Roslen Anacleto
IRRI Rice Seminar Series
Current position: Senior Associate Scientist
Education and training2009, Projects in Controlled Environments (PRINCE2), Singapore1997, MS in Computer Science, University of the Philippines, Los Baños, Laguna1991, BS in Computer Science, University of the Philippines, Los Baños, Laguna
Work experience2009 – present, Senior Associate Scientist, Grain Quality and Nutrition Center, IRRI2005 – 2009, Programmer, Experiment Station, IRRI2003 – 2005, Assistant Professor and Head, MIS Unit, University of the Philippines – Open University2000 – 2003, IT Consultant, various local and international clients1998 – 2000, Academic Head, Systems Technology Institute, Cagayan De Oro City1991 – 1998, Assistant Professor, Central Mindanao University, Musuan, Bukidnon
Research highlights- Keyless data entry for grain quality evaluation- Implemented barcoding for sample labeling and tracking at the quality evaluation laboratory- Currently working on a LIMS implementation for GQNC- Member of the IRRI Experiment Station ISO 14001:2004 certification working group- Conversion of various databases at the Experiment Station from MS Access silos to a true relational database
2
Improving our knowledge of rice quality
Roslen Anacleto, Rosario Jimenez, Adoracion Resurreccion, Jeanaflor Crystal Concepcion, Venea Dara Daygon,
Melissa Fitzgerald
33
– Current tools to measure amylose, gel temp and gel consistency are not globally standardised.
– Consumers do not have consistent adjectives to describe the quality of rice they like.
– New analytical technologies facilitate a surge on new understanding of sensory quality.
– Improved information and communication technologies make global collaboration routine rather than a challenge.
– We are in an era where genotyping is becoming routine.
– Serious investment into new, accurate phenotyping tools would greatly help genotyping work.
Tools to measure quality do not give breeders accurate enough data about eating quality.
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The International Network for Quality Rice
Understanding quality needs collaboration
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The INQR
Researchers who work on rice quality.
80 members from almost every rice quality evaluation program.
Companies who develop instruments for measuring traits of rice quality.
NARES
ARI
PS
6
First INQR meeting 2007
• Survey undertaken to identify priorities for INQR collaboration.
• Top priority was to fix the method to measure amylose by
– Standardising the method amongst all the rice quality labs
– Bringing new science to move from apparent to actual amylose to increase accuracy of amylose measurement.
• The amylose project then began with 30 labs, and by the time it was concluded in 2010, there were 45 labs and the International Standards Organisation involved.
6
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Organisational levels of starch, and where amylose fits
Crystalline lamella
Amorphous lamella
Amylose MoleculeDebranched Amylopectin Molecule
Blocklets
Compound granules
Semi-crystalline zone Amorphous zone
Amylose MoleculeAmylopectin Molecule
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Improving the global measurement of amylose content
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Singing from the same songsheet
• There are many examples of different amylose contents reported for the same variety.
• Rice germplasms are exchanged all over the rice world together with its passport data --- quality data.
• Quality data gives an end-user/breeder expectations.
• If the sending center measures amylose one way and the receiving center another, then the expectations of the end-user are not met.
• It is important that everyone gets the same values for quality data from the same set of samples.
1010
Standardising the measurement of amylose
Round 1
(Determine baseline)
• 17 samples
• 30 laboratories, INQR
• each lab used their own method
•amylose values and details of methods collected
Round 2
(Choose best method)
• 8 methods to test variability
• calibrated standards and values provided
• 17% of values were outliers
• 3 out of 44 labs had no outliers
Round 3
(Precision and proficiency tests)
• 1 method which will be the ISO standard
• 5 standards calibrated by 6 labs (incl. Japan)
• 44 laboratories, INQR
• 18 samples
11
Round 1 – Determining the baseline
• Enormous variability.
• Sample 3: 4 – 40%
• Waxies, which have no amylose: 0 – 12%
• IR64 is sample 11: 15 – 30%
• KDML is sample 13: 10 – 25%
• Several different methods in operation
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-5
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25
30
35
40
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Sample Number
Am
ylo
se C
on
ten
t (%
)
IR 24IR 64
Goami 2
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Round 1: Major source of variability
• The current ISO standard specifies two ways of making a standard curve
– Potato amylose
– Calibrated rice varieties
• 15 labs used potato and 15 used calibrated rice.
• Variability is much higher for labs that used potato.
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0
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25
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35
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Sample Number
Am
ylo
se C
on
ten
t (%
)
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Amylose content using 2 brands of potato amylose, done in one laboratory, by one person
Sample Number
Am
ylo
se C
on
ten
t (%
)
0
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15
20
25
30
35
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Brand 1Brand 2
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Round 2 – Choosing the best method
• 44 participating labs
• 8 methods were used to test variability due to wavelengths, to standing time, and to methods of calibrating the standards
• Two wavelengths were compared: 620 nm and 720 nm
• Two standing time were compared: 0 minutes and 30 minutes.
