Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.

We demonstrate that set-dependent multiplicative scatter correction and set-independent standard normal variate transformations of NIR spectra are linearly related as theoretically expected. It is shown that the mean and standard deviation of the set-mean-spectrum together with the correlation coefficient between each individual spectrum and set-mean-spectrum are required to link these two transformations. It is through these three quantities, that set-dependency is incorporated into spectra derived by application of multiplicative scatter correction. MSC and SNV are two alternative approaches to reduce particle size effects and they are interconvertible.

The aim of this paper was the application of principal component analysis (PCA) 1) to elucidate mutual metabolic relationships between milk fatty acids (FA) and 2) to illustrate the origin of milk FA, in particular C17:1 and cis-9,trans-11 conjugated linoleic acid. Data were combined from 3 experiments with lactating Holstein-Friesian cows offered diets based on grass or legume silage and concentrates. Loading plots of PCA based on milk FA concentrations showed 4 groups of milk FA, having similar precursors or metabolic pathways in the rumen and/or mammary gland: medium-chain saturated FA, de novo synthesized from acetate and beta-hydroxybutyrate; monoenoic milk FA, products of delta9-desaturase activity in the mammary gland; odd chain FA of rumen microbial origin and C18:0, n-6 C18:2, and n-3 C18:3 of dietary origin or the result of rumen biohydrogenation. Loading plots of PCA based on both milk and duodenal FA concentrations as well as on milk FA yields and duodenal FA flows further illustrated the importance of postabsorptive synthesis of the milk medium chain saturated and monoenoic FA and the direct absorption from the blood stream of odd chain FA, C18:0, n-6 C18:2, and n-3 C18:3. In all loading plots, milk oleic acid (C18:1) appeared intermediate between clusters of 18-carbon FA and monoenoic FA, illustrating its dual (dietary and endogenous production) origin. Milk C17:1 was suggested to be a desaturation product of C17:0, in common with other milk monoenoic FA. Finally, the PCA technique, based on milk FA patterns of one experiment, was applied to investigate factors determining cis-9,trans-11 conjugated linoleic acid concentrations in milk. Within the range of diets and cows studied here, we showed changes in cis-9,trans-11 conjugated linoleic acid to be mainly dependent on vaccenic acid supply and to a lesser extent on variation in desaturase activity.

The nutritive value of 17 straws was determined on the basis of their chemical composition, in vitro dry matter (DM) digestibility and rumen fermentation kinetics (from gas production curves measured in vitro). Five roughages were from the cereal species Avena sativa (oat), Hordeum vulgare (barley), Secale cereale (rye), Triticum aestivum (wheat) and Zea mays (maize stover). The other 12 samples were legume straws, two samples from each of the species Cicer arietinum (chickpea), Lens culinaris (lentil) and Phaseolus vulgaris (bean) and one sample from each of the species Lathyrus sativus (chickling vetch), Lupinus albus (white lupin), Pisum sativum (field pea), Vicia articulata (oneflowered vetch), Vicia ervilia (bitter vetch) and Vicia sativa (common vetch). All samples were collected after harvesting from different farms located in León (northwestern Spain). Based on their chemical composition, digestibility and gas production characteristics, species could be clustered into two groups with a significant linkage distance, one for cereal straws that merged at a level of similarity of 80% and the other for legume straws with a degree of similarity of 50%. Species varied widely and significant differences (P < 0.05) were observed between the two groups of straws. Legume straws showed higher crude protein (74 ± 6.1 vs 29 ± 2.2 g kg −1 DM) and lower fibre (584 ± 18.1 vs 793 ± 27.5 g neutral detergent fibre kg −1 DM) contents than cereal straws and, consequently, DM digestibility coefficients (0.670 vs 0.609; standard error of difference 0.0054) and metabolisable energy values (7.4 ± 0.15 vs 5.7 ± 0.24 MJ kg −1 DM) were significantly greater in legume than in cereal straws. Although there were noticeable differences among species within each botanical family, legume straws showed better nutritional quality than cereal straws, indicating that they could be considered promising and interesting sources of roughage for incorporation into ruminant diets.

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.