bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Calibration with Sample Cell Compensation for Determining the Fat Content of Unhomogenised Raw Milk by a Simple near Infrared Transmittance Method
Authors: Chen Jie Yu , Iyo Chie , Kawano Sumio , Terada Fuminori
Published: May 2009

A simple near infrared (NIR) transmittance measuring method for unhomogenised raw milk was developed using test tubes as sample cells and a specially designed sample holder which could control sample temperature. Shortwave NIR spectra (700–1100 nm) of 154 unhomogenised milk samples from eight cows, under different nutritional conditions, were measured at 40°C using this method. The calibration and validation for fat content in raw milk were performed by partial least squares regression in two ways, using either a specific test tube or any individual test tube as a sample cell. A functional calibration model for determining fat content in raw milk with relatively high accuracy (multiple correlation coefficient of 0.99 and SEP of 0.19%) was obtained, which is applicable even when using individual test tubes as sample cells. By examining the loading weight of each factor included in the calibration model for fat determination in milk, it was found that the model consisted of, not only the factor for fat, but also factors which worked as adjusting terms, eliminating any effects caused by the other major components of milk such as lactose, casein and water and by the sample cell.
Predicting Protein Content by near Infrared Reflectance Spectroscopy in Diverse Cereal Food Products
Authors: Kays Sandra E. , Barton Franklin E. , Windham William R.
Published: May 2009

Simultaneous determination of constituents (e.g. dietary fibre, protein, fat) by near infrared (NIR) spectroscopy would increase the speed and efficiency of nutrient analysis while substantially reducing the cost. Previous work has described the development of NIR reflectance models for the prediction of dietary fibre in a diverse group of cereal food products. While NIR spectroscopy has been used to measure protein content in cereal samples comprised of a single grain type, the utility of the NIR technique would be greatly improved if it could be expanded to cereal products derived from a diverse cross-section of grains and formulations. The present study was conducted to investigate the potential of NIR spectroscopy for the analysis of protein in a data set that included products with numerous grains, such as wheat, oats, rice, rye, corn, millet, buckwheat and with a wide range of fat, sugar and fibre contents. In addition, numerous processing techniques and food additives were represented in the data set. Nitrogen content of dry-milled cereal products was measured by combustion analysis (AOAC Method 992.23) and the range in nitrogen values was from 0.65 to 3.31% of dry weight. Milled cereal products were scanned from 1100 to 2500nm with a scanning monochromator. A nitrogen calibration was developed, using a commercial analysis program, with modified partial least squares as the regression method. The standard error of cross validation and R2 for nitrogen ( n=147 calibration samples) were 0.090% and 0.973, respectively. Independent validation samples ( n=72) were predicted with a standard error of performance of 0.079% nitrogen and r2 of 0.984. Because of the diversity of grains in the data set, crude protein was calculated using two nitrogen-to-protein conversion methods and two PLS models were developed for the prediction of crude protein. Crude protein was predicted with a similar precision to nitrogen and the results for both protein models are within the precision required for US nutrition labelling legislation. In conclusion, NIR reflectance spectroscopy can be used for rapid and accurate prediction of nitrogen and crude protein content in a heterogeneous group of cereal products comprised of a wide cross-section of grains and formulations.
Near Infrared Spectrometry and Pattern Recognition as Screening Methods for the Authentication of Virgin Olive Oils of Very Close Geographical Origins
Authors: Bertran E. , Blanco M. , Coello J. , Iturriaga H. , Maspoch S. , Montoliu I.
Published: May 2009

The authentication of foods requires the use of sophisticated and expensive analytical techniques. Thus, there is a need for new, fast and inexpensive analytical methodologies for use as effective screening methods. This paper proposes a NIR spectroscopy-based method for discriminating virgin olive oils of two very similar and geographically close denominations of origin, viz. “Siurana” and “Les Garrigues”, which are made from at least 90% of olives of the Arbequina variety. Two chemometric techniques, artificial neural networks (ANNs) and logistic regression (LR), were tested as classifying tools applied to NIR spectra. The results obtained were quite satisfactory in both cases, in spite of the similarity between the two denominations of origin. The proposed method is intended to fill a gap in the authentication of natural products and allow the discrimination of oil samples, and can be applied to the discrimination of other olive oils which belong to different denominations of origin.
Evaluating near Infrared Techniques for Quantitative Analysis of Carbohydrates in Fruit Juice Model Systems
Authors: Segtnan V.H. , Isaksson T.
Published: May 2009

