Ci spiace, ma questo articolo è disponibile soltanto in English.
Ci spiace, ma questo articolo è disponibile soltanto in English.
ChemometricsConferentia Chemometrica 2017, 3–6 September 2017, Gyöngyös, Farkasmály, Hungary. | chemometrics LINK
“Chaos theory in chemistry and chemometrics: a review” LINK
Predicting herbivore faecal nitrogen using a multispecies near-infrared reflectance spectroscopy calibration. LINK
Calibration transfer of flour NIR spectra between benchtop & portable instruments | directstandardization spectro LINK
Near InfraredFast Detection of Paprika Adulteration Using FT-NIR Spectroscopy LINK
How to analyze food and future requirements for NIR spectroscopy LINK
Infrared“On-Site Analysis of Cannabis Potency Using Infrared Spectroscopy” LINK
Raman5th International Taiwan Symposium on Raman Spectroscopy (TISRS 2017) 27–30 June 2017, Chiayi, Taiwan. LINK
HyperspectralCorning and PrecisionHawk and partnership enables hyperspectral imaging on drones LINK
Employing NIR-SWIR hyperspectral imaging to predict the smokiness of scotch whisky LINK
Defect detection of green coffee by NIR-hyperspectral imaging and multivariate pattern recognition LINK
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“Learning about Spectroscopy with Ocean Optics” LINK
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ChemometricsUV/Vis spectroscopy combined with chemometrics for monitoring solid-state fermentation with … LINK
What is MODEL SELECTION? What does MODEL SELECTION mean? MODEL SELECTION meaning & explanation: LINK
Model selection with multiple regression on distance matrices leads to incorrect inferences LINK
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High-grade, compact spectrometers for Earth observation from SmallSats LINK
FutureMiniature and Micro Spectrometers End-Users Needs, Market & Trends 2015-2021 – Research and Markets – Yahoo Finance LINK
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Non-Destructive Sensor-Based Prediction of Maturity and Optimum Harvest Date of Sweet Cherry Fruit | sensors LINK
IDC unveils its Top 10 Predictions for global Robotics Industry Industry40 Robotics LINK
Global Molecular Spectroscopy Market is expected to reach USD 6.712 billion till 2024. htt… LINK!
Assessing pre-harvest sprouting in cereals using near-infrared spectroscopy-based metabolomics LINK
Rapid screening of commercial extra virgin olive oil products for authenticity: Performance of a handheld NIR device LINK
Viavi Solutions and ESPROS Photonics Corporation Debut New Miniaturized Spectral Sensor and Multispectral Sensor LINK
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Research details developments in the multivariate analysis software industry | MVA LINK
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Investments in AI will triple in 2017. ($47 billion by 2020 per ) CIO CMO | LINK
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WHITE PAPER: A novel knowledge-based Chemometric Software Framework for quantitative NIRS Calibration Modeling LINK
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Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy LINK
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You try as six to ten combinations until one of them selects his favorite calibration model, to then continue to optimize. Since then suddenly fall to outliers, because it goes in depth, so is familiar with the data, we know now the spectra of numbers of outliers and is familiar with the extreme values.
Now, the focus is on the major components (principal components, Latent Variables, factors) and makes sure not to over-fit and under-fit not to. The whole takes a few hours and finally one is content with the model found.
So what would happen if you all in the beginning tried variants found outliers removed and re-evaluated and compared? The results would be better than that of the previous model choice? One does not try out? Because it is cumbersome and takes hours again?
We have developed a software which simplifies this so that also the number of model variations can be increased as desired. The variants generation is automated with an intelligent control system, as well as the optimization and comparing the models and finally the final selection of the best calibration model.
Our software includes all the usual known data pretreatment methods (data pre-processing) and can combine them useful. Since many Preteatments are directly dependent on the wavelength selection, such as the normalization the determined within a wavelength range of the scaling factors to normalize the spectra so that pretreatments with the wavelength ranges may be combined. So a variety of settings sensible model comes together that are all calculated and optimized. For the automatic selection of the relevant wavelength ranges, different methods are used, which are based on the spectral intensities. Thus, for example, regions with total absorption is not used, and often interfering water bands removed or retained.
Over all the calculated model variations as a summary outlier analysis can be made. Are there any new outliers (hidden outlier) discovered, all previous models can be automatically recalculated, optimized and compared without these outliers.
From this great number of calculated models with the statistical quality reviews (prediction performance) the optimum calibration can now be selected. For this purpose, not simply sorting by the prediction error (prediction error, SEP RMSEP) or the coefficient of determination (coefficient of determination r2), but by several statistical and test values are used jointly toward the final assessment of optimal calibration.
Thus we have created a platform that allows the highly automated work what a man can never do with a commercial software.
We therefore offer the largest number of matched to your application problem modeling calculations and choose the best calibration for you!
