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Digital Re-print - November | December 2010 Low-cost Near Infrared Transmission grain analysers for farmers

www.gfmt.co.uk

Measuring the protein, oil and moisture of grains at the farm gate is not a

necessity in all countries. However, where farmers are paid based on the protein and oil content of their grains, then a simple, low-cost NIT (Near Infrared Transmission) analyser can be of significant benefit.

In Australia, South Africa, Canada and some European countries, grain is bought and sold based on protein and oil.

Specifically in Australia and Canada where farms can be producing many thousands of tonnes of wheat, barley and canola per year, and the distances to silos can be as much as 100km, then on-farm grain analysers are becoming a more common investment.

The theory of NIT spectroscopy Near Infrared Transmission spectroscopy

measures protein, oil and moisture in grains by passing light through a 16mm thick cell containing the whole grains.

Infrared energy is absorbed by the chemi-cal bonds that make up the protein, moisture and oil in the grains. Protein is measured from the N-H bonds present in peptide bonds that join together to make long pro-tein molecules.

Moisture, that is water, is measured from the O-H bonds.

And Oil is measured from the C-H bonds that exist in long chain fatty acids which are the building blocks for oils and fats. N-H, O-H and C-H molecules absorb at specific resonant fre-quencies of light.

So by passing infrared light through the sample of grain, energy gets absorbed at the three resonant frequencies in proportion to the concentration of each compo-nent.

Mathematical models are devel-oped using lin-ear regression statistical software that relate the amount of energy absorbed at each frequency to the concentration lev-els. These models are downloaded into the analyser as calibration files.

To analyse a

by Phillip Clancy, NIR Technology Systems, Australia

Low-cost Near Infrared Transmission grain analysers for farmers

Above - Figure 1: The CropScan Loren 1000G On-Farm Analyser

Figure 2: schematic of the diode array spectrometer

Grain&feed millinG technoloGy22 | november - december 2010

FeatureNIR analysis

sample of grain, the sample is poured into the analyser and the NIT spectra is collected. The calibrations are applied and the protein, moisture and oil results are predicted.

Description of the instrument

The CropScan Loren 1000G On-Farm Analyser (see Figure 1), is a diode array spectrometer designed to measure protein, moisture and oil in wheat, barley and canola. Other grains and oil seeds can be measured; how-ever these three seeds make up the bulk of the market in Australia and Canada.

The CropScan 1000G uses a pour through sampling system that allows up to 10 sub samples to be scanned and the average of the protein and moisture or oil and mois-

ture are presented on the screen in around 45 seconds. Figure 2 shows a schematic of the diode array spectrometer.

The advent of low-cost diode array spectrometers, such as the CropScan 1000G, make on-farm grain measurement possible.

The CropScan 1000G has no moving parts except for the sampling brush wheel motor to meter the grain through the sam-ple chamber. This solid state optical system means that the CropScan 1000G is unaf-fected by orientation and vibration. As such the analyser can be placed in a combine, in a tractor or any vehicle and moved from paddock to paddock. Running from a 12 volt DC vehicle power adapter or from a 12 volt battery, the CropScan 1000G can be used virtually anywhere.

Accuracy and precisionA recent in house study to validate the

performance of the CropScan 1000G dem-onstrates the accuracy and precision of the analyser. Table 1 shows the results of analysing

five wheat and five barley samples in duplicate and

compares the results with the laboratory ref-erence values for each sample.

Figure 3 shows the plots for pre-dicted protein vs. the refer-ence protein values while Figure 4 shows the plots for the predicted moisture vs. the reference moisture val-ues.

Table 2 shows the results of analysing the same five wheat and five barley samples on three consecutive days. This test is indicative of the stability of the analyser.

CalibrationsCalibration models for protein, moisture

Table 1: Comparison of 2 CropScan 1000G Analysers vs. the Reference Laboratory (SEP = Standard Error of Prediction)

Sample ID

Protein CropScan

- 004

Protein CropScan

- 015

Ref Protein

Diff Ref

- 004

Diff Ref

- 015

Moisture CropScan

- 004

Moisture CropScan

- 015

Ref Moisture

Diff Ref

- 004

Diff Ref

- 015

W1 9.9 9.7 9.8 0.1 -0.1 9.1 9 8.9 0.2 0.1

W2 10.2 10.5 10.3 -0.1 0.2 11 11.1 11 0 0.1

W3 12.1 11.9 11.9 0.2 0 12.2 12.1 12.1 0.1 0

W4 12.4 12.5 12.7 -0.3 -0.2 9.8 9.6 9.6 0.2 0

W5 13.6 13.8 13.7 -0.1 0.1 11.5 12 11.4 0.1 0.6

B1 8.3 8.4 8.7 -0.4 -0.3 10.8 10.6 10.8 0 -0.2

B2 9.8 9.7 9.6 0.2 0.1 11.1 11.1 11.1 0 0

B3 10.5 10.5 10.3 0.2 0.2 11 11.1 11.1 -0.1 0

B4 11.3 10.9 11.6 0.1 -0.7 12.5 12.4 12.5 0 -0.1

B5 12.6 12.6 12.8 -0.2 -0.2 10 10.2 10.1 -0.1 0.1

SEP - - - 0.22 0.28 - - - 0.11 0.21

Figure 3: Plot of predicted protein vs reference protein

CropScan 1000 Series

Grain Analysers

CropScan 1000B Whole Grain Analyser

Protein and Moisture in Whole Wheat and Barley

Oil and Moisture in Canola

Test Weight Module

Printer Option

Network

CropScan Loren 1000B On Farm Analyser Transportable

12VDC operation

Protein and Moisture in Whole

Wheat and Barley

Oil and Moisture in Canola

For an information pack, please contact: NIR Technology Systems Tel: 612 9771 5444 Fax: 612 9771 5255 Emial: nirtech@nirtech.net

