12th Annual Carolina Swine Nutrition Conference, November 13th, 1996

NIR technology: Can we measure amino acid digestibility and energy values?

Theo van Kempen, Rhône-Poulenc Animal Nutrition, Antony, France

Bottom line:

The NIRS may be used as a tool to predict the digestible amino acid and likely the energy content of feedstuffs. Such information is useful from a quality control perspective to find outliers in incoming raw material and to rank batches of feedstuffs based on nutritionally relevant parameters; for a nutritionist, such information may be used, in combination with laboratory data, to reformulate diets.

Introduction

The feed industry requires rapid and accurate information concerning the nutritive value of feedstuffs. On one hand, such information is needed to negotiate the proper price for a feedstuff, and on the other hand, to correctly include this feedstuff in a complete diet such that the animal’s nutrition requirements are met with the lowest feed cost. Preferably, such information is obtained rapidly so that a routine evaluation of a feedstuff is possible.

Currently, many feedmills use NIRS technology to predict the protein, moisture, fat, and ash contents of feedstuffs to obtain the needed information on a feedstuff. However, these parameters are of limited value since they only correlate to a limited extend with the true nutritional value of a feedstuff. Better parameters to evaluate are the amino acid availability, and the net energy of a feedstuffs.

The objective of this paper is to review the basics of NIRS and the potential of this technique as a tool to routinely evaluate feedstuffs for nutritionally relevant parameters.

Part I: Understanding NIRS

Color

To understand the basic principle of NIRS, it is necessary to understand the basis of color. White light is composed of all the colors in the rainbow. When this white light falls onto an object, certain colors within this spectrum are absorbed by the object, while the rest is reflected (or transmitted). The light reflected can be observed by the eye, and its composite remaining color is interpreted by the eye as the color of the object.

As an example, when sunlight falls onto a clover leaf, both the red and the blue light are absorbed to aid in photosynthesis. The other colors are reflected, and are interpreted by the eye as green.

Physics behind light absorption

The physical basis of light absorption is linked to the nature of molecular bonds and to the nature of light. Light consists of photons traveling all at a fixed speed. While traveling, these photons exhibit a ‘wave’ motion around the path of travel. This wave motion is specific for each color of light, and is often expressed as the frequency (the number of waves traveled per second), or as the wavelength (the distance traveled while completing 1 ‘wave’).

Molecular bonds are the links between atoms within a molecule. These bonds are not static connections, since the atoms they connect vibrate relative to each other. This vibration causes the molecular bond to stretch and compress, resulting in a wave motion of the two atoms relative to each other with a frequency specific to the atoms involved.

When light hits such a vibrating bond, two situations may arise:

1) When the frequency (‘color’) of this light does not match the frequency of the vibration of the molecular bond, then this light is reflected

2) When the frequency (‘color’) of this light matches the frequency of the vibration of the molecular bond, then this light is absorbed, and its energy is used to intensify the vibration (the objects becomes warmer).

Light absorption by feedstuffs

Feedstuffs are made up of mainly organic matter. The molecular bonds which are most common in a feedstuff are thus bonds between hydrogen, carbon, oxygen, sulfur, phosphorus, and nitrogen. The frequency of the vibration between these molecules is such that these bonds generally absorb light in the near-infrared region, or the region which extends just beyond red in the rainbow (not visible for humans).

NIRS uses the principle that molecular bonds absorb specific frequencies of light to obtain information about the number and type of organic bonds present in a feedstuff; in other words, by ‘looking’ at the feedstuff’s color in the near-infrared region.

NIRS Equipment

The principle of a NIRS machine is that a feedstuff is illuminated with light of a specific and known frequency (or wavelength) in the near-infrared region. The absorption of light by the feedstuff is then measured as the difference between the amount of light emitted by the NIRS machine and the amount of light reflected by the sample. The machine layout is explained in Figure 1.

Fig. 1. Schematical layout of NIRS machine. Light is focused (1) onto a concave mirror (2), which separates the light into its composing wavelengths (3). One wave-length is selected (4), and falls onto the feedstuff (5). The amount of this light which is reflected by the feedstuff is measured (6) to obtain the absorption corresponding to one wavelength. By changing the orientation of the mirror (2), different wavelengths of light are selected to obtain absorption measures for all wavelengths of interest. The graph depicting the wavelength tested versus the absorption is called the spectrum, and an example spectrum for coconut meal is presented in the top right corner (7).

When it is known at which frequency specific molecular bonds vibrate then it is possible to deduce from a spectrum the composition of the molecular bonds present in a sample evaluated. Using NIRS to identify simple molecules works indeed that easily.

The NIRS spectrum presented in Fig. 2 is that obtained from DL-methionine. The peaks present in this spectrum are related to the C-H bonds, and tables exist to relate the wavelength at which these peaks were found to the chemical composition of the sample. Spectral information can thus be used to determine the identity of samples.

Fig. 2. NIRS spectrum of methionine. The peaks correspond to C-H bonds in the sample.

Interpretation of feedstuff spectra

Unfortunately, for feedstuffs the picture is more complicated. A large number of molecular bonds are present in a feedstuff, and at any given wavelength, many bonds will absorb light. The resulting spectrum is thus a ‘composite’, where small peaks caused by a specific molecular bond are non-distinguishable (see Fig. 3). This lack of distinguishable peaks means that it is impossible to deduce the composition of a sample from spectral information using tables which indicate which wavelength corresponds with which type of molecular bond.

Fig. 3. NIRS spectrum of soybean meal. No specific peaks are present which allow for a direct quantification of the sample’s composition.

Instead, to predict the composition of feedstuffs it is necessary to make calibrations. A calibration is simply NIRS spectral information correlated with composition values (obtained by, e.g., wet chemistry). This concept is most easily explained by a fictitious example (see Fig. 4). Let us imagine the spectra obtained for 3 soybean samples which are identical except for their protein content. The protein content of these samples was either 44%, 46%, or 48%. In this example, the spectra from these samples deviates only in the region of 1900-2000 nanometer (nm; the wavelength of the light), and this difference must be related to the differences in protein content of the sample, since the samples were otherwise identical.

Plotting the absorption at 1950 nm for each spectrum against the protein level shows that a linear relationship exists between the absorption of light at 1950 nm and the protein level. It is easy to image that such a correlation could be used to predict the protein content of a fourth soybean sample.

In reality, spectra are much more complicated. Protein does not absorb at only one region of the spectra (as seen for methionine in Fig. 2). Additionally, when protein levels increase in a sample, other compounds, e.g., carbohydrates, must decrease, and thus other regions of the spectrum must change. The spectra of the three samples should thus be different in several regions, and it is not so easy to identify a region of the spectrum in which absorption correlates with the protein content of the samples. Nevertheless, this approach is basically the outlined procedure which is used to determine the correlations between spectral information and chemical composition, which are then subsequently used as calibrations to predict unknown samples.

Fig. 4 Fictitious example to illustrate the principle of calibrating: Spectral information is correlated with analytical information (see text for details).

Such calibrations have been successfully applied in the feed industry to predict parameters such as protein, moisture, NDF, ADF, etc. The main reason for this success is the rapidity of the measurement: values can be obtained within 5 minutes of receiving a sample.

Calibration validation

In the process of calculating correlations, the NIRS software has no notion of the functionality of the correlations it determines. When a spectral peak caused by the presence of carbohydrates happens to correlate (negatively) with the protein level in the reference samples, then a calibration might well be developed based on this carbohydrate peak.

Such a calibration is not very useful. Although a relationship exists between carbohydrates and protein in the reference population, this relationship does not hold in the general population. It is thus essential to prevent the use of such calibrations in real-life applications.

In order to improve the chance of developing valid calibrations, the reference database should contain the maximum amount of variation. In practical terms, at least 100 different samples should be used. These samples must be representative of the population to be predicted in the future, and should thus be obtained from different harvest years, the geographical regions from which this feedstuff is obtained, and cover all the genotypes of interest.

The actual functionality of a calibration should be tested in a process called validation. Ideally, a number of samples covering the range of samples which can be encountered in real life are used. All these samples are predicted using the calibration to be tested, and analyzed with the reference technique to obtain the true values. The difference between the predicted and true value is used to calculate the root mean square error of prediction (RMSEP), which is a measure for the accuracy of the calibration.

However, this exercise is tedious and costly, and because of this, NIRS software developers have included ingenious validation procedures in their software. It never hurts, though, to check the validity of a calibration for yourself.

Part II: NIRS to predict the energy content of feedstuffs

Several research groups have developed NIRS calibrations for energy values, however, the majority of this work has been focused on ruminants. Energy predictions for forages are common and are utilized commercially, which suggests that energy can be predicted with an adequate precision (DeBoever et al. 1993). However, the energy content of ruminant feeds is often determined by proximate analyses in combination with some factor of rumen or in vitro digestibility, rather than by in vivo assays.

For poultry, apparent metabolizable energy (AME) predictions were obtained with a prediction error of 80 kcal/kg feed by Valdez and Leeson (1992). However, in the sample population tested by Valdez the AME value of the feedstuffs was linearly dependent on the fat content of the test diet. The utility of NIRS to predict the fat content has been well documented, but unfortunately this does also imply that the calibration generated is not necessarily effective in predicting AME in cases where the fat content does not correlate with the AME content of a diet. Flinn et al. (1994) obtained a prediction error for AME ranging from 90 kcal/kg DM for cereals, to 275 kcal/kg for mixed feeds. This prediction error, however, appears close to the normal variability observed.