Several techniques for measuring quality parameters in foods by the use of near infrared (NIR) technology have been reported. The aim of this experiment is to evaluate the main techniques in order to find the optimal measurement conditions for NIR analysis of carbohydrates in fluid food systems. Two different model systems were studied, each system containing 61 designed samples. The first system was designed to give scatter, and was based on a commercial orange juice. The other system was designed to be scatter-free, and was based on distilled water. To all samples were added the same total amounts of glucose, fructose and sucrose, and measured using the following NIR techniques: transmittance measurements in cuvettes, dry extract diffuse reflectance (DESIR), fibre-optic transflectance and fibre-optic transmittance. Calibration models were made by partial least squares regression in the spectral regions 780–2500nm for DESIR measurements, 1100–1315nm for 10mm pathlengths and 1100–1880+2130–2350nm for 1mm pathlengths. The models were fully cross-validated. Optimal prediction errors (Root Mean Square Error of Prediction, Cross-Validated) for DESIR measurements ranged from 0.020 to 0.030% (w/w), while 1mm cuvette values ranged from 0.008 to 0.012%. For these techniques there were only small differences between juice and water samples. Using fibre-optics, 1mm transmittance gave values in the range 0.068–0.081% for juice samples and 0.022–0.066% for water samples, while 1mm transflectance gave 0.044–0.051% for juice samples and 0.045–0.078% for water samples. 10mm pathlengths provided substantially higher prediction errors than 1mm for all techniques investigated. From these results, two main conclusions can be drawn. First, when measuring off-line, direct transmittance measurements in cuvettes gave better prediction results than DESIR. Second, when using fibre-optics, transflectance gave lower prediction errors than transmittance for scattering samples, while transmittance performed better than transflectance for non-scattering samples.
Variable Selection in near Infrared Spectroscopy Based on Significance Testing in Partial Least Squares Regression
Authors: Westad F. , Martens H.
Published: May 2009

A jack-knife based method for variable selection in partial least squares regression is presented. The method is based on significance tests of model parameters, in this paper applied to regression coefficients. The method is tested on a near infrared (NIR) spectral data set recorded on beer samples, correlated to extract concentration and compared to other methods with known merit. The results show that the jack-knife based variable selection performs as well or better than other variable selection methods do. Furthermore, results show that the method is robust towards various cross-validation schemes (the number of segments and how they are chosen).
NIR Measurement of Moisture Content in Wood under Unstable Temperature Conditions. Part 2. Handling Temperature Fluctuations
Authors: Thygesen Lisbeth G. , Lundqvist Sven-Olof
Published: May 2009

Fluctuations in sample temperature cause peak shifts in near infrared (NIR) spectra of moist, solid wood samples, especially when the temperature varies around 0°C (the freezing point of water). These thermal effects cannot be ignored when NIR and Partial Least Squares (PLS) regression is used for determination of the moisture content of wood outside the laboratory. In this paper, a number of different approaches to the problem are investigated. The approaches may be divided into two different classes according to their basic strategy. One strategy, the soft model strategy, is to represent all relevant temperatures in the calibration set and then produce a global model or a set of local models based on raw or pretreated spectra. This strategy does not require knowledge of the structure of the thermal effects, but a large calibration set representing all relevant temperatures is needed. Three approaches based on this strategy were tested. The other strategy, the transformation strategy, is to develop the moisture content model at one temperature and transform spectra recorded at other temperatures to this temperature. If an effective transformation algorithm can be found, this strategy should require less calibration data. Four new approaches based on the transformation strategy were developed and tested. The three soft model approaches gave similar prediction errors ( RMSEP) for unknown samples (8–9%, expressed as moisture ratio, i.e. the moisture content in percent of the dry weight). None of the approaches based on the transformation strategy gave smaller prediction errors than the soft model approaches, but two of them gave only slightly larger prediction errors ( RMSEP) than the soft model approaches (9–10% moisture).
Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method?
Authors: Dardenne Pierre , Sinnaeve George , Baeten Vincent
Published: May 2009

The four most important regression methods are evaluated on very large data sets: Multiple Linear Regression (MLR), Partial Least Squares (PLS), Artificial Neural Network (ANN) and a new concept called “LOCAL” (PLS with selection of a calibration sample subset of the closest neighbours for each sample to predict). The Standard Errors of Prediction ( SEPs) are statistically tested and the results show that the regression methods are almost equal and that the data matrices are more important than the fitting methods themselves. The types of pre-treatments (Multiplicative Scatter Correction, Detrend, Standard Normal Variate, derivative etc.) of the spectra are too numerous to be able to test all the combinations. For each test, the pre-treatment found as the best with the PLS method is fixed for the other ones. The second part of the paper emphasises the importance of the number of samples. If any agricultural commodity, and probably any kind of product measured by an NIR instrument, can be considered as a mixture of several constituents, the databases built by collecting actual samples bringing new information can reach hundreds, if not thousands, of samples.
A Preliminary Study of Optothermal Spectroscopy: A Potential near Infrared Sample Presentation Method for the Analysis of Milk
Authors: Wilson R.H. , Madsen P.K. , Defernez M. , Tapp H.S.
Published: May 2009

Optothermal (OT) spectroscopy is a potential sample presentation technique for use in the near infrared (NIR). Compared to attenuated total reflectance (ATR) in the mid infrared region it offers the same advantages of ease of use but is not so sensitive to sample morphology. Using model mixtures we have shown that it provides better, linear calibrations for the fat content of milk than ATR, particularly when the fat globule size is not controlled. The potential for on-line stopped flow use is also demonstrated. We conclude OT to be a possible means of introducing NIR into the dairy and related industries.
Analysis of Fat and Dry Matter in Capelin by near Infrared Transmission Spectroscopy
Authors: Solberg Christel , Fredriksen Gjert
Published: May 2009

