bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Temperature on the Predictive Ability of near Infrared Spectroscopy Models
Authors: Blanco Marcelo , Valdés Dámarih
Published: May 2009

Temperature changes alter the position and intensity of near infrared (NIR) spectral absorption bands and thus affect the predictive ability of the associated calibration models. Achieving accurate control of this variable in industrial processes is difficult and variations can have a strong impact on their analytical monitoring. In this work, the effect of temperature changes over the range 25–90°C on the predictions for the ingredients of the esterification reaction between acetic acid and butanol was examined. Spectra for mixtures of the different reactants and products were used to construct calibration models by partial least-squares (PLS) regression and stepwise principal component regression (stepwise PCR). The models were constructed from the temperature ranges, wavelengths, numbers of factors and spectral treatments leading to the highest predictive ability. Based on the results, the variable temperature can also be modelled and the predictive ability of calibration models improved by including partially or completely the effect of temperatures.
Application of a Portable near Infrared Spectrometer for the Manufacturing of Noodle Products
Authors: Kumagai Masanori , Matsuura Natsumi , Li Hua , Ohisa Naganori , Amano Toshio , Ogawa Nobuaki
Published: May 2009

We applied the rapid, non-destructive and convenient near infrared (NIR) spectroscopy method, using a portable NIR spectrometer, to the manufacturing process of noodle products (Inaniwa Udon). Characterisation of wheat flours, which are a primary ingredient of noodle products, was identified easily by the pricipal component (PC) score plots based on the standardised NIR spectra. We assessed these results not only from a practical standpoint, but also for the chemical interpretation of NIR spectra. Without any sample preparation, NIR spectra revealed moisture content in the products. Further investigation revealed that moisture content is proportional to viable bacterial counts in the products, so determining moisture content in products by the rapid, non-destructive and convenient method of using a portable NIR spectrometer, as used in this work, is extremely useful in the Inaniwa Udon quality control process.
Useful Tips for Constructing a near Infrared-Based Quality Sorting System for Single Brown-Rice Kernels
Authors: Rittiron Ronnarit , Saranwong Innapa , Kawano Sumio
Published: May 2009

Information required to develop a high-speed single kernel sorting machine for brown rice is presented. The effects of wavelength region, sample illumination geometry, kernel position and calibration method were examined. To obtain a high-accuracy system, the long wavelength region from 1100 nm to 1800 nm in transmittance mode should be used. An appropriate geometry for spectral acquisition and an alerting system for improper kernel position using principal component analysis are recommended. The choice of calibration methods, partial least squares or multiple linear regression, with similar accuracy is provided. Using the system proposed, sufficiently accurate calibration equations with standard errors of prediction of 0.24% w/w dry basis for moisture and 0.40% w/w dry basis for protein could be obtained.
Prediction of Moisture, Fat and Inorganic Salts in Processed Cheese by near Infrared Reflectance Spectroscopy and Multivariate Data Analysis
Authors: Blazquez Carmen , Downey Gerard , O'Donnell Colm , O'Callaghan Donal , Howard Vincent
Published: May 2009

