Why do people buy Progenesis QI when there is freeware available?

It’s an interesting question and there are many of our users out there with various answers. We decided to ask our users some questions about why they bought Progenesis QI and what difference it has made to their research. Here’s what Research Professor Jace W. Jones had to say on the matter:

Please can you briefly describe your area of research?Jace operating the Synapt G2-S

Our research involves development of mass spectrometry-based platforms that couple biomarker discovery to quantitative validation, from circulating and tissue lipids. In particular, the use of high resolution tandem mass spectrometry to structurally elucidate, identify, and quantify biologically active lipids to further understand disease/injury mechanisms of action and provide insight for drug development targets. To this end, we first design untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments to identify differentially expressed plasma and tissue-bound lipids using in vivo models. Our discovery–based instrument platform of choice is the Waters UPLC coupled to a Synapt G2-S operated in HDMSE acquisition mode. Our typical LC conditions elute lipids over a 20-minute gradient using a UPLC C18 column. The HDMSE data is acquired in both positive and negative ion modes. Experimental parameters vary depending on the particular in vivo model under study but involve multiple biological replicates per condition, per time point. In addition, quality control samples and addition of internal standards are standard operational procedure. The resulting output from this type of workflow is a tremendous amount of analytical data per sample that ideally generates a list of identified lipids that are differentially expressed between the conditions under study.

What problems did you experience prior to using Progenesis?

The data generated from the UPLC-HDMSE workflow is highly complex and results in 1000s of m/z values being identified by a number of analytical parameters, such as retention time, drift time, accurate mass precursor ions, and diagnostic product ions. In order to expedite biomarker discovery and fully utilise the multidimensional data generated on the UPLC HDMSE platform, we realised there was an immediate need for a bioinformatics solution that could efficiently process multidimensional datasets.

What made you convert to Progenesis QI?

We decided to go with Progenesis QI for its ability to handle multidimensional datasets, especially HDMSE workflows. In addition, a primary goal with our discovery/un-targeted mass spectrometry experiments is to generate lipid markers that can then be pipelined for targeted, high-throughput assays. Progenesis QI is an efficient bioinformatics solution that allows us to make the transition from discovery to validation. The ability to process multi-vendor data was also a major selling point.

What difference has Progenesis QI made to your research?

Progenesis QI enables us to efficiently process multidimensional lipidomic datasets in a systematic and straightforward manner. We can also now process HDMSE data on a single software platform.

One of the biggest differences we have seen is our ability to incorporate more biological replicates at the same time including temporal time points and multiple conditions. This gives us the ability to bolster our statistical significance and conduct experiments where we can evaluate potential biomarkers across time over varied conditions.

Please can you give a specific example of the success that Progenesis QI has helped you to achieve?

Progenesis QI has enabled us to increase our lipidomic workflow while increasing the amount of analytical data per sample. Because our data processing has been streamlined with Progenesis QI, we now spend more time on optimizing chromatography (e.g. orthogonal column chemistries) and mass spectrometry acquisition (e.g. ion mobility with tandem mass spectrometry) for more confident lipid identification.

How will it help you in your future research?

The demand for lipidomic experiments from not only our existing collaborators but also from outside researchers has grown steadily over the past couple years. Progenesis QI has enabled us to keep pace with that demand by allowing us to efficiently and confidently process multidimensional lipidomic datasets. This, in turn, expedites the experimental process of generating potential lipid biomarker candidates.

What advice would you give to a metabolomics/lipidomics scientist struggling with similar problems?

The amount of data generated by metabolomic/lipidomic workflows means a tremendous reliance on data processing. Often, the data processing aspect of ‘omics data is time-consuming and beyond the expertise of the scientist performing the experiments. Consequently, having a bioinformatics solution that is efficient, versatile, and reliable is a valuable investment and allows researchers to focus on optimization of their experimental approach and validation studies for potential targets. I highly recommend the use of Progenesis QI as your bioinformatics solution.


