“The age-old concept of data modeling with spectroscopy has been revitalized through the integration of Machine Learning and AI initiatives. The pharmaceutical industry, in particular, is embracing data science, exploring the potential of deep learning and AI tools in spectroscopy applications. The emergence of open-source tools adds transparency to the ‘black box’ of these advanced technologies, sparking discussions around regulatory concerns.”
I’ve been doing data modeling and spectroscopy for 35+ years now and I’ve never felt particularly un-vital (vital–adjective; full of energy, lively). However, there is certainly more vibrancy in chemical data science right now largely due to the hype surrounding Artificial Intelligence/Deep Learning (AI/DL). Rasmus Bro wrote me with “I feel like AI/DL has sparked a new energy and suddenly we are forced to think more about what we are and how we are.” But the part of this bullet that really got my attention is the part I’ve underlined. I wasn’t at the meeting so I don’t know exactly what was said, but I must take issue with the idea that open-source has anything to do with the transparency of ‘black-box’ models.
First off, there is a pervasive confusion between the software that generates models and the models themselves–these are two separate things. The software determines the path that is followed to arrive at a model. But in the end, how you get to a model doesn’t matter (except perhaps in terms of efficiency). What’s important is where you wound up. That’s why I’ve said many times: VALIDATE THE MODEL, NOT THE SOFTWARE THAT PRODUCED IT! I first heard Jim Tung of The MathWorks say this about 25 years ago and it is still just as true. The model is where the rubber meets the road. And so it is also true that, ultimately, transparency hinges on the model.
Why do we care?
Backing up a bit, why do we care if data models are transparent, i.e. explainable or interpretable? In some low-impact low-risk cases (e.g. a recommender system for movies) it really doesn’t matter. But in a growing number of applications machine learning models are used to control systems that affect people’s heath and safety. In order to trust these systems, we need to understand how they work.
So what must one do to produce a transparent model? In data modeling ‘black-box’ is defined as ‘having no access to or understanding of the logic which the model uses to produce results.’ Open-source has nothing to do with the transparency or lack thereof in ‘black-box’ models. It is a requirement of course that for transparency you need to have access to the numerical recipe that constitues the model itself, i.e. the procedure by which a model takes an input and creates an output. This is a necessary condition of transparency. But it doesn’t matter if you have access to the source code that generated or implements e.g. a deep Artificial Neural Network (ANN) model if you don’t actually understand how it is making its predictions. The model is still black as ink.
This is the crux.
Getting to Transparency
The first step to creating transparent models is to be clear about what data went into them (and what data didn’t). Data models are first and foremost a reflection of the data upon which they are based. Calibration data sets should be made available and the logic which was used to include or exclude data should be documented. Sometimes we’re fortunate enough to be able use experimental design to create or augment calibration data sets. In these cases, what factors were considered? This gives a good indication of the domain where the model would be expected to work, what special cases or variations it may or may not work with, and what biases may have been built in.
The most obvious road to transparency includes the use interpretable models to begin with. We’ve been fans of linear factor-based methods like Partial Least Squares (PLS) since day one because of this. It is relatively easy to see how these models make their predictions. There is also a good understanding of the data preprocessing steps commonly used with them. Linear models can be successfully extended to non-linear problems by breaking up the domain. Locally Weighted Regression (LWR) is one example for quantitative problems while Automated Hierarchical Models (AHIMBU) is an example for qualitative (classification) models. In both cases interpretable PLS models are used locally and the logic by which they are constructed and applied is clear.
With complex non-linear models, e.g. multi-layer ANNs, Support Vector Machines (SVMs), and Boosted Regression Trees (XGBoost), it is much more difficult to create transparency as it has to be done post-facto. For instance, the Local Interpretable Model-Agnostic Explanations (LIME) method perturbs samples around the data point of interest to create a locally weighted linear model. This local model is then interpreted (which begs the question ‘why not use LWR to begin with?’). In a somewhat similar vein, Shapley values indicate the effect of inclusion of a variable on the prediction of a given sample. The sum of these estimated effects (plus the model offset) equal the prediction for the sample. Both LIME and SHAP explain the local behavior of the model around specific data points.
It is also possible to explore the global behavior of models using perturbation and sensitivity tests as a function of input values. Likewise, visualizations such as the one below can be created that give insight into the behavior of complex models, in this case an XGBoost model for classification.
XGBoost Decision Surface Visualization
To summarize, transparency, aka “Explainable AI” is all about understanding how models, that are the outputs of Machine Learning software, behave. Transparency can either be built-in or achieved through interrogation of the models.
Transparency at Eigenvector
Users of our PLS_Toolbox have always had access to its source code, including both the code used to identify models and the code used to apply models to new data, along with the model parameters themselves. It is not “open-source” in the sense that you can “freely copy and redistribute it,” (you can’t) but it is in the sense that you can see how it works, which in the context of transparent models is the critical aspect. Our Solo users don’t have the source code, but we’re happy to tell you exactly how something is computed. In fact we have people on staff who’s job it is to do this. And our Model_Exporter software can create numerical recipes of our models that make them fully open and transportable. So with regards to being able to look inside the computations involved with developing or applying models we have you covered.
ANN Model for Fat in Meat showing Shapley Values and Model Sensitivity Test from PLS_Toolbox 9.3.
A Final Word
There are a lot of reasons why one might care about model transparency. We like transparency because it increases the level of trust we have in our models. In our world of chemical data science/chemometrics we generally want to assure that models are making their predictions based on the chemistry, not some spurious correlation. We might also want to know what happens outside the boundary of the calibration data. To that end we recommend in our courses and consulting projects that modeling always begin with linear models as they are much more informative, you (the human on the other side of the screen) stand a good chance of actually learning something about the problem at hand. Our sense is that black-box models are currently way over-used. That’s the part of the AI/DL hype cycle we are in. I agree with the sentiments expressed in Why are we using Black-Box models in AI When we Don’t Need to? and it includes a very interesting example. Clearly, we are going to see continued work on explainable/interpretable machine learning because it will be demanded by those that are impacted by the model responses. And rightly so!
I was asked the other day to provide a list of advantages that our Solo software has over its competitors for chemometrics and machine learning. Well, I don’t spend much time keeping track of what’s in other companies’ software. But I can tell you what is in Solo and why we think it’s a great value. (Apologies in advance for all the acronyms but I’ve included a guide below.)
Solo supports a very wide array of methods for data exploration, regression and classification. The standard PCA, PCR, PLS, MLR and CLS are included of course, but also MCR, SIMPLISMA/Purity, Robust PCA and PLS, O-PLS, PLS-DA, Gray CLS, PARAFAC, PARAFAC2, MPCA, batch data digester, batch maturity, LWR, ANN, Deep Learning ANNs, SVM, N-PLS, XGBoost, KNN, Logistic Regression, user specified Hierarchical Models, Automated Hierarchical Models, SIMCA, UMAP and t-SNE.
