bluuebunny/crossref_metadata_2025 huggingface dataset:
Determination of Capsaicin in the Antirheumatical Plasters by near Infrared Reflectance Spectroscopy: A Comparison of Statistical Methods
Authors: Czarnik-Matusewicz Henryk W. , Korniewicz Adolf
Published: May 2009

The evaluation of near infrared (NIR) reflectance spectroscopy as a method for the determination of capsaicin (8-methyl-N-vanillyl-6-nonenamide)—an active ingredient in the antirheumatical plasters was examined. The analytical procedure for determining the capsaicin was carried out by conventional, time-consuming colorimetric method. Spectra of the 76 plaster samples were recorded in reflectance mode at 2 nm intervals in the range 1100–2500 nm using InfraAlyzer 500 (Bran+Luebbe GmbH). A comparison is made between two regression methods, stepwise multiple linear regression (MLR) and partial least squares regression (PLS). MLR and PLS regression were used for calibrations, with the aid of the software SESAME ver. 2.10 (Bran+Luebbe GmbH). The PLS method showed consistently lower standard error of calibration and higher R values with first and second difference equations. The first difference PLS regression equation resulted in standard error of calibration of 0.018 %, with an R of 0.95. Generalizability of both methods for prediction of capsaicin contents on independent data sets is discussed. Prediction accuracy for independent data sets was increased using PLS regression, but was poor for sample sets with laboratory-measured concentration ranges beyond those of the calibration set. The results in this study indicate that NIR technique has a high applicability to quantitative analysis of capsaicin content in antirheumatical plasters.
Investigating the Polar Qualification System
Authors: Kaffka Károly J. , Gyarmati László S.
Published: May 2009

A new, rapid qualification method was introduced at the 3rd International Conference on Near Infrared Spectroscopy according to which a “quality point” was defined on a two-dimensional “quality plane”. The quality point of the investigated material was given by the center of its spectrum represented in a polar coordinate system. The method was further developed, three interpretations were given for the “center” of the polar spectrum, resulting in three different formulas for determining the x and y coordinates of the quality point. The effect of the change in the amplitude of the absorption peaks, the effect of the noise of the spectrum, the effect of the shifting and tilting the base-line of the spectrum on the location of the quality point were investigated using the three formulas. The results of the investigation and the characteristic features of the three formulas are introduced.
Spectral Amplification in near Infrared Spectrometry
Authors: Meurens Marc
Published: May 2009

“SPECTRAL AMPLIFICATION” is the significant name of a new algorithm of wavelength selection developed to improve the precision of the partial least squares (PLS) calibration of near infrared a (NIR) spectrometer for quantitative chemical analyses. This algorithm amplifies selectively some spectral data by mutiplicative coefficients so that they are predominant in the spectra and lower the prediction error of the PLS calibration. The poster presents a demonstration of “spectral amplification” in the determination of moisture on milk powders by NIR diffuse reflectance spectroscopy.
Non Invasive Analysis of Natural Resin Extracts
Authors: Ionita-Manzatu M. , Scarlat I. , Vasilescu M. , Puica M. , Blagoi G. , Ilie Mihaela
Published: May 2009

The NIR reflection spectra of some purified conifer resin extracts used as a biologically active ingredient in pharmaceutical ointments were employed to reveal the possibility of applying NIR spectroscopy to quality control. The samples were analysed beforehand (by HPLC and compendial methods) in order to characterise them. The fingerprint of this raw material was achieved by using the standard methods recommended by the NSAS operating protocol (for the spectrometer delivered by NIRSystems Inc.). The poor quality samples were also tested by means of same protocol, the limits of method being pointed out.
Can near Infrared-Absorption Spectra Explain the Different Diffusivities of Substituted Benzenes and Alcohols in Octan-1-Ol?
Authors: Geyer W. , Paschke A.
Published: May 2009

The considerable diffusivity difference of substituted benzenes and of alcohols in octan-1-ol1 suggests a strong influence of specific solute-solvent interactions (hydrogen bonding). For the quantification of these effects we dried to detect correlations between the diffusion coefficients in octan-1-ol and the near infrared (NIR)-spectra of the solutions. In our study we chose four aliphatic alcohols, benzene, phenol and seven substituted benzenes. By means of different methods of multivariate analysis (PCR, PLS) we tested the possibilities of the classification and prediction of diffusion coefficients using NIR-spectra. From the preliminary results and under the usual assumption that NIR spectra refer to specific solute–solvent interactions it can be concluded that these interactions also play an important role in the diffusive transport mechanism in liquid octan-1-ol.
Applications of near Infrared Spectroscopy for Qualification of Lubricants and other Mineral Oil Products
Authors: Timm Dagmar , Kurowski Christel , Grummisch Uwe , Meyhack Ulrich , Grunewald Horst
Published: May 2009

