A sensory-calibrated electronic nose method for odor intensity quantification and grade identification in...

Area of Science:

• Food Science
• Sensory Science
• Analytical Chemistry

Background:

• Refrigerator odors from spoiled food and off-gassing impact consumer experience.
• Current odor assessment methods lack objectivity, speed, and human perception alignment.

Purpose of the Study:

• Develop a sensory-calibrated electronic nose (E-nose) for refrigerator odor analysis.
• Quantify odor intensity and identify odor grades aligned with human perception.

Main Methods:

• Human sensory evaluation established odor intensity scales (1 to >20) and grades (weak, moderate, strong).
• An optimized two-sensor array (S3 and S8) was selected using PCA-DI.
• Multivariate regression and Boosted Tree models were used for prediction and classification.

Main Results:

• The E-nose method accurately predicted odor intensity (adjusted R² = 0.8377).
• Odor grade identification achieved high accuracy (93.6% training, 92.0% validation).
• The system demonstrated rapid, objective, and human-aligned odor assessment.

Conclusions:

• The sensory-calibrated E-nose offers a practical solution for refrigerator odor monitoring.
• This methodology provides a framework for intelligent odor management systems.
• The approach enhances objectivity and speed in complex odor analysis.