Near infrared (NIR) transmission spectroscopy can be used for rapid and accurate analysis of chemical constituents in capelin. The NIR spectrum in the region 850–1050 nm was used to predict fat and dry matter in whole minced capelin. The third and first principal component of the multivariate data analysis of the NIR spectra indicated three main different areas, indicating changes in the samples. Best performance was achieved when the calibration model was split into three different areas, depending on the changed biology of the samples: one for pre-spawning capelin (>12% fat); another for the spawning capelin (12–5% fat); and a third one for the post-spawning capelin (6–0% fat). The standard error for prediction ( SEP) of fat was less than 0.25%, and the correlation between the measured and predicted values from NIR analysis was >0.98. The SEP of dry matter was less than 0.5%, and the correlation between the measured and predicted values from NIR analysis was >0.93.
The Relationship between near Infrared Spectra of Radial Wood Surfaces and Wood Mechanical Properties
Authors: Gindl Wolfgang , Teischinger Alfred , Schwanninger Manfred , Hinterstoisser Barbara
Published: May 2009

Near infrared (NIR) spectra taken from solid European larch wood samples subjected to axial bending and compression tests revealed an excellent ability to model the variability of mechanical properties using NIR spectroscopy. By including compression wood specimens, whose strength and elasticity is overestimated when modelled by density, in the investigated sample it could be demonstrated that the model is not just based on the measurement of density, but on density, surface geometry and possibly lignin content and composition. It is concluded that NIR spectroscopy shows considerable potential to become a tool for the non-destructive evaluation of small clear wood specimens, e.g. increment cores.
Study of NIR Spectra, Particle Size Distributions and Chemical Parameters of Wheat Flours: A Multi-Way Approach
Authors: Nielsen J. Pram , Bertrand D. , Micklander E. , Courcoux P. , Munck L.
Published: May 2009

Near infrared (NIR) reflectance spectra contain information about both physical and chemical characteristics of flour samples and have great potential for on-line/at-line quality control in a flour mill. The addition of physical characteristics such as particle size distribution data to the NIR spectra and chemical composition data of wheat flour samples was anticipated to provide a better understanding and translation of multivariate measurements into the operational routines and experiences of mill operators. This was studied using a multi-way model called “Analysis of Common Dimensions and Specific Weights” (COMDIM). By this method the underlying dimensions across several data tables with different numbers of variables are defined and the scores and loadings are interpretable in the same way as in a classical Principal Component Analysis. The method was applied on raw NIR spectra as well as after correcting the NIR spectra using the Standard Normal Variate (SNV). The model output in terms of weights, scores and loadings were highly interpretable and in agreement with common characteristics of wheat flour samples. Four underlying dimensions explained 99.4% of the total variation, both when analysing raw and SNV-corrected spectra. A comparison of the two analyses clearly shows that correcting the spectra puts more emphasis on the chemical information in the spectra. However, even corrected NIR spectra contain considerable information about the particle size properties of the flour samples. It is suggested that the COMDIM model can be a useful tool in the process control in a flour mill and it can be used on a wide range of multi-way data problems to assure a high degree of interpretability.
Obtaining Material Absorption Properties from Remission Spectra of Directly Illuminated, Layered Samples
Authors: Dahm Donald J. , Dahm Kevin D. , Norris Karl H.
Published: May 2009

The effective linear absorption coefficient, K, obtained in transflection from directly illuminated samples made up of layers of thickness d, may be approximately related to the linear absorption coefficient, k, of the material making up the layers through the following empirical equations: k = [(1 – ½ exp(–2 Kd)] K and K = [(1 + exp(–4 kd)] k. The fractions of incident intensity absorbed or remitted by one layer may be modeled by assuming that that the light that moves through a sample has both the characteristics of directed and diffuse radiation.
Calibration Transfer between Miniature Photodiode Array-Based Spectrometers in the near Infrared Assessment of Mandarin Soluble Solids Content
Authors: Greensill C.V. , Walsh K.B.
Published: May 2009

The transfer of predictive models among photodiode array based, short wave near infrared spectrometers using the same illumination/detection optical geometry has been attempted using various chemometric techniques, including slope and bias correction (SBC), direct standardisation (DS), piecewise direct standardisation (PDS), double window PDS (DWPDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT). Additionally, an interpolation and photometric response correction method, a wavelength selection method and a model updating method were assessed. Calibration transfer was attempted across two populations of mandarin fruit. Model performance was compared in terms of root mean squared error of prediction ( RMSEP), using Fearn's significance testing, for calibration transfer (standardisation) between pairs of spectrometers from a group of four spectrometers. For example, when a calibration model (Root Mean Square Error of Cross-Validation [ RMSECV = 0.26% soluble solid content (SSC)], developed on one spectrometer, was used with spectral data collected on another spectrometer, a poor prediction resulted ( RMSEP = 2.5% SSC). A modified WT method performed significantly better (e.g. RMSEP = 0.25% SSC) than all other standardisation methods (10 of 12 cases), and almost on a par with model updating (MU) (nine cases with no significant difference, one case and two cases significantly better for WT and MU, respectively).
Evaluation of Groat Percentage in Oats (Avena Sativa L.) by near Infrared Spectroscopy
Authors: Redaelli Rita , Berardo Nicola
Published: May 2009

