X-FAST. Interne kwaliteit van poreuze levensmiddelen: X-stralentomografie toegepast op product en proces. Pieter Verboven

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Franciscus Aalderink
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1 X-FAST Interne kwaliteit van poreuze levensmiddelen: X-stralentomografie toegepast op product en proces Pieter Verboven

2 X-ray technology Imaging methods o o Radiography (2D) Tomography (3D)

3 X-ray imaging Features o o o o Absorption depending on atomic number Good contrast of internal density differences Air-solid phase Fat-water-protein Good penetration in all foods Macro to (sub-)micron resolution in 3D Little or no sample preparation

4 Challenges Quality, size and resolution of CT images Amount of data Cumbersome analysis Unknown relationship between microstructure and food properties / quality attributes Few online techniques

5 Applications Fruit and vegetables Bakery products Dairy food Confectionary & chocolate Meat products Packaged food

6 European Synchrotron Radiation Facility (ESRF)

10 Jonagold Braeburn Kanzi Conference

11 Jonagold Braeburn Kanzi Conference

12 Ho et al. (2010) Journal of Experimental Botany, 61 (10),

13 X-ray micro- and nano-ct 35,3 µm 22,6 µm 10,7 µm 5,2 µm 3,3 µm 1,3 µm 0.8 0,7 µm 0.5 0,5 µm Foamed gel samples CT images 250 x 250 µm2 250 µm

14 Climate controlled micro-ct Bruker micro-ct 1172 Cooling stage o to -15 C +/- 0.5 C

15 Ice cream

16 Relative volume percentage (%) Microstructure-process relationships Sugar foam: o effect of mixing time 2 min min min

17 Statistical analysis

18 Food structure engineering MAP verpakken van brood (Tetra-project i.s.m. Campus Geel) kruim korst 5 mm 5 mm

19 Berekende [O 2 ] profielen kruim korst 4.5mm 2.6mm (mol m -3 )

20 inline X-ray imaging (2D) Increasing degree of granulation in oranges

21 Inline CT (3D)

22 TomFood, KU Leuven, UGent & UA Inline CT (3D) 1. Recording X-ray projections on conveyer system X-ray source X-ray detectors 2. Fast reconstruct of 3D images 3. Detection algorithms

23 Inline detection algorithms of internal disorders Detect intensity shifts Watersoaked tissues Deydrated areas and cavities Correct (%) Detection rate False negative (%) False positive (%) Apples (n=74)

24 Inline 3D shape and size measurement

25 Foreign materials

26 3D inline CT development Translation+rotation tomography X-ray 3D imaging + X-ray inspection 3D

27 Conveyor belt mock up Source: transmission tube (opening angle 180 ) Translation axis Detector Translation+rotation stage Detector Translation axis Van Hoorebeke et al. U Gent Novel techniques for inspection and engineering of food (micro)structure based on X-ray computed tomography

Inline-CT Limited amount of data in a limited amount of time KU Leuven U Antwerpen U Gent 60 90 120

28 Inline-CT Limited amount of data in a limited amount of time KU Leuven U Antwerpen U Gent Novel techniques for inspection and engineering of food (micro)structure based on X-ray computed tomography

29 Inline-CT Reconstruction 16 projections 128 projections Novel techniques for inspection and engineering of food (micro)structure based on X-ray computed tomography

30 Advanced reconstruction Number of radiographs FBP SART Limited angle FBP SART SART init In collaboration with Antwerp University & Ghent University Novel techniques for inspection and engineering of food (micro)structure based on X-ray computed tomography

detector Feature size (mm/lp) Novel techniques for inspection and

31 In-line CT Analyse/Compare: Quality versus Throughput Throughput (samples/s) 100 cm 80 cm More projections 40 cm 60 cm Larger detector Feature size (mm/lp) Novel techniques for inspection and engineering of food (micro)structure based on X-ray computed tomography

32 3D imaging + X-ray inspection Concept: Compare the radiograph with a simulated radiograph of a reference sample The reference sample contains no defects sample measure radiograph reference sample simulate simulated radiograph Large deviations in the residual image indicate a defect compare No need to develop individual algorithms for different defects! residual image

33 Simulated radiograph Adapted from Palenstijn, W.J., Batenburg, K.J., Sijbers, J., The ASTRA tomography toolbox, in: 13th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE pp Line integrals through 3D volume between detector pixels and source Scan geometry should match the radiographs scanner geometry How to determine the reference sample? simulated measured

34 The reference sample 3D Estimate shape and pose with 3Dvision Assume a model is known Shape / surface rigid (e.g. CAD) deformable (e.g. SSM) Internal density distribution uniform non-uniform 3D scanner fit shape model radiograph scanner compare with radiograph simulated from model

Prediction Potential with perfect reference 98.7 Truth SCAN 1 2 3 4 5 1 19.54 0 0 0 0 2 0 21.

