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

• DOI: 10.1016/j.ijms.2023.117050
• Publication Date: 2023-04-04T06:10:15Z
• Authors: Mehrdad Jahanbani...; Erin Price; Benito A. González; Luis A. Raggi; Shima Javanmardi; Frederic Lens; Barbara Gravendeel; Edgard Espinoza; Fons J. Verbeek
• Keywords: Dart; Species Identification

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