Comparison between SVM (Support Vector Machines) and PLS on spectral dataset
Support Vector Machines (SVM) are parts of the supervised Machine Learning methods. SVM were originally developed for classification objectives (pattern recognition), especially to discriminate between convex or hardly-separable classes. But, they are also very effective for quantitative prediction purposes.
This technique is very interesting to model non-linear relationships between the data or for intricate situations (e.g. complex parameter or concentrations close to the detection threshold).
In order to implement SVMs, 3 parameters must be optimized: a regularization parameter, the margin size and the non-linearity degree of the model, which is much simpler than training Artificial Neural Networks.
When predicting several quantitative parameters based on near-infrared spectroscopic data, SVM models provided significantly better results than PLS regression one (errors divided by 2).
This Machine Learning method brought a significant performance improvement vs PLS regression, due to the non-linearity present in the data sets (fat, protein and humidity contents in meat with NIR spectroscopy).
This project also demonstrated that with a rather small training set size, SVMs could show high performance and generalization power on an independent test set.
Prediction of the aromatic potential of grapes
The French institute of Wine and Vine (IFV) is always looking for improving the winemaking processes, from harvest to bottling process. The IFV often leads collaborative projects, in particular with Vinovalie, a group of wine coop from South -West of France.
One of their main problematic consists in identifying the aromatic potential of the grapes so as to direct the musts towards the most suitable winemaking process.
Aid-decision tools were developed during this project. They allow to define the optimized winemaking process for the grapes. They also make possible to increase the productivity and the wine quality. For the INES wine (AOP Fronton Rosé), the following results were obtained :
- + 10% productivity
- + 18% wines with high aromatic quality
- + 15% increase in price due to a higher aromatic quality
Model updating using the DOP orthogonalization method
In the frame of online monitoring of its polymerization processes, CERDATO, ARKEMA‘s research center, encountered problems in updating its spectroscopic calibrations. After several classical attempts, by adding new samples, their problem persisted.
They called Ondalys to train and support them in the use of different orthogonalization methods, in particular the Dynamic Orthogonal Projection (DOP) method.
Model corrected by DOP obtained better results than those developed with classical methods, while making it possible to diagnose problems that occurred on the production line.
Identification of Raw Materials using Near Infrared spectroscopy (NIR)
In order to gain time and efficiency for the product characterization, one of our customers wished to develop a method for identifying his Raw Materials (powders and liquids) using a Near infrared spectrometer.
This major player in the Pharmaceutical Industry, asked Ondalys to apply robust classification methods and develop robust identification models, that can be used systematically on incoming batches of Raw Materials.
Over 100 different Raw Materials (liquids and powders) are now characterized while arriving, thanks to near infrared spectroscopy and identification models developed by Ondalys.
Modeling for fruit maturity monitoring
The B.I.P. – French interprofessional institute of the prune, wanted to develop a non-destructive decision support tool, applicable directly to the orchard in order to better estimate the maturity of the fruits to predict the optimal harvest date of the plums.
From large databases (more than 6000 samples scanned on a laboratory spectrometer), Ondalys has developed models for quantitative prediction of the sugar and acidity levels of plums.
Following encouraging results, the study continues to transfer the models obtained to a portable spectrometer so that the fruit can be measured directly in the orchard.
Study funded by France AgriMer
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