Near infrared (NIR) spectroscopy is widely used in the food industry as a quality control tool. Usage in the dairy industry is extensive, but reports of its application to processed cheese analysis are few. This work investigated the potential of NIR spectroscopy and multivariate data analysis to determine the moisture, fat and inorganic salts content of processed cheeses stored for up to four weeks. Reflectance spectra (400–2498 nm) and reference values of cheese samples ( n = 64) were collected. Calibrations to predict moisture (37.7–54.8% w/w), fat (23.7–34.2% w/w) and inorganic salts (2.0–4.7% w/w) content were developed by a partial least squares (PLS) regression procedure. Models were developed using five wavelength ranges; 400–2498 nm, 400–750 nm (visible), 400–1100 nm, 750–1100 nm (near near infrared) and 1100–2498 nm (near infrared). Spectral data were used (1) without any pre-treatment, (2) after scatter correction (standard normal variate and de-trending) and (3) the latter plus a second derivative step (10 data point gap size). For both moisture and fat, the preferred models were obtained using the latter treatment. Fat prediction used spectral data between 1100 and 2498 nm ( SECV = 0.45, R = 0.98) with five loadings. For moisture, the preferred prediction was obtained using the wavelength range between 1100 and 2498 nm ( SECV = 0.50, R = 0.99) using four loadings. For inorganic salts calibration, preference was for the model obtained using the second option above on spectral data also in the range 1100–2500 nm ( SECV = 0.26, R = 0.90 with seven loadings). These results are sufficiently accurate to recommend NIR reflectance spectroscopy for off-line quality assessment of processed cheese.
Experimental Design Procedures in the Calibration of Quality Parameters of Alfalfa Pellets from near Infrared Spectra
Authors: Gatius Fernando , Ferran Javier , Puy Jaume
Published: May 2009

Large calibration matrices are usually needed to cover all the variability of forage samples. The present work applies experimental design techniques to reduce the number of samples necessary to calibrate quality properties of pellets of forage. Near infrared spectra were recorded from the raw material which consisted of dehydrated alfalfa milled, pressed and packed in cylindrical pellets, 5 mm in diameter. Partial least squares calibration models for moisture and crude protein, built up from 771 samples, were used as reference. Full factorial, central composite and Box–Behnken designs, using the principal components of the raw spectra as design factors were tested for sample reduction. The effect of increasing the number of replicates for each design point was also studied. Comparisons between models have been made in terms of prediction accuracy, but also looking at the model stability by means of Martens' uncertainty test. Results obtained indicate that the prediction accuracy is similar in all assayed designs and similar to that of the reference model, although the stability of the models obtained using a reduced sample set is lower. Increasing the number of replicates of the design points increases stability.
The Effect of Spectral Pre-Treatments on the Partial Least Squares Modelling of Agricultural Products
Authors: Delwiche Stephen R. , Reeves James B.
Published: May 2009

Spectral pre-treatment, such as scatter correction, smoothing and derivatisation is considered, to the point of being almost folklore, an integral component to the development of near infrared (NIR) partial least squares (PLS) regression equations. This study was undertaken to examine the importance of pre-treatments. Diffuse reflectance NIR (1100–2500 nm) spectra of ground wheat and forages were separately analysed. For ground wheat, the effect of spectral pre-treatment on the PLS equations for protein content and sodium dodecyl sulphate (SDS) sedimentation volume (a protein quality index) was examined. For forages, similar examinations were performed on crude protein content and lignin content. Results indicate that while pre-treatment is indeed important, statistical significance, as determined by the F-test of correlated variances, is often not established. Protein content calibrations tend to be enhanced by scatter correction, as opposed to smoothing or derivatisation, whereas the SDS sedimentation volume and lignin content calibrations favoured these convolution functions. It is recommended that the selection of the best pre-treatment for an analyte be based on the combination of statistical testing and the modeller's judgement.
A Hierarchical Discriminant Analysis for Species Identification in Raw Meat by Visible and near Infrared Spectroscopy
Authors: Arnalds Thorsteinn , McElhinney John , Fearn Tom , Downey Gerard
Published: May 2009

The problem tackled is that of discriminating between chicken, turkey, pork, beef and lamb using visible and near infrared spectra collected from homogenised meat samples. This five-group classification task was treated as a hierarchical sequence of binary splits: white versus red meat, then either poultry versus pork or beef versus lamb, and finally, in the case of poultry, chicken versus turkey. Most of the splits were achieved by linear discriminant analysis applied to principal component scores. The chicken versus turkey split worked better with soft independent modelling of class analogy (SIMCA). One of the attractions of the hierarchical approach is that different methods can be used for different splits. The results, with only two classification errors on 115 validation samples, were better than those achieved in previous analyses of the same data.
Correction of the Temperature Effect on near Infrared Calibration—Application to Soluble Solid Content Prediction
Authors: Chauchard F. , Roger J.M. , Bellon-Maurel V.
Published: May 2009

