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Calibration modeling is a complex and very important part of NIR spectroscopy, especially for quantitative analysis. If the model is badly designed the best instrument precision and highest data quality does not help getting good and robust measurement results. And NIR Spectroscopy requires periodically recalibration and validation.
How are NIR models built today?
In a typical usage in industry, a single person is responsible to develop the models (see survey). He or she uses a Chemometric software that has a click-and-wait working process to adjust all the possible settings for the used algorithms in dialogs and wait for calculations and graphics and then to think about the next modeling steps and the time is limited to do so. Do we expect to find the best use-able or optimal model that way? How to develop near-infrared spectroscopy calibrations in the 21st Century?
Why not put all the knowledge a good model builder is using into software and let the machines do the possibilities of calculations and presenting the result? Designing the software that way, that the domain knowledge is built-in, not just only the algorithms for machine learning and make it possible to scale the calculations to multi-core computers and up to cloud servers. Extend the Chemometric Software with the Domain Knowledge and make as much computer power available as needed.
As it was since the beginning
User → Chemometric Software → one Computer → some results to choose from
==> User’s time needed to click-and-wait for creating results
User → (Domain Knowledge → automatized Chemometric Software) → many Computers → the best models
==> User’s time used to study the best models and reasoning about his product / process
Note that the “Domain Knowledge” here does perfectly support the User’s product and process knowledge to get the things done right and efficient.
Scaling at three layers
- Knowledge : use the domain knowledge to drive the Chemometric Software
- Chemometric Software : support many machine learning algorithms and data pre-processings and make it automatic
- Computer : support multi-core calculations and scale it to the cloud
The hard part in doing this, is of course the aggregation of the needed domain knowledge and transform it into software. The Domain Knowledge for building Chemometric NIR Spectroscopic models is well known and it’s huge and spreads multiple disciplines. Knowledge-driven software for computing helps to find the gold needle in the haystacks. It’s all about scaling that makes it possible. See Proof of Concept.
- NIR users can get help working more efficient and getting better models.
- New types of applications for NIR can be discovered.
- Evaluation of NIR Applications to replace conventional analytical methods.
- Hopeless calibrations development efforts can be re-started.
- Higher model accuracy and robustness can be delivered.
- Automate the experimental data part of your application study.
- Person independent optimization will show new solutions, because it’s not limited by a single mindset => combining all the aggregated knowledge and its combinations.
- Software independent optimization will show new solutions, because none of vendor specific limitations and missing algorithms are present => combining all open available algorithms and there permutations.
- Computing service is included.
Contact us for trial
Your NIR data is modeled by thousands of different useful calibration models and you get the best of them! That was not possible before in such a easy and fast way! See How it works
Imagine that the prediction results can be produced with any kind of software and newest algorithms.
And we just use PLS right to generate models that can be used on all NIR software systems, because PLS is a quasi standard, supported in all major chemometrics software.
Our software framework reached very good results, got gold (rank #1) and silver (rank #2) during well known international NIR Chemometric software shootouts* so far, the competitions are held bi-annual.
|Rank / competitors||Competition / Conference||Year|
|#1 / 1 **||Kaji / ANSIG||2014|
|#1 / 150||Kaji / ANSIG||2012|
|#2 / ???||IDRC / IDRC||2012|
A set of NIR spectral data will be available for downloading from the ANISG website and contestants will be asked to find and explain a “best” chemometric model to robustly predict samples of the same type.
A panel will select the three “best” entries based on the predicted results and spectroscopic explanation of the products and attributes of interest.
The IDRC Competition
The Software Shootout has been a staple of the IDRC. It is a competition amongst participants of the conference that aims at determining the person who developed the best model and obtained the lowest prediction error for a particular problem.
Every IDRC, a new challenge is proposed to participants. The challenge consists of a data set with calibration, test and a validation set.
Participants are given target values for the calibration and test sets but must do their best to develop a model that will predict the validation set as accurately and precisely as possible. Challenges from all sorts of fields of NIRS have been used (agriculture, biomedical, pharmaceutical, soil, …).
*) The author was unable to present the results at the conferences, so this ranking was not official but confirmed by the shootout organizers. Thanks go to Benoit Igne, IDRC 2012 shootout organizer and Steve Holroyd, Kaji Competition organizer at ANISG Conference 2012.
