Transmittance hyperspectral capture system and methodology assessment for blood-liquid serum samples analysis

Hyperspectral imaging analyzed by machine learning algorithms is a powerful tool to classify materials, tissues, molecules and pathogens. By analyzing the electromagnetic spectrum of liquid serum samples, it has been demonstrated that it is possible to predict which patients with possible head trauma injury will have a possible result on computer tomography. This process is being carried out with very complex, slow and expensive spectrometric techniques. To tackle this problem, this study presents a simple hyperspectral imaging system that allows the capture of multiple serum samples with one single scan, without light artifacts as it works in transmittance and without a high data redundancy rate. Throughout this paper, the main characteristics of this system, the pre-processing chain necessary to extract the information from these captures, the working methodology, and the analysis performed are presented. Hyperspectral images of plasma from 405 patients were captured and the signatures obtained from this system were compared with the signatures captured by a spectrometer, which served as a reference system. With a mean correlation of 97.3% and a standard deviation of 3.6%, the presented system not only captures correctly liquid samples, but also provides spatial information and can capture many more samples in a single scan. In addition, a statistical study is presented on which spectrum bands present a higher concentration of information, which will be very beneficial for future analysis.