SLOPE 1st workshop - presentation 5
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Transcript of SLOPE 1st workshop - presentation 5
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Matthias KerschhagglBiRT Workshop, 20/03/15
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Hyperspectral‐/ Chemical Imaging as Key Technology in Sensor Based
Sorting Applications
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Outline
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Who we are
Smart solutions providersince 1987
2006: HSI camera & classificationsystem introduced to market
Real‐time classification and sorting: All‐in‐one device
Industry proven in recycling, mining, food processing and pharmaceutical applications
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Applications
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Metals from slagHousehold waste
C&D Recovered Paper Wood Sorting
Potato Sorting &Grading
E‐Scrap
Nut Sorting
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References RecyclingGlass Sorting
Customer:
EVK products:EOS – Color ImagingARGOS – Conductivity Imaging
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References RecyclingMetal Scrap
Customer:
EVK product:ARGOS –Conductivity Imaging
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References RecyclingPlastic Sorting
Customer:EVK product: HELIOS – Chemical Imaging
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References RecyclingMinerals, Metals, Plastic
Customer:
EVK product:EOS – Color Imaging
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References MiningMineral Sorting
Customer:
EVK product:HELIOS – Chemical Imaging
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References Food SortingDiv. Nut Sorting / Recycling
Customer:
EVK product:HELIOS –
Chemical Imaging
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References Food Processing Potato Sorting
Customer:
EVK products:HELIOS –Chemical Imaging
EOS – Color Imaging
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Hyperspectral Imaging Technology
Camera internal classification and RGB visualization of chemical differences for real‐time analysis
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Outline
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• Recycling
• Food
• Pharmaceuticals
• Mining
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Recycling
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papercardboard
PE‐LD
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Recycling
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PE PVC
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Recycling
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Quality analysis ofpaper
Paper + Glue, 1st derivative, normalized
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Outline
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• Recycling
• Food
• Pharmaceuticals
• Mining
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FoodHSI preview of SE
potatoe
CCI view of SE potatoe
Potatoe after frying
Sugar end
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FoodFood sorting – Blueberries and stems
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Food
Food sorting – Noodles and worms
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Food
Starch
Glucosewater
Simple VIS representation(filter convolution)
of HSI data
CCI representationof HSI data
(PCA: Principal Component Analysis)Quantitative analysis is possible
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HSI Objectives/Trends Monitoring of input streams for quality and process control ‐>
HSI as Process Analysis Technology (PAT) Sorting and monitoring of product flows in one machine using
HSI/CIT Quantitative analysis of chemical constituents as add‐on
Removing foreign materialsand infer quantitative information on e.g. dry matter and glucose in potatos at the same time
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HSI – Quantitative Analysis
wellness.byu.edu
Fig 2: PLS schematics (Wold et al. 2001)
Fernadez‐Novales et al. (2009)
X YPredictors (spectra)
Observations(reference analysis)
X Y
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Dry Matter
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REMINDER 1st inline TEST 2013
HELIOS Camera
R = 0.9 RMS = 1 %
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Inline Dry Matter Measurements
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• Dry matter content tracedby spectral signatures
• Inferred FIR‐filter kernelallows for concentrationprediction
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Dry Matter Machine
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• Integrated in sortingmachine (INSORT Observer)
• Valdidated model(R^2=0.9, RMS=5 %)
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Award
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International FoodTec Award
DLG (Deutsche Landwirtschafts‐Gesellschaft
/ German Agricultural Society)
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Dry Matter
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Dry Matter
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waterchannels
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Dry Matter Statistics
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• DM „heat map“• DM value histogramm• Mean DM• Standard Deviation of
product DM
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Accuracy vs. Precision
Plots taken from http://en.wikipedia.org
higher precision
–lower
accuracy
lower precision
–higher accuracy
Laboratory precision completed with 100% inspection of entire
production stream
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Precision vs. Accuracy
• The intrinsic variance of the product is greater than the precision of the lab reference, so better have full sampling rather than super precision!
• „Better you measure all the fries all the time with reasonable precision rather than 1 out of 100.000 with ultra‐high precision!“
• It is about large scale trends in the whole product stream not about laboratory measurements of unrepresentatively small quantities!
• A RMS of e.g. 1 % for constant monitoring of the product throughout the entire input stream is already way better than what you can do with a precision of 0.1 % every 30 min on a spatially confined sample!
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Courtesy W. Märzinger
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Outline
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• Recycling
• Food
• Pharmaceuticals
• Mining
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Pharmaceuticals
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Pharmaceuticals
R^2=0.98RMSEC = 1.2 mg/cm2
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Pharmaceuticals
R^2=0.98RMSEC = 1.2 mg/cm2
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mg/cm2
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Personalized Medicine
Imprinted set of test specimens with ascending API concentration
Visible image
HSI imagePixels representing wavelength 1.3‐2.3µm
Intensity
unnormalizedspectra
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Drying Processes
In‐situ measurement of moisture content of a sample during drying cycle(left: wet, right: dry)
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Product Identification
Hyperspectral Imaging technology analyzes the chemical consistency and checks tablet integrity
Courtesy of RCPE
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Chemical Composition
Chemical mapping of API content
Agglomerated API spots
1%
11%
5%
15%Courtesy of RCPE
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Outline
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• Recycling
• Food
• Pharmaceuticals
• Mining
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Copper Ores Quantitative Analysis
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λ [px]
I [AU
]
%
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Conclusion
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• HELIOS is a near infrared (NIR) hyperspectral imaging (HSI) smart camera suitable for fast inline applications
• A broad range of applications in food, recycling, miningand pharmaceuticals have been already realized and arerunning in the field.
• HSI has become an established and reliable technology in the field of sensor based sorting.
• Apart from mere sorting solutions the inference of spatiallyresolved, non‐invasive quantitative inline measurementsfor 100 % product control (PAT) have enormous potential adding to classical, precise but low sampling and invasive laboratory based techniques.
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References
M. Kerschhaggl, W. Märzinger, E. Leitner et al. „Inline HSI food inspection and concentration measurements of pharmaceuticals – a report from an industrial environment.” Karlsruhe: KIT Scientific Publishing, Karlsruhe, 2013.
M. Kerschhaggl“Hyperspectral imagery for real‐time quantitative inline analysis,” Sensor‐Based Sorting 2014, GDMB Verlag GmbH, vol. 135, 2014.
M. Kerschhaggl“Hyperspectral Imaging as Process Analysis Technology for inline Applications. Laboratory Precision meets high Sampling Accuracy” Karlsruhe: KIT Scientific Publishing, Karlsruhe, 2015.
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Acknowledgments
M. Jeindl
W. Märzinger
E. Leitner
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