If you are a Progenesis QI user and would like to tell us about your research, please contact us – we’d love to hear from you.

6 ways Progenesis QI can help with your compound identification

Visualisation of the results from a theoretical fragmentation search as done in Metascope

One of the biggest challenges in metabolomics is compound identification – it’s a topic that comes up continually, and something at Nonlinear HQ that we’re constantly trying to help with. The recent releases of Progenesis QI have focussed on improving the process of compound identification, but do you know just how many tools are available within the software?


MetaScope is a tool unique to Progenesis QI, and is the most versatile identification plugin we offer. It can be used to perform a neutral mass search but also allows searching using retention time and CCS values.

For the neutral mass search, MetaScope can read libraries in either SDF, CSV, XLS or XLSX format to give flexibility in the source of your chosen libraries. The ability to search SDFs means you can make use of publicly available libraries, such as HMDB. Thanks to Progenesis SDF Studio, you can customise existing databases by merging multiple files, removing entries or fixing errors. Having the option to search from a CSV / Excel file means you can make your own library without the need to construct an SDF.

MetaScope can also make use of fragmentation data by searching a fragment database or by doing theoretical fragmentation. If you’d like to build your own fragment database, Progenesis QI can help you do that too.


ChemSpider is a web-based chemical structure database with access to over 32 million structures from hundreds of data sources. This tool makes use of those ChemSpider web services, automatically exporting data from Progenesis QI to ChemSpider for searching according to the parameters you select, importing the results, and assigning them against the correct compounds within the software.

As well as being able to define which of the 600+ libraries to search from, and set parameters for precursor tolerance, you can also perform theoretical fragmentation on the search hits, and filter the search by elemental composition and isotope similarity score. Just as for MetaScope and the elemental composition tools, parameter sets can be saved for use with subsequent experiments.


Don’t have access to your own library and don’t want to download one? You can make use of METLIN, a metabolite database containing over 240,000 compounds. METLIN, developed by the Scripps Center for Metabolomics, provides information on names, formulae, theoretical masses, and a link to a webpage detailing identifiers for the compound on various other databases such as KEGG and HMDB.


LipidBlast is a computer-generated MS/MS database produced by the Metabolomics Fiehn Lab. Since theoretical fragmentation searching can be unsuitable for lipids due to the specificity of bond breakages, LipidBlast is a useful alternative.

Elemental Composition Estimation

When you can’t find a database match for your compounds, it may be useful to see the theoretical molecular formulae that match the measured masses and isotope distributions. This tool can also help you to filter down a set of potential hits retrieved from a database search.

Progenesis QI has 3 pre-defined parameter sets: small molecules, lipids, and CHNO (optimised for simple organic), but you can also create your own which can be saved for future use.

Once you have the theoretical formula, you can search this manually in online databases such as PubChem to return potential IDs.

NIST MS/MS Library

The NIST MS/MS library search plugin bundles the NIST 14 LC-MS/MS libraries and performs a combination of neutral mass and MS/MS based searches. This can provide a higher degree of confidence to using just theoretical fragmentation and saves time spent creating your own MSMS library.

Please note that this plugin comes at an additional cost – please contact us for more information.

What next?

We’re always looking for more ways we can improve the identification process, so if there’s a tool you’d like us to link up with, get in touch.

Season’s Greetings from all at Nonlinear!

It’s that time of year again when we close the office for the festive period, and we’d like to take a moment to wish everyone a Merry Christmas and a Happy New Year.

It’s been another busy year for us, with a few highlights worth mentioning:

  • We released 2 updates to Progenesis QI, with v2.0 being released in March, and v2.1 following shortly after in August.
  • We brought out a brand new product, which is FREE to download and use: Progenesis SDF Studio, releasing v1.0 in July following some great feedback to the Beta release of v0.9 in April.
  • We’ve been to conferences all over the world, including the Czech Republic, Canada, various states of the USA and Germany.
  • We welcomed back Gavin Hope to Nonlinear, who re-joined us as a software developer back in September.
  • We also acquired a new member of the team who is proving to possibly be our most popular “employee” yet: Winston the Guide Dog, who is my service dog:

Winston the Guide Dog

The office will be closed from Christmas Day and reopening on Monday 4th January.