This is a poster presentation. Near infrared (NIR) spectroscopy is now recognised worldwide as technology appropriate for industrial quality and process control. NIR is used not only for quantitative analysis but also for identification of materials anywhere in the formulation process: raw materials, intermediates, and finished products. Additives for lubricants like oxidation inhibitors, corrosion inhibitors, and metal deactivators are highly specialised chemicals. Their function is an important part of the lubricant behavior. Some are readily distinguished because their NIR spectra are different. Similar compounds with nearly identical NIR spectra require special chemometric treatment in order to identify them as a proper substance. These same chemometric methods can be used to qualify the material as the specified grade and quality. Water soluble cooling lubricants are used in the metal working industry. The durability of the lubricant in process depends on the amount of impurities by reaction products and hydraulic or other process oils. NIR spectroscopy can be used as quick and effective method to determine the whole oil content and oil impurities continuously (at line) within seconds. The inaccurate and time consuming reference method with the oil tester can be replaced. Along with all the usual benefits of NIR analysis, this identification and qualification method provides a more complete quality assurance program for lubricant production.
Second Derivative near Infrared Studies on the Structural Characterisation of Proteins
Authors: Miyazawa Mitsuhiro , Sonoyama Masashi
Published: May 2009

Fourier Tranform near infrared (FT-NIR) spectra have been measured for several globular proteins which vary widely in the relative amounts of different secondary structures. In order to assist in the interpretation of the NIR spectra in the region between 5000 and 4000 cm−1, FT-NIR measurements of silk fibroin with different secondary structures were also carried out. The second derivative procedures showed that the broad contour near 4610 cm−1 consists of several overlapping components and each relative intensity is significantly changed with the proteins. In particular, the band near 4525 cm−1 seemed to be a characteristic component of sheet structure. On the other hand, low-wavenumber NIR bands in 4500– 4000 cm−1 region were considered to reflect the amino acid compositions of proteins. These experimental data suggested that NIR spectroscopy can be used for structural analyses and characterisations of proteins.
Simultaneous Determination of Fecal Fat, Nitrogen and Water by Fourier Transform near Infrared Reflectance Spectroscopy through a Polyethylene/Polyaminde Film
Authors: Neumeister Volker , Jaross Werner , Henker Jobst , Kaltenborn Georg
Published: May 2009

The determination of fecal fat, nitrogen and water is important to get evidence for malassimilation and for estimating the efficacy of treatment with pancreatic enzymes. Standard methods for the determination of these parameters (van-de-Kamer method for fat determination, Kjeldahl method for nitrogen determination) are expensive, time-consuming and cumbersome for laboratory assistants. Near infrared (NIR) reflectance spectroscopy was evaluated as a potentially attractive alternative method, especially because the simultaneous measurement of fat, nitrogen and water content is possible. After homogenisation parts of stool samples were packed and thermowelded in a bag of polyethylene/polyamide (PE/PA) film to optimize the handling in the laboratory. Two optical systems were tested: 1. Fiber optic, In-Ga-As-detector, weavelength range 1000–2500 nm, area of measurement diameter 4–mm; 2. Integrating sphere, Ge-detector, wavelength range 1000–1800 nm, area of measurement diameter 10 mm. Forty stool samples were used for calibration, another 20 for validation from both healthy children and patients with cystic fibrosis in an age range from 5 to 18 years. The concentrations of fecal compounds were calculated using the chemometric Partial Least Square (PLS) method with the NIR reflectance spectroscopy measurement data. The calibration were carried out based on results of chemical analysis with standard methods. The regression equations of the external NIR reflectance spectroscopy validations were as follows:

1. for the fiber optic system: fat determination y = 0.9737x + 5.7261r = 0.989, nitrogen determination y = 1.0092x+0.0731 r = 0.933, water determination y = 0.9699x+2.0703 r = 0.993;

2. for the integrating sphere system: fat determination y = 1.0308x–1.6797 r = 0.998, nitrogen determination y = 0.9529x+0.5302 r = 0.959, water y = 1.0301x–1.8193 r = 0.993.

The NIR reflectance spectroscopy method is a precise and alternative method for the determination of fecal fat, nitrogen and water. Moreover, handling is simple, time of analysis is short (4 minutes on average) and all calibrated constituents can be analyzed simultaneously. Therefore, we conclude that NIR reflectance spectroscopy is a reliable and useful method for analysis of fecal components in laboratory medicine.
Near Infrared Investigation of Some Lipid Extracts in Order to Ascertain Their Quality
Authors: Blagoi Gabriela , Bleotu Anca , Puica Madalina , Vasilescu Mihaela , Ilie Mihaela
Published: May 2009

Lipid extracts that exhibit similar near infrared spectra due to their chemical composition were investigated to generate their fingerprint by using pattern recognition techniques. Sunflower, buckthorn, wheatgerm, chrysalis, and olive oils were checked for qualitative and quantitative composition by GC-MS techniques and then analysed by using an NIRSystems Pharma device. Good results were obtained for wheatgerm, chrysalis and buckthorn oil (the “quality areas” do not overlay), but contradictory results were obtained for sunflower and olive oil.
Quality Control of Technical Casein—Near Infrared Reflectance Spectroscopy Method
Authors: Czarnik-Matusewicz Henryk W. , Korniewicz Adolf
Published: May 2009

The near infrared (NIR) reflectance spectroscopy method can be used in the routine checking of the technical casein. All the chemical and physical characteristics of the product that influence the NIR spectrum affect the qualification. In order to monitor possible deviations in the preparation, it is advisable to carry out some test during the different manufacturing stages. These test are: determination of water, fat, ash, free and total acidity. A set of 66 ground casein samples was used to calibrate the output from NIR instrument InfraAlyzer 500 (Bran+Luebbe GmbH), taking reflectance readings every 2 nm between 1100 nm and 2500 nm. As soon as the spectral scanning had been completed, the casein samples were subjected to the standard wet chemistry analysis. The spectral data from this calibration set was then statistically manipulated using MLR method with the aid of the software SESAME ver. 2.10 (Bran+Luebbe GmbH) to generate calibration models. These calibrations were then applied to a separate set of 20 samples which, for validation purposes, were also analysed by wet chemistry. The casein samples analysis predictions compared with the wet chemistry results on these samples, with standard errors of determination of 0.1%, 0.2%, 0.2%, 0.2% and 0.5% for water, fat, ash, free and total acidity, respectively. The use of NIR instrumentation and appropriate calibrations is able to result in a significant saving of laboratory resources when large numbers of the technical casein samples are being processed for analysis.
Determination of Rancidity of Soybean Oil by near Infrared Spectroscopy
Authors: Cho Seung-Yong , Kim Jee-Young , Rhee Chul
Published: May 2009

The technique of near-infrared (NIR) reflectance spectroscopy was used in order to test its use as a potential method for determining the rancidity of soybean oils undergoing autoxidation. Commercial soybean oil was used as a substrate and was oxidized at 75°C until the oxidized oil reached the hydroperoxide decomposition stage. Peroxide values (POVs) and acid values (AVs) of the substrate determined by the chemical method were correlated with NIR spectral absorbances. Two wavelengths (2080 and 2020 nm) were selected for the POV calibration, and the resulting coefficient of multiple determination for regression was quite high ( r = 0.9970, SEC = 9.10 meq / kg oil and SEP = 9.67 meq / kg oil). The prediction of oil rancidity by NIR using POV as a reference was found to be accurate in the early stages of oxidation. However, it was undesirable to use the POV calibration equation for the determination of rancidity in the hydroperoxide decomposition stages. Three wavelengths (2008, 1442 and 1752 nm) were used for AV calibration. High correlations were achieved between chemically analyzed AVs and NIR predicted AVs ( r = 0.9987, SEC = 0.127 mg KOH / g oil, and SEP = 0.137 mg KOH / g oil). The AV represented a good indicator for the determination of rancidity of oil undergoing thermal oxidation, whereas evaluation of rancidity using the POV has proven to be difficult due to hydroperoxide decomposition. The result of this study suggested that prediction of rancidity by NIR using POV calibration equation was more precise than using an AV calibration equation in hydroperoxide accumulation stages, whereas the AV calibration equation was a better predictor of oil rancidity than the POV calibration equation at higher POV ranges.
Determination of Degree of Retrogradation of Cooked Rice by near Infrared Reflectance Spectroscopy
Authors: Cho Seung-Yong , Choi Sung-Gil , Rhee Chul
Published: May 2009

Near infrared (NIR) reflectance spectroscopy was used to determine the degree of retrogradation of cooked rice. Cooked rice samples were stored at 4°C for 120 hours, and the degree of retrogradation was measured every six hours during the storage time. Enzymatic method, using glucoamylase, was used as reference method for the determination of the degree of retrogradation. Spectral differences, due to retrogradation of cooked rice, were observed at 1434, 1700, 1928, 2100, 2284 and 2320 nm. 32 samples were used for calibration set and 16 samples were used for validation set. High correlations were achieved between degree of retrogradation determined by enzymatic method and by NIR with multiple correlation coefficient of 0.9753 and a standard error of calibration of 3.64%. Comparable results were obtained with 3.91% of standard error of prediction, when the calibration equation was applied to an independent group of samples. The moisture content of samples tested significantly affected the determination of degree of retrogradation by NIR. The critical moisture content for the determination of degree of retrogradation by NIR was found to be ca. 5% (W.B.). The results suggested that NIR spectroscopy might be used as a potential method for determining both the degree of retrogradation and gelatinization of cooked rice.
Quantitative Analysis of Alcohol, Real Extract, Original Gravity, Nitrogen and Polyphenols in Beers Using NIR Spectroscopy
Authors: Maudoux Marc , Yan Shou He , Collin Sonia
Published: May 2009

This study was to develop a rapid and accurate NIR analysis method for determinations of alcohol, real extract, original gravity, total nitrogen and total polyphenols. Commercial European beers (110 samples) were used to create calibration models between EBC (European Brewing Committee) and NIR spectral data. The optimal correlation coefficients ( r) were 0.94 to 0.98 and the corresponding CV% (coefficients of validation variation) were 4.29, 6.53, 4.50, 6.06 and 4.74 for NIR predictions of alcohol, real extract, original gravity, nitrogen and polyphenols, respectively. The stepwise MLR calibration proved to be a good choice for measurements of alcohol and original gravity, while PLS regression models seem to be better for the predictions of the real extract, nitrogen and polyphenols. Comparisons of results from MLR and PLS, demonstrate that MLR methods (log 1/ R) are better than those of PLS (log 1/ R) in calibration and prediction sets. The reflection mode is better than those of transmission in all above cases.
Application of an Artificial Neural Network to the Identification of Amino Acids from near Infrared Spectral Data
Authors: Sato Tetsuo
Published: May 2009

An artificial neural network (ANN) was trained to identify a group of amino acids from near infrared (NIR) spectral data. The input, the hidden and the output layers were composed of 701 (for raw spectral data, fixed) or 324 (for second derivative of the spectral data, fixed) units, 1 to 100 (changeable) units and 20 (fixed) units, respectively. Using the raw spectral data, the ANN did not converge to a suitable error level. However, when the second derivative spectra were used, whether original or standardised spectra, the error reduced to a suitable level, because this mathematical treatment made their differences in NIR spectra clearer. The ANN was trained for non-pretreated amino acids and then applied to the other prediction sets. When standardised spectra were used, the ANN could almost correctly identify the amino acids not only for non-pretreated amino acids but also for ground samples or samples from different batches. The results obtained by principal component analysis (PCA) were also compared with those by the ANN.
A Review of Process near Infrared Spectroscopy: 1980–1994
Authors: Workman Jerry
Published: May 2009

For this review, papers on NIR process analysis were categorised according to their overall configuration and measurement scheme. This analyser taxonomy was chosen to demonstrate the state of the current use of process analyser terms and to allow the reader to access a broad range of applications by referring to the specific measuring characteristics of the analyser. The basis for this review was a literature search covering the period from January 1980 to January 1994 on the subject of Process NIR and FT-NIR analysis. Each section is in chronological order to allow the reader to observe the development (and refinement) of process NIR from 1980 to the present.
Analyses of Forest Foliage II: Measurement of Carbon Fraction and Nitrogen Content by End-Member Analysis
Authors: Aber John D. , Bolster Katherine L. , Newman Stephen D. , Soulia Margaret , Martin Mary E.
Published: May 2009

Chemical constituents of forest canopies are accepted as key indicators of ecosystem state and function. Remote sensing of these parameters offers the potential for rapid and accurate assessment of rates of key biogeochemical processes over large regions. NASA's Accelerated Canopy Chemistry Program was established to test the accuracy and generality of high-spectral resolution remote sensing in the measurement of lignin, cellulose and nitrogen concentrations in forest canopies.

One of the most straightforward methods for extracting constituent concentration information from spectra is through simple linear mixing models of pre-determined end-member or pure compound spectra. While there are ample reasons to expect that linear mixing models will not work in foliar and whole-canopy samples, end-member analysis might still provide important information in support of standard and emerging statistical techniques, such as linear regression and partial least squares regression on first and second difference spectra, by indicating which parts of the spectrum should contain information on the concentrations of important constituents. The purpose of this paper is to present the results of two approaches to determining the value of end-member spectra for estimating constituent concentrations in foliage of temperate zone forest species. The first examines the spectral changes accompanying each step in the chemical proximate analysis method used to determine concentrations in the laboratory, and from them to infer the spectra of the fractions removed at each step. The second is the combination of known materials which approximate these same fractions in foliage into well-mixed samples to determine whether a simple linear mixing model can be used to predict the spectrum of the resulting mixture.

Results confirm that the combination of linear mixing models and end-member analysis is not an appropriate technique for obtaining quantitative estimates of constituent concentrations for the major components of foliage of native woody plants, nor do we expect that more detailed analyses of plant ultrastructure or foliar spectra will correct the deficiencies identified. However, these results do suggest that the wet chemical procedures used to extract different carbon fractions produce consistent results with regard to the location of spectral features associated with the compounds removed at each step, and that the spectra of the cellulose and lignin isolated by this technique are very similar to those from pure materials. This in turn suggests that statistical techniques such as linear regression on first and second difference spectra and partial least squares methods which allow or correct for non-linear mixing, should be successful.
Analyses of Forest Foliage III: Determining Nitrogen, Lignin and Cellulose in Fresh Leaves Using near Infrared Reflectance Data
Authors: Martin Mary E. , Aber John D.
Published: May 2009

Near infrared laboratory data of whole fresh leaves were evaluated with respect to leaf chemical composition. Near infrared spectra were measured for 211 foliage samples including both broad- and needle-leaved species. A multiple linear regression analysis was used to determine if reflectance data from fresh leaf samples contains information on nitrogen, lignin and cellulose concentrations. Calibration equations were developed for all three leaf constituents, indicating that information on leaf biochemistry is present in the spectra of fresh as well as dried, ground leaf samples.
Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds
Authors: Williams P. C. , Sobering D.C.
Published: May 2009

Near infrared transmittance and reflectance instruments were compared for the determination of protein, oil, moisture and some other constituents and parameters in several grains and seeds of commerce. Both approaches were comparable in accuracy and reproducibility. The importance of optimisation of the wavelength range in whole grain analysis is demonstrated for measurements in both the NIR and visible/NlR wavelength ranges. The RPD statistic, which relates the standard error of prediction to the standard deviation of the original data, is illustrated as a method for the evaluation of calibrations. The concept of monitoring the accuracy of analysis using whole grain calibrations with ground grain calibrations is introduced.
The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra
Authors: Dhanoa M.S. , Lister S.J. , Sanderson R. , Barnes R.J.
Published: May 2009

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.
Rapid Analysis of Whole Blood and Blood Serum Using near Infrared Spectroscopy
Authors: Domján Gyula , Kaffka Károly J. , Jákó János M. , Vályi-Nagy István T.
Published: May 2009

In the present study we describe the relationship between laboratory values obtained with routinely used laboratory analytical methods and near infrared (NIR) spectral data of 126 whole blood and 228 blood serum samples. Spectra were measured with a SPECTRALYZER 1025 (PMC) computerised research analyser. The relationship among laboratory data and values of the second derivative of the log (1/ R) spectra measured at different wavelengths was determined by multiple linear regression (MLR) using three and four term linear summation equations, principal component regression (PCR) and partial least-squares (PLS) regression methods. Along with examples for qualitative detection of protein and lipid in human sera, as well as distinction of albumin and globulin dissolved in physiological saline solution, we describe mathematical models and evaluate their performance for the determination of protein and beta-lipoprotein (β-LP) content of serum as well as oxygen saturation and carbon dioxide pressure in whole blood. Validation of our results yielded a standard error of performance (SEP) of 2.47 g L−1 for protein content and 0.79 TU for β-LP content in blood serum, whereas SEP values of 5.41% for oxygen saturation and 5.27 mm Hg for carbon dioxide pressure in whole blood were found. Our results presented in this preliminary study indicate that NIR measurements can be related to analytical data of whole blood and serum. NIR spectroscopy is a rapid, accurate, cost effective method for determining quality parameters of whole blood and serum and might be a promising new tool in the field of automated clinical laboratory analysis.
The Effect of Light Scattering on NIR Reflectance and Transmittance Spectra of Wheat
Authors: Manley M. , McGill A.E.J. , Osborne B.G.
Published: May 2009

An NIR hardness score based on the reflectance of ground wheat is available (AACC Method 39–70A). However, it is desirable to derive an analogous score applicable to whole wheat so that an on-line measurement of hardness could be used in wheat blending. The intention was to use an algorithm based on the multiplicative effect of scatter on NIR spectra and so an experiment was carried out to determine whether or not the effect of scatter on reflectance and transmittance spectra of whole grain is multiplicative. As no evidence was found to support this model, a different approach will be needed. However, these studies have brought a new insight into the effect of scatter on the NIR spectra of whole grain.
Comparison of Human Panels, Colorimeter and near Infrared Spectroscopy for the Evaluation of Fabric Stain Removal
Authors: Lee K.A. Bunding , O'Brien Jeanne , Kuesten C.L. , Sramek John , Luccas Mary H. , Aldrich Bonnie
Published: May 2009

Near infrared (NIR) spectroscopy was used to analyse fabric stain samples used in a fabric stain prespotter test in which a colorimeter and human panels were used to evaluate the cleaning ability of the prespotters. The purpose of this was to see if the NIR results compared well with the other two techniques and could then be used instead for fabric stain analysis. NIR/visible instruments offer several advantages including determination of coloured and uncoloured components of the stains, ease of comparing the stain before and after cleaning, fast, accurate, reproducible and quantitative analysis, and computer data storage for later comparison. Although the samples were not specifically prepared for this determination, the NIR did give comparable results to the other two techniques for many of the stains analysed.
Fourier Transform near Infrared Absorption Spectroscopy of Gases
Authors: Mosley R.M. , Williams R.R.
Published: May 2009

The application of Fourier transform near infrared spectroscopy (FT-NIR) to the analysis of gas phase molecules is presented. The use of this technique with both sealed laboratory cells and open-path measurements is demonstrated. The gas phase absorption spectra of several environmentally important organic molecules were collected for qualitative comparison. Calibration data and detection limits for several of the compounds are presented. For ammonia, the background equivalent concentration for a 10 cm path was determined to be 1.5 ppm while propane in the same pathlength was found to be 180 ppm. Other applications of this technique, including the use of fibre optics for remote sensing, are also discussed.
Determination of Dry Matter Content and Basic Density of Norway Spruce by near Infrared Reflectance and Transmittance Spectroscopy
Authors: Thygesen Lisbeth G.
Published: May 2009

The purpose of the study was to investigate the feasibility of using near infrared (NTR) spectroscopy in reflectance and transmittance modes for the determination of dry matter content and basic density of Norway spruce ( Picea abies (L.) Karst). The 169 samples varied in moisture content (below fibre saturation, green and fully saturated) and in form (solid wood and shavings). Calibration models were produced using partial least squares regression and the resulting models were validated using test sets. The calibration models for dry matter content showed good predictive ability. The root mean square error of prediction (RMSEP) was 1–2% for dry matter contents ranging between 35 and 95%, corresponding to a relative error between 1 and 6%. Calibration models for basic density were less precise, having RMSEP values between 15 and 26 kg m−3 for basic density values ranging from 313 to 495 kg m−3 (relative error 3–8%). The reason for the limited predictive ability of the basic density models is probably that the part of each sample scanned with NIR spectroscopy was not representative of the whole sample of which the basic density had been determined by the reference method. No marked differences in the predictive ability of calibration models were found between reflectance and transmittance.
Determination of the Authenticity of Orange Juice by Discriminant Analysis of near Infrared Spectra
Authors: Evans D.G. , Scotter C.N.G. , Day L.Z. , Hall M.N.
Published: May 2009

Near infrared spectroscopy was used to discriminate between three sources of orange juice. Three pretreatments and five data transformations to improve discrimination were compared. Principal components analysis of 92 calibration samples was followed by canonical variates analysis using up to 25 principal components. Success rates were compared across pretreatments and transformations for the calibration and test data (50 samples). 100% prediction success was obtained with 25 principal components following no pretreatment and no transformation. Principal component loadings were interpretable.
Use of Principal Component Analysis in a near Infrared Study of the Dissociation Process of Oleyl Alcohol
Authors: Liu Yongliang , Miura Tsuyoshi , Ozaki Yukihiro , Suzuki Masao , Iwahashi Makio
Published: May 2009

This paper demonstrates the usefulness of principal component analysis (PCA) in the study of the dissociation process of oleyl alcohol ( cis-9-octadecen-1-ol) as the pure liquid. Fourier transform near infrared (FT-NIR) spectra of the neat alcohol were measured over a temperature range of 6.5–90°C in the 11,500–6000 cm−1 region. The degree of dissociation of the alcohol was determined by the intensities of the first and second overtones of the OH stretching mode of the monomer. PCA on the obtained spectra were calculated over three different regions, the entire 11,500–6000 cm−1 region and the 11,500–9087 and 9087–6000 cm−1 regions. PCA clearly reveals that there exists a break point at around 45°C in the dissociation process of the alcohol.
Global or Local? A Choice for NIR Calibrations in Analyses of Forage Quality
Authors: Sinnaeve G. , Dardenne P. , Agneessens R.
Published: May 2009

This paper investigates the effect of spectral data pre-treatment by using scatter correction techniques, detrending and derivatives on the standard error of NIR predictive models. It is shown that no particular spectral pre-treatment or no single derivative works best for the three constituents (protein, cellulose, organic matter digestibility) of the three forage databases which we investigated (grass-hay, tropical forages, maize whole plants). The best analytical results are obtained with SNVD, MSC or WMSC treatments. The best results are obtained with a first or second derivative with a segment and a gap of five data points. Local Regression was investigated for the prediction of forage quality. The standard errors of prediction were compared with those obtained with the best global calibration. Trial and error is the only way to fix the number of samples in the subset and the number of terms to retain in the model. Compared to the results for the traditional universal calibration method, the gain in SEP for protein, cellulose and digestibility in grass-hay, tropical forages or maize ranges between 5 and 11%.
Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by vis/NIR Reflectance Spectroscopy. Part II: Principal Components Regression
Authors: Montalvo Joseph G. , Buco Steven E. , Ramey Harmon H.
Published: May 2009

In Part I of this series, both cotton fibre property and reflectance spectra data on 185 US cottons including four Pimas were analysed by descriptive statistics. In this paper, principal components regression (PCR) models for measuring six properties from the cotton's vis/NIR reflectance spectra are critically examined. These properties are upper-half mean length (UHM), uniformity index (UI), bundle strength (STR), micronaire (MIC) and colour (Rd and +b). The spectra were recorded with a scanning spectrophotometer in the wavelength range from 400 to 2498 nm. A variety of spectral processing options, some of which give improved PCR analysis results, were applied prior to the regressions and allowed for testing of over 100 PCR models. All PCR model results are based on the PRESS statistic by one-out-rotation, a fast approximation of the PRESS statistic (to reduce computer time) or on cluster analysis using separate calibration and validation data sets. The standard error of prediction (SEP) of all the properties except UHM compared well to the reference method precision. The precision of the UHM measure by reflectance spectroscopy was strongly influenced by the sample repack error. The SEP of UHM, UI and STR was improved by excluding the Pimas from the data set.
Effects of Water on the Spectra of Model Compounds in the Short-Wavelength near Infrared Spectral Region (14,000–9091 cm−1 or 714–1100 nm)
Authors: Reeves James B.
Published: May 2009

The spectral region from 14,000 to 9091 cm−1 (714–1100 nm) is increasingly being investigated for the analysis of high moisture systems due to its low absorption by water. The objective of this work was to determine if the effects of water on model compounds seen in the 7140–4000 cm−1 (1400–2500 nm) near infrared region occurred in this short wavelength region. Spectra were obtained by diffuse reflectance and transmission using a Fourier transform spectrometer. Spectra were obtained for a variety of organic liquids, liquid/water solutions, solids, wet solids and solutions of solids in water. Solutions included ethanol, acetic acid, acetone, pyridine, sugars, starch, cellulose, gums, amino acids and proteins. The spectral results showed that the effects seen in the 7140–4000 cm−1 (1400–2500 nm) region were also common in the 14,000–9091 cm−1 (714–1100 nm) region (i.e. peak shifts, loss of spectral features etc.). For example, in the long wavelength near infrared region, sugars, such as sucrose and glucose, were distinctively different as crystalline solids, but very similar in solution. In addition, molten glucose and urea appeared virtually identical to their dissolved counterparts indicating a loss of crystallinity to be the source of the changes. Finally, changes in the spectra of other materials, such as acetone, n-butylamine and ethanol (while similar in nature to those previously found in the near infrared) were not identical. Thus, while some shifts in peaks were found to occur with acetone/water mixtures, the dominant effects were changes in the relative intensities of peaks within the acetone spectrum, something not seen in the long wavelength region. Therefore, while the type of spectral effects caused by the presence of water may be similar across various spectral regions, the degree and exact nature of those effects vary with the material in question, the amount of water present and the region in question. Thus, the choice of the spectral region to be used for a specific problem should consider the materials in question, as well as other factors such as the usable pathlength.
Near Infrared Hemoglobinometry
Authors: Kuenstner J.T. , Norris K.H.
Published: May 2009

In this article, near infrared spectroscopic measurements of hemoglobin concentration in whole blood, which are potentially useful in a pulse oximeter modified for non-invasive hemoglobinometry, are presented. The best one-, two-, three- and four-filter calibrations are presented. In addition, a calibration is presented that uses absorbance at three wavelengths, 586 nm, 816 nm and 1300 nm because this calibration is employed by a prototypic non-invasive hemoglobinometer which is under development. For a similar calibration, data is presented that show the relative contributions of the absorbance at each wavelength to the overall measurement. The best three-filter calibration occurred in the longer wavelength near infrared region, and used the absorbance at 1626 nm, 1628 nm and 1638 nm. This calibration gave better performance than the calibrations in the shorter wavelength region.
Using near Infrared Spectroscopy to Monitor the Preparation of Compounds for Screening as Antituberculosis Drugs
Authors: Hearn Michael J. , Celi Paul , Chanyaputhipong Phet-Yoon , Chi Wendy , Kang Joo On , Katz Adena , Shah Ruta , Thai Minh , Ung Podaly
Published: May 2009

Near infrared (NIR) spectroscopy provides an opportune method for assessing the progress of reactions leading to compounds for evaluation as tuberculostats. The advantages of NIR include convenience of sample handling and ease of quantification. Rate estimates obtained from NIR are useful in categorising reactions and adjusting preparative conditions.
Usefulness of a Newly-Developed Fluoride Glass Fibre in near Infrared Light-Fibre Spectroscopy
Authors: Maeda Hisashi , Ozaki Yukihiro , Noda Yukio , Mimura Yoshinori , Nakai Tetsuya , Tani Toshio
Published: May 2009

This paper demonstrates the potential of a newly developed fluoride glass fibre for near infrared (NIR) light fibre spectroscopy. The measurement of a power spectrum using this fibre, on a Fourier transform (FT)-NIR spectrophotometer, reveals that the fibre shows excellent transmission characteristics in the 1000–2500 nm region. FT-NIR spectra in the 1000–2500 nm region have been measured for palmitic acid, potassium ferricyanide, silicone grease and sugar by using the fluoride glass fibre. The obtained spectra and their second derivatives show a high signal-to-noise ratio at least up to 2500 nm.
Letter: Math Pretreatment of NIR Reflectance Data: Log(1/R) vs F(R)
Authors: Dahm Donald J. , Dahm Kevin D.
Published: May 2009

The Kubelka–Munk function, F( R), is not commonly used as a pretreatment of NIR reflectance data. Reasons cited for this in the Handbook of Near Infrared Analysis point to the limitations of the Kubelka–Munk theory in predicting diffuse scattering data, especially for highly absorbing samples. However, the case for this causing a non-linear function of concentration when using F( R) has not been adequately made. A re-examination of data presented shows the non-linearity to be largely explained by the use of methods which could not be expected to produce linear data. Thus, it is as yet uncertain if, when properly applied, the advantages of increased linearity when using the Kubelka–Munk function will outweigh the advantage of relative insensitivity to referenced photometric changes when using log(l/ R) followed by a derivative.
Letter: Practical Consequences of Math Pre-Treatment of near Infrared Reflectance Data: Log (1/R) vs F(R)
Authors: Griffiths Peter R.
Published: May 2009

