{'fraction_non_alphanumeric': 0.01598173515981735, 'fraction_numerical': 0.0031963470319634705, 'mean_word_length': 5.741538461538462, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Measurement of gas-liquid partition coefficient and headspace concentration profiles of perfume materials by solid-phase microextraction and capillary gas chromatography-mass spectrometry

An empirical model describing the relationship between the partition coefficients (K) of perfume materials in the solid-phase microextraction (SPME) fiber stationary phase and the Linearly Temperature Programmed Retention Index (LTPRI) is obtained. This is established using a mixture of eleven selected fragrance materials spiked in mineral oil at different concentration levels to simulate liquid laundry detergent matrices. Headspace concentrations of the materials are measured using both static headspace and SPME-gas chromatography analysis. The empirical model is tested by measuring the K values for fourteen perfume materials experimentally. Three of the calculated K values are within 2-19% of the measured K value, and the other eleven calculated K values are within 22-59%. This range of deviation is understandable because a diverse mixture was used to cover most chemical functionalities in order to make the model generally applicable. Better prediction accuracy is expected when a model is established using a specific category of compounds, such as hydrocarbons or aromatics. The use of this method to estimate distribution constants of fragrance materials in liquid matrices is demonstrated. The headspace SPME using the established relationship between the gas-liquid partition coefficient and the LTPRI is applied to measure the headspace concentration of fragrances. It is demonstrated that this approach can be used to monitor the headspace perfume profiles over consumer laundry and cleaning products. This method can provide high sample throughput, reproducibility, simplicity, and accuracy for many applications for screening major fragrance materials over consumer products. The approach demonstrated here can be used to translate headspace SPME results into true static headspace concentration profiles. This translation is critical for obtaining the gas-phase composition by correcting for the inherent differential partitioning of analytes into the fiber stationary phase.