Barking up the right tree: characterising Garcinia buchananii extracts with Progenesis QI

It’s often said that plants are a rich source of dietary supplements, medicines, and other usefully bioactive phytochemicals. Among these, there are many traditional remedies derived from plants, but these often derive from a specific part of a plant, or historical means of preparation. How, then, to know if this is the best method of obtaining the target compounds? Are they the ‘best’ compounds that plant has to offer? How do different parts of the plant differ from each other for providing bioactive metabolites? The answers to these questions could both help to obtain better yields of such compounds, and to assess whether there is real medical benefit on offer.

Progenesis QI, which we think is a versatile piece of software, is beginning to assist this process, and it turns out that two of its strengths are key to this. Firstly, the ability to rapidly quantify and effectively identify compounds in complex metabolomes; secondly, integrated statistics that allow rapid and robust discovery of biological changes between samples.

These strengths have been brought to bear on Garcinia buchananii, the source of a traditional sub-Saharan African remedy for diaorrhea that has also been claimed to represent a rich source of antioxidants – specifically in its stem bark. However, Dr Timo Stark at Technische Universität München decided to pose several questions – was bark extract truly providing the ‘best’ antioxidant activity; if not, which part of the tree would represent the best source of bioactive antioxidant compounds; and, how did leaf, root, and stem bark extracts differ from each other in their metabolite profiles.

To do this, he analysed G. buchananii extracts from those sources comprehensively, using an Acquity UPLC – Synapt G2-S – HD-MSE Waters technology workflow. This generated a vast array of metabolite data, carrying those twin challenges of identification – always a bottleneck in metabolomics – and accurate, quantitative statistical analysis. However, with Progenesis QI, these need not be intimidating. Our quantify-then-identify co-detection approach with no missing values, multivariate statistical visualisations which can reveal subtle co-ordinated trends in data, flexible and comprehensive range of identification approaches and user-friendly OPLS-DA (discriminant analysis) using an optional integrated analytical package (EZinfo 3.0, Umetrics) combine to make complex analyses much more straightforward. Dr Stark was able to rapidly determine the organs richest in known literature-corroborated antioxidants, differentiate the profile of antioxidants and other compounds associated with each organ, and identify several antioxidant species novel to G. buchananii. In the course of one study a great deal was revealed about the bioactive profile of the plant.

As Dr Stark put it:

“With Progenesis QI we were able to analyse data in a reasonably short time that had previously proved too difficult to analyse. Progenesis is summarizing and illustrating the data, there are direct links to online databases, fragmentation tools can help to verify/identify compounds. It is straightforward.

The power and speed of Progenesis analysis means we can not only get better results from existing experiments but can also analyse larger experiments with more biological replicates to further improve quality of results. Faster hints on compound identification.”

Figure 1

Figure 1. Progenesis QI allowed the detection of antioxidant compounds enriched in particular G. buchananii tissues; in this case, (2R,3S)-morelloflavone in leaf.*

I won’t reiterate the full details of his paper and results here, as there is a better option! Dr Stark himself is presenting a webinar where he will describe his work with Garcinia buchananii and Progenesis QI, and his discoveries, on December the 9th (08:00 PST / 11:00 EST / 16:00 GMT / 17:00 CET) and I would encourage you to register for what promises to be a very interesting presentation. In preparation for that, why not have a read of his paper yourself?

Enjoy the webinar, and if you would like to hear more about how Progenesis QI can assist and improve your own metabolomics studies, please do get in touch.

