Post on 06-Jul-2020
Computer-Aided
Technologies for
Food Risk
Assessment
Candidate: Francesco Rossi
Tutor: Prof. Alfredo Benso
XXX Cycle PhD in Computer and Control Eng.
at Politecnico di Torino
Motivation of PhD in Computer Science
Master’s Degree
Biomedical Engineering
Virtual
Colonoscopy
Prostate Cancer
Research
#machinelearning #computervision #computeraided
Computer-Aided Technologies for Food Risk Assessment
FOODQUALITY
FOODFRAUD
FOODSAFETY
FOODDEFENSE
EconomicalGain
HealthHarm
Unitentional Intentional
Heuristic Molecular
Dairy Farming Analysis“From farm
to Fork”
Food Traceability
Qualitative and
Quantitative certification
STR-DNA Pool Analisys
1. Sample Collection (DNA)
2. STRs selection (20)
3. Genotyping Process (STR)
4. Data extraction (RFU)
2 farms for 12 monthsData
Cow → 2 alleles
Pool → vector of alleles
COWs
POOL
DATA
Predicted
POOL
P1 & P2 SSE
SCORE
CMA-ES
W
HEURISTICSIMULATION
Every dairy product
(i.e. 46 pools) has been
simulated 24 times
Heuristic Analysis
0-50-100 % forgery
DATA
COWs
POOL
DATA
Predicted
POOL
P1 & P2 SSE
SCORE
CMA-ES
W
HEURISTICSIMULATION
Covariance Matrix Adaptation
Evolution Strategy
CMA-ES
COWs
POOL
DATA
Predicted
POOL
P1 & P2 SSE
SCORE
CMA-ES
W
HEURISTICSIMULATION
W
lower boundary=0.5
m
upper boundary=max ( 3
m,1)
COWs
POOL
DATA
Predicted
POOL
P1 & P2 SSE
SCORE
CMA-ES
W
HEURISTICSIMULATION
Predicted POOLPredicted POOL
0
2000
4000
6000
8000
10000
12000CORRECT POOL
0
1000
2000
3000
4000
5000
6000
7000 ALTERATED POOL
COWs
POOL
DATA
Predicted
POOL
P1 & P2 SSE
SCORE
CMA-ES
W
HEURISTICSIMULATION
P1 - forgeryRate of alleles that are included in the pool’s profile but not in the cows‘
P2 - loss by ripeningPercentage of alleles within the cows’ profile but not detected in the pool
COWs
POOL
DATA
Predicted
POOL
P1 & P2 SSE
SCORE
CMA-ES
W
HEURISTICSIMULATION
SSESum of Squared Errors between
original and predicted pool
SCORE=
P1 · P2 · SSE
Introduction
VISUAL FEATURESVISUAL FEATURES
FEATURE ENGINEERING
CLASSIFICATION MODEL
Feature engineering turn your inputs into things the algorithm can understand
COMMON NAME LATIN NAME N°European Anchovy Engraulis encrasicolus 125European Pilchard Sardina pilchardus 107Common Pandora Pagellus erythrinus 20Atlantic Mackerel Scomber scombrus 18Gilt-Head Bream Sparus aurata 22European Hake Merluccius merluccius 19Striped Red Mullet Mullus surmuletus 28
Dataset
Classification Model
339 fishes
ANNs: One-Class & Multi-Class Classifiers
Results
Leave One Out Cross and in-field validation
OCC 100% Acc.MCC 100% Acc (only with sardine and anchovy)
Engraulis encrasicolusSardina pilchardusPagellus erythrinusScomber scombrusSparus aurataMerluccius merlucciusMullus surmuletus
MCC
OCC
Limitations
New order/family/genre/species of fish may require
new features
Segmentation will fail if background is not uniform,
and key-points and features too
The two step for key-points should be improved
Key-points interaction should be avoided
F.I.S.HUBFish Identification Software Hub
Dat
abas
e
Class
ifier
Valid
ation
Mob
ile A
pp
Fillet
s Rec
ognitio
n
ORDER FAMILY GENUS SPECIES
Clupeiformes Clupeidae Clupea harengus
Clupeiformes Clupeidae Sardina pilchardus
Clupeiformes Clupeidae Sprattus sprattus
Clupeiformes Engraulidae Engraulis encrasicolus
