Addressing temperature variations of miniaturized NIR spectrometers: Advancing quantitative models for pharmaceutical analysis

Miniaturized near-infrared (NIR) spectrometers have gained wide popularity in various pharmaceutical applications, particularly for inline processes. However, their quantification capabilities still lag behind those of mature benchtop spectrometers, with miniaturized NIR spectrometers often lacking the accuracy required for pharmaceutical analysis without calibration transfer to a robust benchtop device. Therefore, a comprehensive investigation of their inherent error sources is imperative to enhance their analytical performance. Factors such as compact size, lack of thermal management systems, and operational conditions render miniaturized devices more susceptible to temperature fluctuations. These fluctuations lead to measurement errors, especially when the duration of inline processes limits the frequency of updating background scans. While previous research investigated the impact of sample and ambient temperature variations, we investigated the impact of temperature variations of miniaturized NIR spectrometers itself during sample and background acquisitions. These variations led to emergence of distinct spectral subsets, posing a risk to accuracy when combined in one model and making prediction of one subset by another challenging. We explored calibration transfer (CT) methods to enhance model robustness and maintain prediction accuracy across temperature subsets, with Ridge and LASSO regressions showing superior results. By addressing this error source, it is possible to enhance the accuracy and robustness of miniaturized NIR spectrometers, particularly in prolonged inline measurements.