Volatile-Based Prediction of Sauvignon Blanc Quality Gradings with Static Headspace–Gas Chromatography–Ion Mobility Spectrometry (SHS–GC–IMS) and Interpretable Machine Learning Techniques

2021 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul A. Kilmartin
Keyword(s):  
Machine Learning ◽  
Ion Mobility ◽  
Machine Learning Techniques ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Sauvignon Blanc ◽  
Interpretable Machine Learning ◽  
Static Headspace Gas Chromatography ◽  
Learning Techniques ◽  
Headspace Gas

scholarly journals Assessment of Sauvignon Blanc Aroma and Quality Gradings Based on Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS): Merging Analytical Chemistry with Machine Learning

2020 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul Kilmartin
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Wine Aroma ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Wine Quality ◽  
Sauvignon Blanc ◽  
Static Headspace Gas Chromatography ◽  
Headspace Gas

<div>In this paper, we report on the application of the static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) instrument in the field of wine aroma analysis and its potential in constructing a prediction model for the quality gradings of wines. The easy-to-operate, cost effective SHS-GC-IMS instrument was innovatively used for a non-targeted search for volatile compounds in Sauvignon Blanc wine, with the identification of volatiles seldom before reported. The wine aroma profile acquired by the instrument was organically and innovatively combined with advanced classification models, inspired by the computer science community, to produce high classification accuracy in terms of wine quality gradings. Useful insights were also extracted by using advanced interpretation methods on complex models to learn the important volatiles correlated with wine quality grading.</div>

scholarly journals Assessment of Sauvignon Blanc Aroma and Quality Gradings Based on Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS): Merging Analytical Chemistry with Machine Learning

2020 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul Kilmartin
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Wine Aroma ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Wine Quality ◽  
Sauvignon Blanc ◽  
Static Headspace Gas Chromatography ◽  
Headspace Gas

<div>In this paper, we report on the application of the static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) instrument in the field of wine aroma analysis and its potential in constructing a prediction model for the quality gradings of wines. The easy-to-operate, cost effective SHS-GC-IMS instrument was innovatively used for a non-targeted search for volatile compounds in Sauvignon Blanc wine, with the identification of volatiles seldom before reported. The wine aroma profile acquired by the instrument was organically and innovatively combined with advanced classification models, inspired by the computer science community, to produce high classification accuracy in terms of wine quality gradings. Useful insights were also extracted by using advanced interpretation methods on complex models to learn the important volatiles correlated with wine quality grading.</div>

scholarly journals Assessment of Sauvignon Blanc Aroma and Quality Gradings Based on Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS): Merging Analytical Chemistry with Machine Learning

2020 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul Kilmartin
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Wine Aroma ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Wine Quality ◽  
Sauvignon Blanc ◽  
Static Headspace Gas Chromatography ◽  
Headspace Gas

<div>In this paper, we report on the application of the static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) instrument in the field of wine aroma analysis and its potential in constructing a prediction model for the quality gradings of wines. The easy-to-operate, cost effective SHS-GC-IMS instrument was innovatively used for a non-targeted search for volatile compounds in Sauvignon Blanc wine, with the identification of volatiles seldom before reported. The wine aroma profile acquired by the instrument was organically and innovatively combined with advanced classification models, inspired by the computer science community, to produce high classification accuracy in terms of wine quality gradings. Useful insights were also extracted by using advanced interpretation methods on complex models to learn the important volatiles correlated with wine quality grading.</div>

scholarly journals A Validation Study on the Simultaneous Quantification of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS)

2021 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Bahareh Sarmadi ◽  
Paul Kilmartin
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Aroma Compounds ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Simultaneous Quantification ◽  
Static Headspace Gas Chromatography ◽  
Boltzmann Function ◽  
Headspace Gas

A new quantitative method based on static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) is proposed, which enables the simultaneous quantification of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantification studies, namely, non-linearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantification, repeatability, reproducibility, and recovery were investigated. Both non-linear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. Potential pitfalls that would cause inaccurate quantification i.e., effects of ethanol content and competitive ionization, were also discussed. The performance of the SHS-GC-IMS method was subsequently compared against a currently established method, namely, GC-MS, using actual wine samples. These findings provide an initial validation of a GC-IMS-based quantification method, as well as a starting point for further enhancing the analytical scope of GC-IMS.

scholarly journals A Validation Study on the Simultaneous Quantification of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS)

2021 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul Kilmartin
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Aroma Compounds ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Simultaneous Quantification ◽  
Static Headspace Gas Chromatography ◽  
Boltzmann Function ◽  
Headspace Gas

<p>A new quantitative method based on static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) is proposed, which enables the simultaneous quantification of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantification studies, namely, non-linearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantification, repeatability, reproducibility, and recovery were investigated. Both non-linear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. A potential pitfall that would cause inaccurate quantification <i>i.e.</i>, competitive ionization, is also discussed. These findings provide an initial validation of a GC-IMS-based quantification method, as well as a starting point for further enhancing the analytical scope of GC-IMS.</p>

Identification of terpenes and essential oils by means of static headspace gas chromatography-ion mobility spectrometry

2017 ◽  
Vol 409 (28) ◽  
pp. 6595-6603 ◽  
Author(s):  
Roman Rodríguez-Maecker ◽  
Eduardo Vyhmeister ◽  
Stefan Meisen ◽  
Antonio Rosales Martinez ◽  
Andriy Kuklya ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Essential Oils ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Static Headspace Gas Chromatography ◽  
Headspace Gas

scholarly journals A Validation Study on the Simultaneous Quantification of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS)

2021 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul Kilmartin
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Aroma Compounds ◽  
Headspace Gas Chromatography ◽  
Static Headspace ◽  
Simultaneous Quantification ◽  
Static Headspace Gas Chromatography ◽  
Boltzmann Function ◽  
Headspace Gas

<p>A new quantitative method based on static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) is proposed, which enables the simultaneous quantification of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantification studies, namely, non-linearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantification, repeatability, reproducibility, and recovery were investigated. Both non-linear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. A potential pitfall that would cause inaccurate quantification <i>i.e.</i>, competitive ionization, is also discussed. These findings provide an initial validation of a GC-IMS-based quantification method, as well as a starting point for further enhancing the analytical scope of GC-IMS.</p>

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