1. Implementation of infrared tool at key steps
along the process may improve
the quality management of tomato based products
Sylvie Bureau1, Alexandre Vilas-Boas1, Robert Giovinazzo2, David Page1
1 INRA - Université d'Avignon et des Pays de Vaucluse, UMR SQPOV, Avignon, France
2 SONITO, Maison de l’Agriculture, Avignon, France
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

2. MIRS at different steps along the process
What we know:
The tomato industry lacks of tools to:
Better characterize the technological potential of raw material,
Monitor processes,
Discriminate characteristics of industrial products.
What we want to do with MIRS:
1- Predict quality parameters of raw material
Define the best strategy to build models useful for industry
2- Track products along the processing chain
Discriminate the fresh products and their corresponding HB and CB purées
3- Control the final products
Determine the characteristics of purées and pastes regarding the specification books
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 2 2

3. Why mid-infrared spectroscopy (MIRS)?
● Absorption by samples
● Relationship between spectra and composition
● MIR: a region of the electromagnetic spectrum
( cm-1)
● Link between the fundamental bands and chemical structures
● Easy and rapid acquisition of spectral data
In Laboratory
In Industry
TRIPLE
BONDS
DOUBLE
FINGERPRINT REGION
SKELETAL VIBRATION
X-H HETEROATOMS
1000
2000
2500
3000
3500
1500
FINGERPRINT
REGION
C-H
stretching
O-H
C=O
C-C
C-O
C-N
C=C
C=N
bending
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

4. MIRS at different steps along the process
1- Predict quality parameters of raw material
Define the best strategy to build models useful for industry
2- Track products along the processing chain
Discriminate the fresh products and their corresponding HB and CB purées
3- Control the final products
Determine the characteristics of purées and pastes regarding the specification books
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 4 4

5. 1- MIRS to predict quality parameters of raw material
Methods
- Raw tomatoes studied over 2 years:
- Analyses:
Year 1: 102 samples
Varieties: 29
Locations: 6 (SE and SW France)
Harvesting dates : 7 (July-September)
Maturity stages: 3 (turning, ripe, over ripe)
Year 2: 144 samples
Varieties: 39
Locations: 9 (SE and SW France)
Harvesting dates: 13 (July-September)
Maturity stages: 2 (ripe and over ripe)
Spectrum
acquisition
Homogenization
Classical measurements
Parameters:
- Soluble solids (SSC)
- Titratable acidity (TA)
- Dry matter (DMC)
15 tomatoes
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 5 5
13th ISHS Symposium on the Processing Tomato, Sirmione, Italy, 8-11 June 2014

6. Need a strategy to build relevant models
Variability of fruit quality according to the year
Spectral data
Biochemical data
Year 1
Year 2
Need a strategy to build relevant models
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

7. Methodology used to predict quality parameters
I. Model development
MIRS
Spectrum acquisition
Reference data using
classical techniques . 1
Linear regression (PLS)
Equation of calibration
Dry matter
Soluble solids
Titratable acidity
Cross-validation
II. Prediction of unknown samples . 1
Sample
DMC
SSC TA %
°Brix
meq/100g
16FRADVChaxx2207CADE1
7.4
5.0
6.2
16FRCARChaxx2207CADE1
7.0
4.7
5.4
17FRGONChaxx2207CADE1
7.6
5.1
7.5
17FRH81Chaxx2207CADE1
8.2
5.2
6.4
Equation of calibration
External validation
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

8. Evaluation of model performance
the best model
- Determination coefficient: R2
- Root mean square error: RMSE
- Residual predictive deviation: RPD
RPD > 2.5: good model
Relationship between measured and predicted values:
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management 8
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

9. Comparison of different strategies to build models
M1+2early
Year 1
Year 2 P2 P1
P2 late
Cross-validation
External validation
Model
Prediction
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

10. Model performance: Cross-validation
M1+2early
Year 1
Year 2
Models
Quality trait
RPD R cv 2
RMSE
SSC (°Brix)
0.95
0.16
4.3
0.94
0.13
3.6
0.14
4.5
DMC (%)
0.87
0.25
2.6
0.85
0.41
0.34
3.9
TA (mmolH +
/kg)
0.84
2.19
2.5
0.96
2.23
4.6
0.79
4.15
2.2
Cross-validation
Model performance ↑
R2 ↑ and RMSECV ↓
RPD > 2.5: good prediction
The multi-year model is as good as the single-year models
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 10 10