Gadiformes Gadidae Gadus morhua
Gadiformes Gadidae Melanogrammus aeglefinus
Gadiformes Gadidae Merlangius merlangus
Gadiformes Gadidae Pollachius virens
Gadiformes Merluccidae Merluccius merluccius
Perciformes Sparidae Dentex dentex
Perciformes Sparidae Dentex gibbosus
Perciformes Sparidae Diplodus annularis
Perciformes Sparidae Pagellus acarne
Perciformes Sparidae Pagellus bogaraveo
Perciformes Sparidae Pagellus erythrinus
Perciformes Sparidae Pagrus caeruleostictus
Perciformes Sparidae Pagrus pagrus
Pleuronectiformes Pleuronectidae Hippoglossus hippoglossus
Pleuronectiformes Pleuronectidae Limanda limanda
Pleuronectiformes Pleuronectidae Microstomus kitt
Pleuronectiformes Pleuronectidae Pleuronectes platessa
Pleuronectiformes Pleuronectidae Reinhardtius hippoglossoides
Pleuronectiformes Scophthalmidae Psetta maxima
Pleuronectiformes Scophthalmidae Scophthalmus rhombus
Pleuronectiformes Soleidae Solea vulgaris
Database
ORDER FAMILY GENUS SPECIES
Clupeiformes Clupeidae Clupea harengus
Clupeiformes Clupeidae Sardina pilchardus
Clupeiformes Clupeidae Sprattus sprattus
Clupeiformes Engraulidae Engraulis encrasicolus
Gadiformes Gadidae Gadus morhua
Gadiformes Gadidae Melanogrammus aeglefinus
Gadiformes Gadidae Merlangius merlangus
Gadiformes Gadidae Pollachius virens
Gadiformes Merluccidae Merluccius merluccius
Perciformes Sparidae Dentex dentex
Perciformes Sparidae Dentex gibbosus
Perciformes Sparidae Diplodus annularis
Perciformes Sparidae Pagellus acarne
Perciformes Sparidae Pagellus bogaraveo
Perciformes Sparidae Pagellus erythrinus
Perciformes Sparidae Pagrus caeruleostictus
Perciformes Sparidae Pagrus pagrus
Pleuronectiformes Pleuronectidae Hippoglossus hippoglossus
Pleuronectiformes Pleuronectidae Limanda limanda
Pleuronectiformes Pleuronectidae Microstomus kitt
Pleuronectiformes Pleuronectidae Pleuronectes platessa
Pleuronectiformes Pleuronectidae Reinhardtius hippoglossoides
Pleuronectiformes Scophthalmidae Psetta maxima
Pleuronectiformes Scophthalmidae Scophthalmus rhombus
Pleuronectiformes Soleidae Solea vulgaris
Database
FLATFISH
NORMALFISH
ORDER FAMILY GENUS SPECIES
Clupeiformes Clupeidae Clupea harengus
Clupeiformes Clupeidae Sardina pilchardus
Clupeiformes Clupeidae Sprattus sprattus
Clupeiformes Engraulidae Engraulis encrasicolus
Gadiformes Gadidae Gadus morhua
Gadiformes Gadidae Melanogrammus aeglefinus
Gadiformes Gadidae Merlangius merlangus
Gadiformes Gadidae Pollachius virens
Gadiformes Merluccidae Merluccius merluccius
Perciformes Sparidae Dentex dentex
Perciformes Sparidae Dentex gibbosus
Perciformes Sparidae Diplodus annularis
Perciformes Sparidae Pagellus acarne
Perciformes Sparidae Pagellus bogaraveo
Perciformes Sparidae Pagellus erythrinus
Perciformes Sparidae Pagrus caeruleostictus
Perciformes Sparidae Pagrus pagrus
Pleuronectiformes Pleuronectidae Hippoglossus hippoglossus
Pleuronectiformes Pleuronectidae Limanda limanda
Pleuronectiformes Pleuronectidae Microstomus kitt
Pleuronectiformes Pleuronectidae Pleuronectes platessa
Pleuronectiformes Pleuronectidae Reinhardtius hippoglossoides
Pleuronectiformes Scophthalmidae Psetta maxima
Pleuronectiformes Scophthalmidae Scophthalmus rhombus
Pleuronectiformes Soleidae Solea vulgaris
Database
MANY
