Quantitation of volatile aldehydes using chemoselective response dyes combined with multivariable data analysis

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Background:

• Volatile aldehydes (VAs) are key indicators of food quality and spoilage.
• Accurate quantification of VAs is crucial for food safety and quality control.
• Existing methods for VA detection can be complex and time-consuming.

Purpose of the Study:

• To develop a novel, quantitative method for volatile aldehyde detection using chemoselective response dyes (CRDs).
• To establish robust prediction models for VAs using multivariate data analysis and visible near-infrared spectroscopy.
• To investigate the interaction mechanism between CRDs and VAs using Density Functional Theory (DFT).

Main Methods:

• Utilized chemoselective response dyes (CRDs) for selective detection of volatile aldehydes.
• Acquired multivariate spectral data via visible near-infrared spectroscopy.
• Applied the Synergy-interval Partial Least Squares (Si-PLS) algorithm for quantitative model development.
• Employed Density Functional Theory (DFT) to explore CRD-VA interaction mechanisms.

Main Results:

• Developed quantitative prediction models for VAs with prediction coefficients (Rp) ranging from 0.8399 to 0.9886.
• Achieved low Root Mean Square Error of Prediction (RMSEP) values, all below 0.01.
• Successfully verified models by classifying aging rice samples, with 93% accuracy in the prediction set.
• Identified strong correlations between molecular properties (HOMO-LUMO energy, dipole moment) and CRD-VA interactions.

Conclusions:

• The proposed CRD-based method offers a sensitive and accurate approach for volatile aldehyde quantification.
• Multivariate data analysis, particularly Si-PLS, is effective for building reliable VA prediction models.
• DFT calculations provide valuable insights into the chemical interactions governing the detection process.
• This method has potential applications in food quality assessment and spoilage detection.