NIR Updates from Syngenta (India) · ANJANA RATHNAM, LEAD-ANALYTICS, BA Technologies, South Asia...
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ANJANA RATHNAM, LEAD-ANALYTICS, BA Technologies, South Asia NIR Updates from Syngenta (India) Classification: INTERNAL USE ONLY
Transcript of NIR Updates from Syngenta (India) · ANJANA RATHNAM, LEAD-ANALYTICS, BA Technologies, South Asia...
ANJANA RATHNAM, LEAD-ANALYTICS, BA Technologies,
South Asia
NIR Updates from Syngenta (India)
Classification: INTERNAL USE ONLY
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About Us
Classification: PUBLIC
Syngenta has established an
internal NIR network with
more than 50 members
across 14 Countries
Network members
- NIR users
- NIR experts
- Managers
Aim of the network: To share
knowledge and experience
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Crops Analyzed @ Syngenta with FOSS instruments – Worldwide
Classification: PUBLIC
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FOSS instruments @ Syngenta
Example of measured parameters
Moisture
Protein
Oil
Fatty acids
Starch
Enzyme soluble substance
and many more..
Classification: PUBLIC
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FOSS @ Syngenta India Rice Starch (Amylose*) Tests – Before!
Wet Chemistry Method”
Average Time (12 samples)
2 Hrs/batch
Throughput Rate
50 Samples/Day
Estimated Costs per sample
1.5$ -2 $
Samples Tested @ 2014-15
12000
*Amylose Content (%) – Determining factor for Cooking and Eating Quality
In Rice. Every sample that comes to the lab has to go through this test!
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Wet Chemistry Method”
12 samples
2 Hrs/batch
Throughput Rate
50 Samples/Day
Estimated Costs per sample
1.5$ -2 $
Samples Tested @ 2014-15
12000
Business Requirements
80-100 samples
2 Hrs/batch
Throughput Rate
200 to 300 samples/day
Estimated Costs per sample
0.5$
Expected Capacity
30000 samples
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Chosen Alternative!
NIR (Near Infra Red Spectroscopy)
High throughput
Cost-effective
Quality Data
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Journey with FOSS!
● Called up FOSS India Office
● A live demo was organized by FOSS Service team at
Syngenta Lab
● A key differentiator: Syngenta Samples were used in the
demo!
● Observed deviation in NIR results with Wet chem.
● Service Team has suggested to go for “Calibration” with in-
house samples with varied Amylose data.
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Final Check
With Statistical Tools
Replication
For consistency among 400 samples
Bias Adjustments
For 100% Correlation
Corraboration
Wet Chemistry & FOSS NIR
Calibration
400 Samples
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Key Challenge
Calibration
Complete shift from wet chemistry
Range of Amylose
Amylose Classification
Waxy 1-2%
Very low 2-9%
Low 9-20%
Intermediate 20-25%
High 25-33%
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Correlation with Scores
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y = 1.0018x R² = 0.6363
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NIR Amylose vs Wet Chem
NIR Amylose
Linear (NIR Amylose)
Low Medium High
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Few Limitations
Sample Volume must be adequate – 200 grams
Broken, chalky and damaged samples may result
in lesser Amylose value
Volume & Quality of Sample !!!
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Key Takeaways!
Augmenting Breeding Program, significantly
Saves lot of time and costs – Non destructive method
Improves efficiency of the team
Outstanding troubleshooting efforts from FOSS team
Learning from Rice helped us to implement NIR for Other
Crops (Sorghum)
Learnings from FOSS helped us support NIR in other
Countries/Other Instruments.
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Expectations from FOSS Team
Getting connected to the FOSS calibration experts
Need support from the team in choosing right statistical
tools/calibration models
FOSS team has to adopt “Inclusive approach” during
calibration development.
On-line Networking System for Trouble Shooting
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Customer Centric Team
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THANK YOU
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ANJANA RATHNAM
www.syngenta.com
INDIA