* Reprinted (adapted) with permission from Figure 5 (B), “UPLC-ESI-TOF MS-Based Metabolite Profiling of the Antioxidative Food Supplement Garcinia buchananii”, Timo D. Stark, Sofie Lösch, Junichiro Wakamatsu, et al. Journal of Agricultural and Food Chemistry 63:7169-79; DOI: 10.1021/acs.jafc.5b02544. Copyright 2015 American Chemical Society.

Hi-N Quantitation For Clinical Discovery Proteomics

Progenesis QI for proteomics provides untargeted absolute quantitation of all identified proteins via the Hi-N method. This post explains the method and how it can be a useful tool for discovery proteomics in a clinical setting.

What is Hi-N?

Graph from Silva et al. (2006)Hi-N is a label-free quantitation method allowing absolute quantitation of all identified proteins in a sample, using just a single un-labelled internal standard. Other literature variously describes the method as Top3 or Hi3.

The method relies on a discovery made by Silva et al. (2006) that the average integrated signal intensity of the top 3 most intense tryptic peptides is proportional to the absolute amount of a protein present in a sample. Their graph (right) shows a linear relationship over 2 orders of magnitude for 6 proteins on 6 samples (R2 = 0.9939).

The Hi-N method in Progenesis QI for proteomics chooses, for each protein, the N peptides with the highest abundance and averages their abundances to produce a Hi-N measurement. The number of peptides to consider (N) is configurable, but defaults to 3 as per the majority of literature. By incorporating a known amount of a single internal standard to your samples, the absolute amount of all other proteins can then be calibrated:

Absolute amount of protein A = (Hi-N value for protein A)/(Hi-N value for internal standard) * (Absolute amount of internal standard)

How well does it work?

The principle that the method relies upon (i.e. that the average abundance of the top 3 peptides is proportional to the absolute amount of protein) has been verified by a number of studies, using different instruments and data collection techniques:

The relationship between the average MS signal response of the three most intense tryptic peptides and the absolute quantity of protein can be immediately inferred from the relative ratio of the average MS signal responses. The relative ratios of the average MS signal responses are proportional to the absolute quantities of each protein present in the sample.

Silva et al. (2006) [Waters Q-TOF with LCMSE]

We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements.

Ahrné et al. (2013) [Thermo Orbitrap]

Fig. 2 shows a linearity between the average of the three most intense MS signals of tryptic peptides of one protein and the protein abundance.

Grossman et al. (2010) [Thermo LTQ-FT-ICR]

Further studies have shown the method to have a good dynamic range, high reproducibility and excellent correlation with typical clinical quantitation methods such as routine immunoassays:

The dynamic range of protein abundances spanned four orders of magnitude. The correlation between technical replicates of the ten biological samples was R2 = 0.9961 ± 0.0036 (95% CI = 0.9940 – 0.9992) and the technical CV averaged 7.3 ± 6.7% (95% CI = 6.87 – 7.79%). This represents the most sophisticated label-free profiling of skeletal muscle to date.

Burniston et al. (2014)

One of the key factors required for accurate quantification is high reproducibility of abundance (intensity) measurements. The abundance coefficient of variation (CV) was calculated for all detected peptides in the three data sets (Fig. 6). The average CVs were 0.08 ± 0.1, 0.26 ± 0.09, and 0.18 ± 0.09 for the 4-protein mixture, serum, and tissue data sets, respectively (mean±standard deviation).

Levin et al. (2011)

Our study demonstrates that LCMSE allows reproducible untargeted quantitation of abundant plasma proteins. It gives fair to excellent correlation with immunoassays, and is achieved at low setup costs, without costly isotope-labelled standards used in targeted proteomics approaches. Reasonable variability compared to these targeted-approaches also gives confidence with regard to using this method.

Kramer et al. (2015)

This high correlation with the “gold-standard” of immunoassays suggests that discoveries made using Hi-N will transfer well to further validation studies using targeted methods such as MRM or immunoassays. This makes it a good candidate for quantitation of large numbers of proteins in clinical discovery proteomics.

