Influence of Path Length on the Accuracy of Infrared (IR) Spectroscopy for Bulk Property Measurements

The optical path length of a transmission flow cell is a critical parameter that influences the quantitative accuracy of infrared (IR) spectroscopy. This study investigates how path length affects the accuracy of IR spectroscopy for measuring bulk properties using a generalizable machine learning (ML)-based framework. The approach was demonstrated using an onboard fuel sensing application aimed at predicting the derived cetane number (DCN) of jet fuels and fuel blends from mid-IR spectra in the wavelength range of 5.8-7.2 & micro;m. A range of path lengths, from 1 & times; 10-5 to 500 & micro;m, was examined by simulating spectra through scaling of experimentally measured data. The results showed that extremely short path lengths led to poor prediction accuracy due to insufficient absorption and low signal-to-noise ratio (SNR), while excessively long path lengths caused strong absorption saturation and amplified spectral artifacts, degrading model performance. A broad near-optimal range between 10 and 58.7 & micro;m was identified within the simulation framework, where spectra exhibited adequate absorption, moderate saturation, and minimal distortion. These findings demonstrate that allowing moderate absorption saturation can enhance predictive accuracy by improving SNR at wavelengths most correlated with the target property and emphasize that optimal measurement conditions should be determined based on predictive performance rather than solely maximizing SNR while maintaining absorbance within traditional limits.