Non-destructive fruit quality assessment in packing houses can be carried out using near infrared spectroscopy. However, the prediction performance can be affected by measurement conditions (“external parameters”), such as temperature or variation in stray light. In this work we propose a methodology to reduce the effect of the fruit temperature on sugar content prediction. Two approaches were used to correct for the temperature effect depending on whether the temperature is measurable or not. In the case of measurable temperature, three methods were tested. The first uses a spectrum correction while the second and third are based on regression coefficients which vary with temperature. In case of non-measurable temperature, the studies have led to robust calibration models and to a self-correcting model (where fruit temperature is estimated using spectral data). All techniques were tested with “Golden Delicious” apples. Our study has shown that the most efficient models remove the temperature from the spectral space. These methodologies can be used to minimise other external parameters in NIR calibration.
Kromoscopic Identification of in Vivo Water Variations
Authors: Guthermann Howard E. , Misner Michael W. , Block Myron J.
Published: May 2009

Optical assays for trace analytes in vivo are severely hampered by the predominant optical effects of water throughout the infrared spectrum. These effects must be properly understood and accounted for in order to avoid the occurrence of spurious correlations in data purporting to track concentrations of trace analytes such as glucose. Kromoscopic analysis™, invented for non-invasive optical assay of trace analytes in vivo, uses real time correlations between measurements that are both spatially and temporally congruent and made with multiple detection channels having overlapping spectral sensitivities. In previous work, we have shown how the predominant optical changes due to water movement in vivo can be tracked and accounted for. More in-depth analysis of Kromoscopic data presented here will demonstrate that at least two distinct and uncorrelated modes of optical variation induced by water changes are present during in vivo sampling. Both of these modes of variation must be accounted for to properly minimise their effects on measurements of glucose or other trace analytes in vivo. This minimisation is demonstrated clearly via Kromoscopic analysis, and the resulting requirements of a successful trace analyte measurement are discussed.
Prediction of Shive Content in Pilot Plant Processed Flax by near Infrared Reflectance Spectroscopy
Authors: Sohn Miryeong , Barton Franklin E. , Morrison W. Herbert , Akin Danny E.
Published: May 2009

Shive is the main contaminant in flax fibre and affects fibre quality. In this study, we developed a calibration for determining shive content in flax using near infrared (NIR) spectroscopy and applied the model to pilot plant processed flax to predict shive content. The model based on “ground” mixtures performed best from multiplicative scatter correction after a second derivative treatment of the spectral data, giving a standard error of cross-validation of 0.35% using five factors. Prediction samples were Jordan enzyme-(ER) and Natasja dew-retted (DR) flax that was collected after various stages of processing. When the model was applied to the “ground” flax, a high correlation was obtained between the NIR predicted value and actual shive content, giving a correlation coefficient of > 0.98 for both retted flax samples. However, when the model was applied to the “as-is” flax, a slope and bias were observed. These deviations were corrected by a linear regression between predicted values of “ground” and “as-is” flax. For the NIR analysis of ER flax, the shive content decreased rapidly by the third processing step to 4 to 5% and almost 0% after the last step. For the DR flax, the shive content continuously decreased with processing to about 5% after the last step. The results indicate that it is possibile to measure shive in flax on a commercial processing line.
A New Approach for Estimating Purity of Processed Flax Fibre by NIR Spectroscopy
Authors: Sohn Miryeong , Barton Franklin E. , Akin Danny E. , Morrison W. Herbert
Published: May 2009

