Presentación sobre la imagen hiperespectral en la industria cárnica. Presentación que forma parte de un curso europeo de tecnologías no destructivas para determinar la calidad de la carne.

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Hyperspectral imaging
Ricardo Diaz
Hyperspectral imaging
Ricardo Diaz
TRAINING SCHOOL IN
MONELLS (08/09/14-09/09/14) AND GIRONA (10/09/14)
“NON-DESTRUCTIVE ON-LINE TECHNOLOGIES TO DETERMINE QUALITY OF MEAT AND MEAT PRODUCTS: FUNCTIONING PRINCIPLE AND
CHEMOMETRICS”
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1. Theoretical aspects
- Introduction: concepts
- Operating principle
- Hypercube: spectra or images?
- Multispectral or Hyperspectral?
- Data analysis
- Chemical image construction
- Algorithm parallelization
- Applications
2. Practical application
- Definition of the problem to be solved
- Instrumentation
- Hypercube acquisition
- Signal correction with references
- Points selection
- Pre-treatment
- Model calibration
- Chemical image generation: resulting images
1. Theoretical aspects
- Introduction: concepts
- Operating principle
- Hypercube: spectra or images?
- Multispectral or Hyperspectral?
- Data analysis
- Chemical image construction
- Algorithm parallelization
- Applications
2. Practical application
- Definition of the problem to be solved
- Instrumentation
- Hypercube acquisition
- Signal correction with references
- Points selection
- Pre-treatment
- Model calibration
- Chemical image generation: resulting images

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1. Theoretical aspects1. Theoretical aspects
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1.1. Introduction: concepts
The basis of this technique is based in the interaction between infrared
light and matter, where the light is absorbed in different wavelengths of
the light.
The basis of this technique is based in the interaction between infrared
light and matter, where the light is absorbed in different wavelengths of
the light.

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Spectrum: characteristic fingerprint of matter depending on its composition
in the NIR region
spectrum
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Light diffraction
Light breakdown to measure absorption in different wavelengths
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Image analysis
Application of algorithms to a 2D image to obtain information related
with physical properties: length, size, colour…

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NIR spectroscopy:
Analysis of the NIR spectrum
to measure composition
through the absorption of light
in different wavelengths
Non destructive technique to
obtain chemical information
from one point of the
sample
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Machine vision
Acquisition of images using
image sensor to obtain
information related with physical
properties
Non destructive technique to
obtain physical information
from the whole sample

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Hyperspectral vision
Generation of artificial images
analysing the NIR spectrum of
each pixel of the sample
Non destructive technique to
obtain chemical and physical
information from each point of
the whole sample
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The optics focus a line where the light of each point is diffracted by a
spectrographic optic and is projected on the NIR matrix sensor.
1.2. Operating principle
s
λ

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CAMERA
SPECTROGRAPHIC OPTIC
FOCUSING OPTIC
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Parts of a hyperspectral system:
HYPERSPECTRAL
CAMERA
PC+GPU
PLC
ENCODER
CONVEYOR BELT
IR LIGTH

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HYPERSPECTRAL CAMERA
NIR LIGHT SYSTEM
REJECTION SYSTEM
PC + GPU
CONVEYOR BELT
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Optical configurations:
REFLECTANCE INTERACTANCE TRANSMITANCE

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spectrum of xj
hypercube
λ
Abs
image in λi
s
t
λi
xj
λ
1.3. Hypercube: spectra or images?
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1.4. Multispectral or Hyperspectral approach?
Multispectral approach: selection of several wavelengths
Hyperspectral approach: uses all the wavelengths of the spectrum
λ1
λ2
λ1
λ2

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1.5. Data analysis
1. Machine vision approach: application of image algorithms to the
images in the selected wavelengths.
λ1 λ2
λ1 λ2
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2. Spectroscopic approach: application of multivariate analysis to
estimate composition or classification of each pixel/point of the
sample.
λ1 λn

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Characteristics of multivariate analysis:
- More than one input variable (absorbance in each wavelength) and
more than on output variable.
- Reduction of variables (hundreds of absorbance measurements in
different wavelength) in a reduced set of data (scores) with the
variance of the dataset (hypercube).
- Concentration Maps Generation (e.g. fat determination in meat):
+ PLS (Projection to Latent Structures)
- Classification of each point of the sample (e.g. classification
depending on quality):
+ PCA+AD (Principal Component Analysis +
Discriminant Analysis)
+ PLSDA (Projection to Latent Structures Discriminant
Analysis)
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1.6. Chemical image construction
Assigning a different color to a each pixel depending on its class or
composition, we obtain an artificial image (“chemical image”) representing
composition map of the sample.
Background
Loin
Fat
Bone

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1.7. Algorithm parallelization
High computation capability is needed:
■ Pre-processing algorithm of each spectrum
■ Scores obtaining algorithm
■ Prediction/classification algorithm
Additional processors are needed:
■ FPGA (Field Programmable Gate Array)
■ DSP (digital signal processor)
■ GPU (Graphics Processing Unit)
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Example of time consuming of a hyperspectral image of 320 x 256.
using an Intel Pentium i5 2500K 3.3 GHz with 8 Gb RAM under 64 bits
OS without (blue) and with (red) GPU (GeForce 560 Ti (384 cores).
Algorithm is PCA with CE pre-processing using 6 scores in the model.
Time is seconds.