11. Model performance: External validation
M1+2early
Models
Year 1
Year 2
Predictions P2 P1
P2 late
Quality trait
Models
Prediction R v 2
RMSE
SSC (°Brix) M1 P2
0.31
0.90 M2 P1
0.88
0.25
M1+2early
P2late
0.95
0.11
DMC (%)
0.13
3.89
0.79
2.54
0.81
0.36
TA (mmolH +
/kg)
0.27
33.12
0.16
53.44
2.81
External validation
Year 1
Year 2
The multi-year model is more robust than the single-year models
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 11 11

12. 1- MIRS to predict quality parameters of raw material
Significant result
The multi-year strategy improves the model robustness.
The strategy is adapted to the need of industry:
the early tomatoes participate to the model calibration each year 2 M1
M1+2 3
M1+2+3 M 4
Years 5
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 12 12
13th ISHS Symposium on the Processing Tomato, Sirmione, Italy, 8-11 June 2014

13. MIRS at different steps along the process
1- Predict quality parameters of raw material
Define the best strategy to build models useful for industry
2- Track products along the processing chain
Discriminate the fresh products and their corresponding HB and CB purées
3- Control the final products
Determine the characteristics of purées and pastes regarding the specification books
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 13 13

14. 2- Track products along the processing chain
Methods
1 - Raw material
(2 years, 4 varieties, 2 stresses, 3 dates, 2 blocs)
= 144 batches
2 - Two cooking methods
= 288 purées
3 - MIR spectral data acquisition
4 - Multivariate analysis
PCA: Principal component analysis
FDA: Factorial discriminant analysis
Grinding
(30 sec)
Maceration
(30 min, at room T°)
Microwave heating
(900 W, 0,35sec/g of tomato)
Cold break
Cooling
(in ice, until room T°)
Hot break
(blender, 30 sec)
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 14 14

15. Methodology to discriminate samples
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000 1 2 3 4 5 6 7 8 9 10
F value
Factor effect
Informative region
Spectral data (SNV, smoothing)
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
-0.8
-0.6
-0.4
-0.2
0.2
0.4
0.6
0.8 1
Wavenumber (cm-1)
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
-0.8
-0.6
-0.4
-0.2
0.2
0.4
0.6
0.8 1
Selection
ANOVA
Factorial Discriminant Analysis
FDA
Principal Component Analysis
PCA
Ability to classify samples according to the known groups (processing, variety…)
Representation of samples in a 2D plot
according to the selected spectral data
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 15 15

16. Cartography according to selected spectral regions
Processing
Variety
cm-1
cm-1 HB CB
H10
H13
MIC
TER
FDA
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 16 16

17. Other results… Agricultural practices Processing
MIRS makes possible the discrimination of tomato products according to different factors (varieties, maturity, processing…)
Agricultural practices
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0.02
0.04
0.06
-0.15
-0.05
0.05
0.1
0.15 F1 F2 FR CB HB
Fresh
Cooking
Process routes
Field
Greenhouse
July, 29
July, 28
July, 22
August, 06
August, 04
July, 20
Processing
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 17 17

18. MIRS at different steps along the process
1- Predict quality parameters of raw material
Define the best strategy to build models useful for industry
2- Track products along the processing chain
Discriminate the fresh products and their corresponding HB and CB purées
3- Control the final products
Determine the characteristics of purées and pastes regarding the specification books
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 18 18

19. 3. MIRS to control the Industry products
Experiment:
140 products from 2 plants (juices, purées and pastes)
MIRS in our Lab and reference data from the plant Control Laboratory
SSC (°Brix)
Viscosity (Bostwick)
R2 = 0.98
RMSECV = 0.97
juices
A significant relationship with SSC and viscosity
MIRS makes possible the characterization of the final products
Sylvie BUREAU / Rapid characterization of processed tomato purees using mid-infrared spectroscopy 19 19
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece

20. Summary: Implementation of MIRS tool in Industry
Quality check of the incoming tomatoes to optimize their use
Routinely prediction of SSC, TA and DMC 1
Following the processing route
Monitor the cooking conditions (HB/CB, concentration) 2 3
Traceability and control of the final products (purees, sauces, pastes…)
Determination of their characteristics regarding the specification books
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 20 20

21. Conclusions & Perspectives
Strategy to build models:
Proof of concept at the Lab scale:
Accumulation of data over years
Industrial validation with adapted tools:
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 21 21

22. Thank you for your attention
Sylvie BUREAU / Implementation of infrared tool along the process may improve the quality management
15th ISHS Symposium on the Processing Tomato, June 11th - 15th, 2018, Greece 22 22