Grain&feed millinG technoloGy november - december 2010 | 23

Feature NIR analysis

mium for oil content greater than 40 per-cent. By measuring the protein and oil for grains and oil seeds, the crop can be segregate into dif-ferent silos or bins. By blending higher protein wheat and barley with low pro-tein grains and higher oil canola with lower oil seeds, then it is possible to get more of the farmer’s crop into the premium grades and thereby increase their pay-ments for their crops.

SummaryWith the price of

chemicals, fuel and capital equipment increasing annually, the profitability of farming is decreasing.

Technology is the only option for farm-ers to improve their profitability through improving efficiency. As such, farmers are buying more automated tractors, seeders and com-bines and sprayers with weed indentify-

ing sensors are becoming more common. All these devices offer the possibility of reducing the costs of farming; however they do not provide a means of increasing the price received for the farmer’s crops.

The CropScan 1000G is a relatively low-cost instrument that has the potential of reaching a payback of less than one year.

Instrument investment paybackAlthough different countries have differ-

ent marketing options which will affect the investment payback, the following scenario has been chosen for a typical board acre

farm in Australia. Wheat with pro-

tein above 10.5 per-cent protein attracts a premium of Aus$5 per percent per tonne. Wheat with protein above 13 percent an additional premium is paid. Barley falls into malting grade if the protein is between 9 and 12 percent. Canola attracts a pre-

and oil are developed using a chemometrics software package called NTAS (NIR Technology Analysis Software).

Hundreds and even thou-sands of samples of various grains are scanned, that is the NIT spectra are collected using the CropScan 1000G. A Partial Least Squares (PLS) regression analysis is used to develop a mathematical model that relates the NIT spectra to each of the protein, moisture and oil in the samples.

Separate calibration models are developed for each grain.

The master calibration mod-els provided with the CropScan 1000G have been developed over many years based on sam-ples of wheat, barley and canola collected from many countries. These calibrations are down-loaded into each CropScan 1000G and a set of reference samples are analysed to make slope and bias adjustments to the calibrations so that the instruments is setup to accu-rately and reproducibly analyses new samples of each grain.

The CropScan 1000G includes means of adjusting the slope and bias for each calibra-tion.

This is important in order to align the predicted results with an external laboratory or another instrument’s results. For farmers there is an Autocalibration mode in the instrument’s software that prompts the operator to analyse a reference sample and then adjust the results to align the instru-ment to reference sample.

More inforMation:Phillip ClancyNIR Technology SystemsB1, 366 Edgar Street, Condell Park NSW, Australia

Tel: +61 2 97715444Email: nirtech@nirtech.netWebsite: www.nirtech.net

Table 3: Return on investment analysis

Grains grown and tonnage:

Wheat 2000 tonne

Barley 1500 tonne

Canola 1000 tonne

Premiums paid for protein and oil:

Wheat $5 per percent per tonne

Barley $30 per tonne between 9.5 and 11.5 percent

Canola $10 per tonne over 40 percent oil

Estimated additional tonnage delivered into

premium grades:

Wheat 15 percent = 300 tonne

Barley 30 percent = 500 tonne

Canola 25 percent = 250 tonne

Additional Profit generated:

Wheat $4500

Barley $15000

Canola $2500

Investment: $12500

Return per year: $21000

Figure 4: Plot or predicted moisture vs reference moisture

Table 2: Results of analysing the same five wheat and five barley samples on three consecutive days. This test is indicative of the stability of the analyser

Sample ID

Protein Day 1

Protein Day 2

Protein Day2

RangeMoisture

Day1Moisture

Day2Moisture Day 3

Range

W1 9.9 9.9 10 0.1 9 9 8.8 0.2

W2 10.2 10.6 10.3 0.4 10.9 10.8 10.7 0.2

W3 12 12.1 12.1 0.1 12.2 11.8 11.8 0.4

W4 12.5 12.5 12.4 0.1 9.6 9.5 9.4 0.2

W5 13.7 13.7 13.8 0.1 11.3 11.2 11.1 0.2

B1 8.3 8.9 8.6 0.6 10.8 10.7 10.7 0.1

B2 10.1 10.2 10.2 0.1 11.1 11 11.1 0.1

B3 10.7 10.4 10.5 0.3 10.9 11.1 11 0.2

B4 11.4 11.8 11.4 0.4 12.5 12.6 12.5 0.1

B5 12.5 13.1 12.8 0.6 10.1 10 10.1 0.1

SDD - - - 0.21 - - - 0.09

Grain&feed millinG technoloGy24 | november - december 2010

FeatureNIR analysis

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