• Two methods of calibrating the standards were compared: by iodine (using ISO 6647-1:2007(E)) and also by Size Exclusion Chromatography (by 5 INQR members).
• Eliminated potato amylose and used 4 types of rice varieties according to known amylose contents: waxy, low, intermediate, and high
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The standard: iodine or SEC
Iodine
• Iodine is easy for every lab to do
• Iodine needs to be calibrated against something –reference standards, known rice varieties, potato amylose
• It is not a direct measure of amylose
• We also know that iodine binds to amylopectin.
SEC
• SEC is a direct method to quantify the chains that belong to amylose vs the chains that belong to amylopectin.
• It is not a routine method
• In a network such as ours, it is possible to calibrate standards by SEC and distribute them as reference material.
16
Moving from iodine to SEC calibration values
• Using the standard curve made from iodine values led to higher values for all samples.
• The average difference is not proportional
– Low: 3.5%
– Inter: 5.1%
– High: 6.3%
• Amylopectin contribution increases with increasing amylose. -5
0
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20
25
30
35
40
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Sample number
Am
ylo
se C
on
ten
t (%
)
Values from iodine curveValues from SEC curve
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Testing the relationship between amylose content by SEC and by iodine using both calibration methods
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-5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0
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Calibration values by iodine Calibration values by SEC
Amylose content % by SEC
Am
ylo
se c
on
ten
t %
by
iod
ine
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Sample No.
Am
ylo
se c
on
ten
t (%
)Round 1 vs Round 2 (620-0, 620-30, 720-0, 720-30)
IR 24IR 64
Goami 2
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Other difference is wavelength
• Spectrophotometer scan showing 620nm still has lots of AP while at 720nm AP is almost invisible
• By using the higher wavelength, we are significantly reducing the contribution from amylopectin
• Amylose = AM
• Amylopectin = AP
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800720 760
Wavelength (nm)
560480 520 680600 640
0.4
0.2
0.3
0.1
00
.5
Ab
sorb
ance
AM
90% AP
70% AP
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0.6
0.5
0.4
0.3
0.2
0.1
0
Selecting the best method
• Technically, the best method should be the one that more accurately reports amylose.
• This means calibration by SEC and making the AP/iodine complex invisible, so a wavelength of 720 nm.
• AM-I and AP-I signatures fade through time, thus a standing time of 0 min was chosen.
110100100010000
HighIntermediateLowWaxy
110000 1000 100 10
Chain-Length
No
rmal
ize
d D
RI
Chain-length dist of DB starch
0.7
AM AP
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Performance
SEC calibrated standards measured at different wavelengths at 0 min
IR 24 IR 64 IR 24 IR 64
Goami 2Goami 2
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Round 3 – Precision and proficiency tests
• Rigid test for the 720nm_0_
SEC method
• Five standards were used from each category, each with allele of the Waxy
gene: waxy, very low, low, intermediate, and high
• Six INQR labs with SEC calibrated the standards
• Note that SEC measures true AC so lower values are shown in the chart
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0
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15
20
25
30
0 1 2 3 4 5 6
Standard
Am
ylo
se C
on
ten
t (%
)
Amylose measurements done by six INQR labs based on SEC
calibrated values
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Repeatability and reproducibility conditions
Within a short interval of time
Using different equipmentUsing the same equipment
By different operatorsBy the same operator
At different laboratoriesAt the same laboratory
Measuring on identical material
Using the same test method
Reproducibility conditionsRepeatability conditions
Tang Luping and Björn Schouenborg, 2000, Methodology of Inter-comparison Tests and Statistical Analysis of Test Results – Nordtest project No. 1483-99, p8.
“Within-Lab” “Between-Labs”
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Repeatability and reproducibility conditions
Within a short interval of time
Using different equipment
Using the same equipment
By different operatorsBy the same operator
At different laboratories
At the same laboratory
Measuring on identical material
Using the same test method
Reproducibility conditions
Repeatability conditions
“Within-Lab” “Between-Labs” 5 SEC calibrated standards, 720 nm wavelength, 0 min standing time
18 samples from the same source distributed to 44 participating labs
Agreed time frame for conducting the experiment
Each lab assigned the same technician and equipment
Procedure1
2
3
4
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Boxplot of results
25Sample
Am
ylo
se C
on
ten
t (%
) IR 74Seraup 27IR 64
High
Intermediate
Low
Very low
Waxy
RC 18
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ISO 5725-2:1994, Accuracy (trueness and precision) of measurement methods and results – Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method
Use ISO standard for interlaboratory ring tests
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Yes
Collate results
Outliers found?