How do I use it?

By default, Progenesis QI for proteomics provides relative Hi-N values calculated without an internal standard. This provides you with an abundance measure that is proportional to the absolute amount of protein in your samples, without any additional processing or sample preparation steps.

To obtain absolute measurements for all proteins in your experiment, you simply need to add a known amount of internal standard to each sample. Then it’s a simple case of telling Progenesis the accession and amount of your internal standard added. Progenesis will automatically re-calibrate your abundance values to provide absolute measurements (in fmol). So with just the addition of a single internal standard, you get absolute quantitation of all proteins in your sample effectively “for free” – with no additional analysis steps.

Protein quant options in the automatic processing tool Protein quantitation options in Progenesis QI for proteomics

You can configure your internal standard (refered to as “calibrant” in Progenesis QI for proteomics) either at the automatic processing set up wizard, or later on in the workflow when reviewing your identified proteins.

Why should I use it?

The label-free Hi-N method provides quantitative precision similar to labelled methods, without the greater expense, preparation time and variability the labelling process brings. The label-free approach is applicable to any kind of sample, in comparison to some labelled approaches – not all labelling methods are applicable in all scenarios, and in some methods only a subset of proteins are actually labelled.

Quantitative measurements in label-free proteomics have typically only allowed for relative “cross-run” comparison. Such measurements can only be validly compared for a single protein across runs. The linearity of the Hi-N method allows, in addition, comparison between proteins in the same run, providing much more information about the relative amounts of different proteins in your samples.


In conclusion, the Hi-N method provides a useful tool for quantitation in clinical discovery proteomics. The measurements obtained correlate well with routine immunoassays and labelled approaches, so make it likely that discoveries will transfer well to MRM/immunoassay validation studies. The only extra sample preparation required is the addition of a known amount of a single (non-labelled) internal standard to each sample.

Progenesis QI for proteomics performs Hi-N quantitation (without an internal standard) by default. Absolute quantitation using an internal standard is simply a case of entering the standard’s accession and spiked amount. If you’d like to find out more, get in touch, or download Progenesis QI for proteomics and try it for yourself.


Silva, J. C., Gorenstein, M. V., Li, G. Z., Vissers, J. P. C., & Geromanos, S. J. (2006). Absolute quantification of proteins by LCMSE – a virtue of parallel MS acquisition. Molecular & Cellular Proteomics, 5(1), 144-156.

Ahrné, E., Molzahn, L., Glatter, T., & Schmidt, A. (2013). Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics, 13(17), 2567-2578.

Grossmann, J., Roschitzki, B., Panse, C., Fortes, C., Barkow-Oesterreicher, S., Rutishauser, D., & Schlapbach, R. (2010). Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. Journal of proteomics, 73(9), 1740-1746.

Burniston, J. G., Connolly, J., Kainulainen, H., Britton, S. L., & Koch, L. G. (2014). Label-free profiling of skeletal muscle using high-definition mass spectrometry. Proteomics, 14(20), 2339-2344.

Levin, Y., Hradetzky, E., & Bahn, S. (2011). Quantification of proteins using data-independent analysis (MSE) in simple and complex samples: A systematic evaluation. Proteomics, 11(16), 3273-3287.

Kramer, G., Woolerton, Y., van Straalen, J. P., Vissers, J. P. C., Dekker, N., Langridge, J. I., Benyon, R. J., Speijer, D., Sturk, A. & Aerts, J. M. F. G. (2015). Accuracy and Reproducibility in Quantification of Plasma Protein Concentrations by Mass Spectrometry without the Use of Isotopic Standards. PloS one, 10(10), e0140097.

Food for thought at RAFA 2015

Last week, I attended the 7th symposium on Recent Advances in Food Analysis with some of my colleagues from NLD and Waters.