35 Prediction Potential with perfect reference 98.7 Truth SCAN scan 1 scan 2 scan 3 scan 4 scan 5

36 Rigid shape model Internally uniform 2 shapes Toroid Ellipsoid 5 defect shapes Variable intensity 1. Apply random rotation and generate point cloud to simulate 3D-vision 2. Fit model to point cloud (Iterative closest point / Procrustes) 3. Generate radiographs 4. Classify Added noise Naïve bayesian classifier on sum of pixel values in residual images.

37 Classification (N = 5000)

38 PC2 Morphable bodies: Statistical shape model PC1 Generate a set of shapes with corresponding shapes Apply PCA on point coordinates -3 sd -3 sd +0 sd +3 sd Results in a mean shape and displacement vectors (principal components) for each point +0 sd i.e. a new shape is a linear combination of the mean shape and the principal components +3 sd Different scale in images: my mistake

39 Estimating shape and pose Iterate Rigid registration (iterative closest point) Least squares optimization of every point in model to nearest neighbour in point cloud Until error is acceptable Sensitive to local minima Alternative: Schneider, D.C., Eisert, P., Fitting a morphable model to pose and shape of a point cloud, in: Vision, Modeling, and Visualization Workshop Proceedings. pp

40 Non-rigid shape model Internally uniform random fit residual 1. Generate random shape 2. Apply random rotation and generate point cloud to simulate 3D-vision 3. Fit model to pose and shape of point cloud 4. Generate radiographs 5. Classify Added noise Naïve bayesian classifier on sum of pixel values in residual images.

41 To do Add defects to random shapes Generate large datasets to asses classification rates Implement non-uniform density distribution

X-FAST doelstellingen Product- en procesontwikkeling: o ontwikkelen van een testfaciliteit voor microstructuuranalyse met X-stralentomografie o onderzoeken van de relatie tussen structuur en

42 X-FAST doelstellingen Product- en procesontwikkeling: o ontwikkelen van een testfaciliteit voor microstructuuranalyse met X-stralentomografie o onderzoeken van de relatie tussen structuur en sensorische of functionele eigenschappen Online niet-destructieve kwaliteitscontrole: o o online meetmethode van de interne samenstelling en dimensies detectie van defecten en vreemde voorwerpen

43 Praktische aanpak Is de microstructuur, het defect of het vreemd voorwerp meetbaar met X-stralen? Is dit accurater/sneller/gemakkelijker/betrouwbaarder dan met een andere methode? Zijn er structurele verschillen op basis van een verschillende samenstelling of bereidingswijze? Zijn deze structuurverschillen bepalend voor de beoogde producteigenschappen? Kan dit op een kostefficiënte manier worden geïmplementeerd in de productontwikkeling of kwaliteitscontrole?

44 X-FAST Werkplan WP1. Microstructuur 1.1 Cases 1.2 X-stralentomografie 1.3 Analyse 1.4 Adviezen Mijlpalen M1.1 M1.2 M1.3 WP2. Online inspectie 2.1 Cases 2.2 X-stralen inspectie 2.3 Analyse 2.4 Adviezen Mijlpalen M2.1 M2.2 M2.3

45 Toepassingen Ontbijtkoek o o o Inline defecten meten Gaten vorm/grootte Breuk/beschadiging in niet-doorzichtige verpakking Samenstelling Inpasbaarheid op lijn Oorzaken defecten Vreemde voorwerpen Plastic Optimalisatie 2D scanner

46 rubber glas Toepassingen Fruitpuree o Vreemde voorwerpen Glas Rubber Detectie 2D/3D Inline toepassen kiwi framboos aardbei 2D

47 rubber glas Toepassingen Fruitpuree o Vreemde voorwerpen Glas Rubber Detectie 2D/3D 3D

48 Toepassingen Chocolade o Vreemde voorwerpen Metaal Plastic Haren Productsamenstelling o % vulling o % toevoegingen Productvorm o afwijkingen

49 Discussie en actiepunten

50 Thanks to

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