This repeatability in the reference chemistry, only the

This study presents
results concerning the use of FT-MIR spectroscopy to predict a set of plasma
parameters included in the metabolic profiles of horses. To build prediction
models that accurately predict parameters in unknown samples, the calibration
dataset should contain samples that represent all possible sources of variation
that can be encountered when the prediction model is used on an unknown sample.
For this reason, it is very important to select samples that will provide the
largest range of information for building the calibration dataset 7. The horses selected for this study
were all clinically healthy; consequently, the variability observed in the data
is representative of normal physiological conditions.

In the absence of clinical
signs of disease, individuals in a population can be characterized by the
presence of high variability in the concentrations of many blood biomarkers and
in the occurrence of subclinical conditions of disease (i.e., the presence of acute-phase
proteins). The concentrations of the blood parameters measured in the present
study were within the reference ranges reported for horses without clinical
signs of disease 21–23.

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The accuracy of infrared
analysis is affected by the quality of the reference assays used 24. Precision is associated with the
variability in the actual value being measured when an assay is replicated.
When a particular assay is run repeatedly on the same sample and the results
obtained have little variability, the assay is said to have high precision 25. Large variability in the observed
results indicates low assay precision. In our study the reference assays used
to analyze some blood parameters were characterized by not having optimal
repeatability between runs, with a CV greater than 10% for haptoglobin and
between 3 and 10% for creatinine and HDL and LDL cholesterol concentrations and for PON,
MPO, FRAP, ceruloplasmin, and GGT activityEd1 . For these blood parameters, the prediction
models where characterized by an RPD lower than 2, which is considered to be
the minimal threshold for acceptable approximate quantitative predictions 20. Among these blood variables that
do not exhibit optimal repeatability in the reference chemistry, only the prediction
models of HDL and LDL cholesterol could be considered nearly acceptable. Improvement
in the reference assays for all these blood parameters that do not exhibit optimal
repeatability could improve the predictive ability of the prediction model
based on FT-MIR spectroscopy.

With the
exception of inorganic P, the prediction models of all the minerals analyzed in
this study were characterized by poor prediction ability. Similar results were
observed, mainly for electrolytes, in our previous study on plasma from dairy
cows 8 and in studies on cow milk. In
particular, the concentrations of Mg, Na and K were poorly predicted by FT-MIR
spectroscopy 26. In contrast, Soyeurt et al. 26 and Toffanin et al. 27 have suggested the potential of
FT-MIR spectroscopy to predict Ca and P content in cow milk. Because the
correlations between Ca and P concentrations estimated by the FT-MIR
predictions and the known milk components were inferior to the correlation
calculated based on the cross-validation, Soyeurt et al. 26 concluded that these calibration
equations were obtained from a real spectral absorbance. Consequently, the
prediction of Ca and P concentrations in milk by using FT-MIR spectra is related
to the real spectral absorbance and not estimated on the basis of the
correlation of these concentrations with those of other known milk components
also predicted by FT-MIR.

previously observed 8, the current study still confirms the
difficulty in predicting mineral content in plasma with FT-MIR spectroscopy,
particularly minerals that are not included in organic compounds. The difficulty
of predicting mineral content in blood observed in our study is likely due to
two main reasons: First, among the minerals measured in the blood, a large
proportion were in ionized form and were not included in organic compounds. Approximately
50% of total Ca in plasma is in the ionized form, and approximately 45% is
linked to protein, while approximately 70% of Mg is ionized 28. Second, in this study, and as
previously observed in dairy cow plasma 8, the variability observed for the
minerals in our dataset was lower compared to the variability observed for
other blood parameters. As previously stated, the development of better prediction
models requires a dataset containing samples with greater variability, leading
to the improvement of the predictive ability of FT-MIR spectroscopy. To
increase the variability of these indices, it would be useful to include, in
the population, some animals with clinical diseases or physiological conditions
that alter the mineral blood concentration.

prediction models of the enzymatic activities measured in this study had poor
predictive ability. As indicated in our previous work 8, the inadequate estimation was
probably due to the non-normal distribution of the data, with a very low
proportion of high values in this dataset. The logarithmic transformation did
not improve the predictive ability of the prediction models. A better
assessment of enzymatic activities can be obtained by using a database with
greater variability; in particular, a representative number of samples with
high values could improve the predictive ability of FT-MIR spectroscopy.
Moreover, the activities of the enzymes were measured with the reference
methods, but the absolute concentrations of the enzymes were not quantified. It
is therefore possible that discrepancies exist between the amount and activity
of the enzymes.

Excellent prediction ability was obtained for several of the parameters, mainly parameters
associated with energy and protein metabolism. Among the energy-related
parameters, an excellent calibration curve was obtained for total cholesterol, with
RER and RPD values greater than the threshold values (10 for RER and 3 for RPD)
suggested for excellent prediction 18,20. Acceptable results were also obtained for
cholesterol fractions and for triglycerides. Among the parameters associated
with protein metabolism, excellent prediction models were obtained for total
protein, albumin, globulin, and urea. Very high RER and RPD values were
obtained for total protein and protein fractions. These results confirm those
obtained in our previous study on the plasma of dairy cows 8. The results obtained with FT-MIR for the total
protein and protein fractions are particularly interesting given that Fourier-transform
infrared spectroscopy is a widely used tool in many different fields and is even
used to evaluate secondary and tertiary protein structure 29,30, to study differentiation of plasmin and
plasminogen in milk 31, and to evaluate secondary and tertiary
structural changes in bovine plasminogen 32, and those in casein 33 and casein fractions in goat milk 34. The ability of FT-MIR to predict the concentrations
of protein fractions (i.e., albumin and globulin) offers the opportunity for
more extensive evaluations than just the evaluation of protein metabolism. In fact,
plasma albumin concentration is determined by the hepatic synthetic rate, which
is normally in equilibrium with degradation. As indicated by Tennant and Center
35, hypoalbuminemia may be caused by defective
albumin synthesis associated with severe hepatocellular disease or may be
caused by increased albumin loss resulting from glomerulopathy (protein-losing
nephropathy), severe intestinal inflammation, or inflammatory conditions 36. In acute or chronic inflammatory conditions, a
rise in total protein concentrations caused by elevated globulin fractions may
occur; albumin concentrations are often decreased in these situations. The
combined effect of these changes is a decrease in the albumin/globulin ratio. Often,
the total protein concentration is within the reference range, whereas the albumin/globulin
ratio is decreased; therefore, the albumin/globulin ratio is of greater
clinical significance than the total protein concentration 37. In severe, chronic hepatopathy, concentrations
of IgM, IgG, and IgA tend to be elevated. Both decreased albumin and increased
globulin result in a decrease in the albumin/globulin ratio 35. Therefore, FT-MIR also offers the possibility,
by the prediction of the concentrations of protein fractions, to obtain
information associated with the health status and welfare of animals and to
diagnose many different problems, including conditions associated with the liver,
kidney and gastrointestinal tract and inflammatory conditions.

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