Solo has a sophisticated point and click user interface (see below) for graphical data editing and model building which users find very intuitive. Plots are easily customizable with class sets, color by properties, etc.
Solo supports the most extensive set of data preprocessing methods: centering, scaling, smoothing, derivatives, automatic WLS baseline, selected points baseline, Whitaker filter, EEM filtering, MSC, EMSC, detrend, EPO, GLSW, despiking, Gap-Segment derivatives, OSC, normalization, PQN, SNV, block scaling, class centering, Pareto and Poisson plus build your own math functions. User selectable preprocessing order and looping for more flexibility.
Solo does full cross-validation including all preprocessing steps, and includes a variety of cross validation methods as well as customizable specified data splits.
Solo supports a host of methods for variable selection: i-PLS, VIP, SR, Genetic Algorithms, r-PLS and stepwise.
Solo includes a full suite of methods for calibration transfer: DS, PDS, DW-PDS, SST, OSC, plus they can be inserted before or between preprocessing steps as required with the Model Centric Calibration Transfer Tool (MCCT).
Solo includes the Model Optimizer which can be used to create, calculate, compare and rank linear and non-linear models with a variety of preprocessing and selected variables
Solo is available on all three major platforms: Windows, MacOS and Linux. Models and data files are completely compatible between platforms.
Solo includes the Report Writer which makes models easy to document by creating PowerPoint or web pages from models. Solo maintains data history and includes model caching for preserving traceability.
Solo optional add-ons include Model_Exporter which allows users to export models as numerical recipes as well as Python and MATLAB code so they can be applied online and in handheld devices. Solo also works with Solo_Predictor, a stand-alone, configurable, prediction engine for online use. The MIA_Toolbox add-on allows users to seamlessly apply all the methods above to hyperspectral images.
Solo is completely compatible with PLS_Toolbox for use with MATLAB. PLS_Toolbox has all the features of Solo, including the point and click interfaces and graphical data editing, but also allows users to access all the functionality from the command line and incorporate these methods in user specified scripts and functions for ultimate flexibility. This allows users to work the way they want (command line or point and click) and still work together.
Solo gives users access to the Eigenvector HELPDESK, user support that is prompt and actually helpful. HELPDESK is manned by our developer staff, the people that actually write the software. Need more help on specific applications? Eigenvector offers consulting services.
Despite all of its advantages, Solo costs less than other major chemometric packages. We publish our price list so you can compare. We offer single-user and floating licenses that work great for small or large groups.
Eigenvector Research produces a variety of software products for chemometrics and machine learning and we often get asked how they work together. Here’s the roadmap!
We have two main packages for modeling, our MATLAB® based PLS_Toolbox, and our stand alone Solo (with versions for Windows, macOS and Linux). Solo is the compiled version of PLS_Toolbox, so in practice they are nearly identical, the difference being that if you are using PLS_Toolbox under MATLAB you also have access to the command line versions of all the functionality. PLS_Toolbox and Solo are highly interfaced point and click programs and include PCA, PLS, PCR, MCR, ANNs, SVMs, PARAFAC, MPCA, PLS-DA, SIMCA, kNN, etc., a very wide array of preprocessing methods, and also tools for specific tasks such as calibration transfer/instrument standardization.
Eigenvector Software for Chemometrics and Machine Learning
MATLAB®-based
Stand-alone
General Modeling & Analysis
PLS_Toolbox Our flagship product 30 years in the making. Point and click or command line access to the widest array of chemical data science tools and methods.
Solo The stand-alone version of PLS_Toolbox, available for Windows, macOS and Linux. Point and click chemometrics and machine learning.
Hyperspectral & Multivariate Image Analysis
MIA_Toolbox Add-on to PLS_Toolbox, allows seamless use of modeling methods on hyperspectral data plus additional image analysis tools.
Solo+MIA The stand-alone version of PLS_Toolbox plus MIA_Toolbox. Point and click modeling of hyperspectral images.
Model Export for Online Predictions
Model_Exporter Add-on to PLS_Toolbox, turns models into numerical recipes or code for application in other software or platforms.
Solo_Predictor Full featured online prediction engine applies any PLS_Toolbox or Solo model to new data, MIA_Toolbox compatible.
If you are doing hyperspectral imaging then you can add our MIA_Toolbox to PLS_Toolbox, or choose Solo+MIA. This allows use of all the above methods directly on hyperspectral images plus adds a few more image specific tools.
If you want to automate model application (say you want to get a PLS model online and have it make predictions as new data comes in) there are two main routes. Solo_Predictor is a full featured stand alone prediction engine that can apply any model you make in PLS_Toolbox/Solo and there are a variety of ways to communicate with it, the most common being socket connections. Solo_Predictor is compatible with hardware from many of our Technology Partner spectrometer companies.
Model_Exporter, on the other hand, creates numerical recipes and code required to apply models to new data streams in a variety of languages including MATLAB and Python. These recipes can then be compiled into other programs or run on hand held devices (such as ThermoFisher’s TruScan or Si-Ware’s NeoSpectra).
So what software should you buy? PLS_Toolbox or Solo?
Buy PLS_Toolbox if you …
— already have access to MATLAB, it’s less expensive than Solo
— know you want to automate pieces of your modeling process
— want to customize plots using MATLAB
— want to access additional functionality from other MATLAB toolboxes
Buy Solo if you …
— want to work only within visual interfaces
— don’t need to script or program
— prefer the lower cost of Solo compared to MATLAB + PLS_Toolbox
The term chemometrics was coined by Svante Wold in a grant application he submitted in 1971 while at the University of Umeå. Supposedly, he thought that creating a new term, (in Swedish it is ‘kemometri’), would increase the likelihood of his application being funded. In 1974, while on a visit to the University of Washington, Svante and Bruce Kowalski founded the International Chemometrics Society over dinner at the Casa Lupita Mexican restaurant. I’d guess that margaritas were involved. (Fun fact: I lived just a block from Casa Lupita in the late 70s and 80s.)
Chemometrics is a good word. The “chemo” part of course refers to chemistry and “metrics” indicates that it is a measurement science: a metric is a meaningful measurement taken over a period of time that communicates vital information about a process or activity, leading to fact-based decisions. Chemometrics is therefore measurement science in the area of chemical applications. Many other fields have their metrics: econometrics, psychometrics, biometrics. Chemical data is also generated in many other fields including biology, biochemistry, medicine and chemical engineering.
So chemometrics is defined as the chemical discipline that uses mathematical, statistical, and other methods employing formal logic to design or select optimal measurement procedures and experiments, and to provide maximum relevant chemical information by analyzing chemical data.
