A novel method to analyse DART TOFMS spectra based on Convolutional Neural Networks: A case study on methanol extracts of wool fibres from endangered camelids

Abstract

Monitoring the illegal trade of wool fibres of wild vicuña (Vicugna vicugna) and guanaco (Lama guanicoe) is highly desirable. The high market value of fleece from these camelid species poses a threat to their wild populations. A previous study showed that direct analysis in real time time-of-flight mass spectrometry (DART-TOFMS) effectively identifies wool fibres to species. Producing high-resolution data in a short period of time makes DART-TOFMS a reliable identification tool, even though data analysis can still be improved. The present study proposes a novel data analysing pipeline based on Convolutional Neural Networks (CNN), applicable to any kind of DART-TOF MS data. We tested our proposed method on keratin fibres of four camelid species (Vicugna vicugna: n = 19; Vicugna pacos: n = 20; Lama guanicoe: n = 20, and Lama glama: n = 20). Analyses showed that selecting 512 ions with the highest relative intensity provides the best resolution and yields 100% accuracy for species identification.