Untargeted rapid differentiation and targeted growth tracking of fungal contamination in rice grains based on headspace‐gas chromatography‐ion mobility spectrometry

Author(s):  
Shuang Gu ◽  
Zhenhe Wang ◽  
Jun Wang
2021 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Bahareh Sarmadi ◽  
Paul Kilmartin

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.


Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2888
Author(s):  
Shuang Chen ◽  
Jialing Lu ◽  
Michael Qian ◽  
Hongkui He ◽  
Anjun Li ◽  
...  

This paper proposes the combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometrics as a method to detect the age of Chinese liquor (Baijiu). Headspace conditions were optimized through single-factor optimization experiments. The optimal sample preparation involved diluting Baijiu with saturated brine to 15% alcohol by volume. The sample was equilibrated at 70 °C for 30 min, and then analyzed with 200 μL of headspace gas. A total of 39 Baijiu samples from different vintages (1998–2019) were collected directly from pottery jars and analyzed using HS-GC-IMS. Partial least squares regression (PLSR) analysis was used to establish two discriminant models based on the 212 signal peaks and the 93 identified compounds. Although both models were valid, the model based on the 93 identified compounds discriminated the ages of the samples more accurately according to the goodness of fit value (R2) and the root mean square error of prediction (RMSEP), which were 0.9986 and 0.244, respectively. Nineteen compounds with variable importance for prediction (VIP) scores > 1, including 11 esters, 4 alcohols, and 4 aldehydes, played vital roles in the model established by the 93 identified compounds. Overall, we determined that HS-GC-IMS combined with PLSR could serve as a rapid and accurate method for detecting the age of Baijiu.


2020 ◽  
Vol 13 (10) ◽  
pp. 7524-7532 ◽  
Author(s):  
Anggita Rosiana Putri ◽  
Maria José Aliaño-González ◽  
Marta Ferreiro ◽  
Widiastuti Setyaningsih ◽  
Abdul Rohman ◽  
...  

2020 ◽  
Author(s):  
Wenyao Zhu ◽  
Frank Benkwitz ◽  
Paul Kilmartin

<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>


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