In spite of being a nearly perfect word to capture what we do here at Eigenvector, there are two significant problems encountered when using the term Chemometrics: 1) In spite of the existence of the field for nearly five decades and two dedicated journals (Journal of Chemometrics and Chemometrics and Intelligent Laboratory Systems), the term is not widely known. I still run into graduates of chemistry programs who have never heard the term, and of course it is even less well known in the related disciplines, and less yet in the general population. 2) Many that are familiar with the term think it refers to a collection of primarily projection methods, (e.g. Principal Components Analysis (PCA), Partial Least Squares Regression (PLS)), and therefore other Machine Learning (ML) methods (e.g. Artificial Neural Networks (ANN), Support Vector Machines (SVM)) are not chemometrics regardless of where they are applied. Problem number 2 is exacerbated by the current Artificial Intelligence (AI) buzz and the proclivity of managers and executives towards things that are new and shiny: “We have to start using AI!”
Typical advertisement presented when searching on Artificial Intelligence
This wouldn’t matter much if choosing the right terms wasn’t so critical to being found. Search engines pretty much deliver what was asked for. So you have to be sure you are using terms that are actually being searched on. So what to use?
A common definition of artificial intelligence is the theory and development of computer systems able to perform tasks that normally require human intelligence. This is a rather low bar. Many of the models we develop make better predictions than humans could to begin with. But AI is generally associated with problems such as visual perception and speech recognition, things that humans are particularly adept at. These AI applications generally require very complex deep neural networks etc. And so while you could say we do AI this feels like too much hyperbole, and certainly there are other arguments against using this term loosely.
Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Most researchers (apparently) view ML as a subset of AI. Do a search on “artificial intelligence machine learning images” and you’ll find many Venn diagrams illustrating this. I tend to see it as the other way around: AI is the subset of ML that uses complex models to address problems like visual perception. I’ve always had a problem with the term “learning” as it anthropomorphizes data models: they don’t learn, they are parameterized! (If these models really do learn I’m forced to conclude that I’m just a machine made out of meat.) In any case, models from Principal Components Regression (PCR) through XGBoost are commonly considered ML models, so certainly the term machine learning applies to our software.
Google Search on ‘artificial intelligence machine learning’ with ‘images’ selected.
Process analytics is a much less used term and particular to chemical process data modeling and analysis. There are however conferences and research centers that use this term in their name, e.g.IFPAC, APACT and CPACT. Cheminformatics sounds relevant to what we do but in fact the term refers to the use of physical chemistry theory with computer and information science techniques in order to predict the properties and interactions of chemicals.
Data science is defined as the field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data. Certainly this is what we do at Eigenvector, but of course primarily in chemistry/chemical engineering where we have a great deal of specific domain knowledge such as the fundamentals of spectroscopy, chemical processes, etc. Thus the term chemical data science describes us pretty well.
So you will find that we will use the terms Machine Learning and Chemical Data Science a lot in the future though we certainly will continue to do Chemometrics!
That’s not a headline you see very often. Usually it’s “Under New Management.” But here at Eigenvector Research we’re proud of our stability. I wrote the first version of our MATLAB-based PLS_Toolbox while I was in graduate school thirty-one years ago. I still oversee its development along with our other software products.
In 1990 Partial Least Squares (PLS) regression was still fairly novel. PLS_Toolbox 1.0 included it, of course, along with a non-linear version of PLS and a number of tools for Multivariate Statistical Process Control (MSPC) including Principal Components Analysis (PCA). The goal then, as it is now, was to bring new multivariate modeling methods to users in a timely fashion and in a consistent and easy to use package.
PLS_Toolbox 1.0 Manual, 1990.
Neal B. Gallagher joined me in 1995 to form Eigenvector Research, Inc. He has been contributing to PLS_Toolbox development for almost 27 years now, along with consulting and teaching chemometrics, (i.e. chemical data science). Our senior software developers R. Scott Koch, Bob Roginski and Donal O’Sullivan have been with us for a combined 45 years (18, 15 and 12 respectively). That continuity is one reason why our helpdesk is actually so helpful. When you contact helpdesk with a question or problem we can generally get you in touch with the staff involved in writing the original code.
To assure that continuity going forward we’ve brought some younger developers on board including Lyle Lawrence and Sean Roginski. (Lyle was still sleeping in a crib and Sean wasn’t born yet when PLS_Toolbox first came out-ha!) Both have taken deep dives into our code and have been instrumental in the recent evolution of our software. Primarily on the consulting side of EVRI, Manny Palacios brings his youthful energy and extensive experience to our clients’ data science challenges.
PLS_Toolbox/Solo Analysis Interface with Integrated Deep Learning ANN from scikit-learn and TensorFlow.
Over the years we have developed and refined PLS_Toolbox along with our standalone software Solo, adding many, many new routines while advancing usability. Currently we are completing the process of integrating new methods from the Python libraries scikit-learn and TensorFlow into the soon to be released PLS_Toolbox/Solo 9.0. So when we bring you new methods, like Deep Learning Artificial Neural Networks (ANNDL, shown above) or Uniform Manifold Approximation and Projection (UMAP, below) you can be sure that they are implemented, tested, supported and presented in the way that you’ve come to expect in our software. They have the same preprocessing, true cross-validation, graphical data editing, plotting features, etc. as our other methods.
PCA of Mid-IR Reflectance Image of Excedrin Tablet with Corresponding UMAP Embeddings
Now, 25+ years in, we’re moving forward with the same vision we’ve had from the beginning: bring new modeling methods to the people that own the data in a consistent straightforward package. This same old management is working to assure that far into the future!
The development of a machine learning model is typically a fairly involved process, and the software for doing this commensurately complex. Whether it be a Partial Least Squares (PLS) regression model, Artificial Neural Network (ANN) or Support Vector Machine (SVM), there are a lot of calculations to be made to parameterize the model. These include everything from calculation of projections, matrix inverses and decompositions, computing fit and cross-validation statistics, optimization, you name it, it’s in there. Lots of loops and logic and checking convergence criteria, etc.
On the other hand, the application of these models, once developed, is typically quite straight forward. Most models can be applied to new data using a fairly simple recipe involving matrix multiplications, scalings, projections, activation functions, etc. There are exceptions, such as preprocessing methods like iterative Weighted Least Squares (WLS) baselining and models like Locally Weighted Regression (LWR) where you really don’t have a model per se, you have a data set and a procedure. (More on WLS and LWR in a minute!) But in the vast majority of cases effective models can be developed using methods whose predictions can be reduced to simple formulas.
Enter Model_Exporter. When you create any of the models shown at right (key to acronyms below) in PLS_Toolbox or Solo, Model_Exporter can take that model and create a numerical recipe for applying it to new data, including the supported preprocess steps. This recipe can be output in a number of formats, including MATLAB .m, Python .py or XML. And using our freely available Model_Interpreter, the XML file can be incorporated into Java, Microsoft .NET, or generic C# environments.
So what does all this mean?
Total model transportability. Models can be built into any framework you need them in, from process control systems to hand-held analytical instruments.
Minimal footprint. Exported model also have a very small footprint and minimal computing overhead. This means that they can be made to run with minimal memory and computing power.