Compute replication means and std. dev.
Discard discordant data
Check the results for consistency and outliers**
Compute the general mean, repeatability std. dev., and
reproducibility std. dev., etc.
Recalculate replication means and std. dev.
Discard or correct outliers
Report the results
Form A
Form B,Form C
Statistical AnalysisReport
No
**ISO 5725 suggests two ways of checking for consistency and outliers:
1. Graphical – Mandel’s k and h statistics2. Numerical – Cochran’s and Grubb’s
tests
Statistical analysis flowchart
ISO 5725-2:1994(E)1
2
3
4
5
6
7
8
Excel worksheets
Formulas are givenin the standard
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Using graphical techniquein detecting outliers(Mandel’s k and h Tests)
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0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
lab1 lab2 lab4 lab6 lab7 lab10 lab11 lab12 lab13 lab15 lab17 lab18 lab21 lab22 lab23 lab24 lab25
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Sample 11
Sample 12
Sample 13
Sample 14
Sample 15
Sample 16
Sample 17
Sample 18
Laboratory
Man
de
l’s k
-sta
tist
icWithin-laboratory consistency statistic
According to ISO 5725-2:1994(E)
1% significance5% significance
k=1.72
k=2.10
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0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
lab26 lab27 lab28 lab29 lab30 lab32 lab33 lab36 lab37 lab38 lab41 lab42 lab46 lab47 lab54 lab64 lab65
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Sample 11
Sample 12
Sample 13
Sample 14
Sample 15
Sample 16
Sample 17
Sample 18
Laboratory
Man
de
l’s k
-sta
tist
ic
1.72
2.10
k=1.72
k=2.10
Within-laboratory consistency statistic (continuation)According to ISO 5725-2:1994(E)
1% significance5% significance
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0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
lab4 lab6 lab10 lab13 lab26 lab28 lab30 lab47 lab54 lab65
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Sample 11
Sample 12
Sample 13
Sample 14
Sample 15
Sample 16
Sample 17
Sample 18
Laboratory
Man
de
l’s k
-sta
tist
ic
k=1.72
k=2.10
Within-laboratory consistency statistic (outliers)According to ISO 5725-2:1994(E)
1% significance5% significance
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-6.00
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
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2.00
3.00
4.00
5.00
6.00
lab1 lab2 lab4 lab6 lab7 lab10 lab11 lab12 lab13 lab15 lab17 lab18 lab21 lab22 lab23 lab24 lab25
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Sample 11
Sample 12
Sample 13
Sample 14
Sample 15
Sample 16
Sample 17
Sample 18
Between-laboratory consistency statisticAccording to ISO 5725-2:1994(E)
Laboratory
Man
de
l’s h
-sta
tist
ic
Sample 2
Sample 3
Sample 4
Sample 5
k=1.91
h=2.45
k=-1.91
h=-2.45
1% significance5% significance
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-6.00
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
lab26 lab27 lab28 lab29 lab30 lab32 lab33 lab36 lab37 lab38 lab41 lab42 lab46 lab47 lab54 lab64 lab65
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Sample 11
Sample 12
Sample 13
Sample 14
Sample 15
Sample 16
Sample 17
Sample 18
Laboratory
Man
de
l’s h
-sta
tist
ic
k=1.91
h=2.45
k=-1.91
h=-2.45
Between-laboratory consistency statistic (continuation)According to ISO 5725-2:1994(E)
1% significance5% significance
34
-6.00
-5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
lab12 lab26 lab30 lab54 lab65
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Sample 11
Sample 12
Sample 13
Sample 14
Sample 15
Sample 16
Sample 17
Sample 18
Laboratory
Man
de
l’s h
-sta
tist
ic
h=2.45
k=-1.91
h=-2.45
Between-laboratory consistency statistic (outliers)According to ISO 5725-2:1994(E)
1% significance5% significance
k=1.91
35
Summary of outliers using Mandel’s k and h statistics
Lab designationsLab designations
3030183026, 28, 659
3026, 47, 6517304, 268
3030, 6516306, 107
3030153026, 30, 656
3026, 651412, 3013, 655
30261312, 54264
54, 65471212283
12, 6528, 541112, 30, 5430, 472
3028, 30, 471026, 5441
Between-LabWithin-LabSampleBetween-LabWithin-LabSample
17 14 16 11
Total within-lab outliers - 33 (10 unique labs)
Total between-lab outliers - 25 (5 unique labs)
Total outliers - 58
4 labs in common – 26,30, 54, 65
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Using numerical techniquein detecting outliers(Cochran’s and Grubbs’ Tests)
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26c, 28c,30g,65c
4c, 26c,30g
12g, 30g
12c, 26c, 30c, 65c
12g,13c,30c,65c
4c,12g,26c,54g,65c
4c,12g,18c,26g,28c,30g, 54g
12g, 26g,30g,47c,54g, 65g
Outlier/Reasons
432445760Number of excluded outliers
6.5733.5903.5274.0273.3005.4676.3623.4727.138Reproducibility limit
