Finding features - variable extraction strategies for dimensionality reduction and marker compounds identification in GC-IMS data

Christmann, Joscha; Rohn, Sascha; Weller, Philipp

doi:10.1016/j.foodres.2022.111779

Cited by 20 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, to the classification metrics the PLS coefficients and variable importance in projection scores were characterized. There was no identifiable characteristic marker compound for any of the species, the information on class differences is in the ratios between the peaks …”

Section: Resultsmentioning

confidence: 99%

“…There was no identifiable characteristic marker compound for any of the species, the information on class differences is in the ratios between the peaks. 24 A second observation is that the S. cerevisiae batches seem to form two clusters. The samples further to the right side of the plot are the ones taken in the second half of the fermentation.…”

Section: Hs-gc-ims Instrumentation and Measurementmentioning

confidence: 98%

See 1 more Smart Citation

Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS

Christmann,

Weber,

Rohn

et al. 2024

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

Gas chromatography combined with ion mobility spectrometry (GC-IMS) is a powerful separation and detection technique for volatile organic compounds (VOC). This combination is characterized by exceptionally low detection limits in the low ppbv range, high 2-dimensional selectivity, and robust operation. These qualities make it an ideal tool for nontarget screening approaches. Fermentation broths contain a substantial number of VOC, either from the medium or produced by microbial metabolism, that are currently not regularly measured for process monitoring. In this study, Escherichia coli, Saccharomyces cerevisiae, Levilactobacillus brevis, and Pseudomonas fluorescens were exemplarily used as model organisms and cultivated, and the headspace was analyzed by GC-IMS. Additionally, mixed cultures for every combination of two of the microorganisms were also characterized. Multivariate data analysis of the GC-IMS data revealed that it is possible to differentiate between the microorganisms using PLS-DA with a prediction accuracy of 0.92. The mixed cultures could be separated from the pure cultures with accuracies between 0.87 and 1.00 depending on the organism. GC-IMS data correlate with the optical density and can be used to follow and model growth curves. The root mean squared errors ranged between 10 and 20% of the maximum value, depending on the organism. Peak identification with reference compounds did not reveal specific marker compounds, rather the pattern was found to be responsible for the model performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Hs-gc-ims Instrumentation and Measurementmentioning

confidence: 98%

Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS

Christmann,

Weber,

Rohn

et al. 2024

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mi et al [86], instead, studied the volatile composition of chili peppers, in particular the flavonoid-related species, discriminating two genotypes of them by PCA and PLS-DA. Christmann et al developed a chemometric method based on PCA and PLS-DA to unfold high-dimension GC-IMS data and find the most relevant variables (i.e., chromatographic peaks in the 2D IMS-plot) [87] and tested a Python free package to elaborate GC-IMS data in an user-friendly way [88].…”

Section: Gas Chromatography (Gc) and Chemometricsmentioning

confidence: 99%

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

et al. 2023

View full text Add to dashboard Cite

Instrument automation, technological advancements and improved computational power made separation science an extremely data-rich approach, requiring the use of statistical and data analysis tools that are able to optimize processes and combine multiple outputs. The use of chemometrics is growing, greatly improving the ability to extract meaningful information. Separation–multidetection generates multidimensional data, whose elaboration should not be left to the discretion of the operator. However, some applications or techniques still suffer from the lack of method optimization through DoE and downstream multivariate analysis, limiting their potential. This review aims at summarizing how chemometrics can assist analytical chemists in terms of data elaboration and method design, focusing on what can be achieved by applying chemometric approaches to separation science. Recent applications of chemometrics in separation analyses, in particular in gas, liquid and size-exclusion chromatography, together with field flow fractionation, will be detailed to visualize the state of the art of separation chemometrics, encompassing volatile, soluble and solid (colloidal) analytes. The samples considered will range from food chemistry and environmental chemistry to bio/pharmaceutical science.

show abstract

“…24 Currently, HS-GC-IMS has been successfully applied in various elds of food science, including food fraud detection, authenticity control, and avor analysis. 25,26 The HS-GC-IMS technique has the advantages of high sensitivity and specicity, which make it uniquely suited for the traceability of the origin of agricultural products. As a nondestructive assay, HS-GC-IMS does not affect the nature of the food samples during the assay process and is therefore suitable for rapid detection of food products without compromising their quality and integrity.…”

Section: Introductionmentioning

confidence: 99%

Geographical differentiation of garlic based on HS-GC-IMS combined with multivariate statistical analysis

Cui,

Liu,

Zhang

et al. 2024

Anal. Methods

View full text Add to dashboard Cite

show abstract

Finding features - variable extraction strategies for dimensionality reduction and marker compounds identification in GC-IMS data

Cited by 20 publications

References 26 publications

Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS

Nontargeted Volatile Metabolite Screening and Microbial Contamination Detection in Fermentation Processes by Headspace GC-IMS

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

Geographical differentiation of garlic based on HS-GC-IMS combined with multivariate statistical analysis

Contact Info

Product

Resources

About