Department of Food and Nutritional Sciences
Process Analytical
Technology Tools in Dairy
Processing
Colette C. Fagan
November 21, 2013
c.c.fagan@ reading.ac.uk
© Universit y of Reading 2013
www.reading.ac.uk
INTRODUCTION:
PAT AND DAIRY PROCESSING
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2
Process Analytical Technology (PAT)
• PAT system
–
–
–
–
–
designing, analysing, controlling
timely measurements (i.e., during processing)
critical quality and performance attributes
raw and in-process materials
Final product quality
• Analytical includes:
– chemical, physical, microbiological, mathematical, and risk
analysis
– conducted in an integrated manner
• PAT Tools
SCI: On-Line and At-Line Analytical Technologies in the Industrial Biotechnology Sector
PAT Food Industry
• PAT tools & analyzers known within food industry for
decades
• Previous focus to implement on-/in-line analyzers in the
production process just to have real-time measurements
to monitor the production
• More recent focus moved away from implementation of
on-/in-line analyzers for monitoring product attributes
• Now: Use of PAT technologies to understand and control
the whole manufacturing process
• Consistently ensure a predefined quality at the end of the
manufacturing process.
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Process Variation
• uncontrolled disturbances
– milk changes
– temperature of surroundings
• Ideally -> no change to uncontrolled variables.
• Real world -> change uncontrolled variables -> variable
product quality
• Determine optimum settings for the process set point
during the manufacturing process
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Product
Unit operation
Technology
NIR
Raw milk
Cheese
Butter
Cream
cheese
Whey
MIR
Compositional analysis
****
Authenticity
*
Compositional analysis
****
Fermentation
**
**
Compositional analysis of raw materials
****
****
Compositional analysis
****
****
Fermentation
**
***
Compositional analysis
***
**
****
Fractionation
***
Compositional analysis
***
WFC
Compositional analysis
**
Milk powder Compositional analysis
C
***
***
Compositional analysis
WPC
T
***
Compositional analysis of raw materials
Separation
MW
***
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*
6
PAT TOOLS IN CHEESE MANUFACTURE
RESEARCH DEVELOPMENTS
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Technological Steps Cheese Processing
Milk reception
Curd Cutting
Salting
Syneresis
Ripening
Milk pretreatments Milk coagulation
Molding
Pressing
Cheese packaging
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Milk Coagulation & Curd Cutting
Cutting time determination
Visual
observation
Fixed time
Objective methods
Inline
methods
Non Inline
methods
CoAguLite Sensor
CoAguLite
sensor
(Reflectronics, KY USA)
(Payne & Hicks, 1992)
Optical methods
Probe
Optical fibers
Colloidal particles
Light source
880 nm
1200µm
Light
backscatter
I (v)
Ø 600µm
Cutting time = β * tmax
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Curd Syneresis
Miniature
Large Field of View (LFV) Sensor spectrometer
Vat wallPolarizing
plate
Quartz rod
Milk
Fiber optic cable
Power
supply
Light Source
Glass Optical
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At-Line Analytical
window
probeTechnologies in the Industrial Biotechnology Sector
LFV Response - Temperature
1.6
1.6
21.6 °C
30.0 °C
1.4
Intensity Ratio
1.2
1
0.8
0.6
0.4
1.2
1
0.8
0.6
0.4
0.2
0
20
40
60
80
100
120
140
0.2
0
20
40
Time (min)
940 nm
960 nm
980 nm
940 nm
CoAguLite
60
Time (min)
960 nm
80
980 nm
1.6
38.4 °C
1.4
Intensity Ratio
Intensity Ratio
1.4
1.2
1
0.8
0.6
0.4
0.2
0
940 nm
20
40
960 nm
Time (min)
60
980 nm
80
100
CoAguLite
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CoAguLite
Modelling LFV Sensor Response
Rt = R∞ + (R0 − R∞ )e
R∞ = light backscatter ratio during
syneresis at an infinite time
R0 = light backscatter ratio during
syneresis at cutting time
kLFV = kinetic rate constant (min-1)
for the LFV sensor response during
syneresis
1.5
LFV reflectance ratio
Rt = light backscatter ratio during
syneresis at time t (min)
− k LFV t
1.2
1.0
0.7
h
0.4
0
20
40
60
80
Time from cutting (min)
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100
Prediction Model
• Curd moisture as a function of processing time
predicted with SEP of 1.72%
CM t = CM ∞ + (CM 0 − CM ∞ )e
− kCM t
CM ∞ = (β 0 + β1T + β 2t max + β 3 Fm + β 4 FPm )
kCM = β 5T 2 + β 6t max + β 7 k LFV 15
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Predicted curd moisture content (%)
90
80
y = 0.9994x
R2 = 0.95
n = 540
Model XV
70
60
50
50
60
70
80
90
Measured curd moisture content (%)
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Cheese Ripening
• Cheddar Cheese
• Grading performed after one month
• 3 month to assign each batch to a category.
