Swine production has been facing substantial economic challenges in recent years, due to poor crop yields and increased competition for raw materials from the biofuel industry. As a consequence, feed prices have been variable and more industrial by-products have become available. At the same time, we have experienced increasing sustainability demands on animal production, for example to reduce nutrient release in effluent, while producing more and cheaper food for an increasing world population. All this has driven the swine industry to implement more professional, accurate and precise practises.

1. S
wine production has been facing substantial
economic challenges in recent years, due to
poor crop yields and increased competition
for raw materials from the biofuel industry.
As a consequence, feed prices have been
variable and more industrial by-products
have become available. At the same
time, we have experienced increasing
sustainability demands on animal
production, for example to reduce nutrient release in effluent,
while producing more and cheaper food for an increasing world
population. All this has driven the swine industry to implement
more professional, accurate and precise practises.
With feed costs accounting for 50 to 80 percent of total variable
production costs, nutrition continues to be an area of major focus.
The key target for nutritionists is to provide the animal with the
correct amount of nutrients to support optimal performance. Both
excess and a lack of nutrients are likely to result in economic
losses, through higher costs and/or lower animal performance.
Thus, it is important for the nutritionist and raw material
purchaser to have correct information on the composition and
nutritional value of available ingredients. Accurate and regular
analysis of feedstuffs and complete feed, to confirm diets are
correctly formulated, is a key quality control measure.
To ensure consistency in diets, nutritionists traditionally
used proximate analysis from approved laboratories where
ingredients and feeds are analysed for their nutritional contents.
Unfortunately, the majority of these analyses are time-consuming
and expensive which restricts the number of samples that can
be analysed and creates a delay between sampling and receiving
results of the analyses. Alternatively, a Near Infra-red (1100-2500
nm wavelength) Reflectance spectrometer (NIR) can be used to
predict composition, as this technology is cost effective and fast.
This allows nutritionists to get almost immediate feedback on
in-coming ingredients and out-going feeds, and to analyse many
more samples at a much-reduced cost. However, NIR has much
greater potential uses in animal production. This article will
discuss the use of NIR in feedstuff analysis and diet formulation,
and opportunities to extend this technology beyond standard
analysis to support greater efficiencies in swine production.
Predicting feed composition
NIR can predict chemical and physical properties by relating
vibrational spectra obtained on a set of known samples to
reference analytical methods performed on the same sample set.
The resulting calibration
can be used to predict
the composition of
unknown samples of the
same type of materials.
NIR offers important
advantages over
traditional methods, in
that it is rapid, non-
destructive, requires no chemicals and hence produces no waste.
It is easy to operate, once calibrated, and requires minimal sample
preparation.
It is common practice for nutritionists to formulate diets with
average compositional data for ingredients, taking either a
book value or actual analytical data, and often a safety margin
based on the expected variability in the data. Safety margins
can vary, depending on the formulator and the feedstuff, usually
varying between zero (average data used) and one standard
Taking NIRbeyond feedstuffs
- analysis to enhance pork production profitability
by Hadden Graham, AB Vista Feed Ingredients and Chris Piotrowski, AB Vista Feed Ingredients and
Ming Yang Tan, Aunir Singapore
Table 1. Range in DE (MJ/kg as fed) and intake index (0-100) of cereal grains in pigs (from Black and Spragg, 2010)
As-fed basis Wheat Triticale Barley Sorghum Pearl millet1 Rice1
Faecal DE (kcal/kg) 12.8 - 15.1 11.3 - 14.6 10.8 - 14.7 14.1 - 15.2 13.9 - 14.4 14.5 - 14.6
Ileal DE (MJ/kg) 9.34 - 13.40 7.99 - 12.90 6.08 - 12.9 11.5 - 13.7 12.6 - 13.3 13.7 - 14.0
Faecal DE intake index 40.0 - 85.0 42.0 – 100.0 34.0 - 90.0 37.0 - 96.0 - -
42 | Milling and Grain
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2. deviation from the average. Adjusting the nutritional value of
the ingredient based on the standard deviation ensures that the
majority of feeds produced will provide the expected level or
higher of any nutrient.