In oats ( Avena sativa L.), groat percentage (GP) is defined as the amount of hull-less kernels obtained after de-hulling, expressed as a percentage of the weight of the sample. GP is considered a good marker of milling yield in oats, but so far a quick and reliable method to define it has been lacking. In the present study, a group of oat cultivars, grown in Italy in 1998–2000, was used to develop near infrared (NIR) spectroscopy calibration equations for this parameter. The material for the analysis was represented by whole kernels. The coefficient of determination R2c was 0.80. The ability of the equation to predict GP was then verified using a separate set of oat samples. These preliminary results indicated that NIR spectroscopy could be considered a good tool to predict GP in oat cultivars; analysis of whole kernels allowed time to be saved by avoiding the grinding stage in sample preparation.
Application of near Infrared Transmittance Spectroscopy to the Estimation of Protein and Lipid Contents in Single Seeds of Soybean Recombinant Inbred Lines for Quantitative Trait Loci Analysis
Authors: Tajuddin Teuku , Watanabe Satoshi , Masuda Ryoichi , Harada Kyuya , Kawano Sumio
Published: May 2009

The evaluation of genetic and environmental components related to protein and lipid contents requires a large number of seeds to be analysed. In this study, the use of non-destructive analysis using near infrared (NIR) transmittance spectroscopy was investigated for the determination of protein and lipid contents in intact single seeds of soybean [ Glycine max (L.) Merrill] recombinant inbred lines (RIL). Calibration equations were developed from spectral information gathered between 700 and 1100 nm. Prior to the calculation, the original spectra were transformed to the second derivative spectra. This work demonstrated that separating seed into two groups (≥ or <6 mm), followed by second derivative transformation and selection of the firstwavelength couldreduce the sample size effect remarkably. Furthermore, calibration and validation equations were developed from the combination of F8 and F9 seeds in order to correct the bias which occurred when F9 seeds were analysed using the F8 only calibration. All the equations were obtained with four optical terms. The protein content equation in the ≥6 mm group resulted in correlation coefficient ( R) of 0.88 and standarderror of prediction ( SEP) of 1.32% protein, while inthe <6 mm group theresults were0.87for R and 1.57% protein for SEP. For the lipid constituent, R and SEP were 0.80 and −0.14% in the ≥6 mm group and 0.72 and −0.09% in the <6 mm group, respectively. These equations were then applied to estimate the protein and lipid contents of soybean RIL F9 generation and the implications of these results are discussed.
A Comparison of Fourier and Wavelet Transforms in the Processing of near Infrared Spectroscopic Data: Part 1. Data Compression
Authors: Fearn Tom , Davies Anthony M.C.
Published: May 2009

The advent of spectral imaging and recent trends towards the compilation of large spectral databases have caused renewed interest in the compression of near infrared spectra for purposes of storage. A comparison of approaches using Fourier and wavelet transforms shows that wavelets are generally, though not always, more efficient than Fourier at compressing near infrared spectra. The Daubechies extremal phase wavelet of order 4 is a good choice for this purpose.
Estimating the Composition of Tomato Juice Products by near Infrared Spectroscopy
Authors: Goula Athanasia M. , Adamopoulos Konstantinos G.
Published: May 2009

The use of near infrared (NIR) reflectance spectroscopy for the rapid and accurate measurement of moisture, sugar, acid, protein and salt was explored in a diverse group of tomato juice products. Partial and overall calibrations were performed on four different tomato juice products. Partial calibrations for each product included samples of the specific product, whereas overall calibration used samples of all the products. Samples were analysed employing traditional chemical methods and scanned using an Instalab 600-Dickey-John NIR apparatus to obtain NIR spectra. Calibrations were achieved with the use of multilinear regression between chemical and spectral data from each calibration data set. A separate set of samples was used to validate the calibrations. Linear regression was applied to compare the results obtained by NIR spectroscopy for all constituents of the validation set with those obtained by the reference methods. In addition, the root mean square error of prediction ( RMSEP), the bias and the correlation coefficients ( r and r′) were calculated. All of the statistical parameters were better with overall than with partial calibrations. Prediction ability of overall calibration was very good for all the constituents. r and r′ values were higher than 0.9488 and 0.9453, respectively, RMSEP values were smaller than 0.1067, whereas bias varied from −0.020 to 0.016. The partial calibrations are considerable less variable with the correlation coefficients r and r′ ranged from 0.8890 to 0.9477 and from 0.7202 to 0.8518, respectively, RMSEP varied from 0.0647 to 0.4942 and bias from −0.365 to 0.071. NIR measurement as performed by the Dickey-John Analyser was proved a rapid and accurate method for analysis of tomato juice samples and may be used as a replacement for conventional expensive and time-consuming wet chemistry methods.
Direct near Infrared Analysis of Sugar Cane Clear Juice Using a Fibre-Optic Transmittance Probe
Authors: Tewari Jagdish , Mehrotra Ranjana , Irudayaraj Joseph
Published: May 2009

Determination of sugars in sugarcane juice is an important task in sugar refineries as well as in food industries because of its continuous evolution during the fermentation process. A rapid and accurate method for the determination of total sugars, also termed as the ‘Pol’ value, using a near infrared (NIR) fibre optic probe has been developed and validated. Clear sugarcane juice samples were collected from the sugar industry during the entire sugarcane season to represent a wide range of concentration of components such as sucrose, glucose and fructose. The samples were scanned in the NIR region (5400–6400 cm−1) using the fibre-optic transmittance probe and analysed by partial least squares (PLS). The standard polarimetric method was used to obtain reference values for the calibration model from NIR results. The calibration model developed was able to assess the Pol value in unknown sugarcane clear juice samples accurate to an R2 value of 0.936. The technique has potential in the rapid measurement of Pol content in sugarcane clear juice for the sugar industry.
Starch Index Determination of Apple Fruit by Means of a Hyperspectral near Infrared Reflectance Imaging System
Authors: Peirs Ann , Scheerlinck Nico , De Baerdemaeker Josse , Nicolaï Bart M.
Published: May 2009

A new technique, based on hyperspectral near infrared reflectance imaging, was developed to determine the maturity stage of pre-climacteric apple fruit. The technique is advantageous with respect to existing machine vision techniques as it avoids the use of toxic iodine solution. It was shown that the starch concentration obtained in each position of the fruit was continuously measured compared with the discrete values (stained/not stained) obtained with the traditional technique. The difference between the starch area and non-starch area was classified using a threshold value of the first principle component score image. The technique may be simplified in the future, using a number of bandpass filters, which will speed up the application while the purchase costs will decrease considerably. It can be considered as a model system to map quality attributes of apple fruit.
Rapid Spectroscopic Analysis of Marzipan—Comparative Instrumentation
Authors: Christensen Jakob , Nørgaard Lars , Heimdal Hanne , Pedersen Joan Grønkjær , Engelsen Søren Balling
Published: May 2009

This study had the dual purpose of applying vibrational spectroscopy to measure marzipan composition and of comparing a number of infrared and near infrared (NIR) set-ups and sampling techniques. A set of 32 marzipan samples of nine different recipes were analysed chemically and measured on six different spectrometer set-ups. Calibrations were developed using partial least squares regression (PLSR) to moisture and sugar content. Interval PLS (iPLS) was applied to all the recorded data sets to find optimal spectral intervals (of 100 nm) for the calibrations. The root mean square error of cross-validation ( RMSECV) for the prediction of the moisture was found to be 0.36% w/w moisture (range 7–19%). The sugar content was predicted with a RMSECV of 1.43%w/w(range 33–68%). Despite large variations in spectral range, instrumental design and sampling devices the different NIR spectrometer set-ups yielded calibration results with only small differences. The results are discussed in terms of the robustness of NIR instrumentation for rapid non-destructive food analysis and underline the holistic information provided in the NIR spectral range.
Determination of Lubricant Base Oil Properties by near Infrared Spectroscopy Using Different Sample and Variable Selection Methods
Authors: Lima Flávio S.G. , Araújo Maria. A.S. , Borges Luiz E.P.
Published: May 2009

The use of near infrared (NIR) spectroscopy and chemometrics for the prediction of seven lubricant base oil properties was studied. The partial least-squares calibration models were developed using spectra acquired on an acousto-optic tunable filter (AOTF)-NIR spectrophotometer equipped with a fibre-optic probe in the 1100–1700 nm region. In this work, 88 samples obtained from three Brazilian refineries over a period of two years were employed. Different variable selection and sample selection methods were also investigated. The partial least squares regression (PLSR) models thus obtained showed good correlation coefficients for the properties studied and the calculated root mean square errors of prediction (relative density = 0.0005 g cm−3; molecular weight = 12 g mol−1; flash point = 7°C; temperature 50% recovered = 6.5°C; aromatic carbon assay = 0.5%; naftenic carbon assay = 1.0% and parafinic carbon assay = 1.4%) were lower than half the reproducibility values defined by the ASTM methods, except for the relative density. These results indicate, therefore, the potential of the NIR spectroscopy and chemometric techniques for on-line lubricant oil production process control.
Near Infrared Spectroscopy and Pattern Recognition Methods Applied to the Classification of Vinegar According to Raw Material and Elaboration Process
Authors: Sáiz-Abajo M.J. , González-Sáiz J.M. , Pizarro C.
Published: May 2009

