Phytothermotherapy (“grass baths”) is a traditional phytotherapy for rheumatism consisting of taking baths in hot fermenting grass. Scientific studies have demonstrated its efficiency in treating several rheumatic diseases. However the efficiency and repeatability of the therapy is dependent on the wild fermentations, determining sometimes the appearance of unpleasant conditions leading to the early abandonment of the therapy. The metabolism undergoing in the grass baths is unknown and there is not an established method to evaluate and predict grass baths quality. The aim of this study is to establish a simple VOCs profiling method able to evaluate the grass baths, predicting their evolution, through the identification of marker volatiles related to the best conditions and/or the spoilage. After replicating in real scale the traditional grass baths, the volatile profiles were measured using passive diffusion samplers injected in a thermal desorption-comprehensive GC×GC-TOF-MS. The high dimensionality of the data coupled with the limited number of time points, required a rigorous method development for the analysis of the data, achieved through the development of a novel R package for variable selection in GC×GC data matrices. The further application of a fuzzy clustering approach demonstrated to be a useful tool dealing with short time series, allowing to discard un-trending volatiles and giving a clear snapshot of the main trends in the data. A broad coverage of the volatolome was provided, thus suitable to describe the main metabolic changes ongoing in the grass baths. Coupling this data with the temperature and pH, and comparing it to the data from similar processes, like silage and compost, we demonstrated that the established method can be helpful to evaluate short time series, allowing us to obtain a list of volatiles as candidate markers for the quality of the grass baths. The established method gave a list of markers applicable to real scale grass baths to predict spoilage; furthermore it provides a list of volatiles where to search for candidate markers with reported health-related effects and can be used to generate hypothesis on the mechanisms of action of the treatment.

Applying novel approaches for GC × GC-TOF-MS data cleaning and trends clustering in VOCs time-series analysis / Narduzzi, Luca; Franciosi, Elena; Carlin, Silvia; Tuohy, Kieran; Beretta, Alberto; Pedrotti, Franco; Mattivi, Fulvio. - In: JOURNAL OF CHROMATOGRAPHY. B. - ISSN 1570-0232. - STAMPA. - 2018:1096(2018), pp. 56-65. [10.1016/j.jchromb.2018.07.012]

Applying novel approaches for GC × GC-TOF-MS data cleaning and trends clustering in VOCs time-series analysis

Narduzzi, Luca;Franciosi, Elena;Mattivi, Fulvio
2018

Abstract

Phytothermotherapy (“grass baths”) is a traditional phytotherapy for rheumatism consisting of taking baths in hot fermenting grass. Scientific studies have demonstrated its efficiency in treating several rheumatic diseases. However the efficiency and repeatability of the therapy is dependent on the wild fermentations, determining sometimes the appearance of unpleasant conditions leading to the early abandonment of the therapy. The metabolism undergoing in the grass baths is unknown and there is not an established method to evaluate and predict grass baths quality. The aim of this study is to establish a simple VOCs profiling method able to evaluate the grass baths, predicting their evolution, through the identification of marker volatiles related to the best conditions and/or the spoilage. After replicating in real scale the traditional grass baths, the volatile profiles were measured using passive diffusion samplers injected in a thermal desorption-comprehensive GC×GC-TOF-MS. The high dimensionality of the data coupled with the limited number of time points, required a rigorous method development for the analysis of the data, achieved through the development of a novel R package for variable selection in GC×GC data matrices. The further application of a fuzzy clustering approach demonstrated to be a useful tool dealing with short time series, allowing to discard un-trending volatiles and giving a clear snapshot of the main trends in the data. A broad coverage of the volatolome was provided, thus suitable to describe the main metabolic changes ongoing in the grass baths. Coupling this data with the temperature and pH, and comparing it to the data from similar processes, like silage and compost, we demonstrated that the established method can be helpful to evaluate short time series, allowing us to obtain a list of volatiles as candidate markers for the quality of the grass baths. The established method gave a list of markers applicable to real scale grass baths to predict spoilage; furthermore it provides a list of volatiles where to search for candidate markers with reported health-related effects and can be used to generate hypothesis on the mechanisms of action of the treatment.
1096
Narduzzi, Luca; Franciosi, Elena; Carlin, Silvia; Tuohy, Kieran; Beretta, Alberto; Pedrotti, Franco; Mattivi, Fulvio
Applying novel approaches for GC × GC-TOF-MS data cleaning and trends clustering in VOCs time-series analysis / Narduzzi, Luca; Franciosi, Elena; Carlin, Silvia; Tuohy, Kieran; Beretta, Alberto; Pedrotti, Franco; Mattivi, Fulvio. - In: JOURNAL OF CHROMATOGRAPHY. B. - ISSN 1570-0232. - STAMPA. - 2018:1096(2018), pp. 56-65. [10.1016/j.jchromb.2018.07.012]
File in questo prodotto:
File Dimensione Formato  
Narduzzi et al JChromB 2018.pdf

Solo gestori archivio

Descrizione: Articolo principale
Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.01 MB
Formato Adobe PDF
2.01 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/211803
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact