David S. Himmelsbach

Two-Dimensional Vibrational Spectroscopy II: Correlation of the Absorptions of Lignins in the Mid- and Near-Infrared:

Two-dimensional (2D) statistical correlation of mid- and near-infrared spectra was used as a means to assist with qualitative spectral interpretation of the lignin carbohydrate fractions from plant materials. Cross-correlation by least-squares is used to assess changes in both regions that result from changes in the set of sample spectra. The technique has been applied to a set of specially prepared “lignin” samples that differ in lignin content, species of origin, and method of preparation. Dispersive near-infrared (NIR) and Fourier transformed mid-infrared (FT-IR) diffuse reflectance spectra (DRIFTS) were obtained on each of the samples, and point-for-point 2D cross-correlation was obtained. The technique permits the correlation of the combination and overtone bands of the NIR region with the fundamental vibrations in the mid-infrared (MIR) region. The results show a broad correlation of aromatic C-H stretch at 3022 cm−1 in the MIR to 1666-nm location in the NIR region and other locations to a lesser extent all across the NIR from 1400 to 2500 nm. A narrower region of correlation was found for the phenolic O-H stretch in the MIR at 3663 cm−1 to 1428 nm and 1938 nm in the MIR. Multiple narrow correlations occur in the area of the summation bands from 1450 to 1650 cm−1, indicative of substitution patterns and correlated areas of C-H and O-H stretch in the NIR.

Chemical, microscopic, and instrumental analysis of graded flax fibre and yarn

A series of flax fibre and yarn samples that had been commercially graded low, medium, and high quality were analysed by light microscopy, wet chemical analysis, Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy to determine characteristics which could be related to quality ratings for each sample type. Light microscopy revealed fragments of cuticular and epidermal material bound to the fibres. As the quality ratings improved, fewer of these fragments were found and greater separation of the fibre bundles to smaller bundles and, in some cases, elementary fibres occurred indicating more efficient retting. Chemical evaluation showed that, as quality of the yarns increased, amounts of fatty acid and long-chain alcohols as well as dihydroxy fatty acids decreased. Chemical data on fibre did not show consistent trends with quality. Raman spectroscopy showed increasing amounts of cellulose and decreasing amounts of aromatics and hydrocarbons with increasing quality, which paralleled the chemical data. NMR analysis showed nearly equal amounts of crystalline cellulose regardless of quality for both fibre and yarn samples. The strengths and weaknesses of each analytical method are discussed. This initial study suggested that chemical constituents characteristic of cutin and waxes could be used as an initial marker of quality. © 1999 Society of Chemical Industry

Two-dimensional Fourier transform Raman correlation spectroscopy determination of the glycosidic linkages in amylose and amylopectin.

Amylose and amylopectin are two major carbohydrates in cereal and cereal food products. Both of these polysaccharides have complex conformations that affect their physical, chemical, and biological activities,1 despite the fact that they are mainly made up of a-(1→4)-linked Dglucose residues. Generally, amylose has been considered to be a linear polymer through a-D-(1→4) glycosidic linkages (Fig. 1), although now there is evidence that amylose is not completely linear. Amylopectin is a branched polymer, and a branch point occurs approximately every 20–25 glucose units when a chain of a-D(1→4) glucose units is linked to the C-6 hydroxymethyl position of a glucose molecule through an a-D-(1→6) glycosidic linkage. Thus, about 4–5% of the glucose units in amylopectin are involved in branch points. Clearly, both the relative proportions of amylose to amylopectin and a-D-(1→6) branch points depend on the source of the starch.2 Raman spectroscopy has found considerable applica-

Two-Dimensional Vibration Spectroscopy of Rice Quality and Cooking

ABSTRACT Rice samples were taken from a study of rice milling properties that affect quality. The spectra of milled and cooked samples were taken in the near-infrared, mid-infrared, and Raman region. These spectra, two regions at a time, were regressed by a two-dimensional technique to develop contour maps that indicated the correlation of two spectral regions. These relationships demonstrate that it is possible to recognize the hydration effects caused by gelatinization (cooked samples vs. milled rice). Three water (O-H stretch) spectral bands (960, 1445, 1,930 nm) in the near-infrared (NIR) show marked differences between milled and cooked rice. The difference spectra indicated that there were additional phenomena occurring besides the addition of water. These differences are apparent in both C-O-H and N-H bands, which indicate that water is interacting with both starch and protein. The two-dimensional technique developed in this laboratory was used to get a better interpretation of what occurs during c...

Rice Quality by Spectroscopic Analysis: Precision of Three Spectral Regions

ABSTRACT Three types of spectroscopy were used to examine rice quality: near infrared (NIR), Raman, and proton nuclear magnetic resonance (1H NMR). Samples from 96 rice cultivars were tested. Protein, amylose, transparency, alkali spreading values, whiteness, and degree of milling were measured by standard techniques and the values were regressed against NIR and Raman spectra data. The NMR spectra were used for a qualitative or semiquantitative assessment of the amylose/amylopectin ratio by determining the 1–4 to 1–6 ratio for glucans. Protein can be measured by almost any instrument in any configuration because of the strong relationship between the spectral response and the precision of the reference method. Amylose has an equally strong relationship to the vibrational spectra, but its determination by any reference method is far less precise, resulting in a 10× increase in the standard error of cross-validation (SECv) or standard error of performance (SEP) with R 2 values equal to that of the protein m...

Raman and NIR Spectroscopic Methods for Determination of Total Dietary Fiber in Cereal Foods: Utilizing Model Differences

This work evaluates the complementarity in the predictive ability of three Raman and three near-infrared reflectance (NIRR) partial least-squares regression (PLSR) models for total dietary fiber (TDF) determinations of a diverse set of ground cereal food products. For each spectral type (R or N), models had previously been developed from smoothed (D0), first-derivative (D1), or second-derivative (D2) spectral data. The NIRR and Raman models tend to have very different sets of outliers and uncorrelated errors in TDF determination. For a single spectral type, the prediction errors of various preprocessing methods are partially complementary. The samples are very diverse in terms of composition, but the main problem groups were narrowed to high-fat, high-bran, and high-germ samples, as well as and those containing synthetic fiber additives. Raman models perform better on the high-fat samples, while NIRR models perform better with high-bran and high-synthetic samples. Raman models were better able to accommodate a wheat germ sample, even though this sample type was poorly represented by the calibration set. Two methods are presented for utilizing the complementarity of the spectral and processing techniques: one involves simple averaging of predictions and the other involves avoidance of outliers by using statistics generated from the sample spectrum to choose the best model(s) for determination of the TDF value. The single best model (N-D1) has a root-mean-squared prediction error of 2.4% TDF. The best model of prediction averages yields an error of 1.9% (combining N-D0, N-D1, N-D2, R-D0, and R-D1). An error of 1.9% was also obtained by choosing a single prediction from the six models by using statistics to avoid outliers. With the selection of the best three models and averaging their predictions, an error of 1.5% was achieved.

Transfer of Near-Infrared Calibration Model for Determining Fiber Content in Flax: Effects of Transfer Samples and Standardization Procedure

The transfer of a calibration model for determining fiber content in flax stem was accomplished between two near-infrared spectrometers, which are the same brand but which require a standardization. In this paper, three factors, including transfer sample set, spectral type, and standardization method, were investigated to obtain the best standardization result. Twelve standardization files were produced from two sets of the transfer sample (sealed reference standards and a subset of the prediction set), two types of the transfer sample spectra (raw and preprocessed spectra), and three standardization methods (direct standardization (DS), piecewise direct standardization (PDS), and double window piecewise direct standardization (DWPDS)). The efficacy of the model transfer was evaluated based on the root mean square error of prediction, calculated using the independent prediction samples. Results indicated that the standardization using the sealed reference standards was unacceptable, but the standardization using the prediction subset was adequate. The use of the preprocessed spectra of the transfer samples led to the calibration transfers that were successful, especially for the PDS and the DWPDS correction. Finally, standardization using the prediction subset and their preprocessed spectra with DWPDS correction proved to be the best method for transferring the model.

Effect of random noise on the performance of NIR calibrations

The proliferation of applications for near infrared (NIR) spectroscopy has been fostered by advances in instrumentation and statistics. NIR analytical instrumentation is becoming more stable and reliable. Chemometrics is playing an important role in qualitative and quantitative NIR spectra analysis. The objective of this study was to evaluate the performances of four commonly used calibration models: (1) stepwise multiple linear regression (SMLR); (2) classical least-squares (CLS); (3) principal component regression (PCR); and (4) partial least-squares (PLS) in NIR spectroscopy analysis when random noise is present in the optical data. A conceptually simple procedure for comparing the performance of the four calibration methods in the presence of different levels of random noise in spectra data has been introduced here. This procedure, using the computer simulation data and real spectra of tobacco, has provided useful information for understanding the effects of random noise on the performance of multivariate calibration methods. Both numerical and graphical results will be shown.

Two-dimensional vibration spectroscopy. V: Correlation of mid- and near infrared of hard red winter and spring wheats

Two-dimensional correlation spectroscopy across the near infrared (NIR) and mid-infrared (MIR) regions have been used to explain the NIR spectra of hard red winter and spring wheat and provide additional confidence in analytical models developed with empirical data. Recent studies have shown that the major C–H stretching vibrations and some of the aromatic C–H and ring stretching vibrations and the minor vibrations in the “fingerprint” region are correlated also. The technique has been expanded to include Raman spectra. The Raman spectra were enhanced with Maximum Likelihood methods to improve signal-to-noise (S/N) while maintaining resolution. This was necessary to eliminate the effects of fluorescence which degrades S/N. The use of NIR lasers at 1.1 μm generally eliminates fluorescence as a problem, but it is still quite prevalent in agricultural materials. The original study did not show any significant correlations to aromatic functionality. However, the band at 1552 nm correlates to the Raman and not to the MIR. This band has shown up in NIR spectroscopy models for the determination of lignin, but is not readily observed in the MIR. Thus it correlates to a Raman active rather than a MIR active band. The same phenomena are observed for the amide I, II and III bands for wheat. The interesting features from NIR and MIR are that there are correlations that distinguish winter from spring wheat. These, and the Raman spectra of wheat, will be shown. These studies show that multiple regions of the electromagnetic spectrum can be, and in deed need to be, used to interpret adequately the spectral and statistical results we have traditionally obtained in the NIR.

NIR-FT/Raman spectroscopy for nutritional classification of cereal foods

ABSTRACT The classification of cereals using near-infrared Fourier transform Raman (NIR-FT/Raman) spectroscopy was accomplished. Cereal-based food samples (n = 120) were utilized in the study. Ground samples were scanned in low-iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN2) detector over the Raman shift range of 202.45~3399.89 cm-1. Samples were classified based on their primary nutritional components (total dietary fiber [TDF], fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components ...