Near infrared (NIR) spectroscopy is used to classify vinegar samples according to the raw material of origin (white, red, aged and balsamic wine, malt, cider and molasses) and elaboration processes such as ageing in wood or must addition. Balsamic vinegar from Modena and Sherry wine vinegar are also included in the study; these vinegars are sometimes aged using a battery of barrels or “ soleras and criaderas” system that give the final product a special character. The differences between these samples and the other vinegar samples are also described in this study. Multivariate analysis techniques, cluster analysis, minimum-spanning tree, potential functions and discrimination via regression methods, apart from soft independent modelling of class analogy (SIMCA) and unequal class models (UNEQ) class-modelling techniques, were used to extract the information from the NIR spectral variables. These methods enable the development of models to classify the different groups of vinegar samples and provide accurate information on the specific characteristics of the vinegars related to the elaboration process of the several raw materials. A high percentage of correct classifications were obtained for wine, cider, malt, Balsamic and Sherry vinegars, ranging from 85.7 to 100%. Molasses vinegar was grouped with non-aged wine or cider samples depending on its acetic grade.
Prediction of Roasting Colour and other Quality Parameters of Roasted Coffee Samples by near Infrared Spectroscopy. A Feasibility Study
Authors: Esteban-Díez I. , González-Sáiz J.M. , Pizarro C.
Published: May 2009

Today, the coffee industry has a growing need to have rapid reference methods at its disposal that can serve as a basis for developing robust and reliable calibration models, which can later be used in the determination of future samples. This study focuses on four of the most important constituents of espresso and roasted coffee that are essential from a quality assurance standpoint: total acidity, caffeine content, chlorogenic acids and roasted bean colour. Thanks to the conjunction of pre-processing methods and multivariate calibration with near infrared (NIR) spectroscopy, separate regression models have been developed which can be used as a rapid method for the prediction of these four quality parameters. Special attention could be given to the unexpected potential of NIR measurements to provide colour quality information relating to roasted coffee samples.
Spectral Pre-Treatment for Diffuse Transmittance Linearity Improvement
Authors: Švedas Vitas
Published: May 2009

The linearity of diffuse transmittance spectra was considered on the basis of the Kubelka–Munk theory. The sub-linear dependence of –log T on the component concentration was determined and a linearisation method is suggested. The main point of linearisation is solving a cubic equation with absorbance to be linearised as an unknown variable and with the equation coefficients being power functions of the sample scattering capacity. The performance of the method is tested by employing the spectra of adsorbed water in the 1796–2004 nm range. The cellulose sheet was a matrix for adsorbed water molecules. Seventeen water content values from 0.6% to 22.7% (w/w) were prepared for calibration–prediction procedures. Raw spectra and linearised spectra were analysed by principal component analysis and partial least squares (PLS) regression. PLS cross-validation shows the 2.85-fold decrease of the prediction error after linearisation.
Examination of Spruce Wood Biodegraded by Ceriporiopsis Subvermispora Using near and Mid Infrared Spectroscopy
Authors: Schwanninger M. , Hinterstoisser B. , Gradinger C. , Messner K. , Fackler K.
Published: May 2009

The wood colonising white-rot basidiomycete Ceriporiopsis subvermispora is able to degrade lignin in preference to cellulose. To differentiate between fungal strains and to estimate their delignification behaviour, both in an early stage of degradation and over a specific period, is important for the wood industry. Mid infrared (MIR) and near infrared (NIR) spectra were taken from 60 milled spruce wood samples and their total lignin content was determined by wet laboratory methods. Good correlations were found between the MIR band–height ratio (H1510 cm−1 / H897 cm−1) and the lignin content ( r = 0.965) and between the NIR band height at 5978 cm−1 (1673 nm) taken from spectra in the second derivative mode and the lignin content ( r = 0.956). Furthermore, good linear correlations between the band–height ratios calculated from the MIR spectra and the amplitudes of the band around 5978 cm−1 (1673 nm) of NIR spectra in the second derivative mode were found for the calibration samples ( r = 0.934) and for the fungal-treated samples ( r = 0.984). The good correlation found between the MIR band–height ratio and the band height from NIR spectra in the second derivative mode could be interesting if calibrations exist for MIR (or NIR) to predict samples measured in the NIR (or MIR). MIR and NIR spectra recorded from milled spruce wood shavings that had been subjected to fungal treatment with three strains of Ceriporiopsis subvermispora (CBS 347.63, FPL 90.031 and FPL 105.752), for a period of up to 14 days, were investigated to see if these spectroscopic techniques could replace chemical methods. It is shown that the relative degree of delignification can be obtained directly from NIR spectra in the second derivative mode measuring the amplitude of a distinct band and from MIR spectra normalised with respect to the band at 897 cm−1. Subjecting the spectra to principal component analysis (PCA) made it possible to study the time course along a PC axis. The use of an appropriate NIR wavenumber range subjected to PCA led to a scores plot that made it possible to differentiate between the three strains of C. subvermispora along one axis. It was also possible to give a time course and an indication of the relative degree of delignification along the second axis. In both cases, 99% of the data variance was explained with the first two PCs. A similar time course was obtained from MIR spectra, but the strains could not be separated well. Besides strain differentiation and examination of delignification, some practical applications (for example, in the pulp and paper industries, fungi-screening, evaluation of wood preservatives) are discussed. The results clearly demonstrate that it is possible to compare and differentiate between the strains without applying time-consuming chemical methods. The examination of NIR spectra is sufficient.
Analysis of Quality Constituents of Natural Alpine Swards with near Infrared Reflectance Spectroscopy
Authors: Confalonieri Massimo , Lombardi Giampiero , Bassignana Mauro , Odoardi Miriam
Published: May 2009

