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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
Janine Hahne, B.Sc.
Unity Scientific GmbH
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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