Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical...

Area of Science:

• Food Science
• Analytical Chemistry
• Sensor Technology

Background:

• Growing consumer demand for high-quality food necessitates advanced quality assessment methods.
• Organoleptic properties (smell, taste, appearance) are key consumer concerns.
• Electronic sensing systems (e-noses, e-tongues, e-eyes) mimic human senses to detect chemical signals in food.

Purpose of the Study:

• To review the application of e-nose, e-tongue, and e-eye systems combined with chemometrics for food quality evaluation.
• To analyze trends and methodologies in sensor-based food analysis.
• To identify future research directions in the field.

Main Methods:

• Systematic literature review of 122 articles from the Web of Science database.
• Focus on studies utilizing e-nose, e-tongue, and e-eye devices for food analysis.
• Analysis of chemometric approaches employed, including linear and nonlinear methods.

Main Results:

• Exploratory linear methods were most common, followed by classification and regression techniques.
• Nonlinear approaches, particularly AI and machine learning, demonstrated superior performance in classification and regression.
• These advanced methods offer significant potential for improving food quality assessment.

Conclusions:

• E-sensing systems coupled with chemometrics are valuable tools for food quality evaluation.
• Nonlinear chemometric methods, especially AI and machine learning, provide enhanced analytical capabilities.
• Future research should explore big-data applications of AI/ML for more accurate food analysis and safety.