Flax must be retted, in which bast fibres are separated from non-fibre components, and then mechanically processed to clean the fibres before industrial application. In the USDA Flax Fiber Pilot Plant, flax is first cleaned through four separate modules and then passed through a Shirley Analyzer to further clean fibres for high-value applications such as textiles. Often, multiple passages through the Shirley Analyzer are employed to obtain higher quality fibres, but it is difficult to determine when the limit for cleanliness is reached by this method. Further, it is clear that materials other than the woody shive components are being removed by Shirley-cleaning, and a method is needed to assess cleanliness beyond the measure for shives. In this study, we attempted to establish an index to determine the degree of purity of flax fibre during the secondary cleaning stage for high quality fibre. Dew-retted (DR) flax and enzyme-retted (ER) flax, which had been first processed through the USDA Flax Fiber Pilot Plant and assessed for shive content, were processed with 10 repetitions of cleaning through the Shirley Analyzer. For both flax samples, absorbances at 1730, 1766, 2312 and 2350 nm decreased with successive Shirley-cleaning steps. These wavelengths appeared to originate from the epidermal layer (EL) that was associated with the flax fibre, an index was calculated using 11 training samples and validated using 10 independent test samples from the same flax samples. Index values gradually decreased with successive Shirley-cleaning steps for both retted flax samples; a lower index value indicated cleaner fibre. Different curves were apparent for the two flax samples, suggesting variations in the cleanliness of the starting material or perhaps differencess in fibre composition. The results suggest it is possible to determine the extent of cleaning of flax fibre using NIR spectroscopy beyond that for shive content based on the epidermal layer of the plant.
Estimation of the Physical Wood Properties of Pinus Taeda L. Radial Strips Using Least Squares Support Vector Machines
Authors: Cogdill R.P. , Schimleck L.R. , Jones P.D. , Peter G.F. , Daniels R.F. , Clark A.
Published: May 2009

Near infrared (NIR) spectroscopy offers a rapid method for estimating many important wood properties, including air-dry density, microfibril angle ( MFA) and SilviScan estimated stiffness (EL(SS)). Wood property calibrations may be improved by using non-linear calibration methods. In this study, we compare calibrations developed using partial least squares (PLS) regression and least-squares support vector machine (LS-SVM) regression, a relatively new technique for modelling multivariate, non-linear systems. LS-SVM regression provided the strongest calibration statistics for all wood properties. For an equivalent number of latent variables, the predictive performance of the MFA LS-SVM calibrations were superior to those of the corresponding PLS calibration, while predictive results for air-dry density and EL(SS) were similar for both calibration methods.
The Influence of Spectral Resolution on the Quantitative near Infrared Spectroscopic Determination of an Active Ingredient in a Solid Drug Formulation
Authors: Kolomiets Olga , Siesler Heinz W.
Published: May 2009

This short communication is intended to demonstrate the influence of spectral resolution on the quantitative determination of an active ingredient in a solid drug formulation by diffuse reflection near infrared spectroscopy. As model system we have chosen a mixture of the active ingredient acetylsalicylic acid (aspirin) with cellulose and starch as excipients.
Potential for On-Site Analysis of Hog Manure Using a Visual and near Infrared Diode Array Reflectance Spectrometer
Authors: Saeys W. , Darius P. , Ramon H.
Published: May 2009