This means that our results are faster, more accurate, robust and objective basis (person independent) and quite easy for you to apply.
You have the full control of the models supplied by us, because we provide a clearly structured and detailed blueprint of the complete calibration, with all settings and parameters, with all necessary statistical characteristics and graphics.
Using this blueprint, you can adjust the quantitative calibration model itself in the software you use, understand and compare. You have everything under control form model creation, model validation and model refinement.
Your privacy is very important to us. The NIR data that you briefly provide us for the custom calibration development will remain of course your property. Your NIR data will be deleted after the job with us.
Interested, then do not hesitate to contact us.
That is common practice. But is this good practice?
And nobody asks, how long, how hard have you tried, how many trial have you done, if this really the best model that is possible from the data?
And imagine the cost of the data collection including the lab analytics!
And behind this costs, have you really tried hard enough to get the best out of your data? Was the calibration done quick and dirty on a Friday afternoon? Yes, time is limited and manually clicking around and wait in such kind of software is not really fun, so what are the consequences?
Now I come to the most important core point ever, if you own expensive NIR spectrometer system, or even many of them, and your company has collected a lot of NIR spectra and expensive Lab-reference data over years, do you spend just a few hours to develop and build that model, that will define the whole system’s measurement performance for the future? And ask yourself again (and your boss will ask you later), have you really tried hard enough, to get the best out of your data? really?
What else is possible? What does your competition do?
There is no measure (yet) what can be reached with a specific NIR data set.
And this is very interesting, because there are different beliefs if a secondary method like NIR or Raman can be more precise and accurate, as the primary method.
What we do different is, that our highly specialized software is capable of creating large amounts of useful calibrations to investigate this limits – what is possible. It’s done by permutation and combination of spectra-selection, wave-selection, pre-processing sequences and PC selections. If you are common with this, then you know that the possibilities are huge.
For a pre-screening, we create e.g. 42’000 useful calibrations for the mentioned data set. With useful we mean that the model is usable, e.g. R² is higher than 0.8, which shows a good correlation between the spectra and the constituent and it is well fitted (neither over-fitted nor under-fitted) because the PC selection for the calibration-set is estimated by the validation-set and the predictive performance of the test-set is used for model comparisons.
Here the sorted RMSEP values of the Test Set is shown for 42’000 calibrations.
You can immediately see that the manually found performance of 0.49 is just in the starting phase of our optimization. Interesting is the steep fall from 1.0 to 0.5 where manually optimization found it’s solutions. A range where ca. 2500 different useful calibrations exist. The following less steep fall from 0.5 to 0.2 contains a lot more useful models and between 0.2 to 0.08 the obvious high accurate models are around 2500 different ones. So the golden needle is not in the first 2500 models, it must be somewhere in the last 2500 models in the haystack.
That allows us to pick the best calibration out of 42’000 models, depending on multiple statistical evaluation criteria, that is not just the R² or RPD, SEC, SEP or RMSEP, (or Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Multivariate AIC (MAIC) etc.) we do the model selection based on multiple statistical parameters.
To compare the calibration models by similarity it is best viewed with dendrogram plots like this (zoomed in), where the settings are shown versus the models overall performance similarity. In the settings you can see a lot of different permutations of pre-processings combined with different wave-selections.
Here is a part of our calibration report, that exactly describes the data used in the calibration set (CSet), the validation set (VSet) and the test set (TSet). The numbers are the number ids of the spectra in your delivered NIR data file.
The calibration method settings and parameters are
Waveselection : the variable selection or wavenumber selection or wavelength selection
Pretreatments : the spectral data pre-processing
PCs : the number of Principal Components (PC) or Latent Variables (LV)
Method : the modeling method algorithm used, e.g. PLS
Then the statistical analysis of the PLS model by the different sets (CSet, VSet, Tset).
Statistical analysis of calibration, validation and test results : 1 Name, 2 Unit, 3 N : number of spectra, 4 N : number of samples, 5 Average spectra count per sample, 6 Reference values, 7 Min, 8 Mean, 9 Median, 10 Max, 11 Standard deviation, 12 Skewness : left (-) or right (+) lack of symmetry, 13 Kurtosis : flat (-) or peaked (+) shape, 14 Model statistics, 15 RPD, 16 R², 17 RMSEC, RMSEP, RMSET : root mean square of prediction errors, 18 SEC, SEP, SET : standard error (bias corrected), 19 Bias, 20 Skewness of prediction errors, 21 Kurtosis of prediction errors, 22 Intercept, 23 Slope, 24 Intercept (reverse), 25 Slope (reverse), 26 Sample Prediction Repeatability Error, 27 Sample Prediction Repeatability Error (of Missing data MSet)
This shows how we deliver the optimal settings. With the statistical values, the NIR model predicted values of all spectra and additional plots you are able to compare with your re-built model to verify that the models perform nearly equally.