Modeling of the Mass Spectrometric Response Factors in Non-target Analysis

Area of Science:

• Analytical Chemistry
• Environmental Science
• Computational Chemistry

Background:

• Volatile organic compounds (VOCs) are significant air pollutants requiring accurate quantification.
• Mass spectrometry (MS) is a key technique for VOC analysis, but response factors vary.
• Developing predictive models for MS response factors can improve VOC determination.

Purpose of the Study:

• To establish a quantitative structure-property relationship (QSPR) model for predicting MS response factors.
• To encode chemical structures of VOCs using molecular descriptors.
• To validate the model's predictive accuracy for environmental applications.

Main Methods:

• Experimental measurement of MS response factors for 36 saturated and unsaturated VOCs.
• Utilizing multiple linear regression (MLR) to build the QSPR model.
• Employing leave-one-out cross-validation to assess model performance and error.

Main Results:

• A robust QSPR model was successfully established using molecular descriptors and MLR.
• Cross-validation demonstrated a 15% error in predicting response factors, deemed sufficient for air analysis.
• The model showed good predictive ability for diverse VOCs containing C, H, and halogen atoms.

Conclusions:

• The developed QSPR model provides a reliable method for estimating MS response factors.
• This approach facilitates simultaneous qualitative and quantitative analysis of atmospheric VOCs.
• The study offers a valuable tool for environmental monitoring and air quality assessment.