Table 1 Soybean sample composition

Detection of Roundup Ready™ Soybeans by Near-Infrared Spectroscopy

S.A. Roussel*, C.L. Hardy**, C.R. Hurburgh, Jr.*, G.R. Rippke* Iowa State University,. Ames, IA

* Grain Quality Laboratory ** Center for Crops Utilization ResearchDepartment of Agricultural & Biosystems Engineering

The controversy over genetically modified (GMO) grains has created the need for a rapid screening method for grain handlers. Maintaining purity of an otherwise indistinguishable product is not an operation familiar to commodity markets.

Near-infrared analyzers would be well suited to screening inbound deliveries, if there were sufficient spectral differences between GMO and conventional varieties. While NIR units cannot detect DNA, spectral differences may be caused by larger structural changes accompanying the modification.

Roundup Ready™ soybean (RR) was the first genetically modified grain to be widely adopted by US farmers (0% of soybean acres grown in 1996 to more than 50% in 1999).

10th International Diffuse Reflectance Conference (IDRC-2000), August 13-18th, 2000, Chambersburg, PA.

Figure 2 shows the region of greatest spectral response to be 900–950nm.

A preliminary study demonstrated a spectral difference between RR and conventional soybeans (Figure 1). Samples of known varieties from Iowa county

Additional samples (Table 1) were scanned in 1999 on near infrared spectrometers (Figure 3).

The objective of this study is to develop the protocol for distinguishing Roundup Ready™ from conventional soybeans using near infrared spectrometry.

RR Non-RRSample set(nRR/nnon-RR)

FactorAvg. Std. Dev. Avg. Std. Dev.

Moisture 11.5 0.63 10.9 0.81

Protein 37.5 0.93 36.5 0.84Sample Set I

1998 harvest samples(53/61) Oil 18.9 0.48 19.5 0.69

Moisture 11.2 0.63 10.7 1.71

Protein 36.0 1.50 35.8 1.61Sample Set II

1999 harvest, breeder, strip plot samples (308/341) Oil 18.5 0.82 18.3 1.00

Moisture 8.73 0.96 9.2 0.93

Protein 35.7 1.29 35.5 1.45Sample Set III

1999 Iowa SoybeanYield Test samples (3882/3482) Oil 18.2 0.84 18.3 0.80

Basis 13% moisture, predicted by the Infratec, calibration SB00994

After the preliminary study based on Sample Set I, the general progression of evaluation was:

1. New PLS calibrations and ANN models (Figure 5) were created using Sample Sets I & II (Table I). The constituent values as well as the spectra were used as input variables.2. PLS, ANN (Figure 5), and LWR (Figure 6) calibrations were generated using a very large database (Sample Set III in Table I).

3.2

3.3

3.4

3.5

3.6

3.7

850 870 890 910 930 950 970 990 1010 1030Wavelength (nm)

ln (1

/t)

Roundup Ready Soybeans(51 samples)

Non-Roundup Ready Soybeans

(63 samples)

Near-infrared transmittance spectra, Infratec 1229; Roundup Ready vs. Non-Roundup Ready soybeans

strip plots in 1998 comprised a calibration set of 53 RR soybeans and 61 non-RR

soybeans (Table 1). On an independent set of 39 samples, this calibration classified

RR soybeans with 85% accuracy and non-RR soybeans with 95% accuracy.

However, it gave less than 70% accuracy on the following year’s grain (1999).

-300

-200

-100

0

100

200

300

850 870 890 910 930 950 970 990 1010 1030

Wavelength (nm)

Reg

ress

ion

coef

ficie

nt v

alue Water band, 960 nm

Active Region

Regression coefficients for 1998 Roundup Ready soybean calibrations

CH-, CH2-, CH3-, R-OH activity

Figure 2

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

850 870 890 910 930 950 970 990 1010 1030

Wavelength (nm)

Ave

rag

e d

iffe

ren

ce, R

R -

no

n-R

R, l

n (

1/T

) u

nits

Original calibration set (n=114)

Original validation set (n=39)

Early harvest 1999 (n=43)

Wet breeder samples (n=96)

Dry breeder samples (n=270)

1999 strip plots (n=107)

Differences in NIR transmission spectra (Roundup Ready - non-Roundup Ready)

in 6 soybean sample sets, 1998-1999

Figure 4

Figure 4 shows the Infratec spectral differences between RR and non-RR samples. The direction of differences (RR higher than non-RR) remained but the magnitude of the difference was not consistent. Figure 4 indicates that a linear PLS model might not describe the differences accurately.