The castle at nightThe congress was held in the beautiful city of Prague in the Czech Republic, famous for its old architecture and buildings like the astronomical clock, Charles Bridge and the castle. It was my first time in the Czech Republic and Prague so I took this opportunity to do some sightseeing of Prague by night, with my colleague Martin Wells.

It was a busy 4 day meeting with a lot of interesting presentations on food safety, food contaminants, food fraud and authenticity, and natural toxins.

On Tuesday, there was a great presentation from Zoltan Takats about REIMS (Rapid Evaporative Ionization Mass Spectrometry) and the iKnife, which was originally developed for surgical cancer tissue detection. Interestingly, it can also be used for instantaneous characterisation of different food types, as discussed in one of our previous posts.

There was also the brilliant talk from Chris Elliott on food fraud and food authenticity. After  the horsemeat scandal in 2013, that first hit the UK then spread right across Europe, he was asked by the UK government to conduct an independent review of the UK food supply system. The objective was to determine what had gone wrong and to suggest measures to make the system much more robust from fraud. This resulted in the Elliott Review report.

A good turn out for a talk on CCS values

There is currently great interest in how advances in analytical science can help to combat food fraud, and we’re pleased to say that Progenesis QI is one of these emerging tools being put to the test for suitability for food authenticity studies.

If you haven’t tried Progenesis QI yet, please get in touch for a FREE trial.

Big society for small molecules

Last week I was invited to attend Waters’ Metabolomics user meeting of 2015, held at their UK headquarters in Wilmslow, Cheshire. There was a great turnout, with a good variation in applications, from institutes all over the world.

Waters UK headquarters

The meeting kicked off after lunch with an introductory talk from Ian Edwards (Waters UK), who gave an overview of the latest solutions offered by Waters for metabolomics. This included a mention of Progenesis QI which was a cue for me and my colleague, development manager Ian Morns, to introduce ourselves. It’s been a while since I was last in a lab, so it was nice to see the latest advancements, particularly with regards to UPLC and the advantages it offers over HPLC.

The rest of the afternoon was filled with talks from metabolomics researchers from across the globe. Dr Panagiotis Vorkas, Imperial College London, UK, presented “Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage” with particular focus on the applications to cardiovascular disease. Panagiotis highlighted that there are particular challenges for clinical metabolomics due to the vast physiochemical diversity of the human metabolome, and how discovery metabolomic profiling is now possible thanks to advancements in instrument technology.

This was aptly followed by a talk from Dr Alexander Fauland, Karolinska Institutet, Sweden on “Overcoming challenges and developing methods for targeted metabolomics of lipid mediators” with particular reference to the application to better diagnostics and treatments to prevent death by anaphylaxis.

Sharing is caring

The rest of the talks were equally engaging, with a few making references to their use of Progenesis QI and the extended statistics module, EZinfo. The main message of the day was definitely centred around the need for more sharing within the community, particularly for libraries, since this is a main limitation for the field currently.

At Nonlinear, we’ve been providing tools to help researchers curate their own neutral mass databases by combining MOL files in Progenesis SDF Studio, and also to create their own fragment databases within Progenesis QI. Whilst it was great to see so many references to people using these tools, it’s still got us thinking more about the need for a community driven project for curating libraries. With that in mind, I have a few questions for you:

In fact, Waters are already working on a community CCS library which they intend to distribute to users of their latest instrument, Vion. CCS is more consistent and reproducible than other measurements such as retention time and fragments, which makes it an ideal community library subject, but there’s still a need to collect libraries for anyone who isn’t fortunate enough to have access to a Waters ion mobility system. A community project needs to be driven by the whole community, so please do get involved by voting on the above poll.

Missed us?