In this study, a diode array near infrared (NIR) spectrometer that can be mounted on a mobile agricultural machine was tested under lab conditions for its effectiveness as a rapid, on-site hog ( Sus domesticus) manure composition sensor. One hundred and sixty nine diverse hog manures, collected from farms in the northern part of Belgium, were assayed by conventional wet chemical analysis and spectroscopy for the following constituents: dry matter content, organic matter content, pH, total Kjeldahl nitrogen, ammonium nitrogen, phosphorus, potash, calcium, sodium and magnesium. Samples were scanned (two fills, three measurements each) on a Zeiss Corona 1.7 VisNIR diode array instrument equipped with an OMK measuring head, ranging between 400 and 1700 nm (five scans per measurement) using a sample cell (50 mm diameter) for diffuse reflectance scanning from the bottom. Based on the absorbance–constituent and constituent–constituent correlations, the constituents were divided into two groups: one for those in the solids (dung) and one for those in the liquid (urine). The pH was not correlated to any other constituent and, therefore, treated separately. Partial least squares calibrations were developed for the constituent groups and for each of the constituents separately using leave-one-out cross-validation, showing a common spectral basis for each constituent group. To investigate the possibility of developing a cost-effective sensor, regression calibrations were developed using a few wavelengths selected based on the partial least squares regression and validated by leave-one-out cross-validation. Results demonstrated that: (1) approximate quantitative predictions can be made for total N, NH4–N and K, (2) a discrimination between high and low values can be made for DM, OM, P and Mg, (3) prediction of Ca, Na and pH are not possible (yet), (4) one measurement and one filling of the cup give a reliable measurement, (5) a cost-effective sensor measuring only a few appropriately selected wavelengths might be able to give comparable results, (6) further work will be necessary to optimise sample presentation and calibration procedures and (7) mounting a diode array instrument on a slurry tanker can make environmentally and economically sound nutrient management possible.
Non-Destructive Determination of Carbohydrate Content in Potatoes Using near Infrared Spectroscopy
Authors: Chen Jie Yu , Miao Yelian , Zhang Han , Matsunaga Ryuji
Published: May 2009

The performance of near infrared spectroscopy as a simple technique for the non-destructive determination of carbohydrate content in potatoes was examined. An interactance method was adopted to measure near infrared spectra (700–1100 nm). A good calibration model with reasonable accuracy (correlation coefficient of 0.93 and standard error of prediction of 0.98%) was developed using partial least square (PLS) regression. The PLS calibration model utilised effectively two characteristic absorption bands of 990 nm and 916 nm, assigned to potato starch. The plots of the regression coefficients were thought to be useful for understanding the calibration model structure.
Near Infrared Spectroscopy for Determination of Total and Exchangeable Cations in Geologically Heterogeneous Forest Soils
Authors: Chodak Marcin , Khanna Partap , Horvath Balazs , Beese Friedrich
Published: May 2009

Sustainable forest management requires information on a number of soil properties. Therefore fast methods of soil analysis are needed. The objective of this study was to test the ability of near infrared (NIR) spectroscopy to predict the total and exchangeable Na, K, Ca, Mg, Mn, Fe and Al, the cation exchange capacity ( CEC), the base saturation ( BS) and the total contents of Zn, Cu, Cd and Pb in geologically heterogeneous forest soils. The samples ( n = 100) were collected from five sites covered by beech ( Fagus sylvatica L.) forest stands and from four depths. The soils were analysed for total contents of Na, K, Ca, Mg, Mn, Fe, Al, Zn, Cu, Cd and Pb after pressure digestion in HNO3 and for contents of exchangeable Na, K, Ca, Mg, Mn, Fe and Al in NH4Cl. The spectra were recorded between 400 and 2500 nm at 2 nm intervals. Principal components analysis revealed significant spectral differences among the samples from different sites. The global NIR models predicted well the mean values of the total contents of all cations, Zn, Pb, the exchangeable K, Mg, Fe and BS in upper (0–10 cm) and lower (10–20 cm) layers of the studied soils: the correlation coefficients ( r2) of the linear regression (measured against predicted values) varied between 0.90 and 1.00 and the regression coefficients ( a) ranged from 0.94 to 1.07. The contents of Cu, CEC and the exchangeable Na, Ca and Al were predicted satisfactorily ( r2 = 0.87–0.98, a = 0.86–1.14). The global models overestimated the values of total Ca and exchangeable Ca, Mn and Fe in the lower parts of their ranges resulting in biased estimations of the means at some of the considered sites. Splitting the sample population into spectrally similar groups enabled the development of local calibrations, which improved the prediction accuracy (lower standard errors of prediction) for most of the analysed constituents and removed the bias in the estimations of exchangeable of total Ca and Ca, Mn and Fe. The obtained results indicated the usefulness of NIR spectroscopy for determination of a number of soil constituents in geologically heterogeneous forest soils.
Near Infrared Reflectance Spectroscopy of Oil in Intact Canola Seed (Brassica Napus, L.). I. Attributes of the Intact Seed Spectrum
Authors: Golebiowski T.
Published: May 2009