Our chemometric software framework can significantly reduce the time spent for NIR method development and fine optimization. The time saving can be achieved through highly automated experiments and the usage of cloud computing. Calibrations are built and evaluated using automated good practices protocols resulting in useful, precise and robust Calibrations. The high number of experiments enables a deep screening of the solution domain to find the optimum calibration settings, something currently unavailable in standard chemometric software.
**) We were the only participator that got the 4 competition tasks (4-times more than usual) completed in that short time and submitted the fully documented results. After the competition, the information was given, that the data was originated from forages and the constituents were dry matter, organic matter digestibility, protein and ash. Thanks go to Daniel Cozzolino, Kaji 2014 Competition organizer.
All the below categories are implemented by using multiple different algorithms and formulas which leads to many different calibrations.
Steps in modeling
- Data Cleaning – (bad data, missing values, duplicate elimination, spectral quality / intensity / noise, input value typing errors, …)
- Initial Calibration set up – selection of calibration, validation and test samples
- Wavelengths selection
- Data preprocessing, pretreatments
- Method calculation
- Choosing the number of Principal Components / Latent Variables
- Validation of calibration model / Statistics of performance – (accuracy, precision, linearity, repeatability, range, distribution, robustness / stability, sensitivity, simplicity, etc.)
- Outlier examination and removal
The problem of choosing the optimal number of factors to find the optimum between underfitting and overfitting is solved by having multiple methods and protocols implemented leading to multiple calibrations.
The evaluation and the selection of the best calibration is based on many individual statistical values including the most popular RMSEP, SEP, Bias, SEC, R2 and PCs etc.
Results and Reporting
A detailed calibration report is provided detailing the best available calibration containing all calibration parameter settings and statistics of prediction performance of the calibration set, the validation set and the test set. A visual expression of the calibration is provided with the most importance plots.
Our service works with any quantitative NIR spectra data set in the standard JCAMP-DX format and uses mainly PLS and PCR to be compatible with other chemometric calibration software.
We help you to find the optimal settings for higher NIR accuracy and reliability. You can build your own custom NIR calibration model with this valuable settings. We offer a quantitative NIR Calibration development and optimization service. New: White Paper about the details, what’s behind.
Increase Your Profit with optimized NIR Accuracy
- going closer to your product specification limits and maximize profitability
- optimizing your models yield to process optimization and optimizing productivity
- compete against other NIR vendors in a feasibility study (NIR salesman)
- compatible with any NIR vendor
- no installation, no learning
- quantitative NIR Calibration Development as a Service
- help users avoid common pitfalls of method development
- before you validate and approve your solution for use in production process:
- check if a better calibration can be found,
- compare your calibration with other experts solutions.
- no cumbersome trial-and-error modeling steps
- calculation time is spent on our high performance infrastructure
- fast results, developed calibrations within days
- fix costs, depends only on data size (not hourly rate for service)
- huge saving in method development costs
- easy to plan
There are many published standards and guidelines (protocols) available for developing NIR calibrations from Standards Consortium such as ASTM, EMEA, ICH, IUPAC, ISO, USP, PASG etc. as well as many good recommendations and guidelines found in various textbooks and papers.
The difficulty with so many ‘Protocols’ for the NIR user is to have them all available and in their thought processes during calibration work and in addition to execute, check and challenge all calibrations generated manually. This is time consuming and sometimes boring repetitive work.
To simplify this for the person generating the NIR Calibrations, we have collected the good practices protocols and integrated them into our service that automates the calibration building and evaluation procedures.
to part 2
5. Calibration Precision
What do you believe, can NIR calibration models be more precise than reference values?
6. Calibration Maintenance
How often do you update your quantitative calibrations per year?
7. Quantitative Calibrations
How many quantitative (%) calibrations do you have in use?
8. Quantitative Parameters
In all your quantitative calibrations, how many parameters (properties) you have in total?
9. Qualitative Calibrations
How many qualitative (identification) calibrations do you have in use?
Please vote and see the assessments below.
1. Calibration Developers
How many persons in your company are able to develop a NIR Calibration?
2. Calibration Development
How much time do you spend to develop a calibration model?
3. Chemometric Software
Which Chemometric Software are you using for NIR?
4. NIR Spectrometer Brand
Which NIR Spectrometer Brand do you use?
Please vote and see the assessments below.