If you weren’t able to attend this meeting, we’ll be at RAFA, Prague, Czech Republic on November 3rd – 6th. We’d love to talk to you, particularly about your thoughts on community libraries. Hopefully we’ll see you soon. Smile

HUPO 2015: Proteomics, Progenesis and poutine

Just over 2 weeks ago I arrived in Vancouver, British Columbia, Canada to attend HUPO 2015 – my first conference with Nonlinear. I was accompanied by Dave from our UK office, Mark and Jonathan who are based in the US, and also Rob who works for Waters in their UK headquarters.

We had some free time on the Sunday morning to get acquainted with the city, and what better way to do so than by going on a seaplane tour to take in all the sights from above? As well as taking to the skies (and the sea), we also explored the area by foot – the Gastown Steam Clock and the Digital Orca statue were definite highlights.

Digital OrcaGastown Steam ClockBeautiful view of Vancouver from the seaplane

The plenary talks were held on the Sunday evening, with the Eagle Talking Stick being passed from speaker to speaker as a nod to Vancouver’s Native American heritage. Aled Edwards gave a very engaging talk about the current state and possible future of the drug discovery field, including a tribute to the late Frances Oldham Kelsey, who was born on nearby Vancouver Island and was responsible for preventing the use of thalidomide in the US.

Monday was our first full day on the booth. Walking from the hotel to the conference centre, in beautiful weather, coffee in hand with a stunning sunrise emanating golden rays across the wakening city sure made a positive change from my usual daily commute!

There were some great talks throughout the week, especially those on standardisation and QC. The Proteome Standards Initiative and ProteomeXchange Consortium session on Tuesday morning was particularly excellent, showing that data exchange, searching and reprocessing are very much becoming a reality. Albert Sickmann also gave a great talk during Wednesday’s “Standardisation in proteomics” session, providing an excellent perspective on the need for QC – something we also focussed on with our release of v2 of Progenesis QI for proteomics.

TobyAside from the talks, it was great for me to meet customers face to face, as well as some of my Waters colleagues who are based further afield. I also enjoyed meeting HUPO’s celebrity exhibitor, Toby the therapy dog.

I’m pleased to say I thoroughly enjoyed HUPO and my first visit to Vancouver – it’s a city with a lot to offer (not just poutine which may be my new favourite thing!). Next year’s host, Taipei, has a lot to live up to!

Missed us at HUPO?

Don’t worry, check out our events page to see where we are next. We hope to see you soon!

"Welcome back!" to Gavin Hope, Principal Software Engineer

We’ve been busy as always at Nonlinear HQ, having been all over the world over the course of the summer attending conferences. We’ve released v2.1 Progenesis QI which sees our first steps into working with Direct Sample Analysis techniques with the iKnife. We’ve also released a free SDF management tool, Progenesis SDF Studio. So, to help us to keep up the pace, we’ve expanded our development team by welcoming back Gavin Hope.


Gavin last worked for Nonlinear back in 2006 before seeking new challenges elsewhere, first as a developer and then as a project manager. He’s now returned to software development and specifically to Nonlinear as he missed the passion of our development team, making high quality software, with a close relationship with our users.

Like a lot of our developers, Gavin is a big fan of strategy board games and a regular attendee of our weekly games night. When not gaming, he enjoys snowboarding, although this hobby has been put somewhat on hold with the recent arrival of his son, whom he’s hoping to have snow-mobile as soon as possible!

Interested in a career at Nonlinear Dynamics?

If Nonlinear Dynamics sounds like somewhere you’d like to work and you think you can strengthen our team, you might want to check out our recruitment page.

From bees or not from bees: that is the question!

Dr Chris Buck and the next generationToday’s post is a guest post, kindly provided by Dr Chris Buck, an LC-MS Field Support Specialist for Waters Australia. He’s recently had an article published in Chromatography Today, looking at food adulteration – specifically, at adulteration of honey products. Here, he presents an overview of some of the issues that article covered and how Progenesis QI was key in the monitoring of both honey and adulterant markers. Over to you, Chris!

