Alberto Ferrer Multivariate Statistical Engineering Group (GIEM) Dpt of Applied Statistics, Operations Research and Quality Universitat Politècnica de València
In this webinar Feb 17, 15 pm CET, Alberto Ferrer will address the potential of Latent Variables-based Multivariate Statistical Models such as Partial Least Squares Regression (PLS) for facing some challenges in Industry 4.0 by exploiting its property of being able to model “causality” in the latent space even in the case of using historical data, typical of these highly digitalized environments.
For any mass spectrometry based analytical assay it is considered best practice to include mechanisms for assessing the quality of acquired analyte concentrations. This is particularly important for untargeted metabolomics, where many hundreds of metabolites may be (relatively) quantified in parallel, with metabolite identification performed post hoc, making it is impossible to calibrate each metabolite to an internal standard gradient, and equally, making it impossible to ensure optimal peak-shape for all detected features. This leaves the acquired data open to unwanted within- and between-batch variation throughout a given experiment. For over a decade, the use of repeat-injection pooled quality control samples has proven to be a popular means of monitoring the precision of acquired data. In this webinar I will briefly outline current best practices and demonstrate a new software package (qcmxp.org) for assessing and potentially improving the precision of untargeted metabolomics data collect using these protocols.
Dr. David Broadhurst is Professor of Metabolomic Epidemiology & Biosystems Data Science at Edith Cowan University, Perth, Western Australia. He has been an active member of the metabolomics community for over 25 years. In 2022 he was made a lifetime honorary fellow of the Metabolomics Society for his work promoting best practice in design of experiments, biostatistics and machine learning.
We have been working with untargeted GC-MS for decades here so it is about time that we now finally put some data out there for you to play with. The first data set comes from a bachelor project on apple wine fermentation. Have fun!
We keep finding that we left out some data sets and functions when we transferred to our new home page here. So keep sending us notifications if you miss something.
We have made a small interactive program for learning about Control Charts (Shewhart, CUMSUM). The program is an educational tool, that has been made freely available.
The app runs in MATLAB either locally hosted or through MATLAB online. A small user guide is available . Note, the program can be made available as a standalone application using the MATLAB compiler to produce an *.exe file.
Are you interested in learning a simple and free tool for turning untargeted GC-MS data into peak tables. And do so with less time, less user-dependence and with more analytes recovered. Then you may want to learn how to use PARADISe, a stand-alone Windows program for just that. We are running a two-day course on how to use the software:
PARADISe : a user friendly software for untargeted analysis of Gas Chromatography Mass Spectrometry (GC MS) data
Traditionally, GC-MS data analysis follows a targeted approach that involves several time consuming steps (integration and quantification), usually carried out sample by sample, being also subject to inter-user variability. Furthermore, interesting compounds are often left undiscovered due either to practical reasons or analytical limitations (limit of detection and quantification). PARADISe is a user-friendly tool for GC-MS deconvolution and identification It allows to perform untargeted analysis, meaning that all compounds present in the samples are considered, while overcoming the problems mentioned above.
Audience The course is intended for GC-MS users at any level of expertise in any scientific field, working in both academic and industrial environment Basic knowledge of chemometrics or statistics is advisable, but not mandatory.
This course will provide the participants a complete overview of the software, from theory to practice Participants are encouraged to bring and work with their own data, otherwise we will provide them with a dataset.
Monday, November 13th 9-12 Theoretical background. The data science behind the tool Monday, November 13th 12.30-15 Getting started with PARADISe Tuesday, November 21 st 9-15 Discussion of your experience so far, troubleshooting, good practices and challenges
Red wines, 44 samples, produced from the same grape (100% Cabernet Sauvignon), harvested in different geographical areas, have been collected from local supermarkets in the area of Copenhagen, Denmark. Details on the geographical origins and number of wine samples analysed are given in Table 1.
Table 1. Geographical origin of the analysed red wines
Origin
Wine samples
Argentina
6
Chile
15
Australia
12
South Africa
11
Total
44
The wine samples have been analyzed using head space GC-MS and FT-IR analytical instruments. The FT-IR was a commercial WineScan instrument provided by FOSS Analytical A/S.
GC-MS data
For each sample a mass spectrum scan (m/z: 5-204) measured at 2700 elution time-points was obtained providing a data cube of size 44×2700×200. In Figure 1 an example of a chromatogram for one red wine sample is shown.
Figure 1. Typical chromatogram showing the TIC (Total Ion Count) of one red wine sample.
In the figure the abundance at each scan is found by summing the contribution of all intensities of mass channels investigated (m/z: 5-204).
FT-IR data
For all wine samples 14 quality parameters were predicted from the IR spectra (Figure 2) using the FOSS WineScan build-in calibration models (Table 2).
Figure 2. Typical IR spectrum of one red wine sample. The water band regions around 1545-1710 cm-1 and 2968-3620 cm-1 should be excluded from the data analysis.
Table 2. Quality parameters measured on the WineScan instrument and used in MVP (units shown in brackets)
#
Quality parameter
1
Ethanol (vol. %)
2
Total acid (g/L)
3
Volatile acid (g/L)
4
Malic acid (g/L)
5
pH
6
Lactic acid (g/L)
7
Rest Sugar (Glucose + Fructose) (g/L)
8
Citric acid (mg/L)
9
CO2 (g/L)
10
Density (g/mL)
11
Total polyphenol index
12
Glycerol (g/L)
13
Methanol (vol. %)
14
Tartaric acid (g/L)
Get the data
The data are available in zipped MATLAB 6.x format. Download the data and write load Wine_v6 in MATLAB.
If you use the data we would appreciate that you report the results to us as a courtesy of the work involved in producing and preparing the data. Also you may want to refer to the data by referring to:
T. Skov, D. Balabio, R. Bro (2008). Multiblock Variance Partitioning. A new approach for comparing variation in multiple data blocks. Analytica Chimica Acta, 615 (1): 18-29
Zip-file information
Variable
Description
Dimensions
Aroma_compounds
Peak areas of aroma compounds
44×57
Class
Classes of wines (see Table 1)
44×1
Data_GC
Three-way data
44×2700×200
Elution_profiles
Summed mass dimension – see Figure 1
44×2700
IR_spectra
IR spectra without waterband
44×842
IR_spectra_with_waterband
IR spectra with waterband – see Figure 2
44×1056
Label_Aroma_comp
Label aroma compounds
1×57
Label_Elution_time
Label elution time in minutes
1×2700
Label_Mass_channels
Label m/z
1×200
Label_Pred_values_IR
Label quality parameters
1×14
Label_Wine_samples
Label wine samples ARG: Argentina AUS: Australia CHI: Chile SOU: South Africa
