Optimization of thermal desorption conditions of stir bar sorptive extraction facilitated by machine learning

Stir bar sorptive extraction is an effective technique for trapping odorants, but there are limited studies on the effect of varying thermal desorption conditions on desorption efficiency of odorants. Therefore, we conducted this study to explore the relationship between desorption conditions and desorption efficiency for 18 odorants with diverse physicochemical properties using instrumental analysis and mathematical modeling. We trained four types of machine learning models using a dataset comprising 864 different combinations of four desorption conditions (each three levels) and physicochemical properties. The prediction value of the selected model was validated using a validation dataset of six new odorants. The Random Forest model had the highest performance (R = 0.910). The order of feature importance using this model was as follows: cryo-focusing temperature, molecular weight, log P, boiling point, desorption temperature, desorption time, and helium flow. For testing on new odorants, the correlations between predicted and experimental data for terpene (R = 0.99), alcohol (R = 0.98), ester (R = 0.92), sulfide (R = 0.89), phenol (R = 0.88), and aldehyde (R = 0.61) were determined.

Automated summary

Stir bar sorptive extraction is effective for trapping odorants, and this study investigated the relationship between desorption conditions and efficiency for different odorants. Four machine learning models were trained using a dataset of four desorption conditions and physicochemical properties. The Random Forest model performed the best, and its prediction value was validated using a dataset of new odorants.