For pigs, digestible energy calibrations were developed by Van der Meer et al. (1988). This paper, though, only lists the standard error of the calibration (a measure of how well the calibration describes the population used to make the calibration), which is of little value for the true prediction error of a calibration. The success of NIRS in the ruminant area suggests, nevertheless, that the NIRS is capable of predicting energy values for feeds or feedstuffs for swine. In addition, the theoretical basis of NIRS suggests that energy predictions should be achievable. Until a serious attempt to do so is published, though, it remains to be seen if it is feasible in real life.

Part III: NIRS to predict the digestible amino acid content of feedstuffs

Calibration development

Since 1981, Rhône-Poulenc Animal Nutrition has tested feedstuffs in poultry for ileal amino acid digestibility according to the procedure of Green et. al (1986). Most samples used in these tests have been retained in cold storage. Currently, a large database exists containing samples from virtually every category of feedstuffs that would be of interest to the feedstuff industry and derived from many countries around the world.

The samples contained in this database were used for the development of NIRS calibrations. Course samples, such as grains, were ground using a Retch grinder (model ZM1000, Haan, Germany) with a 1 mm screen, while the other samples were scanned as-is. Prior to scanning, samples were acclimated to the NIRS room temperature of 23°C. Scans were made using a NIRSystems model 6500 NIRS (Perstorp Analytical, Höganäs, Sweden; a complete setup costs in the order of $75000) equipped with a natural product cell. These scans were subsequently correlated to the ileal digestibility data obtained in vivo.

The validation procedure used to test calibrations is as follows: one sample was deleted from the database, after which a calibration was developed using all other samples in the database. This calibration was subsequently used to predict the deleted sample. This procedure was repeated for all the samples in the database, and the difference between the true and predicted values was used to calculate the prediction error (RMSEP or Root mean square error of prediction).

Calibrations were developed and tested for either specific feedstuffs (e.g., only meat & bone meal), feedstuff categories (e.g., all animal protein products, including e.g., fish meal, meat & bone meal, etc.), or a global database (containing all feedstuffs). In principle, a calibration based on only one feedstuff yields the best results. However, this requires that sufficient samples are present in the database to cover all possible variation. Including samples from a different category increases the variation, and may improve the prediction accuracy of the calibration.

Table I. Root mean square errors of prediction (RMSEP; g/100g) and derived r²_rmsep values for meat & bone meal within the global (based on all feedstuffs), product group (based on feedstuffs of animal origin), product calibration (based on meat & bone meal samples). (The three-letter amino acid code followed by a ‘P’ indicates ‘poultry digestible’; nd=not determined)

	Lys	LysP	Met	MetP	Thr	ThrP
	Population statistics:
mean	2.85	2.21	0.85	0.70	1.87	1.42
stdev	0.74	0.62	0.26	0.21	0.42	0.35
	Global calibration:
RMSEP	0.23	0.30	0.14	0.13	0.12	0.14
r²rmsep	0.90	0.77	0.72	0.63	0.92	0.80
	Animal meal calibration:
RMSEP	0.26	0.31	0.14	0.13	nd	0.16
r²rmsep	0.87	0.74	0.69	0.62	nd	0.80
	Meat & bone meal calibration:
RPSEP	0.32	0.35	0.14	0.13	0.13	0.16
r²rmsep	0.81	0.67	0.72	0.62	0.90	0.79

The validation results for meat & bone meal samples for the three different database types tested are provided in Table I. The RMSEP (expected prediction error) and r² values (variation explained by the calibration) as presented in this table were equal or better (lower for RMSEP, higher for r²) when using the global calibration in comparison with the animal meal or meat & bone meal calibration. For the other animal meals (fish meal and poultry byproducts), similar results were obtained (results not shown). Based on this database comparison test, it can thus be concluded that for animal meal samples, the global calibration outperformed the other calibrations.

For products with lower protein levels, such as cereals and oilseed meals, product and product group calibrations performed better than the global calibration (data not shown).

These data illustrate that it is important to consider which database is used to obtain a calibration. Since the global database performed best for the animal meal samples, a global calibration was subsequently developed for all total and essential amino acids. The calibration and validation statistics for a part of this calibration are shown in Table II.

Table II. Population statistics and performance (g/100g) for a part of the calibration developed using the ‘global’ database. (The three-letter amino acid code followed by a ‘P’ indicates ‘poultry digestible’.)

	ISI statistics		validation
	SECV	1-VR	RMSEP	r²rmsep
Lys	0.19	0.98	0.25	0.97
LysP	0.23	0.97	0.29	0.94
Met	0.09	0.96	0.11	0.94
MetP	0.09	0.95	0.10	0.94
SAA	0.14	0.96	0.19	0.94
SAAP	0.14	0.93	0.18	0.90
Thr	0.10	0.99	0.13	0.98
ThrP	0.12	0.97