General aspects
Just as a calibration curve is necessary for determining the protein or P content of a
sample, it is necessary to develop a calibra- tion for the determination of solid feed composition by NIRS. However, the two procedures are as different as the samples in question. In the case of protein or P in solution, for all intents and purposes one has a single analyte (protein or P content) changing in an otherwise constant media (water, buffers, etc.). Also, the analyte at best represents a very small percentage of the total system. When using NIRS to determine feed composition (i.e. neutral and acid detergent fibre (NDF and ADF), crude protein (CP), lignin, etc. (Van Soest, 1994)), one has a solid medium (feed itself) in which the analyte (NDF, etc.) is not only the object of the determination, but also constitutes a significant fraction of the medium itself. Also, unlike the earlier calibrations for protein or P in which the media (water, buffers, etc.) are generally non-absorbing at the wavelength chosen,
with feeds the media consists of other com- ponents of the feedstuff which also have absorption bands in the NIR, and generally at the same wavelengths as the component of interest. This can be seen in Fig. 9.3, where the NIR spectra of casein and cellu- lose are shown. For a hay or silage, the NIR spectrum thus consists of overlapping spectra of the many different constituents present, each one different, but, like cellu- lose and casein, often having absorptions in the same or similar regions. Except for pure and simple materials such as acetone, spectra do not consist of sharp, individual peaks. Thus, developing a calibration becomes a very complicated process. As a result, calibration development has resulted in NIRS becoming almost synony- mous with the terms multivariate statistics and chemometrics.
Multivariate simply means that there are multiple variables (wavelengths in the Fig. 9.1. Diffuse reflectance for a ground solid showing: (a) reflection from the cell cover; (b) specular reflection from the sample; (c) absorption and diffuse reflection from the sample; and (d) total absorption by the sample.
case of NIRS) involved in finding a calibra- tion equation for the constituent of interest.
The field of using multivariate statistical procedures for the quantitative and qualita- tive analysis has come to be known as chemometrics, and is an integral part of utilizing NIRS.
Since the constituent of interest is both the analyte being determined and part of the media or matrix, and since all the other constituents in the sample are most probably also changing in relative amounts from sample to sample, it is extremely important that the proper samples be used when developing a calibration. Because all the organic constituents in a feed sample are likely to absorb NIR radiation at all wavelengths to at least some degree, and because all or most of the constituents will vary simultaneously in relative amounts present, one determines the values for a given constituent not only in terms of itself, but also in terms of everything else present. As a result, the structure of a data set is probably the most important part of developing and utilizing NIRS, and the
misunderstanding of this point probably leads to more problems and disillusion- ment with NIRS than anything else.
Sample sets and calibration development In using a single wavelength, it is easy to see that anything (other than changes in the analyte) that might alter the absorbance will result in some error in the determina- tion of the analyte. For example, in Fig.
9.4, if spectrum A were to be shifted to the right to give the results in B (temperature changes can easily cause such peak shifts), then the absorbance value read at the wavelength where the peak should be would result in an incorrect value for the analyte. In Fig. 9.4C and D, we see the effects of two kinds of baseline shift. In Fig.
9.4C, the entire spectrum has shifted upwards (this is very common in NIR spec- tra due to particle size differences between samples), resulting in higher absorbance values, although a simple baseline correc- tion can be made. In Fig. 9.4D, the baseline
Absorbance
1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm)
High-quality lucerne hay
Wheat straw
Fig. 9.2. NIR spectra of high-quality lucerne hay and wheat straw.
is tilting from left to right, also resulting in incorrect absorbance values, but, in this case, the error varies in intensity from 0 on the left to a maximum on the right side of the peak. This type of error is called multi- plicative scatter (Isaksson and Kowalski, 1993) and is also common among ground feed samples due to particle size and absorbance differences, but requires more than a simple baseline shift for correction.
In reality, as opposed to this simplification, multiplicative scatter is a function of the absorbance values (i.e. the greater the absorbance the larger the shift). Finally, in Fig. 9.4E, we see the result when a second component is present (F), and the resulting spectrum E is a combination of the two component spectra (A and F). In this case, using the absorption at the peak also gives incorrect results. This could occur if, for example, a sample was tested using a cali- bration built from samples which had no component F. Since no F was ever present in the calibration set, there would be no compensation built into the calibration for it. This, combined with the fact that virtu-
ally anything different about samples (physical or compositional) or the condi- tions under which spectra were obtained (instrumental, environmental, etc.) can alter NIR spectra and thereby influence the accuracy of a calibration, probably explains more about the nature and problems of NIR calibration development, maintenance and use than anything else. Since NIR calibra- tions often use many wavelengths, one can imagine the total effect of many such spec- tral alterations on a calibration.
Although developing a calibration or equation to determine the composition of feed samples from their NIR spectra, one is trying to find relationships between the spectra obtained and the component of interest (NDF, ADF, etc.) in order to deter- mine the composition of new samples using only their spectra. Therefore, any- thing which is different between the spectra used to develop the calibration initially, and the spectra of future samples, which is not due only to compositional variations which have been included in the calibration, can result in errors. A list of
Absorbance
1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm)
Multipoint baseline corrected
Cellulose
Casein
Fig. 9.3. Baseline-corrected NIR spectra of cellulose and casein.