NIR has been used in the feed industry for over 30 years, and
is now approved by the AOAC to determine moisture, nitrogen
(crude protein) and acid detergent fibre (ADF) in feed and
forages. However, there is some scepticism across the industry
regarding the accuracy of NIR to predict feed composition
relative to wet chemistry. Some of this is due to the use of poor
or inappropriate NIR calibrations, and some to poor sampling
techniques; NIR can only predict the composition of samples
similar to those used to develop the calibration, and the variation
can never be less than that of the methods used to provide the
data build the calibration.
It is common to assume that a wet chemistry result is always
better than a NIR result; however, Undersander (2006) reported
that when crude protein results differ, a re-run of the wet
chemistry agreed with the NIR 80 percent of the time. This
demonstrates that, as might be expected, there is less risk of
making a mistake when taking a NIR spectrum than when
running a laboratory analysis. However, the real advantage of
NIR is that it is cheaper and quicker to analyse a number of
samples for a range of analyses than to run one wet chemistry
analysis, giving the formulator a much more complete real-time
picture of the overall composition as well as variation within feed
ingredients.
Predicting nutritive value
Feedstuffs are usually purchased on the basis of parameters
such as test weight and crude protein content, both unrelated to
a greater or lesser degree to their value in feed. Consequently,
Rao (2012) indicated that approximately half of incidences of
poor performance in a US commercial broiler company were
related to the use of incorrect feedstuff nutritive values. The
traditional method of predicting the energy value of feedstuffs
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3. or feeds is to use any of a number of published equations to
calculate the productive energy from the analysed nutrient
content. These equations are usually developed from trials where
a diet of known composition was fed to the target animals and the
productive value, such as net or digestible energy, determined.
The weaknesses of this approach are well known; for example,
the assay methods used to develop the prediction equations
may be different from those used to analyse the feedstuffs in
question, and the feedstuffs or diets used in the animal trials may
not represent those used commercially. Further, animal trials are
prohibitively expensive and time-consuming. The production
advantages of accurate feed formulations, based on NIR analyses
rather than book values, in promoting extra broiler performance
was recently demonstrated by Soto et al. (2013).
Starting in 1996, a major research program has been undertaken
in Australia to develop NIR calibrations to predict the nutritive
value of commonly used feedstuffs across several animal species,
including ruminants, pigs and poultry. Close to 4000 cereal grain
and protein feedstuffs were surveyed, and over 350 of these were
fed to animals (>100 for swine) to determine available energy
and intake index as well as composition, reactive lysine and
standardised ileal amino acid digestibility (Black and Spragg,
2010, Black et al., 2014). The energy value (faecal DE) of cereals
for pigs varied within and between cereal types (Table 1), ranging
to as high as 4 MJ/kg for barley. It was estimated that, taking
cereals at US$250/t as an example, a 1 MJ/kg difference would
be worth between US$15-20/t in swine feed. With well over
100 million tons of cereals used in swine feeds per annum, this
equates to potential savings of several billion dollars to the swine
feed sector worldwide for energy alone!
Analysing all incoming feed raw materials, even by wet
chemistry, would be both time consuming and expensive and the
delay in receiving results would make this practically ineffective.
However, this Australian project has used animal data to develop
NIR calibrations to predict energy content and intake index (from
0 - 100) as well as composition, allowing incoming raw materials
to be quickly analysed and segregated on arrival at the mill. The
value of using NIR to determine the composition of in-coming
feedstuffs has recently been demonstrated by an integrated UK
company. By simply segregating in-coming wheat and soybean
meal into either high- and low-protein bins for each, this
company was able to save over US$3 per ton in feed formulations
as well as close to US$20000 per annum on wet chemistry costs.