The feasibility of near infrared (NIR) reflectance spectroscopy in determining various dry matter constituents such as crude protein, neutral detergent fibre, acid detergent fibre and ash in mixed samples of natural alpine swards has been investigated. Samples were collected in different altitudinal and geographic locations exploited by grazing animals. They were previously analysed by conventional chemical methods, scanned using an NIRSystems 5000 monochromator and spectra were treated using different math pre-treatments and applying different calibration algorithms to achieve the best predictive performances. First and second derivatives of each NIR spectrum were used for all statistical analyses. Step-up, step-wise and modified partial least squares (MPLS) regression methods were applied to develop reliable calibration models between the NIR spectral data and the results of wet analyses. MPLS models fitted chemical data better than linear regression models for CP, ADF and ash, while step-wise worked better for NDF. The results demonstrated that NIR reflectance spectroscopy can be used as a routine testing method to estimate accurately, rapidly and non-destructively three important constituents of mixed alpine forage species, namely crude protein, neutral detergent fibre and ash. For other determinations, such as acid detergent fibre, our results were slightly less accurate and satisfactory but may be useful for separation of samples into groups.
Efficient Spectroscopic Calibration Using Net Analyte Signal and Pure Component Projection Methods
Authors: Cogdill Robert P. , Anderson Carl A.
Published: May 2009

In the wake of FDA's finalisation of the process analytical technology guidance to industry, the application of near infrared (NIR) spectroscopy for quality analysis in pharmaceutical manufacturing has continued to grow. The required level of variation needed to develop a NIR method often exceeds that observed in a well-controlled pharmaceutical production process. This insufficiency can be addressed by developing non-production samples to introduce range, but at high cost in labour and complexity. The recently-introduced pure-component projection (PCP) method utilises the information in the spectral characteristics of pure sample constituents to reduce NIR spectra to a univariate signal, thereby mitigating the need for non-production samples. The PCP method is compared to net analyte signal (NAS) processing and PLS regression calibration when relatively little calibration data are available. The predictive performance of all algorithms was observed to be similar, although NAS and PCP have advantages in selecting the optimal number of latent variables for calibration. PCP holds a definite advantage as the only algorithm capable of producing a sensitive, linear regression coefficient vector without chemical reference data or non-production samples.
The Component Analysis of Bottled Red Sufu Products Using near Infrared Spectroscopy
Authors: Lu Chao , Han Donghai
Published: May 2009

Sufu (fu-ru), a cheese-like product originating in China, is one of the most popular fermented soybean foods in China. This paper indicates the possibility of using near infrared (NIR) spectroscopy as a rapid, non-destructive method to estimate the quality attributes of sufu products. The partial least squares (PLS) method was applied to build models to predict the contents of water, salt, soluble protein and free amino nitrogen in bottled red sufu product. Both optical fibre and integrating sphere accessories were used to collect the representative reflected spectra. Multiplicative scatter correction (MSC) was used to eliminate the effect of light scatter and the first-order derivation of the spectra was used to reduce the effect of absorption of glass bottles. The coefficients of determination ( R2) of the four components above were 0.96, 0.98, 0.94 and 0.94, respectively using an integrating sphere, and 0.92, 0.98, 0.96 and 0.92, respectively using an optical fibre. The root mean square errors of prediction ( RMSEP) of those were 0.99, 0.21, 0.49 and 0.033%, respectively using an integrating sphere and 1.63, 0.28, 0.34 and 0.041%, respectively when using an optical fibre. The correlations ( R2) between the components of sufu blocks and those of the surrounding dressing mixture had been found to be 0.18 for water, 0.24 for soluble protein, 0.92 for free amino nitrogen and 0.89 for salt. These coefficients would be applicable for potential on-line analysis. The preliminary results of those models indicated that NIR spectroscopy has a potentially useful role in the measurement of the component content of sufu samples.
Determination of Ephedrine Hydrochloride in Mixtures of Ephedrine Hydrochloride and Pseudoephedrine Hydrochloride Using near Infrared Spectroscopy
Authors: Dijiba Yanga K. , Niemczyk Thomas M.
Published: May 2009

There are a number of situations where there is a need to determine the concentrations of components in solid-state mixtures without dissolving the samples. The experiments described here were designed to demonstrate that diffuse reflectance near infrared spectroscopy coupled with partial least squares (PLS) data analysis can be used to determine the minor component in a mixture of structurally-similar solid-state compounds, in this case mixtures of ephedrine hydrochloride and pseudoephedrine hydrochloride. It is shown that when care is taken to produce homogeneous calibration samples very good results can be obtained; in this case, cross-validated standard error of predictions of 2.30 wt% when the minor component spanned the concentration range of 0 to 50 wt% and 0.30 wt% when the minor component spanned the concentration range of 0 to 5 wt%. Results are presented that indicate that the amount of data available to the PLS calibration routine relative to the range over which the calibration is performed can limit the precision and accuracy of the determinations.
Determination of Tobacco Constituents with Acousto-Optic Tunable Filter-Near Infrared Spectroscopy
Authors: He Zhihui , Lian Wenliu , Wu Mingjian , Chen Ya , Tang Liyun , Luo Jia
Published: May 2009

