SLOPE 1st workshop - presentation 5

www.evk.biz© EVK DI Kerschhaggl GmbH 2014

Matthias KerschhagglBiRT Workshop, 20/03/15

Hyperspectral‐/ Chemical Imaging as Key Technology in Sensor Based

Sorting Applications

Outline

www.evk.biz© EVK DI Kerschhaggl GmbH 2014 matthias.kerschhaggl@evk.biz

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

Applications

Metals from slagHousehold waste

C&D Recovered Paper Wood Sorting

Potato Sorting &Grading

E‐Scrap

Nut Sorting

References RecyclingGlass Sorting

Customer:

EVK products:EOS – Color ImagingARGOS – Conductivity Imaging

References RecyclingMetal Scrap

Customer:

EVK product:ARGOS –Conductivity Imaging

References RecyclingPlastic Sorting

Customer:EVK product: HELIOS – Chemical Imaging

Recovered Paper Sorting

8sieglinde.kepplinger@evk.biz

References RecyclingMinerals, Metals, Plastic

Customer:

EVK product:EOS – Color Imaging

References MiningMineral Sorting

Customer:

EVK product:HELIOS – Chemical Imaging

References Food SortingDiv. Nut Sorting / Recycling

Customer:

EVK product:HELIOS –

Chemical Imaging

References Food Processing Potato Sorting

Customer:

EVK products:HELIOS –Chemical Imaging

EOS – Color Imaging

www.evk.biz© EVK DI Kerschhaggl GmbH 2014 matthias.kerschhaggl@evk.biz

Hyperspectral Imaging Technology

Camera internal classification and RGB visualization of chemical differences for real‐time analysis

www.evk.biz© EVK DI Kerschhaggl GmbH 2014 matthias.kerschhaggl@evk.biz 14

HELIOS – Spectral Ranges

Outline

• Recycling

• Food

• Pharmaceuticals

• Mining

Recycling

papercardboard

PE‐LD

Recycling

PE PVC

Recycling

Quality analysis ofpaper

Paper + Glue, 1st derivative, normalized

Outline

• Recycling

• Food

• Pharmaceuticals

• Mining

FoodHSI preview of SE

potatoe

CCI view of SE potatoe

Potatoe after frying

Sugar end

FoodFood sorting – Blueberries and stems

Food sorting – Noodles and worms

Starch

Glucosewater

Simple VIS representation(filter convolution)

of HSI data

CCI representationof HSI data

(PCA: Principal Component Analysis)Quantitative analysis is possible

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

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)

Dry Matter

REMINDER 1st inline TEST 2013

HELIOS Camera

R = 0.9 RMS = 1 %

Inline Dry Matter Measurements

• Dry matter content tracedby spectral signatures

• Inferred FIR‐filter kernelallows for concentrationprediction

Dry Matter Machine

• Integrated in sortingmachine (INSORT Observer)

• Valdidated model(R^2=0.9, RMS=5 %)

International FoodTec Award

DLG (Deutsche Landwirtschafts‐Gesellschaft

/ German Agricultural Society)

Dry Matter

waterchannels

Dry Matter Statistics

• DM „heat map“• DM value histogramm• Mean DM• Standard Deviation of

product DM

Accuracy vs. Precision

matthias.kerschhaggl@evk.biz 33

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

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!

Courtesy W. Märzinger

Outline

• Recycling

• Food

• Pharmaceuticals

• Mining

Pharmaceuticals

37matthias.kerschhaggl@evk.biz

Pharmaceuticals

R^2=0.98RMSEC = 1.2 mg/cm2

Pharmaceuticals

R^2=0.98RMSEC = 1.2 mg/cm2

mg/cm2

matthias.kerschhaggl@evk.biz

Personalized Medicine

Imprinted set of test specimens with ascending API concentration

Visible image

HSI imagePixels representing wavelength 1.3‐2.3µm

Intensity

unnormalizedspectra

Drying Processes

In‐situ measurement of moisture content of a sample during drying cycle(left: wet, right: dry)

Product Identification

Hyperspectral Imaging technology analyzes the chemical consistency and checks tablet integrity

Courtesy of RCPE

Chemical Composition

Chemical mapping of API content

Agglomerated API spots

15%Courtesy of RCPE

Outline

• Recycling

• Food

• Pharmaceuticals

• Mining

Sandstone/Slate Classification

Copper Ores Quantitative Analysis

λ [px]

matthias.kerschhaggl@evk.biz

Conclusion

• 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.

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.

Acknowledgments

M. Jeindl

W. Märzinger

E. Leitner

SLOPE 1st workshop - presentation 5

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