ft-nir and process optimisation - qcl · 2019. 11. 20. · dairy liquids and ft-nir •starter...
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FT-NIR and Process Optimisation
November 2019
Dr. Mark Whatton
Technical Projects Manager
QuadraChem Laboratories Ltd
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Contents
Infrared Spectroscopy Overview
Process Optimisation Cases
•Butter / Spreads
•Dairy Liquids
FT-NIR
FT-NIR Hardware For Process Optimisation
Making Optimisation Work
Summary
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Infrared
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Infrared Spectroscopy
Carbon, Hydrogen, Nitrogen, Oxygen, etc. atoms bonded to form molecules
Molecule bonds absorb IR light
The amount of IR absorbed at different wavelengths is measured
The amount of IR absorbed equal to the amount of molecule present
The analyser calculates the composition of different components
Beer-Lambert Law
A = alc
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Infrared Spectrum
In MID IR individual components can be observed in the spectrum absorbing infrared light in proportion to their concentration
Fat B 2850cm-1
Fat A 1750cm-1
Protein 1547cm-1
Lactose 1040cm-1
(water)
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MIR Spectrum Changes
Fat B C-H Fat A
C=O
Changing FAT Level The Fat A should be applied for milk products whereas Fat B is applied at measurement of individual cow samples or samples containing free fatty acids (FFA).
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NIR Spectrum Changes
It is possible to observe changes in NIR spectra relating to components in some cases
NIR systems use calibration models to calculate component properties
However overlapping bands and shifting peaks make this haphazard
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Then Why Use NIR?
NIR Absorptions are “Forbidden”
• In theory they shouldn’t happen
In Reality “occasionally”
they do
• NIR Waves travel through matter until absorbed
NIR reflected back from
matter
• Missing absorbed NIR seen in spectrum
NO SPECTROSCOPIC
SAMPLE PREPARATION!
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FT-NIR
9
Wavelength Accuracy
Signal to
Noise
Long Term
Stability Water vapour !
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Salt Analysis
Salt is a Crystalline solid and therefore does not absorb IR light
However Salt does dissolve, surrounding water molecules altering the H-O bond
This is seen as a very small water peak change, which with FT-NIR can be accurately quantified (+/- 0.05% Salt)
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Factory Process Optimisation
11
Different requirements for the applications in lab versus in factory
Accurate information presented to Operators in clear way that can be used to affect change
Factory has capability to tune process to achieve optimisation
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Butter Process Optimisation
ROI – Butter Production - Moisture max 16%
Before Inline Optimisation Optimisation
Production Optimisation
Analyser Variation
Old Method +/- 0.6 More Accurate Method +/- 0.15
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Milk Process Optimisation
13
Raw Milk Cream
Skim
Cream
Semi Skim
Semi Skim >1.50% FAT
Illegal Excellent Real Poor
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Process Optimisation
14
Fast trim at start-up and after production stop
Production closer to target
Warning about production drift outside targets
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FT-NIR Hardware for Optimisation
• Factory Suitable – Vibration, Protection, etc.
• Food Safe
• CIP/SIP Compatible
• SCADA / OPC Integration
• Useful To Operators
• No Down Time
15
Diffuse Reflectance Sampling
Spoon Probe Sampling
Backscatter Sampling
Transmission Probe Sampling
Transmission Cell Sampling
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Production Integration
16
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Factory Spectrometer
17
Isolated from plant vibration
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Factory Operator Terminal
18
+ SCADA Integration
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Achieving Optimisation with FT-NIR
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Useful Results
Calibration Support
Reference Analysis
Sampling Error
Process Understanding
Expectations
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In-Line Accuracy Potential
20
Liquid WPC Butter & Spreads
Lab Analyser
Lab Analyser
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Dairy Liquids and FT-NIR
• Starter Calibrations – FT-NIR Bench-top Platform –no
pumping system, no cell ware wear, no maintenance, no ongoing calibration
– Models directly transferred to In-Line System
– Rapid commissioning
21
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Technical Support
22
Maintaining Accuracy
Solving Problems
New Products
Changing Processes
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FT-NIR & Optimisation
Know the Problem
Expectations Fit For Purpose NIR Hardware
High Quality Reference Analysis
Technical Support
Factory Engagement
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For More Information
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