The amylose project

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

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Improving our knowledge of rice quality

Roslen Anacleto, Rosario Jimenez, Adoracion Resurreccion, Jeanaflor Crystal Concepcion, Venea Dara Daygon,

Melissa Fitzgerald

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– 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

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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.

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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.

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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

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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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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Sample Number

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ylo

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)

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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Amylose content using 2 brands of potato amylose, done in one laboratory, by one person

Sample Number

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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.

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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

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Sample number

Am

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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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Calibration values by iodine Calibration values by SEC

Amylose content % by SEC

Am

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ine

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Sample No.

Am

ylo

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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

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00

.5

Ab

sorb

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AM

90% AP

70% AP

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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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Standard

Am

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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

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3

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Boxplot of results

25Sample

Am

ylo

se C

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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

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**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

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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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lab1 lab2 lab4 lab6 lab7 lab10 lab11 lab12 lab13 lab15 lab17 lab18 lab21 lab22 lab23 lab24 lab25

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Laboratory

Man

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l’s k

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icWithin-laboratory consistency statistic

According to ISO 5725-2:1994(E)

1% significance5% significance

k=1.72

k=2.10

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Laboratory

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Within-laboratory consistency statistic (continuation)According to ISO 5725-2:1994(E)


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lab4 lab6 lab10 lab13 lab26 lab28 lab30 lab47 lab54 lab65

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Laboratory

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Within-laboratory consistency statistic (outliers)According to ISO 5725-2:1994(E)


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Between-laboratory consistency statisticAccording to ISO 5725-2:1994(E)

Laboratory

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Sample 2

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k=1.91

h=2.45

k=-1.91

h=-2.45


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Laboratory

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h=2.45

k=-1.91

h=-2.45

Between-laboratory consistency statistic (continuation)According to ISO 5725-2:1994(E)


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lab12 lab26 lab30 lab54 lab65

Sample 1

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Laboratory

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h=-2.45

Between-laboratory consistency statistic (outliers)According to ISO 5725-2:1994(E)


k=1.91

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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%

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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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% Amylose by Old Method

% A

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ose

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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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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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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

The amylose project

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