Artificial Intelligence-Supported Spatial Scanning for Enhanced Real-Time Spectral Analysis of Heterogeneous Media

In spectroscopic analysis, the development of cost-effective handheld infrared spectrometers has revolutionized monitoring practices, particularly in industries like agriculture and food production. Analysis time and efficiency of processing the raw spectral data are further boosted by the implementation of machine learning models for identification and quantification purposes. However, the inherent spectral variations arising from the random spatial arrangement of inhomogeneous granular media pose accuracy challenges. To enable real-time monitoring with enhanced precision, a scanning approach is presented herein. It involves the utilization of microelectromechanical systems-based infrared spectral sensors with spot sizes of 3 to 20 mm, facilitating spectrospatial averaging by scanning over the sample surface. A model is developed for the absorbance repeatability of the nonhomogeneous samples. It takes into account the effects of spatial and temporal noise, the sample scanned area, and the optical throughput of the device, also revealing an optimum point for the light spot size. Through experimental validation performed on wheat, chosen among the major cereals, we demonstrate that the spatial scanning method considerably improves absorbance repeatability of the 10-mm sensor by up to 5 times compared to stationary measurements, showing a good agreement with the theoretical model. The impact of sample scanning on artificial intelligence-based chemometrics model accuracy is rigorously assessed using heterogeneous feed samples, revealing remarkable enhancements in the accuracy of prediction models. The 10-mm sensor exhibits a substantial reduction in root mean square error for protein (−60%) and for moisture (−43.5%), 2 major nutritional quality indicators of cereal grains.