Predicting lecithin concentration from differential mobility spectrometry measurements with linear regression models and neural networks

• Published in: Talanta (Oxford) 2021-04, Vol.225, p.121926-121926, Article 121926
• Main Authors: Anttalainen, Anna, Mäkelä, Meri, Kumpulainen, Pekka, Vehkaoja, Antti, Anttalainen, Osmo, Oksala, Niku, Roine, Antti
• Format: Article
• Language: English
• Subjects: Concentration prediction; Differential mobility spectrometry; Ion Mobility Spectrometry; Lecithin; Lecithins; Linear Models; Neural networks; Neural Networks, Computer; Phospholipids; Regression analysis; Spectrum Analysis
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2020.121926

Differential mobility spectrometry (DMS) analysis of electrosurgical smoke can be used to distinguish cancerous and healthy tissues. Mass spectrometry studies of surgical smoke have revealed phospholipids as the key compounds enabling this discrimination. Lecithin is a mixture of phospholipids encountered in tissues. We hypothesized that DMS is capable of detecting and quantifying lecithin from water solution in headspace chamber, paving way for analysis of surgical smoke. We measured different lecithin concentrations in a biologically relevant range considering healthy and cancerous tissues with DMS and trained regression models to predict the analyte concentration. The models were internally cross-validated and externally validated. The best cross-validation results were obtained with convolutional neural networks, with root mean square error (RMSE) = 0.38 mg/ml. This is the first demonstration of estimation of analyte concentration from DMS measurements with neural networks. The best external validation results were acquired with sparse linear regression methods, with RMSE varying from 0.40 mg/ml to 0.41 mg/ml. The results demonstrate that DMS is sufficiently sensitive to detect biologically relevant changes in phospholipid concentration, potentially explaining its ability to detect cancerous tissue. In the future, we aim to reproduce the results by using surgical smoke as the medium. In this scenario, the complex background of surgical smoke will be the main challenge to overcome. Predicting concentration with neural networks also lays the foundation for wider analytical usage of DMS. [Display omitted] •Differences in phospholipid concentration are key aspects in cancerous tissue detection.•We predicted the phospholipid concentration from differential mobility spectrometry (DMS) data.•DMS can detect cellular level differences in phospholipid solutions from headspace chamber.•Non-linear regression models such as neural networks are suitable for concentration prediction.