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NIR

Feed Analysis

Easy, Fast and Accurate

More Product safety in compound feed production

Authors:

Josef Schweizer, Dipl. Ing. agr. (FH)

Unity Scientific GmbH, Germany

jschweizer@unityscientific.com

Janine Hahne, B.Sc.

Unity Scientific GmbH

jhahne@unityscientific.com

Production with risks

Extreme fluctuations of market prices, not at least due to speculation of high value ingredients on a

global market, represent a high risk for the industrial production of compound feed. Price erosion on

the market for compound feed products is often in contrast to high acquisition costs. The develop-

ment of prices for compound feed is following the same mechanisms that also rules the difficult to

predict agricultural markets and that has led to extreme fluctuations of prices for agricultural prod-

ucts like grain and pork meat. Such an environment puts highest requirements on the optimization of

competitive feed recipes and not at least also on production and quality control. The implementation

of a high-performance analytical system, comprised of a NIR spectrometer and a calibration package

for compound feed and single feeds is here demonstrated for the example of the „Mischfutterwerke

Mannheim GmbH“ (Compound Feed Mannheim corporation, previously Muskator Mannheim).

Fig. 1: Aerial view Mischfutterwerke Mannheim GmbH, Germany

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Market-driven variety of products

Feed mills with a large customer base of small and medium sized agricultural business in a rural area

have to manage a quality assurance program for an enormously wide

and specialized array of products. With the new label „mifuma“ Mann-

heim serves not only the classical segment of farm animals with high

quality feed for ruminants, pork and poultry, but addresses also needs

for feeding rabbits, pigeons, horses, dogs, cats, birds, fish and rodents,

which is a traditional market of high economic importance. Bag

products are allocated for markets in Germany, Austria, Switzer-

land, France and Luxemburg. Bulk feed is distributed to the

regions of Bavaria, Baden-Württemberg, Hesse, Rhineland-

Palatinate, the Rhine area and the Benelux countries. As the

plant is located at the Mannheim harbour, raw materials can be

shipped cost effectively from all over the world by ship, wagons

or trucks. Mannheim is focused on the production of premium

high quality feed.

Quality assurance from purchasing the raw materials to the feeding trough

Quality assurance of feed starts with the control of ingredients and ends at the farm. Up to 30 differ-

ent raw materials are controlled for quality before acceptance and distribution into silos for storage

before processing. Highest attention is paid to the biological condition, smell, appearance, hygiene,

possible infestation of mould or microbes and of course to the nutritional value.

A complete nutritional analysis starts with the determination of the moisture content, followed by

crude protein, crude fat, crude fibre, crude ash, starch, sugar, ADF, NDF and the calculation of the

energy value according to the DLG formula. The goal is to let no feed ingredient or feed pass the

gates of the plant without complete nutritional analysis and confirmation of the declared quality.

In the field the farmer is assisted by an experienced team of feed experts. The own basic feed is

complemented with feed concentrate from Mannheim to achieve the best results whether it maybe

meat-, egg- or milk production. This is why quality feed from Mannheim has such a high importance

for livestock keepers.

Fig. 2: Shipping of compound feed

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More confidence in quality by NIR spectroscopy

Taking into account the multitude of feed ingredients and the complex matrix of compound feed,

that are often comprised by more than 10 different ingredients, the demands on technology and

calibrations of a feed analyser are extremely high.

The „Mischfutterwerke Mannheim GmbH“ have made the decision to buy the SpectraStar 2400 from

Unity Scientific. This NIR-Spectrometer scans the wavelength-range from 1200 to 2400 nm and deliv-

ers a complete NIR spectrum with a resolution of 1.0 nm and a

wavelength accuracy of 0.2 nm. With an exceptionally low

noise below 20 µAU (absorption units) it is positioned as a high

performance spectrometer.

The very compact system with integrated PC and touch screen

is delivered inclusive calibrations and necessary accessories for

the analysis of feed ingredients and compound feed. It is also

possible to transfer existing calibrations from other NIR-

systems.

A ground sample is filled into the feed

cell, which guarantees a constant

packing, and automatically scanned by

rotation over the measuring window.

Multiple scans are averaged resulting

in an extremely good repeatability.

The complete measuring cycle, inclusive the presentation of results, takes

about 1 minute.

Fig. 3: SpectraStar 2400 Feed Analyzer

Fig. 4: Station for filling the

measuring cell

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Calibration package for feed

Part of the Unity feed solution is the „INGOT“ calibration package for feed ingredients and compound

feed, which has proven to be excellent in praxis as it can be

individually adjusted to the mix of raw materials and products

of the respective customer. Unity Scientific and the agricultural

lab Aunir owned by the company AB Agri Ltd in the UK have

established cooperation in the field of NIR-feed analysis. The

Aunir specialists dispose of an enormous NIR-calibration-

database that has been built over many years in cooperation

with numerous feed mills all over Europe. Reference values have been established using exclusively

approved reference methods.

Parameter Reference method

Moisture Loss of weight/ drying oven

Crude protein N- Determination acc. to Dumas or Kjeldahl

Crude fat Acid hydrolysis and solvent extraction

Crude fibre Weende method

Crude ash Incineration at 540° C in a furnace

Starch Polarimetric

Sugar Luff-Schorl method

ADF modified ADF method (van Soest)

NDF Neutral detergent method (van Soest)

Table 1: Reference methods for the calibration of feed ingredients and compound feed

A considerable improvement of the power of prediction models in the InGot package was achieved

by adding the experiences of the Belgian agricultural research centre „Centre de Recherches

Agronomiques“ in Gembloux under the direction of Dr. Pierre Dardenne.

