“The potential of applying this kind of multivariate analysis to SIFT-MS analysis is huge”


Jessica Creak Syft Technologies Application Scientist

Jessica Creak

BA(Hons), MA(Cantab)


Applications Scientist

Jessica graduated from the University of Cambridge in 2013 with a degree in Natural Sciences and specializing in chemistry. Before joining Syft in 2016 she completed a two-year leadership development course managed by TeachFirst, which saw her teaching chemistry in a secondary school located in a low socioeconomic area of Mid-Wales in the UK.

Jessica enjoys working within the food, flavour and environmental applications at Syft and is a keen blogger for our website!

Outside of work Jessica enjoys walking, travelling and team sports.

 


Fig.1. Shows the principle component models for green coffee beans from five different countries of origin.

The Syft Technologies Voice200ultra Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) instrument is the perfect platform for generating quality data for multivariate statistical post-processing. Our exclusive LabSyft software is compatible with statistical software which allows data to be processed as soon as it is created.

How does it work?


The first step in the process is creating your statistical model. You do this by using a “training data set” consisting of samples from each defined class. In the coffee example below, the classes were defined by country of origin, but this could also relate to pass/fail product samples. The classification models are built using principal component analysis (PCA) whereby only the significant components within a sample are retained.

For example, in a full scan mass spectrum this would be particular peaks or ratios of peaks. Alternatively, it could be a subset of specific compounds.
Once the model is created, a SIMCA (soft independent modelling by class analogy) approach can be used to classify new samples. This is done by projecting the observations onto each principal component model and calculating the goodness of fit.

In figure 1. you can see the principle component models for green coffee beans from five different countries of origin.

Methodology


    1. Green coffee beans of various origins (Brazil, Colombia, Ethiopia, Guatemala, and Sumatra (Indonesia) were obtained from a New Zealand importer.
    2. Single beans were placed in 10-mL sample vials (seven replicates per origin) and equilibrated at room temperature (~22 °C) for 30 minutes in a standard sample vial tray on a GERSTEL Multipurpose Sampler (MPS).
    3. Analysis was carried out using a Voice200ultra SIFT-MS instrument in full scan mode integrated with the GERSTEL MPS.
    4. Multivariate statistical analysis was performed using the Pirouette software package (from Infometrix).

Opportunities


The potential of applying this kind of multivariate analysis to SIFT-MS analysis is huge: rapid sample analysis combined with automatic classification allows companies to quickly check the quality and the authenticity of incoming product.

Posted by Jessica Creak, Applications Scientist at Syft Technologies.

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