In the present work, we developed an acousto-optic tunable filter near infrared (AOTF-NIR) reflectance spectroscopy method for determining tobacco constituents. The NIR spectra of 443 tobacco samples were collected using an AOTF-NIR spectrometer. Calibration models for total sugar, reducing sugar, nicotine and potassium in tobacco were built by partial least squares (PLS) regression for which regression R2 were 0.984, 0.987, 0.988 and 0.920, respectively and the average relative standard deviations between the predicted values and the measured values were 2.68%, 2.88%, 4.18% and 8.38%, respectively. The results indicate that the AOTF-NIR technique is feasible for the analysis of tobacco constituents and provides a foundation for on-line monitoring in the tobacco process. To our knowledge, this is the first attempt to analyse tobacco constituents with AOTF-NIR spectroscopy.
Near Infrared Spectroscopy, Cluster and Multivariate Analysis—Characterisation of Silica Materials for Liquid Chromatography
Authors: Huck Christian W. , Ohmacht Robert , Szabo Zoltan , Bonn Günther K.
Published: May 2009

Near infrared (NIR) reflectance spectroscopy is a well established method within the analytical chemistry and a wide range of applications, for example in the field of food and pharmaceutical products, is covered. In the era of material science and nano technology, we brought together, in this feasibility study, silica chemistry with NIR. NIR, in the fibre-optics mode, is used for morphological studies. It enables differentiation between silica materials in a three-dimensional factor-plot depending on their functionalisation and physical properties such as particle diameter and pore diameter with Q-values of 0.95 and 0.99. The well established, common reference methods for the determination of surface area and porosity are the so called Brunnauer Emmett Teller method and size-exclusion chromatography. Both methods are time-consuming and require a lot of experience. The recently elaborated NIR method offers a physicochemical quantitative description at the nano-scale level of particle size, specific surface area, pore diameter, pore porosity, pore volume and total porosity with high linearity of R2 > 0.97 for calibration and r2 > 0.98 for validation and a bias of < 2.48 × 10−14. Compared to the reference method, which enables the determination of the individual parameters with a relative standard deviation between 6 and 28%, the NIR method shows RSD% between only 0.010 and 13.7%, which means a fundamental improvevement in precision. The measurement takes only a few seconds so high sample throughput is guaranteed. The suitability of the newly established method in material science is demonstrated, as an example, on the silica stationary phases for liquid chromatography.
Remote near Infrared Instrument Cloning and Transfer of Calibrations to Predict Ingredient Percentages in Intact Compound Feedstuffs
Authors: Pérez-Marín D. , Garrido-Varo A. , Guerrero-Ginel J.E.
Published: May 2009

This study examined the possibility of transferring calibrations to predict ingredient inclusion percentages in intact compound feedstuffs. For standardisation purposes, the cloning algorithm proposed by Shenk and Westerhaus was used. Three different cloning sets were evaluated for the design of seven spectral correction files or cloning matrices. Six of these correction files, when applied to spectra obtained from the satellite instrument, yielded results similar to those obtained using the master instrument. Moreover, using a validation set ( N = 15) to compare predicted values from unstandardised and standardised satellite spectra with master spectra using the same calibrations, the standard error of differences ( SED) was greatly reduced with all six cloning matrices. This indicates the success of the tuning method for standardisation and transfer of ingredient composition prediction equations in compound feedstuffs analysed unground.
Potential of near Infrared Transflectance Spectroscopy to Detect Adulteration of Irish Honey by Beet Invert Syrup and High Fructose Corn Syrup
Authors: Kelly J. Daniel , Petisco Cristina , Downey Gerard
Published: May 2009

Near infrared (1100–2498 nm) transflectance spectroscopy was used to detect beet invert syrup (BI) and high fructose corn syrup (HFCS) adulterants in artisanal Irish honey. The sample set investigated comprised authentic ( n = 83), BI-adulterated ( n = 56) and HFCS-adulterated ( n = 40) honeys. Soft independent modelling of class analogy was used to classify honeys as authentic or adulterated while partial least squares regression (PLS1) was used to predict the adulteration level. Spectral data were investigated in three forms: raw, after multiplicative scatter correction and after second derivative transformation. The best classification model was obtained using raw spectral data. The preferred models for prediction of percentage adulteration involved PLS1 of multiplicative scatter corrected spectra (adulteration with BI) and second derivative transformation (adulteration with HFCS). The present study has demonstrated that near infrared spectroscopy could be used as a rapid screening tool for detection of BI and HFCS adulteration in Irish honey.