This study examines the application of the VIAVI microNIR, a portable near-infrared device, for the qualitative and quantitative analysis of illicit drugs. Predictive models were developed using data from Gas Chromatography-Mass Spectrometry (GC-MS) analyses, the reference method. The primary challenge addressed was the harmonization of data from various GC-MS sources to optimize the accuracy of NIR spectral predictions. The research focused on methodologies for data integration and model development, utilizing advanced statistical methods and ensemble machine learning techniques to improve the predictive accuracy of NIR models in the face of multisource GC-MS data variability. Emphasis was placed on algorithm optimization and sophisticated data processing techniques necessary for robust model formation.
Furthermore, the study explored the scalability challenges of these models for broad deployment across various devices, highlighting the need for maintaining accuracy and consistency in different operational settings. Strategies such as device calibration validation, real-time data analysis, and implementation of strict quality control measures to ensure stable and reliable predictions on a large scale were implemented.