Optimization of electronic nose drift correction applied to tomato volatile profiling

Area of Science:

• Analytical Chemistry
• Food Science
• Sensor Technology

Background:

• Electronic-noses (e-noses) offer a promising method for evaluating the volatile profiles of food products like tomatoes.
• Sensor drift and standardization remain significant challenges for routine e-nose application.

Purpose of the Study:

• To develop and validate a comprehensive strategy for correcting sensor drift in e-nose analysis of tomato volatile profiles.
• To assess the effectiveness of the proposed drift correction methods in enabling reliable and scalable sample analysis.

Main Methods:

• Implemented a multiplicative drift correction procedure combined with Partial Least Squares (PLS) adaptation for short-term drift.
• Utilized a synthetic reference standard mix for long-term drift correction and sequence standardization.
• Employed Partial Least Squares Discriminant Analysis (PLS-DA) for data visualization and sample grouping.

Main Results:

• Achieved a drastic reduction in sensor signal relative standard deviation (RSD), ranging from 75.7% to 99.7%.
• Demonstrated 94.4% success in grouping identical tomato materials in PLS-DA graphical representations.
• Validated the e-nose strategy for analyzing large sample sets across multiple sequences.

Conclusions:

• The integrated drift correction strategy effectively solves sensor drift and standardization issues, enabling routine e-nose use for tomato volatile profiling.
• E-nose analysis provides a cost-effective and scalable alternative to gas chromatography for assessing volatile profiles.
• Developed expandable maps of volatile profile similarities aid in material selection for breeding and evaluating pre/postharvest procedure impacts.