After recent revelations of fake honey products being sold in Australia labelled as real honey, the Australian Competition & Consumer Commission (ACCC) Commissioner, Sarah Court, declared in a statement that products sold under the label of ‘honey’ must be produced entirely by honey bees. It had been shown, however, that an imported product made from corn and sugar cane (and no honey) was being labelled as a domestic pure honey product. This sort of dishonesty and mislabelling of products can take place to substitute alternative products of lesser value, cheating consumers and potentially can be disastrous for allergy sufferers. It’s also reminiscent of scares such as the 2008 Chinese milk scandal where diluted milk was spiked with melamine to fake higher protein content. The question arises: can a food safety lab tell the difference between a good product and a bad product, be it fake or adulterated in some way?

Applying chemometrics to the problem

In my recent article in Chromatography Today, I describe an experiment to assess the application of chemometrics to a test case of honey adulteration. Food safety is of great concern at the Australian National Measurement Institute, and my goal was to demonstrate a clear and unambiguous comparison of honey, likely substitutes, and mixtures of honey with those substitute products in a very rapid manner.

Quite often, the process of identifying molecules to use as markers can require a great deal of research, but literature may have few answers as to what should be present in a product and how much should be there. Finding such molecules through a literature search, developing a multiple reaction monitoring (MRM) mass spectrometer method and testing the method with standards and different products could take months; not only to source the high purity standards, but also to optimize the methods of extraction and analysis. And that’s not to mention that a devious product counterfeiter may spike in exactly those same high purity standards, if they are available, to fool product testing.

Rapid turnaround with Progenesis QI

My approach was to apply high resolution mass spectrometric fingerprinting of honey and the adulterants, using a Waters quadrupole time of flight instrument (Xevo G2 QTof) and the Progenesis QI software to process the data. A major goal was to show the groundwork for testing a food product could be set very rapidly.

The honey, wheat glucose syrup based product, and “golden” cane sugar syrup were purchased in the morning of day one. Later that day, the samples were prepared for analysis, with the preparation of mixtures of adulterants with honey in addition to the individual food products. Before injection, sample preparation was a simple dilution in water for each sample and filtration via 0.22 micron microcentrifuge filters. The sample set was then run overnight using triplicate injections of each sample onto C18 reverse phase chromatography with positive ion mode acquisition to target basic and hydrophobic compounds. This was followed by repeat injections of all samples onto an amide HILIC column coupled to negative ion mode acquisition to target polar compounds such as simple sugars and acidic compounds.

Data was collected from the instrument on Day 2 and, by lunchtime, data had been processed using Progenesis QI. Further data analysis was done over the next few days but effectively it could be seen the experiment had worked well, and compound markers of interest were already tabulated, in less than two days.

Identifying markers

Using a UPLC for chromatography coupled to the QTof, I was able to generate an Exact Mass Retention Time (EMRT) data set that could clearly differentiate the samples of honey, its potential adulterants, and honey adulterated with the other foods. This process is also sometimes called mass fingerprinting, as a very large number of components are observed in every injection, creating a very extensive and detailed picture of what makes up each sample. The size and complexity of the data set is where Progenesis QI is invaluable in allowing comparison of groups of injections and the application of multivariate statistics to find components that are unique or clearly elevated in the different foods.

PCA plot in EZ info

Potential markers – either highly specific to the honey or found at higher levels in the adulterants – were identified.  These markers could be further used to develop targeted analysis via other instruments more plentiful in food labs, such as MRM on tandem quad mass spectrometers like the Waters TQS.  Progenesis QI can, in combination with carefully designed experimental methods, make any adulterated product stand out from the crowd of good products based upon unusual chemical components, without any preconceived idea what the tell-tale components will be.

3D montage in Progenesis QI


Christopher Buck. Streamlining the Use of High Resolution Mass Spectrometry Data to Fingerprint Adulterated Honey using Multivariate Data Analysis to Facilitate Food Product Quality Control.  (2015)  Chromatography Today 8 (3), pages 48-52.