiated between various beef-pork mixture ratios.
• A minimum detection level of 10% pork adulteration in beef was achieved.
• The generated dataset provides a robust foundation for developing advanced detection algorithms.

Conclusions:

• The electronic nose is a viable tool for detecting pork adulteration in beef.
• The dataset supports research in food authentication, Halal verification, and the development of sensor-based analytical methods.
• This work contributes to enhancing food safety and combating fraudulent practices in the food industry.