Order of magnitude faster execution. Lightweight recipe produces predictions much faster than the original model.
Complete transparency. There’s no guessing as to exactly how the model gets from measurements to predictions, it’s all there.
Simplified model validation. Don’t validate the code that makes the model, validate the model!
This is why our customers in many industries, from analytical instrument developers to the chemical process industries, are getting their models online using Model_Exporter. It is creating a revolution in how online models are generated and executed.
And what about those cases like WLS and LWR noted above? We’re working to create add-ons so exported models can utilize these functions too. Look for them, along with some additional model types, in the next release.
Is it for everybody? Well not quite. There are still times where you need a full featured prediction engine like our Solo_Predictor that has built in communication protocols (e.g. socket connections), scripting ability, and can run absolutely any model you can make in PLS_Toolbox or Solo (like hierarchical and even XGBoost). But we’re seeing more and more instances of companies utilizing the advantages of Model_Exporter.
Join the Model_Exporter revolution for the compact, efficient and seamless application of your machine learning models!
BMW
Models Supported: Principal Components Analysis (PCA), Multiple Linear Regression (MLR), Principal Components Regression (PCR), Partial Least Squares Regression (PLS), Classical Least Squares (CLS), Artificial Neural Networks (ANN), Support Vector Machine Regression (SVM), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine Discriminant Analysis (SVM-DA), Artificial Neural Network Discriminant Coming Soon: Analysis (ANN-DA), Locally Weighted Regression (LWR), Logistic Regression Discriminant Analysis (LREG-DA).
Preprocessing Methods Supported: General scaling and centering options including Mean and Median Centering, Autoscaling, Pareto and Poisson Scaling, Multiplicative Scatter Correction (MSC), Savitsky-Golay Smoothing and Derivatives, External Parameter Orthogonalization (EPO), Generalized Least Squares Weighting (GLSW), Extended Least Squares, Standard Normal Variate (SNV), 1-, 2-, infinity and n-norm Normalization, Fixed point spectral baselining. Coming Soon: Iterative Weighted Least Squares Baselining (WLS) and Whittaker Baslining.
As with most of you, we here at Eigenvector are very concerned about the COVID-19 outbreak and its impact on our families, friends, customers and communities. We continue to monitor its evolution closely, especially in regard to our employees and their families. The direct effect of COVID-19 on the daily business of Eigenvector, however, is modest. For over 25 years now everybody, or as we say, EVRIbody in our organization has worked from home. We’re all set up to work remotely as that is how we’ve always done it. We expect to be working our usual hours for the foreseeable future with perhaps a few exceptions to work with and around spouses, children and parents now occupying our homes or needing our assistance.
We recognize that many of our colleagues are experiencing work disruptions and many are now working from home. As experienced home workers we offer here a few tips for making your home office productive and keeping your work/life balance intact.
Start early and try to work regular hours.
Shower, shave and dress as if you were going to the office. (The EigenGuys aren’t very good at this, especially the shaving part.)
If you have a monitor get the level correct and set your keyboard up in a way that supports your wrists.
Get a good chair and resist the urge to work in places that don’t promote good posture.
A headset with noise cancellation and a good microphone is useful for Webex meetings, conference calls, regular phone calls and simply screening out distractions.
Check in with co-workers frequently.
Plan some time for exercise even if it’s just a walk around the block or some stretching and sets of sit-ups and push-ups. I like mid-morning for this.
Try to stay out of the kitchen (this is tough) unless you’re using your break time to start long prep time meals (crock-pot, pizza dough, etc.).
Set a “closing time” and try to stick to it. Spouses/partners can enforce this by setting a beer on the homeworker’s desk at the appointed time. (This always works on me!)
Many of our software users are among these new homeworkers and we are working to accommodate them. In particular, users that work with our floating license versions of PLS_Toolbox or Solo may have trouble reaching EVRI’s Floating License Server when working from home. We have just posted a video addressing this issue: Using Floating License Software Remotely. As always, if you have any questions regarding our software or online courses please contact our helpdesk and we will respond promptly. And to our consulting clients: we are available as always.
Our 15th Annual Eigenvector University, originally scheduled for April 26-May 1, has been postponed until August 16-21. We will of course continue to monitor the situation to determine if additional delays are warranted.
Finally, we also have plans to offer additional resources online. We will step up the frequency of our “EVRI-thing You Need to Know About..” webinar series to twice per month. The next one, “EVRI-thing You Need to Know About Performing PLS-DA” is Wednesday, March 25 at 8:00 am PDT, (16:00 CET). We also plan to offer a couple of our short courses via webinar in the coming months. We will let you know when plans are finalized.
I’ve just returned from Conference Chimiométrie 2020, the annual French language chemometrics conference, now in its 21st edition. The conference was held in Liège and unfortunately there wasn’t much time to explore the city. I can tell you they have a magnificent train station, Gare de Liège is pictured at right.
The vibrant French chemometrics community always produces a great conference with good attendance, well over 120 at this event, and food and other aspects were as enjoyable as ever thanks to the local organizing committee and conference chair Professor Eric Ziemons.
Age Smilde got the conference off to a good start with “Common and Distinct Components in Data Fusion.” In it he described a number of different model forms, all related to ANOVA Simultaneous Components Analysis (ASCA), for determining if the components in different blocks of data are unique or shared. What struck me most about this talk and many of the ones that followed is that there are a lot of different models out there. As Age says, “Think about the structure of your data!” The choice of model structure is critical to answering the questions posed to the data. And it is only with solid domain knowledge that appropriate modeling choices are made.
In addition to a significant number of papers on multi-block methods, Chimiométrie included quite a few papers in the domain of metabolomics, machine learning, Bayesian methods, and a large number of papers on hyper spectral image analysis (see the Programme du Congrès). All in all, a very well rounded affair!
I was pleased to see a good number of posters that utilized our PLS_Toolbox and in some instances MIA_Toolbox software very well! The titles are given below with links to the posters. We’re always happy to help researchers achieve an end result or provide a benchmark towards the development of new methods!
A colleague wrote to me recently and asked if Eigenvector was considering rebranding itself as a Data Science company. My knee-jerk response was “isn’t that what we’ve been for the last 25 years?” But I know exactly what she meant: few people have heard of Chemometrics but everybody has heard about Data Science. She went on to say “I am spending increasing amounts of time calming over-excited people about the latest, new Machine Learning (ML) and Artificial Intelligence (AI) company that can do something slightly different and better…” I’m not surprised. I know it’s partly because Facebook and LinkedIn have determined that I have an interest in data science, but my feeds are loaded with ads for AI and ML courses and data services. I’m sure many managers subscribe to the Wall Street Journal’s “Artificial Intelligence Daily” and, like the Stampeders on Chilkoot Pass pictured below, don’t want to miss out on the promised riches.
