Generalized two-dimensional (2D) correlation spectroscopy has been applied to analyze near-infrared (NIR) spectra of milk with different protein and fat concentrations. The NIR spectra of milk show rather poor signal-to-noise ratios compared with those of a protein or fat solution and have changing baselines from one spectrum to another. Poor signal-to-noise ratio and variations in baseline are common problems for NIR spectra of real-world samples. This study aims at expanding the utility of generalized 2D correlation spectroscopy to complicated multicomponent biological systems. In order to overcome the above two problems, we have employed multiplicative scatter correction (MSC) and smoothing as pretreatment procedures of the milk spectra selected for the calculation of 2D NIR correlation. 2D synchronous correlation spectra in the 2000–2400 nm region constructed from protein or fat concentration-dependent spectral changes of milk sharply enhance bands assignable to proteins or fats, respectively. It has been found that a power spectrum along the diagonal line in a synchronous spectrum very effectively shows the contribution of a particular component to the NIR spectra of milk. In fact, for example, the power spectrum for the fat concentration-dependent spectral changes of milk is very close to an NIR spectrum of fat itself. Two-dimensional asynchronous correlation spectra demonstrate the existence of bands that cannot be identified even by calculation of second derivatives and chemometrics analysis of the spectra. The asynchronous spectra also elucidate interaction between fats, proteins, and water.

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T wo-dimensional (2D) corre l ation spectro s c o py has recently received keen interest because it is a totally n ew and powerful spectral analytical method [1][2][3][4]. The 2D correlation spectroscopy enables us to obtain spectral information not readily accessible from one-dimensional spectra by spreading spectral peaks over the second dimension [1][2][3][4][5][6][7]. Band assignments and studies of inter-and intramolecular interactions become easier by selective correlations between various bands in synchronous and asynchronous 2D correlation spectra. Probing the specific order of the spectral intensity va ri ations is also possible by inspecting the asynchronous 2D spectra. Two-dimensional (2D) correlation analysis hasbeen applied to analyze protein and fat concentration-dependent near-infrared (NIR) spectral variations of milk. Synchronous and asynchronous 2D correlation spectra of milk enhance spectral resolution and provide information about concentration-dependent intensity changes not readily accessible from one-dimensional spectra. The asynchronous 2D correlation map shows marked differences between the protein and fat concentration-dependent spectral changes.

This study aimed to estimate by near infrared (NIR) spectroscopy the microbial nitrogen content (MN) of feed residues from in sacco degradability trails and duodenal digesta of sheep. NIR spectra from 50 samples of duodenal digesta, and from in sacco residues-110 samples of alfalfa hay and 38 samples of maize silage were obtained using an NIRSystems 4250 spectrophotometer. The microbial nitrogen (MN) content of part of the alfalfa hay in sacco residues (78 samples) was calculated from the percentage of 15 N enrichment compared to enrichment in the original samples; for the rest of the alfalfa samples and samples of maize silage residues were determined by diaminopimelic acid (DAPA) as a bacterial marker, and MN of duodenal digesta samples by the purine N (RNA equivalent) content as a microbial marker. The calibration equations were developed by modified least squares as the calibration method. The microbial content of all kinds of samples was accurately calibrated and crossvalidated. A standard error of cross validation (SECV) of 0.418 g microbial N kg -1 DM, a coefficient of determination for the cross validation of 0.925 and a ratio of standard deviation of population and the SECV of 3.88 were obtained for the alfalfa 15 N labelled hay residues. For maize silage residues, the corresponding values were 0.832, 0.938 and 3.90, and for duodenal digesta samples the values were 1.05, 0.962 and 5.19, respectively. Prediction of MN as percentage of total N of the samples gave approximately the same level of accuracy. For example, the SECV was 2.35% units, cross-validation R 2 was 0.953, SD/SECV was 4.60 for alfalfa 15 N labelled hay residues. Despite the different origin of the analysed samples (feed residues and duodenal digesta), the NIR spectroscopy determination of MN content of all samples was based on spectral data at very similar wavelengths. The study indicated that NIR spectroscopy has the potential to predict microbial nitrogen content and to distinguish MN from total N content of in sacco feed residues and duodenal digesta.