2.8592.5751.2241.5901.5962.4352.1902.2641.693Repeatability limit
7.8795.425-70.1898.15010.8557.0238.1828.57671.607Reproducibility covariance (%)
3.4273.891-24.3623.2175.2503.1282.8175.59216.986Repeatability covariance (%)
2.3471.2821.2601.4381.1791.9522.2721.2402.549Reproducibility std. dev. (sR)
1.0210.9200.4370.5680.5700.8700.7820.8090.605Repeatability std. dev. (sr)
29.79223.635-1.79517.64610.85827.80227.76914.4593.560General mean
303132303029272834Number of valid laboratories
987654321Sample
Repeatability/Reproducibility Statistics (samples 1-9)
Note on the suffixes:- “c” denotes rejection due to Cochran’s statistic - “g” denotes rejection due to Grubbs’ statistic
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30g, 54g26c,30g,47c,
54g, 65c
30c, 54g,65c
30c26c,30g, 65c
26c, 30g47c, 54g, 65g
12g, 54c,65g
28c, 30c, 47c
Outlier/Reasons
253132333Number of excluded outliers
9.6583.1862.6017.8184.8924.9992.8403.1694.862Reproducibility limit
3.4251.5011.0512.6492.7223.1191.9511.7551.952Repeatability limit
11.670-82.071-71.18710.5328.49210.56815.56617.17812.904Reproducibility covariance (%)
4.139-38.673-28.7663.5694.7256.59310.6949.5125.180Repeatability covariance (%)
3.4491.1380.9292.7921.7471.7851.0141.1321.737Reproducibility std. dev. (sR)
1.2230.5360.3750.9460.9721.1140.6970.6270.697Repeatability std. dev. (sr)
29.555-1.387-1.30526.50920.57416.8936.5166.58913.458General mean
322931333132313131Number of valid laboratories
181716151413121110Sample
Note on the suffixes:- “c” denotes rejection due to Cochran’s statistic- “g” denotes rejection due to Grubbs’ statistic
Repeatability/Reproducibility Statistics (samples 10-18)
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Summarized results
Laboratories with repeatability problems
-> lab 26, 28, 30, 47, 54, 65
Laboratories with reproducibility problems
-> lab 12, 26, 30, 54, 65
Laboratories with both problems!
-> lab 26, 30, 54, 65
4 out of 34
11.76%
4040
Analysis of lab proficiency
With repeatability issues : 26, 28, 30, 47, 54,65With reproducibility issues : 12, 26, 30, 54, 65
IRRI GQNC
4141
The result of the amylose project
• Completion of the 1st INQR standardisation project.
• New method and calibrated standards were launched by ISO at the INQR symposium at the IRRC28 in November 2010 in Hanoi.
• Providing calibrated standards and testing to achieve uniform measurement is only possible in a collaborative network.
• Each year, newly grown standards and calibration values (from 6 labs) will be distributed to INQR members.
• We are getting inquiries from companies who want to buy the standards...
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What this means for breeders
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-5
0
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10
15
20
25
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35
-5 0 5 10 15 20 25 30 35
% Amylose by Old Method
% A
myl
ose
by
Ne
w M
eth
od
VHHILVLW
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So then…
• Previously the categories were waxy, very low, low, intermediate, high.
• That will not change.
• Current work is done in establishing the relationship between the values obtained using both methods
• This relationship would be determined through rigorous computational methods on precise data sets
• Any immutable relationship will be used as correction factor should breeders/rice scientists refer to old values
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44
Timeline to integrate the new method
• ISO has approved the method to be the new standard.
• At IRRI we are currently collecting data from both methods.
• We intend to switch fully to the new method by the end of this year.
• Other INQR labs are following this timeline.
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Conclusions
• Rigorous analytical and computational procedures were done to move from apparent to actual amylose content
• A platform for collaborative work is in place --- INQR
• Current classifications for amylose content: waxy, very low, low, high, and very high will not change
• Work is in progress to determine the relationship between old and new amylose values
• A series of other collaborative work will be undertaken to improve existing tools for measuring quality
• Current research activities at the Grain Quality and Nutrition Laboratory progressively improve our understanding of rice quality
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46
Acknowledgement
The authors thank
• The INQR participating laboratories and other contributing members
• PBGB breeders and GRC for providing quality samples
• The GQNC technicians: Teodie, Johnny, Boy, Dennis, Leah, Ferdie, and Lucy for their usual outstanding support