Oh boy. Déjà vu. In the late 80s and 90s during the first Artificial Neural Network (ANN) wave there were a slew of companies making similar promises about the value they could extract from data, particularly historical/happenstance process data that was “free.” One slogan from the time was “Turn your data into Gold.” It was the new alchemy. There were successful applications but there were many more failures. The hype eventually faded. One of biggest lessons learned: Garbage In, Garbage Out.
I attended The MathWorks Expo in San Jose this fall. In his keynote address, “Beyond the ‘I’ in AI,” Michael Agostini stated that 80-90% of the current AI initiatives are failing. The main reason: lack of domain knowledge. He used as an example the monitoring of powdered milk plants in New Zealand. The moral of the story: you can’t just throw your data into a ML algorithm and expect to get out anything very useful. Perhaps tellingly, he showed plots from Principal Components Analysis (PCA) that helped the process engineers involved diagnose the problem, leading to a solution.
Another issue involves what sort of data is even appropriate for AI/ML applications. In the early stages of the development of new analytical methods, for instance, it is common to start with tens or hundreds of samples. It’s important to learn from these samples so you can plan for additional data collection: that whole experimental design thing. And in the next stage you might get to where you have hundreds to thousands of samples. The AI/ML approach is of limited usefulness In this domain. First off it is hard to learn much about the data using these approaches. And maintaining parsimony is challenging. Model validation is paramount.
The old adage “try simple things first” is still true. Try linear models. Use your domain knowledge to select sample sets and variables, and to select data preprocessing methods that remove extraneous variance from the problems. Think about what unplanned perturbations might be affecting your data. Plan on collecting additional data to resolve modeling issues. The opposite of this approach is what we call the “throw the data over the wall” model where people doing the data modeling are separate from the people who own the data and the problem associated with it. Our experience is that this doesn’t work very well.
There are no silver bullets. In 30 years of doing this I have yet to find an application where one and only one method worked far and away better than other similar approaches. Realize that 98% of the time the problem is the data.
So is Eigenvector going to rebrand itself as a Data Science company? We certainly want people to know that we are well versed in the application of modern ML methods. We have included many of these tools in our software for decades, and we know how to work with these methods to obtain the best results possible. But we prefer to stay grounded in the areas where we have domain expertise. This includes problems in spectroscopy, analytical chemistry, chemical process monitoring and control. We all have backgrounds in chemical engineering, chemistry, physics, etc. Plus collectively over 100 man-years experience developing solutions that work with real data. We know a tremendous amount about what goes wrong in data modeling and what approaches can be used to fix it. That’s where the gold is actually found.
Eigenvector Research, Inc. was founded on January 1, 1995 by myself and Neal B. Gallagher, so we’re now 25 years old. On this occasion I feel that I should write something though I’m at a bit of loss with regards to coming up with a significantly profound message. In the paragraphs below I’ve written a bit of history (likely overly long).
PLS_Toolbox Floppy Disks 1994-1997
We started Eigenvector with each of us buying a Power Mac 8100 with keyboard, mouse and monitor. These were about $4k, plus another $1700 to upgrade the 8Mb RAM it came with to 32Mb. Liz Callanan at The MathWorks gave us our first MATLAB licenses-thanks! PLS_Toolbox was in version 1.4 and still being marketed under Eigenvector Technologies. Our founding principle was and still is:
Life is too short to drink bad beer, do boring work or live in a crappy place.
That’s a bit tongue-in-cheek but it’s basically true. We certainly started Eigenvector to keep ourselves in interesting work. For me that meant continuing with chemometrics, data analysis in chemistry. New data sets are like Christmas presents, you never know what you’ll find inside. For Neal I think it meant anything you could do that let you use math on a daily basis. Having both grown up in rural environments and being outdoor enthusiasts location was important. And the bit about beer is just, well, duh!
As software developers we found it both interesting and challenging to make tools that allowed users (and ourselves!) to build successful models for calibration, classification, MSPC etc. As consultants we found a steady stream of projects which required both use of existing chemometric methods and adaptation of new ones. As we became more experienced we learned a great deal about what can make models go bad: instrument drift, differences between instruments, variable and unforeseen background interferents, etc. and often found ourselves as the sanity check to overly optimistic instrument and method developers. Determining what conclusions are supportable given the available data remains an important function for us.
Our original plan included only software and consulting projects but we soon found out that there was a market for training. (This seems obvious in retrospect.) We started teaching in-house courses when Pat Wiegand asked us to do one at Union Carbide in 1996. A string of those followed and soon we were doing workshops at conferences. And then another of our principles kicked in:
Let’s do something, even if it’s wrong.
Neal Teaching Regression at EigenU
Entrepreneurs know this one well. You can never be sure that any investment you make in time or dollars is actually going to work. You just have to try it and see. So we branched out into doing courses at open sites with the first at Illinois Institute of Technology (IIT) in 1998, thanks for the help Ali Çinar! Open courses at other sites followed. Eigenvector University debuted at the Washington Athletic Club in Seattle in 2006. We’re planning the 15th Annual EigenU for this spring. The 10th Annual EigenU Europe will be in France in October and our third Basic Chemometrics PLUS in Tokyo in February. I’ve long ago lost count of the number of courses we’ve presented but it has to be well north of 200.
Our first technical staff member, Jeremy M. Shaver, joined us in 2001 and guided our software development for over 14 years. Our collaborations with Rasmus Bro started the next year in 2002 and continue today. Initially focused on multi-way methods, Rasmus has had a major impact on our software from numerical underpinnings to usability. Our Chemometrics without Equations collaboration with Donald Dahlberg started in 2002 and has been taught at EAS for 18 consecutive years now.
So what’s next? The short answer: more of the same! It’s both a blessing and a curse that the list of additions and improvements that we’d like to make to our software is never ending. We’ll work on that while we continue to provide the outstanding level of support our users have come to expect. Our training efforts will continue with our live courses but we also plan more training via webinar and in other venues. And of course we’re still doing consulting work and look forward to new and interesting projects in 2020.
In closing, we’d like to thank all the great people that we’ve worked with these 25 years. This includes our staff members past and present, our consulting clients, academic colleagues, technology partners, short course students and especially the many thousands of users of our PLS_Toolbox software, its Solo derivatives and add-ons. We’ve had a blast and we look forward to continuing to serve our clients in the new decade!
I attended Metabolomics 2019 and was pleased to find a rapidly expanding discipline populated with very enthusiastic researchers. Applications ranged from developing plants with increased levels of nutrients to understanding cancer metabolism.
Metabolomics experiments, however, produce extremely large and complex data sets. Consequently, the ultimate success of any experiment in metabolomics hinges on the software used to analyze the data. It was not surprising to find that multivariate analysis methods were front and center in many of the presentations and posters.
At the conference I saw some nice examples using our software, but of course not as many as I would have liked. So when I got home I put together this table comparing our PLS_Toolbox and Solo software with SIMCA, R and Python for use with metabolomics data sets.
Compare Software for Metabolomics
PLS_Toolbox
Solo
SIMCA
R/Python
Available for Windows,
Mac and Linux?