The selection of F( R) or (1/ R) as the metric for quantitative diffuse reflection spectrometry depends on the application and on the spectral region. For mid-infrared measurements, where dilution in a non-absorbing matrix is usually necessary and samples are often physical mixtures of the individual components, the use of the Kubelka–Munk function, F( R), is often preferred. For diffuse reflection measurements of agricultural products and commodities in the near infrared region, where dilution in a non-absorbing matrix is unnecessary and the concentration of the analytes rarely varies by more than a factor or two, the use of log(1/ R) is preferable. For any diffuse reflection measurement where the spectral baseline is irreproducible, band intensities vary with the baseline in F( R) spectra but not in log (1/ R) spectra.
Near Infrared Reflectance Spectroscopy in the Prediction of Sensory Properties of Beef
Authors: Hildrum K.I. , Isaksson T. , Næs T. , Nilsen B.N. , Rødbotten M. , Lea P.
Published: May 2009

Near infrared (NIR) spectroscopy in the prediction of sensory hardness, tenderness and juiciness of bovine M. Longissimus dorsi muscles has been studied. Principal component regressions (PCR) of sensory variables from NIR reflectance measurements on frozen/thawed beef of 120 heat treated samples yielded multivariate correlation coefficients of cross-validation of 0.74, 0.70 and 0.61 for hardness, tenderness and juiciness, respectively. The corresponding correlation coefficients for NIR measurements of fresh (non-frozen) samples were approximately 0.1 units lower for all sensory variables. Predicting Warner Bratzler (WB) shear press values from NIR measurements gave a correlation coefficient similar to that for prediction of sensory hardness. The univariate correlation coefficient between sensory hardness and WB shear press values was 0.90.
Analysis of Polymer Pellets Obtained from Two Extruders Using near Infrared Spectroscopy
Authors: Hall Jeffrey W. , Grzybowski Denise E. , Monfre Stephen L.
Published: May 2009

The near infrared spectroscopic determination of additive levels in polypropylene pellets obtained from two process extruders/pelletisers is described. A quotient-term multiple linear least-squares spectroscopic model was derived that characterises analyte concentration and corrects for spectroscopic differences within the matrix due to the extruder/pelletisers. The analytical error of the spectroscopic model is comparable to the reference method used to derive the spectroscopic model.
Differentiation of Male, Female and Dead Silkworms While in the Cocoon by near Infrared Spectroscopy
Authors: Jin Tongming , Liu Ling , Tang Xiaowei , Chen Hang
Published: May 2009

The feasibility of using a non-destructive method to differentiate male (M), female (F) and dead (D) worm cocoons of silkworms has been studied with near infrared (NIR) spectroscopy over the region 680–1235 nm on a sample set of 205 fresh cocoons. On the basis of the spectral data of the first and second derivatives, characteristic vectors were extracted by multiple linear regression and a Bayes critical function was established. This function was used to test 375 samples and the success rate was 95.7%. This method of sex judging is thus preferable to the conventional use of the weight and size of a cocoon which achieved a success rate of only 82.9% on the same samples. Experiments showed that the differences between NIR spectra of M and F cocoons mainly result from the property of silkworm cocoons rather than their incunabulum.
Non-Supervised Neural Categorisation of near Infrared Spectra. Application to Pure Compounds
Authors: Cáceres-Alonso Paloma , García-Tejedor Alvaro
Published: May 2009

Near infrared (NIR) spectroscopy is recognised world-wide as a powerful tool for substance quantification and identification provided that good data analysis tools are used. Most of the identification algorithms use supervised learning and require previous knowledge of existing categories to construct the mathematical models that will be later used at runtime. The use of non-supervised neural learning algorithms is not a common tool in identification of near infrared spectra, although they are widely employed as a pattern recognition technique. Problems analogous to NIR identification have already been solved by means of non-supervised neural networks. Their main advantages are the ability to learn from examples as well as the processing speed, once they are trained. We present in this work a preliminary study of a non-supervised classifier built using Kohonen self-organising maps (SOM). The result is a model useful for identification of a group of NIR spectra belonging to 15 pure products. The accuracy of the classification is discussed. The generalisation of the method for more complex data is still an open issue.
The Authentication of Basmati Rice Using near Infrared Spectroscopy: Some Further Analysis
Authors: Krzanowski W.J.
Published: May 2009

The feasibility of using near infrared transmission spectroscopy to discriminate between Basmati and other long-grain rice samples has been demonstrated previously by Osborne et al. In their analysis they pooled samples from different countries of origin into the single category “other” and used the multivariate techniques of principal component analysis and linear discriminant functions to arrive at their conclusions. We reanalyse here their data but without such a major pooling of samples, retaining four groups in the discrimination. Using the multivariate techniques of partial least squares, orthogonal canonical variates and a recently proposed search for “extremeness”, we demonstrate complete support for the previous conclusions.
Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data
Authors: Hana M. , McClure W.F. , Whitaker T.B. , White M. , Bahler D.R.
Published: May 2009

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.