–
–
–
–
Mild sold 3 to 5 months
Medium sold 6 to 8 months
Mature sold 9 to 12 months
Vintage 12 to 18 months.
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NIR / FTIR spectroscopy:
monitoring cheese ripening
Age
(a) rubbery ( b) crumbly
(c) chewy (d) massforming
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Foss
NDC Infrared Engineering
InfraLab e-Series At-Line Analyzer
www.ndcinfrared.com
DA 7250 NIR Analyser
Perten
http://www.foss.us
http://www.perten.com/
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in the Industrial Biotechnology Sector
17
Hyperspectral Imaging (HSI)
HSI emerging platform technology that integrates conventional computer vision and
spectroscopy. It combines the advantages of both technologies by providing spatial,
spectral, and multi-constituent information, while also being sensitive to minor
constituents.
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NIR-HSI Image of
cheese
Reflectance
Typical NIR Spectra
RGB Image of
cheese
Wavelength
(nm)
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Sector
The Potential Of Hyperspectral
Imaging To Map Variations In
Cheese Composition
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Materials & Methods
• Cheese Manufacture and Analysis
• Cheeses produced in triplicate (10 L milk)
• Recombined milk
– 3 milk fat levels (0, 2.5 & 5.0 g/100 g)
– constant protein level (3.3 g/100 g)
• Gel cut when the storage modulus (G') = 35 Pa
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Materials & Methods
• Hyperspectral Imaging
• Hyperspectral images obtained
• Pushbroom line-scanning HSI instruments (DV Optics Ltd.,
Padua)
– NIR system 880-1720 nm, resolution: 7 nm, 320 × 280 pixels
– Spectral Scanner software: image acquisition
– Internal & external
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Materials & Methods
• Analysis
• Four subsamples of each
cheese extracted using a
cheese borer
– moisture (2 subsamples)
– fat content (2 subsamples)
• average spectrum corresponding to each subsample was
extracted, pretreated by standard normal variate, & subjected
to PLS regression using full cross validation (n = 36).
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Moisture prediction maps
1
2
3
4
5
6
a
b
c
d
NIR SNV data.1 – 6 indicates the
number of LV in the model
applied to the image, colour bars
below images represents the
average moisture content (a: 59.6
%, b: 45.9 %, c: 50.4 %, d: 61.5 %, e:
45.0, f: 51.1 ) for that cheese
subsample, (a-c: calibration set
images; d-f: test set images).
e
f
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Fat prediction maps
1
a
b
c
d
e
2
3
4
5
6
NIR SNV data. 1 – 6 indicates the
number of LV in the model
applied to the image, colour bars
below images represents the
average fat content (a: 31.1 %, b: 0
%, c: 19.8 %, d: 31.4 %, e: 0%, f:
20.44%) for that cheese
subsample, (a-c: calibration set
images; d-f: test set images)
f
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Fat Content Prediction (a) 24%, ( b) 27%, and (c) 38%.
29
a
32
b
28
31
27
30
26
29
25
28
24
27
23
26
22
25
21
24
20
23
22
19
c
43
42
41
40
39
38
37
36
35
34
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What is required
• Number of PAT tools
• the PAT strategy will require buy-in from personnel who
will be operating the technology
• Process Development/Research
• Process understanding
• Process improvement
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Effect of including Jersey milk in Holstein-Friesian milk supplies on
Cheddar cheese yield and quality
APPLICATION OF PAT TOOLS
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The project background
Pocock Memorial
Trust
Dartington Cattle
Breeding Trust
Dairy Science & Technology
PAT Tools
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Conclusions
• PAT Framework
– Not fully exploited in food industry
• Develop appropriate sensing technologies
• Traditional, emerging , robust, cost
• Buy-in from all levels in industry
– Product & Process understanding
– Sustainability: environmental, economic
• Integrate food science, process engineering, statistic,
photonics, data management
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Acknowledgments
• Dr Aoife Gowen
• Prof Colm O’Donnell
• Prof Fred Payne
• Dr Donal O’Callaghan
• Dr Colm Everard
• Prof Manuel Castillo
• Dr Jim Burger
• Dr Alistair Grandison
• Ms Julie Bland
SCI: On-Line and At-Line Analytical Technologies in the Industrial Biotechnology Sector