FEW
Examples
Melanogrammus aeglefinus
m=0,052
Psetta maxima
m=0,015
m is the metricgood
Engraulius encrasicolus
m= 0,023
Examples m is the metricwrong
Sardina pilchardus
Sprattus sprattus
m= 1,129
Hippoglossus hippoglossus
Microstomus kitt
m=1,612
Merlangius merlangus
Pollachius virens
m= 1,741
FISHUB Database
Global Acc. 94%
e.g. Dentex dentex Acc. 86%
Diplodus annularis Acc. 96%
Sardina pilchardus Acc. 96%
The Species Acc. is higher when
the number of picture per species
are elevated and vice-versa
IN FIELD
with DNA analysis
69 fish sampled with pictures and
DNA barcode
9 fraud found by DNA
5 fraud discovered by App
62 correct identification by App
Validation
RESULTS+
+
FILLET
Normal image
Microscope
Toluidine
blue stain
Molecular
sensor
Legend
Fillets Recognition EXPLORATORY TASK
The Near-Infrared spectroscopic is a method that
makes use of the electromagnetic spectrum from about
700 nm to 2500 nm. It can penetrate tissues and it is
useful to probe bulk material with essentially no
preparation
Acquisition Visualization Data Model
NIR - SCiO
METHOD DetailsGlobal Accuracy
%
1 normal img 59.8
2 microscope img 49.5
3 normal img + staining 56.6
4 microscope img + staining 53.2
5 SCiO NIR 98.3
+
+
For the POC we selected 10 fish fillets for each considered species:
Solea solea, Pleuronectes platessa, Pangasianodon hypophthalmus.
For methods 1-2-3-4 the pictures were collected and processed with image feature extraction
techniques such as: Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Feature
(SURF) and Gray Level Co-Occurrence Matrix (GLCM). Eventually a SVM classifier was used.
3 random scans per fillets
40 fillets per species
• Gadus morhua• Pleuronectes platessa• Pollachius virens• Epinephelus costae• Synaptura cadenati• Sebastes norvegicus• Merluccius merluccius• Scomber scombrus
RESULTS
98% Accuracy
Latest News
Computer-Aided
Technologies for
Food Risk
Assessment
Candidate: Francesco Rossi
Tutor: Prof. Alfredo Benso
XXX Cycle PhD in Computer and Control Eng.
at Politecnico di Torino
N° Name 15 STR 10 STR
1 AGLA29 ok ok
2 MB025 ok ok
3 Z27077 ok ok
4 BMS2142
5 MB071 ok ok
6 SRC276
7 MB064
8 BM1706 ok ok
9 HUJ625 ok
10 BMC1207 ok
11 AGLA232 ok
12 BM3507
13 BM4602
14 BMC6020 ok ok
15 INRA133 ok ok
16 BMS0607 ok ok
17 BMC4214 ok ok
18 BM720 ok
19 BMS4044 ok ok
20 BM0143 ok
ProcessedAssumes Beer-Lambert model is valid, and transforms the measured signal to be linear with concentration by doing a log transform and adjusting the result for noise and deviations from the model.
NormalizedPerforms normalization of the signal. This is meant to compensate for changing measurement conditions (e.g. varied scanning distances) that typically occur from sample to sample. Y axis still means reflectance but in normalized units instead of raw reflectance.
Processed and NormalizedFirst assumes Beer-Lambert model (Processed) and then normalizes the results to compensate for differences in the optical path between samples. This is useful, for example, when there is variation in the thickness of the samples.
(log(R))” and NormalizedSimilar to Processed and Normalized, uses a more aggressive form of Processed. Adds more noise, but in some cases may be the only way to create a good model.