| Title: | Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome |
| Author(s): | Caratti A; Squara S; Bicchi C; Tao Q; Geschwender D; Reichenbach SE; Ferrero F; Borreani G; Cordero C; |
| Address: | "Dipartimento di Scienza e Tecnologia del Farmaco, Universita di Torino, Via Pietro Giuria 9, Turin I-10125, Italy. GC Image LLC, Lincoln, NE, USA. GC Image LLC, Lincoln, NE, USA; Computer Science and Engineering Department, University of Nebraska - Lincoln, Lincoln, NE, USA. Department of Agricultural, Forestry and Food Sciences, Universita di Torino, Grugliasco TO, Italy. Dipartimento di Scienza e Tecnologia del Farmaco, Universita di Torino, Via Pietro Giuria 9, Turin I-10125, Italy. Electronic address: chiara.cordero@unito.it" |
| DOI: | 10.1016/j.chroma.2023.464010 |
| ISSN/ISBN: | 1873-3778 (Electronic) 0021-9673 (Linking) |
| Abstract: | "Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables 'computers and systems to derive useful information from digital images' giving access to higher-level information and 'take actions or make recommendations based on that information'. Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability" |
| Keywords: | Gas Chromatography-Mass Spectrometry/methods *Volatile Organic Compounds/analysis Artificial Intelligence Food Computers Augmented visualization Butter volatilome analysis Chromatograms registration and alignment Comprehensive two-dimensional gas chromato; |
| Notes: | "MedlineCaratti, Andrea Squara, Simone Bicchi, Carlo Tao, Qingping Geschwender, Daniel Reichenbach, Stephen E Ferrero, Francesco Borreani, Giorgio Cordero, Chiara eng Netherlands 2023/04/29 J Chromatogr A. 2023 Jun 21; 1699:464010. doi: 10.1016/j.chroma.2023.464010. Epub 2023 Apr 23" |