Combining the long experience of Unity Scientific in the development of NIR-systems with the com-

petence of Aunir has resulted in a highly productive solution for NIR-feed analysis. A solution with

calibrations for single feed, feed supplements, feed concentrates and compound feed for different

livestock like poultry, ruminants, pork and horses. Feed ingredients covered include cereals, oil seeds

and legumes as well as products from the oil, starch, dairy, brewery and milling industries.

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Poultry Ruminants Swine Horses Concentrates and

supplements

Cereals

Turkey Beef Finisher Racehorse Turkey Barley

Chicken Calf Sow Pony Broiler Maize

Layer Dairy Piglet grower Horse Swine Oat

Broiler Lamb Pre-Starter Pork Triticale

Piglets Wheat

Layer Rye

Ruminants

Dairy cows

Legumes Products from

cereal mills and

starch production

Dairy products Products from oil produc-

tion and breweries

Oilseeds

Beans Citrus

Dried whey

powder Maize gluten Rape seed

Pees Corn gluten

Skimmed milk

powder Linseed expeller Soybean

Lupines Grass meal

Whole milk

powder Spend grains

Tapioca Rape seed meal

Bran Soybean meal

Maize gluten Sunflower meal

Maize germ

meal DDGS

Table 2: Scheme of calibrations for feed and feed ingredients

PCA-statistics

Each group of prediction models contains compre-

hensive amounts of feed data. The NIR spectra of

these feed samples show similarities with respect to

main constituents. This can be shown with help of a

statistical method called principal component analy-

sis (PCA). Theoretically it would be possible to

measure e.g. all types of cereals with one single pre-

diction model. In practice models are made for each

individual grain, e.g. wheat, barley, oats etc., in or-

der to have the possibility for individual bias adjust-

ments and to be able to report for each individual

cereal. Similar is done for other groups.

A prediction model is continuously further developed. Constantly new data reflecting harvest year,

variety and location from all over the world are added. The update of calibrations is part of the pack-

age.

Fig. 5: Three dimensional depiction of characteristics using the

Unity Chemometric Software

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Integration of a new measuring method

After installation of the applications in the instrument first measurements

can be performed. To start with the systematic error (BIAS) for each in-

stalled prediction model is determined by calculating the differences be-

tween the reference values determined in the lab and the predicted NIR-

values. These data are entered into the software to adjust the basic model

and the NIR system can now be used for analysing the different products.

Before the new measuring method can be integrated into the quality as-

surance system it has to be validated. For this purpose the reference sam-

ples used for the BIAS adjustment and additional samples from production

have been used. In total a set of 39 samples (see table 3) has been pre-

pared.

Product Number Moisture Crude-

protein

Crude

fat

Crude-

fibre

Crude-

ash

Sugar Starch

Turkey finisher 2 x x x x x x x

Beef finisher 4 x x x x x

Dairy 9 x x x x x x x

Swine finisher 3 x x x x

Horse 3 x x x x x

Rabbit 3 x x x x x

Layer 2 x x x x x x

Wheat bran 5 x x x x x

Maize 3 x x x x x x x

Rape seed meal 2 x x x x x x x

Table 3: Set of samples used for validation

For validation an independent set of samples is selected representative for the measured ranges and

feed products. The results from this validation are compiled in table 4. In a feed mill the moisture

content is by nature in a very narrow range (SD= 1,4). Notable is the low SD for fat, as only samples

with fat contents between 3,0 % – 5,0 % were represented.

Parameter Number of

samples

SD (measuring

range)

MIN MAX SEP Validation R2

Moisture 39 1,4 7,3 12,9 0,26 0,96

Crude protein 36 9,1 5,5 41,8 0,56 0,99

Crude fat 39 1,0 1,8 7,0 0,40 0,84

Crude fibre 38 5,9 1,9 25,4 0,54 0,99

Crude ash 33 4,8 1,1 10,6 0,48 0,95

Sugar 17 2,3 2,9 10,5 0,57 0,89

Starch 15 18,1 6,1 64,4 0,82 0,99

Table 4: Results of the validation using an independent set of samples

Fig. 6: The new NIR Feed Analyzer

in practical use

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After grinding the pellets and coarse meals using a Retsch mill (2 mm sieve) two

test samples were obtained. One sample was measured by the NIR system and the

second was sealed in a plastic bag and send to a reference lab for chemical deter-

mination of the reference values.

Evaluation of the prediction model is made by calculating the SEP, an expression of

the average difference between predicted and reference values, and by the square

of the multiple regression coefficient (R2) that shows how much of the variance is

explained by the prediction model. As the systematic error (BIAS) already has been corrected for,

only n (number of samples), SD, SEP and R2 need to be determined for the validation of the model.

Conclusion

The validation set could be predicted with a high linear correlation to the reference values (R2= 0.9).

The standard error of prediction (SEP= 0.3 – 0.8) as a measure of the average deviation from the ref-

erence value is very good. The validation set covers a range that is of practical importance and allows

with almost 40 samples significant conclusions that allow the new analysis system to be used for the

tested feed and feed ingredients without any concern.

A validation for the remaining feed and feed ingredients is presently on-going.

Fig. 7: 2 mm screen

(Retsch)

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Graphical presentation of the validation

Wasser = moisture, Rohfett (Extr) = Crude fat, Rohasche = Crude ash, Rohprotein = Crude protein,

Rohfaser = Crude fibre, Stärke = Starch, Gesamtzucker = Total sugar

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