Machine learning for spectroscopic data

1. Detecting Nutrients by Means of Near Infrared Spectra
Group:
Farid, Oleksii, Radim, David
Mentor:

2. Detecting Nutrients by Means of Near Infrared Spectra
Group:
Farid, Oleksii, Radim, David
Mentor:
PABLO0OoOoOoO!

3. Motivation

4. Motivation

5. Goal
 Predict % protein of a unknown animal feed sample
 Clustering samples with similar nutrient content
 Compete with results of domain experts

6. Goal
 Predict % protein of a unknown animal feed sample
 Clustering samples with similar nutrient content
 Compete with results of domain experts

7. Methods - Regression
1. Metrics: RMSE and MAD
2. Savitzky-Golay-filter and scaling
3. Models:
 Expert baseline:
 PLS regression
 SVM
 Random Forest
 NN/CNN
 Stacking

8. Methods - Clustering
1. Preprocessing:
 Savitzky-Golay-filter
2. Dimension reduction and feature selection:
 Manual selection (domain knowledge)
 PCA
 PLS
 t-SNE
 Autoencoder
3. Clustering:
 K-means

9. Results

10. Results

11. NADEGE -
PLS UNSC.
Jean-Claude
Stacked-PLS
JOHAN C
classes 9
+ PLS STACKED
YOANN 8
Classes +
PLS SVM
LUDOVIC
LWR
Bootstrap BRIAN
MATTHIEU
Local
spectra +
pls
RMSE 5.121 1.830 2.189 0.9113 0.870 1,8622 0.774 0.948 0.742
MAD*1.48 4.865 1.790 0.823 0.8738 0.765 0.7370 0.662 0.574 0.558
MAD 3.287 1.209 0.556 0.5904 0.517 0.4980 0.447 0.388 0.377
Results
 Results between 3. and 4. place in the competition
with domain experts (France)

12. NADEGE -
PLS UNSC.
Jean-Claude
Stacked-PLS
JOHAN C
classes 9
+ PLS STACKED
YOANN 8
Classes +
PLS SVM
LUDOVIC
LWR
Bootstrap BRIAN
MATTHIEU
Local
spectra +
pls
RMSE 5.121 1.830 2.189 0.9113 0.870 1,8622 0.774 0.948 0.742
MAD*1.48 4.865 1.790 0.823 0.8738 0.765 0.7370 0.662 0.574 0.558
MAD 3.287 1.209 0.556 0.5904 0.517 0.4980 0.447 0.388 0.377
Results
 Results between 3. and 4. place in the competition
with domain experts

13. Future work
 Outlier detection and removal
 Using cluster information for regression

14. Thanks for your attention
and
many thanks to Pablo for your
support!!