Yes
Yes
Windows only
Yes
Comes with User Support?
Yes
Yes
Yes
No
Point-and-click GUIs for
all important analyses?
Yes
Yes
Yes
No
Command Line available?
Yes
No
No
Yes, use is mandatory
Source Code available
for review?
Yes
Yes, same
code as
PLS_Toolbox
No
Yes
Includes PCA, PLS, O-PLS?
Yes
Yes
Yes
Add-ons available
Includes ASCA, MLSCA?
Yes
Yes
No
Add-ons available
Includes SVMs, ANNs
and XGBoost?
Yes
Yes
No
Add-ons available
Includes PARAFAC,
PARAFAC2, N-PLS?
Yes
Yes
No
Add-ons available
Includes Curve Resolution Methods?
Yes
Yes
No
Add-ons available
Extensible?
Yes
No
Yes, through Python
Yes
Instrument standardization,
calibration transfer tools?
Yes
Yes
No
No
Comes complete?
Yes
Yes
Yes
No
Easy to install?
Yes
Yes
Yes
No
Cost
Inexpensive
Moderate
Expensive
Free
So it’s easy to see that PLS_Toolbox is in the sweet spot with regards to metabolomics software. Yes, it requires MATLAB, but MATLAB has over 3 millions users and is licensed by over 5000 universities world wide. And if you don’t care to use a command line Solo includes all the tools in PLS_Toolbox and doesn’t require MATLAB. Plus, Solo and PLS_Toolbox share the same model and data formats. So you can have people in your organization that use only GUIs work seamlessly with people who prefer access to the command line.
So the bottom line here is:
If you are just getting started with metabolomics data PLS_Toolbox and Solo are easy to install, include all the analysis tools you’ll need in easy to use GUIs, are transparent and are relatively inexpensive.
If you are using SIMCA, you should try out PLS_Toolbox because it includes many methods that SIMCA doesn’t have, the source code is available, its more easily extensible, works on all platforms, and it will save you money.
If you are using R or Python, you should consider PLS_Toolbox because it is fully supported by our staff, has all the important tools in one place, sophisticated GUIs, and is easy to install.
Ready to try PLS_Toolbox or Solo? Start by creating an account and you’ll have access to free fully functional demos. Questions? Write to me!
Chimiométrie 2019 was held in Montpellier, January 30 to February 1. Now in its 20th year the conference attracted over 150 participants. The conference is mostly in French, (which I have been trying to learn for many years now), but also with talks in English. The Scientific and Organizing Committee Presidents were Ludovic Duponchel and J.M. Roger, respectively.
Eigenvector was proud to sponsor this event, and it was fun to have a display table and a chance to talk with some of our software users in France. As usual, I was on the lookout for talks and posters using PLS_Toolbox. I especially enjoyed the talk presented by Alice Croguennoc, Some aspects of SVM Regression: an example for spectroscopic quantitative predictions. The talk provided a nice intro to Support Vectors and good examples of what the various parameters in the method do. Alice used our implementation of SVMs, which adds our preprocessing, cross-validation and point-and-click graphics to the publicly available LIBSVM package. Ms. Croguennoc demonstrated some very nice calibrations on a non-linear spectroscopic problem.
I also found three very nice posters which utilized PLS_Toolbox:
Preliminary appreciation biodegradation of formate and fluorinated ethers by means of Raman spectroscopy coupled with chemometrics by M. Marchetti, M. Offroy, P. Bourson, C. Jobard, P. Branchu, J.F. Durmont, G. Casteran and B. Saintot.
By all accounts the conference was a great success, with many good talks and posters covering a wide range of chemometric topics, a great history of the field by Professor Steven D. Brown, and a delicious and fun Gala dinner at the fabulous Chez Parguel, shown at left. The evening included dancing, and also a song, La Place De la Conférence Chimiométrie, (sung to the tune of Patrick Bruel’s Place des Grands Hommes), written by Sylvie Roussel in celebration of the conference’s 20th year and sung with great gusto by the conferees. Also, the lecture hall on the SupAgro campus was very comfortable!
Congratulations to the conference committees for a great edition of this French tradition, with special thanks to Cécile Fontange and Sylvie Roussel of Ondalys for their organizational efforts. À l’année prochaine!
I logged in to LinkedIn this morning and found a discussion about Python that had a lot of references to PLS_Toolbox in it. The thread was started by one of our long time users, Erik Skibsted who wrote:
“MATLAB and PLS_Toolbox has always been my preferred tools for data science, but now I have started to play a little with Python (and finalised my first on-line course on Data Camp). At Novo Nordisk we have also seen a lot of small data science initiatives last year where people are using Python and I expect that a lot more of my colleagues will start coding small and big data science projects in 2019. It is pretty impressive what you can do now with this open source software and different libraries. And I believe Python will be very important in the journey towards a general use of machine learning and AI in our company.”
This post prompted well over 20 responses. As creator of PLS_Toolbox I thought I should jump in on the discussion!
In his response, Matej Horvat noted that Python and other open source initiatives were great “if you have the required coding skills.” This a key phrase. PLS_Toolbox doesn’t require any coding skills _at all_. You can use it entirely in point-and-click mode and still get to 90% of what it has to offer. (This makes it the equivalent of using our stand-alone product Solo.) When you are working with PLS_Toolbox interfaces it looks like the first figure below.
Of course if you are a coder you can take advantage of the ability to also use it in command line mode and build it into your own scripts and functions, just like you would do with other MATLAB toolboxes. The caveat is that you can’t redistribute it without an additional license from us. (We do sell these of course, contact me if you are interested.) When you are working with Python, (or developing MATLAB scripts incorporating PLS_Toolbox functions for that matter), it looks like the second figure.
Like Python, PLS_Toolbox is “open source” in the sense that you can actually see the code. We’re not hiding anything proprietary in it. You can find out exactly how it works. You can also modify if you wish, just don’t ask for help once you do that!
Unlike typical open source projects, with PLS_Toolbox you also get user support. If something doesn’t work we’re there to fix it. Our helpdesk has a great reputation for prompt responses that are actually helpful. That’s because the help comes from the people that actually developed the software.
Another reason to use PLS_Toolbox is that we have implemented a very wide array of methods and put them into the same framework so that they can be evaluated in a consistent way. For instance, we have PLS-DA, SVM-C, and now XGBoost all in the same interface that use the exact same preprocessing and are all cross-validated and validated in the same exact way so that they can be compared directly.
If you want to be able to freely distribute the models you generate with PLS_Toolbox we have have a tool for that: Model_Exporter. Model_Exporter allows users to export the majority of our models as code that you can compile into other languages, including direct export of Python code. You can then run the models anywhere you like, such as for making online predictions in a control system or with handheld spectrometers such as ThermoFisher’s Truscan. Another route to online predictions is using our stand-alone Solo_Predictor which can run any PLS_Toolbox/Solo model and communicates using a number of popular protocols.