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Hyperspectral inspection system
in real time in ainia pilot plant
Hyperspectral inspection system
in real time in ainia pilot plant
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1.8. Applications
Concentration of the AP in pharmaceutics
©ainia

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Detection of hazelnut shells
©ainia
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Applications in meat
• Quality Control: measurement of chemical and physical properties with a
non-destructive method analysing the 100% of the batch. E.g. tenderness,
pH, water activity, fat, moisture…
• Classification of samples based on the quality: RFN (reddish, firm y
non exudative), PSE (pale, soft y exudative) y DFD (dark, firm y dry)
• Composition control of the whole production: moisture, protein and
fat
• Foreign bodies detection independently of its density
• Defect detection in carcasses: tumours, strokes, faecal residues

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Foreign bodies detection in meat products
Diaz R et al. 2011. Proc. Eurosensors XXV ©ainia
Detection and identification of contaminants PET, HDPE, LDPE film, metal,
insect, bone and fat in meat products such as pork loin with detection
sensitivity from 1 mm.
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Prediction of drip-loss, pH, and color for pork using a
hyperspectral imaging technique
J. Qiao et al. Meat Science 76 (2007) 1–8
Correlation coefficient for predicting the water activity of 0.77, pH of 0.55 and 0.86
color.
J. Qiao et al. Meat Science 76 (2007) 1–8

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Determination of the intramuscular fat % in meat by NIR
hyperspectral vision system
© ainia
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In a batch of 28 samples with 400 faecal residues was achieved by detecting the 95%
hyperspectral vision system 400 to 1000 nm.
K.C. Lawrence et al., J. Near Infrared Spectrosc. 14, 223–230 (2006)
Contaminant detection on poultry carcasses

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Prediction of beef tenderness using hyperspectral vision
Classification error of 96.4% among 111 samples of beef (longissimus dorsi) in tender,
intermediate and hard.
G. K. Naganathana et al. Computers and electronics in agriculture 64 (2008) 225–233
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Poultry skin tumor detection with hyperspectral system
Through a selection of 8 spectral bands in the VIS / NIR could be detected 32 of 40
skin tumors.
S. Nakariyakul, D.P. Casasent / Journal of Food Engineering 94 (2009) 358–365

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Prediction of drip-loss, pH, and color for pork using a
hyperspectral imaging technique
Classification of pork loin steaks in RFN (reddish, firm and non-exudative), PSE (pale,
soft and exudative) and DFD (dark, firm and dry)
D. Barbin et al. / Meat Science 90 (2012) 259–268
RFN PSE DFD
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NIR hyperspectral imaging for rapid and reagentless
determination of Enterobacteriaceae on chicken fillets
PLSR model based on 3 wavelengths (930, 1121 and 1345 nm) to estimate the
Enterobacteriaceae presence with a root mean squared errors (RMSEs) > 0.47 log10
CFU g-1.
Y.-Z. Feng et al. / Food Chemistry 138 (2013) 1829–1836

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https://www.youtube.com/watch?v=mTmsWQP8Mpw

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2. Practical application2. Practical application
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2.1. - Definition of the problem to be solved
Map generation composition of pork chops with
hyperspectral vision in the near-infrared:
- Sample preparation
- Instrumentation
- Hypercube acquisition
- Signal correction with references
- Pre-treatments
- Model calibration
- Validation
- Chemical image generation

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Hyperspectral camera:
Camera XEVA-CL (1100 y 2500 nm) with 320x256 px
Spectrograph Imspector N25E
Optics 22mm
Conveyor belt with servomotor
75 W NIR halogen lamps
PC Intel i5, 8 Gb RAM with GPU
Processing Ainia’s software for acquisition, data processing and
implementation in real time.
2.2. - Instrumentation
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Instrumentation tuning
- Turn on the lamps
- Turn on the SWIR camera
- Focusing the optics
- Speed regulation
- Adjustment of lighting conditions
- Set the integration time

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2.3. - Hypercube acquisition
Acquisition of the video sequences (hypercubes):
- Black reference
- White reference
- Sample sequences
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2.4. - Signal correction with references
Signal correction avoid external influences caused by light changes
Black reference: internal noise from the sensor
White reference: variations of light in time
Rλ - Bλ
Wλ- Bλ
R Reflectance in λwavelength
B Black reference in λwavelength
W White reference in λwavelength

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2.4. – Points selection
Selection of the point to extract the spectra of each class or sample to
build the data matrix for calibration
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2.5. - Pre-treatments
Spectrum pre-treatments: a method to improve the quality of the
spectrum and to solve some problems during the acquisition process.
The most commonly used methods are:
■ Autoscale: center and scale
■ Mean center
■ Signal Normal Variate (SNV)
■ Multiplicative Scatter Correction (MSC)
■ Savitzky-Golay derivatives

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2.6. - Model calibration
Chemometrics analysis:
PLSDA (Projection to Latent Structures Discriminant Analysis)
Choose cross validation “random subsets”
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2.7. - Chemical image generation: resulting images

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Thanks for your attention
Ricardo Diaz rdiaz@ainia.es