As indicated above, extending this to the more variable energy
value would save much greater sums.
High phosphate prices, increasing environmental pressures
and more effective enzyme products have encouraged feed
manufacturers to increasingly replace inorganic phosphates
with phytases. However, the extent of phosphorus release by
phytases depends to a large extent on the phytate content of the
diet. As phytate levels can vary between and within feedstuffs,
it is difficult to accurately predict the phytate content of a final
feed. While several laboratory methods are available to determine
phytate levels in feeds, these are all relatively expensive and time
consuming. Recently, NIR calibrations based on an enzymatic
laboratory method were developed to give the real-time
prediction of the phytate content of feedstuffs and diets, allowing
feed manufacturers to maximise phytase inclusion and thus feed
cost savings (Santos and Bedford, 2012).
Delivering NIR services
Today NIR equipment is usually laboratory based and loaded
with appropriate calibrations. This presents some challenges; for
example, sample delivery to the laboratory can result in delays
that eradicate the advantages of speed of analysis. Further,
calibrations quickly become outdated; this requires updated
calibrations to be updated on a frequent and on-going basis.
Recent developments in NIR hardware have allowed the
production of robust, portable, battery-operated units. This
allows the analysis to be carried out at the point of interest, for
example at the grain silo or feed mill intake. Further, in-line
NIR equipment is currently available that allows feedstuffs to be
monitored during harvesting or feeds to be continually analysed
during production in the feed mill. Software and communications
developments have allowed web-enabled NIR services, where
spectra are downloaded to a master machine containing all
appropriate calibrations, with instantaneous feedback. This
has several advantages; for example the analyst can pay on
an “as-used” basis rather than paying a fixed up-front fee for
a calibration, independent of sample numbers. This can also
give the analyst access to a wide range of calibrations, and the
calibrations can be updated regularly as they essentially sit on
one computer.
Novel uses of NIR
Beyond the standard prediction of dietary composition, NIR
use has recently been extended into, for example, sample
identification. Work in other areas suggests that, providing
suitable standards are available, NIR can be used to confirm the
growing condition of feedstuffs. Another example of an extension
of NIR technology is the determination of mixer profiles in feed
manufacture. Mixer profiles are usually determined by analysing
the variation (percent CV) in components such as salt/sodium or
protein/nitrogen in 5-10 feed samples. However, this approach
will include the variation in the assay procedure used to analyse
the component chosen, and the result could thus be considered to
only apply to the specific component analysed. Thus, if sodium
is chosen, the mixer profile will reflect primarily the variability
in the dispersion of added salt. This can be overcome by looking
at the CV across the NIR spectra of a series of samples. The CV
as estimated from ten samples of feed taken from a mixer run for
1-5 minutes clearly shows an optimal mixing time of 3-4 minutes,
and that the NIR gives the same result as the analysis of specific
feed components, but with lower variability (Table 2).
NIR is currently used to analyse feedstuff and feed
compositions for quality control within the swine feed industry.
However, developments in hardware and software present the
possibility of using this technology to determine the value of
incoming raw materials as well as to control in-line and in real-
time the accuracy of feed formulations. This is potentially worth
several billion dollars in terms of feed cost savings and more
predictable animal performance for the worldwide swine industry.
In the future we can expect to see laboratory, hand-held and in-
line NIR equipment used widely in the purchase feedstuffs and in
feed manufacture.
References available on request.
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Table 2. Influence of mixing time on mixer profile (percent CV) as
determined by NIR or the chemical analysis of several feed constituents
Mixing time
(min)
NIR Sodium Crude Protein Crude fat
1.0 19.2 81.4 40.3 49.5
2.0 4.4 55.5 8.5 9.3
3.0 2.9 8.1 4.0 6.3
4.0 1.0 11.1 3.2 3.8
5.0 3.4 5.9 4.0 5.5
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