PLS_Toolbox is just one piece of the complete chemometrics solutions we provide. We offer training at our renowned Eigenvector University and many other venues such as the upcoming course in Tokyo, EigenU Online, and an extensive array of help videos. And if that isn’t enough we also offer consulting services to help you develop and implement new instruments and applications.
So before you spend a lot of valuable time developing applications in Python, make sure you’re not just recreating tools that already exist at Eigenvector!
Integration of Eigenvector’s multivariate analysis software with Metrohm’s Vis-NIR analyzers will give users access to advanced calibration and classification methods.
Metrohm’s spectroscopy software Vision Air 2.0 supports prediction models created in EVRI’s PLS_Toolbox and Solo software and offers convenient export and import functionality to enable measurement execution and sample analysis in Metrohm’s Vision Air software. Customers will benefit from data transfer between PLS_Toolbox/Solo and Vision Air and will enjoy a seamless experience when managing models and using Metrohm’s NIR laboratory instruments. Metrohm has integrated Eigenvector’s prediction engine, Solo_Predictor, so that users can apply any model created in PLS_Toolbox/Solo.
Data scientists, researchers and process engineers in a wide variety of industries that already use or would like to use Eigenvector software will find this solution appealing. PLS_Toolbox and Solo’s intuitive interface and advanced visualization tools make calibration, classification and validation model building a straightforward process. A wide array of model types, preprocessing methods and the ability to create more complex model forms, such as hierarchical models with conditional branches, make Eigenvector software the preferred solution for many.
“This a win-win for users of Metrohm NIR instruments and users of Eigenvector chemometrics software” says Eigenvector President Dr. Barry M. Wise. “Thousands of users of EVRI software will be able to make models for use on Metrohm NIR instruments in their preferred environment. And users of Metrohm NIR instruments will have access to more advanced data modeling techniques.”
Researchers benefit from Metrohm’s Vis-NIR Instrument and Vision Air software through instruments covering the visible and NIR wavelength range, intuitive operation, state-of-the art user management with strict SOPs and global networking capabilities. Combining the solutions will create an integrated experience that will save time, improve product development process and provide better control of product quality.
Key Advantages PLS_Toolbox/Solo:
Integration of Solo_Predictor allows users to run any model developed in PLS_Toolbox/Solo
Allows users to make calibration and classification models in PLS_Toolbox and Solo’s user-friendly modeling environment
Supports standard model types (PCA, PLS, PLS-DA, etc.) with wide array of data preprocessing methods
Advanced models (SVMs, ANNs, etc.) and hierarchical models also supported
Key Advantages Vision Air:
Intuitive workflow due to appealing and smart software concept with specific working interfaces for routine users, and lab managers
Database approach for secure data handling and easy data management
Powerful network option with global networking possibility and one-click instruments maintenance
The ICNIRS conference was held June 11-15 in Copenhagen, Denmark, where close to 500 colleagues gathered for the largest forum on Near-Infared Spectroscopy in the world. The conference featured several keynote lectures, classes taught by EVRI associate Professor Rasmus Bro, and also held several poster sessions where over 20 conference attendees displayed their research using EVRI software! We’d like to feature some of the posters and authors below: thanks for using our software, everyone!
Scott Koch joined Eigenvector in January, 2004, and quickly made himself indispensable. Although his title is Senior Software Engineer, Scott commented the other day, “we wear so many hats in a small company that I don’t know if a title is really useful.” He tackles a variety of jobs at EVRI including interface and database design, software version control and general troubleshooting. Scott is fluent in MATLAB, SQL, Java, Subversion and Python and contributes to our products, e.g.PLS_Toolbox and Solo, as well as working with our clients on custom applications. You can also catch him as a guest blogger for Undocumented Matlab, where he discusses his work involving Matlab-Java programming.
In addition to working at Eigenvector, Scott is also an outdoor enthusiast and distance runner, and can often be seen flying through the trails of the west coast. Here he is running the Broken Arrow Trial near Sedona, AZ. He’s also an avid backcountry skier and rally car driver.
Says Barry, President of EVRI: “When we hired Scott there was one thing on his resume that really caught my eye: ‘PSIA level II Ski Instructor: Possess an uncanny ability to coax terrified beginners down steep slopes and back onto the chair lift.’ Scott has been all that and more with regard to helping users with our software, and he’s filled a lot of gaps in our development process I didn’t even realize we had. We’re so glad Scott is part of our team!”
Thanks Scott for all your hard work! We have a lot of fun hanging out with you, and you inspire us with your athletic passions and drive to make this company better.
Last month I had the pleasure of attending Chimiométrie XVII. This installment ran from January 17-20 in the beautiful city of Namur, BELGIUM. The conference was largely in French but with many talks and posters in English. (My French is just good enough that I can get the gist of most of the French talks if the speakers put enough text on their slides!) There were many good talks and posters demonstrating a lot of chemometric activity in the French speaking world.
EVRI was also proud to sponsor the poster contest which was won by Juan Antonio Fernández Pierna et al. with “Chemometrics and Vibrational Spectroscopy for the Detection of Melamine Levels in Milk.” For his efforts Juan received licenses for PLS_Toolbox and MIA_Toolbox. Congratulations! We wish him continued success in his chemometric endeavors!
Finally I’d like to thank the organizing committee, headed by Pierre Dardenne of Le Centre wallon de Recherches agronomiques. The scientific content was excellent and, oh my, the food was fantastic! I’m already looking forward to the next one!
Model_Exporter is EVRI’s software for turning multivariate/chemometric models into formats which can be compiled into online applications. It offers an alternative to our stand-alone prediction engine Solo_Predictor. Model_Exporter allows users of our MATLAB® based PLS_Toolbox and stand-alone Solo to easily create a numerical recipes of their models. These recipes give the step by step procedure that take a measurement and calculate the desired outputs, such as concentration, class assignment, prediction diagnostics, etc. This includes applying all preprocessing steps along with the model (PCA, PLS, PLS-DA etc.) itself. When Model_Exporter is installed, models can be exported into predictor files in a variety of formats via the file menu in the Analysis window as shown below.
Model_Exporter also includes two versions of the freely-distributable Model_Interpreter. Either the C# or Java version of the Model_Interpreter can be used by any 3rd party program to add the ability to parse an exported model in XML format. Simply point the interpreter at an XML exported model and supply the data from which to make a prediction. The interpreter applies the model and returns the results. Model_Interpreter has no licensing fees and is appropriate for use on standard processors and operating systems or on handheld devices run by reduced instruction set processors (e.g. ARM). Your application doesn’t need to know anything about the preprocessing or model being used.
Version 3.0 of Model_Exporter was released in early October along with its associated stand-alone Solo+Model_Exporter version 7.9. This release includes support for Support Vector Machine (SVM) regression and classification models as well as Artificial Neural Network (ANN) regression models.
