Development of volatile compounds fingerprints by headspace‐gas chromatography‐ion mobility spectrometry in concentrated tomato paste and distillate during evaporation processing

2020 ◽  
Author(s):  
Dan Wang ◽  
Rongrong Lu ◽  
Yue Ma ◽  
Shuang Guo ◽  
Xiaoyan Zhao ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Volatile Compounds ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Headspace Gas Chromatography ◽  
Tomato Paste ◽  
Headspace Gas

Analysis of the volatile compounds associated with pickling of ginger using headspace gas chromatography ‐ ion mobility spectrometry

2019 ◽  
Vol 34 (6) ◽  
pp. 485-492 ◽  
Author(s):  
Xiao Li ◽  
Wenjia Cui ◽  
Wenliang Wang ◽  
Yueming Wang ◽  
Zhiqing Gong ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Volatile Compounds ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
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 ◽  
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 Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry in Combination with Chemometrics for Detecting the Age of Chinese Liquor (Baijiu)

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2888
Author(s):  
Shuang Chen ◽  
Jialing Lu ◽  
Michael Qian ◽  
Hongkui He ◽  
Anjun Li ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Goodness Of Fit ◽  
Accurate Method ◽  
Headspace Gas Chromatography ◽  
Least Squares Regression ◽  
Chinese Liquor ◽  
Headspace Gas ◽  
Discriminant Models

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.

Adulteration detection of essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry

2021 ◽  
pp. 131373
Author(s):  
Xinjing Dou ◽  
Liangxiao Zhang ◽  
Ruinan Yang ◽  
Xiao Wang ◽  
Li Yu ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Sesame Oil ◽  
Headspace Gas Chromatography ◽  
Headspace Gas
Sign in / Sign up

Export Citation Format

Share Document