Figure 3Foss/Tecator Infratec 1229 Grain Analyzer

To prevent confounding, the data was balanced in other constituents (Moisture, Protein, Oil) known to affect the spectra (Table 1).

The classification accuracy of various models was compared: Partial Least Squares (PLS) (UnscramblerTM v. 7.5) Locally Weighted Regression (LWR) (MatlabTM v. 5.3) Artificial Neural Networks (ANN) (MatlabTM v. 5.3)

Materials

Methods

850 875 900 925 950 975 1000 1025 10503.4

3.45

3.5

3.55

3.6

3.65

3.7

3.75

3.8

3.85

3.9

Soybean NIR spectrum

Wavelengths (nm)

ln(1

/T)

Roundup Readysoybeans

Non Roundup Ready soybeans

Moisture

Protein

Oil

Fiber

Pri

ncip

al

Co

mp

one

nts

Figure 5: ArtificialNeural Networks

The best ANN model (Figure 5) used 25 principal components generated from the 100-wavelength spectra and 4 constituent

5 to 8 hidden nodes, and 2 outputs

values (moisture, protein, oil, and fiber) as inputs,

encoding for RR and non-RR classes.

Calibration set Predicted test set% correct

classificationModel and parameters Year (RR/non-RR) Year (RR/non-RR) RR Non-RRPLS 16LVs 66 72PLS+

a17LVs

Sample Set I & II 308 / 341 Sample Set II (cv)

308 / 34171 76

PLS 12LVs 62 53PLS+

a10LVs 59 69

ANN (25+4:8:2)b

Sample Set I & IICalibration

272 / 275Sample Set I & II

Test Set37 / 55

81 87PLS

a16LVs Sample Set III

c4302 / 3880 Sample Set III

(cv)4302 / 3880 89 87

PLS 16LVs 77 79PLS+

a15LVs 77 79

LWR 17LVs, 850ng 93 92ANN (25+4:5:2)

Sample Set III 3882 / 3482 Test Set III 420 / 398

91 89

Table 2 Roundup Ready™ soybean classification results

(cv) -Classification errors computed using cross-validation LVs - Latent variables ng - neighborsa Includes the constituents (moisture, protein, oil and fiber) as independent variablesb (25+4:8:2): Input nodes: 25 principal components + 4 constituents; hidden nodes: 8; output nodes: 2c Sample Set III and Test Set III were combined for cross-validation.

Results

300500

700900

11001300

1500

13 14 15 16 17 18 19

7

7.5

8

8.5

9

9.5

10

10.5

11

Number of neighbors

LWR prediction of the 818-sample test set

Number of latent variables

Pe

rce

nta

ge

of c

lass

ifica

tion

err

or

8.3%

7.6%

Figure 6Figure 6 shows the various parameters tested with LWR:

300 - 1500 neighbors 13 - 19 latent variables α = 0 - 1(proportion of

x- and y-distances) Spectra + constituents

or spectra onlyThe lowest classification error (7.6%) was obtained with 850 neighbors and 17 latent variables and α=0 (Figure 6).

The discrimination between RR and conventional soybeans has been achieved using non linear (ANN) and local (LWR) models a on large NIR spectral database.

The optical standardization of spectra issued from different NIR spectrometers may further improve the GMO classification.

The difference in the organic composition between RR and non-RR soybeans detected by NIR measurements must be more clearly defined.

Among the 3 models tested, Locally Weighted Regression achieved the best classification performance (92.4% accuracy). Artificial Neural Networks classified the RR soybeans nearly as accurately as did LWR. Partial Least Squares produced unsatisfactory classification accuracy.

Figure 1

Conclusions

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