These changes represent a significant addition to Model_Exporter making it even more unique in the chemometrics world. No other chemometric modeling product offers anything as transparent, flexible or unencumbered by licensing. You can get more info about Model_Exporter by consulting the Release Notes and the Model_Exporter Wiki page.
Users with current maintenance can access these versions now from their account. If expired, maintenance can be renewed through the “Purchase” tab.
The MathWorks released MATLAB R2014b (version 8.4) last week, and right on its heels we released PLS_Toolbox 7.9. R2014b has a number of improvements that MATLAB and PLS_Toolbox users will appreciate, specifically with graphics. The new MATLAB is more aesthetically pleasing to the eye, easier for the Color Vision Deficiency (CVD) challenged, and smoother due to better anti-aliasing. An example is shown below where the new CVD-friendly Parula color map is used to indicated the Q-residual values of the samples.
But the most significant changes in R2014b are really for people (like us) that program in MATLAB. For instance, TMW didn’t just change the look of the graphics, they actually changed the entire handle graphics system to be object oriented. They also added routines useful in big data applications, and improved their handling of date and time data. When you start the new MATLAB the command window greets you with this:
“Some existing code may need to be revised to work in this version of MATLAB.” That is something of an understatement. In fact, R2014b required the update of almost every interface from PLS_Toolbox 7.8. Revising our code to work with R2014b required hundreds of hours. But the good news for our users is that we were ready with PLS_Toolbox 7.9 when R2014b was released AND, as always, we made our code work with previous versions of MATLAB (back to R2008a). This, of course, is the significant difference between a supported commercial product and freeware. Not only do you get new features regularly, but you can rely on it being supported as operating systems and platforms change.
So if you look at the Version 7.9 Release Notes, you won’t see a lot of major changes. Instead, we took the time to assure compatibility with R2014b and made many minor changes to improve usability and stability.
The new MATLAB will allow our command-line and scripting users to do their science more efficiently and present their result more elegantly. These improvements will benefit us as well, and will ultimately translate into continued improvement in PLS_Toolbox and Solo.
On New Year’s day 2014 Eigenvector Research, Inc. (EVRI) celebrated its 19th birthday and began its 20th year. The momentum that carried us into 2013 built throughout the year and resulted in our largest year-over-year software sales increase since 2007. Our best three software sales months ever have all been within the last five months. Clearly our partnering with analytical instrument makers and software integrators plus our tools for putting models on-line are striking a responsive chord with users.
The consulting side of our business also continues to be very busy as we assist our clients to develop analytical methods in a wide variety of applications including medical, pharmaceutical, homeland security (threat detection), agriculture, food supplements, energy production and more.
The third leg of our business, chemometrics training, continued unabated as we taught on-site courses for government and industry, courses at conferences and held the 8th edition of our popular Eigenvector University (EigenU). We enter 2014 firing on all cylinders!
Major additions to PLS_Toolbox and Solo in 2013 included the Model Optimizer, Hierarchical Model Builder, a new Artificial Neural Network (ANN) tool, and several new file importers. We will soon release an additional ANN option along with new tools for instrument standardization/calibration transfer. Also on the horizon, a major new release of Solo_Predictor will include an enhanced web interface option and additional instrument control and scripting options.
2014 includes a busy schedule with conferences, talks, conference exhibits and short courses. Below is a listing of where you’ll be able to find us:
January 21-24, IFPAC, Arlington, VA. BMW to present “Mixed Hierarchical Models for the Process Environment” and “A Multivariate Calibration Model Maintenance Road Map.”
March 2-6, Pittcon Chicago, IL. NBG and RTR will be at the EVRI exhibition booth.
April 27-May 2, EigenU 2014, 9th Annual Eigenvector University, Seattle, WA. Join the complete EVRI staff for 6 days of courses and events.
May 6-9, EuroPACT, Barcelona, Spain. BMW to give plenary address “Model Maintenance: the Unrecognized Cost in PAT and QbD” and a condensed version of our “Chemometrics without Equations” short course.
June 1-4, CMA4CH, Taormina, Italy. JMS to teach short course and talk TBD.
August 2-8, IDRC, Chambersburg, PA. NBG to attend, talk TBD.
September 14-18, ICRM, Nijmegen, The Netherlands. NBG to give keynote “An Overview of Hyperspectral Image Analysis in Chemometrics.”
September 28-October 3, SciX 2014, Reno, NV. JMS Chemometrics Section Chair, talks and courses TBD.
November 10-13, EigenU Europe, Hillerød, Denmark. Courses led by BMW and Eigenvector Associate Rasmus Bro.
November 17-19, EAS 2014, Somerset, NJ. EVRI sponsor of Award for Achievements in Chemometrics. Courses and talks TBD.
We’re especially excited about this year’s Eigenvector University. This ninth edition of EigenU will include all our usual events (poster session, PowerUser Tips & Tricks, workshop dinner) plus five new short courses. Special guest Age Smilde will lead “Chemometrics in Metabolomics” and Rasmus Bro will present “Modeling Fluorescence EEM Data.” The other three new courses are “Calibration Model Maintenance,” “PLS_Toolbox Beyond the Interfaces” and “Getting PLS_Toolbox/Solo Models Online.” We expect EigenU 2014 to be an especially fun and fruitful learning experience.
We look forward to working with you in 2014!
BMW
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On the other hand, the application of these models, once developed, is typically quite straight forward. Most models can be applied to new data using a fairly simple recipe involving matrix multiplications, scalings, projections, activation functions, etc. There are exceptions, such as preprocessing methods like iterative Weighted Least Squares (WLS) baselining and models like Locally Weighted Regression (LWR) where you really don’t have a model per se, you have a data set and a procedure. (More on WLS and LWR in a minute!) But in the vast majority of cases effective models can be developed using methods whose predictions can be reduced to simple formulas. 
Many of our software users are among these new homeworkers and we are working to accommodate them. In particular, users that work with our floating license versions of PLS_Toolbox or Solo may have trouble reaching EVRI’s Floating License Server when working from home. We have just posted a video addressing this issue: Using Floating License Software Remotely. As always, if you have any questions regarding our software or online courses please contact our helpdesk and we will respond promptly. And to our consulting clients: we are available as always. 
I’ve just returned from Conference Chimiométrie 2020, the annual French language chemometrics conference, now in its 21st edition. The conference was held in Liège and unfortunately there wasn’t much time to explore the city. I can tell you they have a magnificent train station, Gare de Liège is pictured at right. 
Development of plant phenotyping tools for potato resistance against Phytophthora infestans by François Stevens et. al. 
Oh boy. Déjà vu. In the late 80s and 90s during the first Artificial Neural Network (ANN) wave there were a slew of companies making similar promises about the value they could extract from data, particularly historical/happenstance process data that was “free.” One slogan from the time was “Turn your data into Gold.” It was the new alchemy. There were successful applications but there were many more failures. The hype eventually faded. One of biggest lessons learned: Garbage In, Garbage Out. 
