SLOPE 1st workshop - presentation 3

SLOPE and BiRT Hyperspectral Imaging Workshop - BOKU-UFT , Tulln Austria, 20th March 2015 1

Detection of undesirable substances

by NIR hyperspectral imaging spectroscopy and chemometrics in agriculture

Philippe Vermeulen,

Juan Antonio Fernández Pierna, Pierre Dardenne, Vincent Baeten

(CRA-W Belgium)


NIR hyperspectral imaging

applications:

Detection and quantification

of contaminants

in the cereals production control sector


Study case: ergot

• Reemerging of the ergot presence in cereals

– For the farmer, yield reduction (10%)

– For the feed/food sector, high toxicity risk

• Ergot is a sclerotium formed by

the fungi Claviceps purpurea

including ergot alkaloids

• EU legislation:

– 500 mg/kg of sclerotia in food

– 1000mg/kg of sclerotia in feed


Reference method

• The existing microscopy method provides an

elegant early warning tool for ergot

contamination but is time-consuming

Sorting by visual observation Confirmation by microscope


Detection based on one mean

spectrum of the sample

Detection based on the analysis

of several subsamples

What can we do using NIRS?


- Measurement in reflection mode

- Wheel of 30 vials

- Analysis time: +/- 4 hours !!!

for 3 wheels replicated 4 times

by sample

What can we do using NIRS?


Pure ergot

Bottom

ergot

Positive

vials

Negative

vials

What can we do using NIRS? PCA on 324 spectra of one sample adulterated at 0,05%


What can we do using NIRS? Set of 7 laboratory samples with 0 0,01 0,05 0,1 0,15 0,5 and 1%

of ergot in cleaned wheat


Contaminants in food

and feed: Inexpensive

detection for control of

exposure

(FP7 project n°211326)


• NIR camera setup

NIR line scan camera: features

- Wavelength range: 1000-2500 nm by step of 6 nm

- 1 line = 320 pixels = 320 spectra

- field of view: 10 cm

- Analysed surface = continuous

- Time of acquisition = 50 millisec/pixel line

- Speed of the conveyor belt = 3 mm/sec

- 1 pixel = 275 µm * 275 µm = 0.075 mm2

1000 1500 2000 25000.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Wavelength (nm)

Absorb

ance (

Log 1

/R)

Ergot

Wheat


Ergot detection in wheat by NIR imaging

Wheat

kernels Ergot

Bodies

The number of pixels counted

for each class of the model

is also provided.


Results of ergot bodies detection Set of 7 laboratory samples with 0 0,01 0,05 0,1 0,15 0,5 and 1%

of ergot in cleaned wheat

Corrective factor weight/area

Reference value: % weight

Predicted value: % area * 0,8


Peer reviewed Journal article


NIR line scan camera: instrument

in demonstration at NUTRECO

Tray

NIR camera SWIR

ImSpector

N25E

Spectra

Camera

(Specim Ltd)

Light source


Ergot detection in wheat by NIR imaging

Prediction results of the SIMCA (Soft Independent

Method of Class Analogy) model

The number of pixels counted for

each class of the model and …

(object quantification)

… the distribution of groups

of pixels detected as ergot

are also provided

(object identification)

Wheat

kernels

Ergot

Bodies


Results of ergot bodies detection Set of 7 samples (2009-2010)

wheat, rye Set of 6 samples (2011)

rye, triticale, oat

BurgerMetrics

Instrument (Pilot

imaging system)

SisuChema

Instrument (Commercial

imaging system)

Corrective factor weight/area

Reference value: % weight

Predicted value: % area * 0,8


ABC special issue: CONffIDENCE outputs


Other applications

• ergot detection in black oat


Other applications

• Multicontaminants detection: ergot, datura, …

Mixture of wheat, black oat, rape seed, ergot and datura


Video


Peer reviewed Journal article


Benefits of the method

for a feed Company

Classical microscopy NIR hyperspectral imaging

High skilled personal Low skilled personal

15 min / 250g A few minute / 250 g

Reduced samples Large samples (sampling more

representative)

Dedicated to ergot Multiple contaminants


NIR hyperspectral imaging

applications:

assessment of resistant plants

in sugar beet breeding program


Nematodes cysts counting

by

NIR hyperspectral imaging system


What is the issue ?

Yield reduction in relation to the cyst

nematode number in the sugar beet

roots

Brown and white cyst

nematode (Heterodera

schachtii)


How to control the contamination?

Cyst nematode development cycle

Tolerant sugar beet plants breeding

Cysts at different development

stages (optical microscope)


Status of the analytical aspects?

Cysts counting by optical microscopy using a

camera system at SES VANDERHAVE

Cysts = red spots


NIR line scan camera: instrument

SWIR ImSpector N25E spectral camera (Specim Ltd)

Conveyor belt (Burgermetrics)

Parameters

- Wavelength range: 1000-2500 nm by step of 6 nm

- 1 line = 320 pixels = 320 spectra

- 1 box = 100 000 spectra

- Time of acquisition for 1 box= 1 min NIR image acquisition of one box


Spectral libraries

Spectral data preprocessed

1200 1400 1600 1800 2000 2200 2400-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Variables

Mean

1 Cyst

2 Root

3 Substrat


Data treatment: Applying of 3 discrimination equations and DBSCAN

Image at 1720 nm Picture DQ110053-08 Removing background (Eq1)

Removing substrat (Eq2) Removing root (Eq3) DBSCAN

Discrimination models


DQ110053-08 – Plot 302 - Susceptible plant

Ref value: 80 Predicted value: 80

1 cyst = 4 pixels

Nematodes cysts counting by line scan HIS


DQ110053-20 – Plot309 – Tolerant plant Ref value: 49 Predicted value: 31

1 cyst = 4 pixels



10 Tolerant plants 10 Susceptible plants

Reference values 40 (24-51) 69 (51-82)

Predicted values 21 (5-49) 58 (26-82)

(1 cyst = 4 pixels)



Cercospora leaf spots assessment

by

NIR hyperspectral imaging system


Samples (DQ100742-01 to 08) Instrument : Line scan HIS

Cercospora leaf spots assessment by line scan HIS

4 cerco tolerant plants

4 cerco susceptible plants



DQ100742-02 : Prediction on-line (PLSDA model)



DQ100742-02 3 days after




Crop residues Toxic plants (Senecio) Animal/vegetal feed

Melamine in soybean meal)

Collagen inside the bones Collagen (COL+)

No collagen (COL-)

1200 1400 1600 1800 2000 2200 24001000

2000

3000

4000

5000

6000

7000

8000

Wavelengths (nm)

Abs

orba

nce

Collagen classes: mean spectra

COL+

COL-

Dry fruits

Poster

And many other applications …


Thank you for your attention

Ph. Vermeulen and all the team

Contact: [email protected]

Acknowledgements


http://chimio2016.sciencesconf.org

Contact: [email protected]

2 Days training: 17-18 January 2016

2 Days conference: 19-20 January 2016

Education

SLOPE 1st workshop - presentation 3