This study examined the attributes of the oil signal embedded in the near infrared (NIR) reflectance spectra of intact canola seed. The oil/spectrum relationship was examined with the aid of the following techniques: comparison of raw log 1/ R spectra, investigation of absorption difference spectra and investigation of oil/wavelength correlations. The 1100–2500 nm reflectance spectrum of canola oil contains three major segments, 1150 to 1250 nm, 1650 to 1800 nm and 2250 to 2380 nm, that denote absorption of the constituent. The spectrum of intact canola seed closely resembles the spectrum of oil. However, the strength of oil absorption in the seed is significantly reduced, particularly at the longer wavelengths. Examination of the NIR spectra revealed that the oil signal embedded in the reflectance spectrum of intact canola seed was primarily affected by the spectral characteristics of the seed. Signal correction tools—standard normal variance, detrending and the second order derivative—applied to the data showed an unreliable efficiency in minimising scattering defects in the spectra. The absorption difference spectra displayed variable patterns in the manifestation of differences in the oil concentration between canola samples. These differences could be observed either in all or only in some of the oil-specific segments of the spectrum. Correlograms illustrated a very complex spectrum/oil relationship, displaying a large number of wavelengths strongly correlated to the oil across the entire 1100–2500 nm spectrum, including a considerable number of wavelengths, which displayed chance correlations. Therefore, NIR analysis of oil in the intact canola seed preferably should involve the full spectrum information and should involve the use of latent variables, rather than the original wavelength space.
A Simple Method of Instrument Standardisation for a near Infrared Sorting Machine: The Utilisation of Average Spectra as Input Vectors
Authors: Saranwong Sirinnapa , Kawano Sumio
Published: May 2009

A new concept of spectral adjustment was examined to solve the problem of calibration transfer in a near infrared (NIR) sweetness sorting machine. Instead of pairing the absorbance values of each sample measured by master and slave instrument, the average spectrum of each instrument was used. Two identical NIR instruments, equipped with interactance fibre optics, were used as model instruments and a calibration equation for Brix determination of apples was developed as a model calibration. For each instrument, master and slave, each average spectrum was calculated from the spectra of an identical group of apples and then spectral matching of the slave instrument to the master was performed. The curve fitting method, either linear regression or polynomial, could not work well. However, by adding the difference spectrum, the differences of average absorbance values at wavelength i between the master and slave instruments, the spectrum of each sample measured by the slave instrument could successfully standardise the slave instrument. The matching was successfully done by adding the difference spectrum between the two average spectra to the slave instrument's spectra. It was found that the method could be used to compensate for both the wavelength shift and photometric change and it also proved that the method was applicable for both multiple linear regression and partial least squares equations.
In-Line Analysis of Ground Beef Using a Diode Array near Infrared Instrument on a Conveyor Belt
Authors: Hildrum Kjell Ivar , Nilsen Bjørg Narum , Westad Frank , Wahlgren Nils Magnus
Published: May 2009

In-line monitoring of the proximal composition of ground beef on a conveyer belt has been tested using a near infrared (NIR) reflectance instrument with a diode array detector. Sixty batches of coarsely ground beef were processed under industry conditions and monitored continuously. After removing signals originating from the belt itself, the remaining data were used to make partial least squares models for all samples at two different grinding sizes. The correlation coefficients were in the range 0.93–0.96, while the full cross-validated errors for fat and water were between 1.6 and 2.4%. The corresponding errors for protein were 0.5–0.8%. A forward variable selection method based on jack-knifing yielded similar results. The predictions were generally best for the smallest grinding size. The loading plots showed that there was a high interdependency between calibrations for the different components. Before implementation of the calibrations, they must be re-tested under industry conditions on new independent batches.
Removal of Conveyor Belt near Infrared Signals in In-Line Monitoring of Proximal Ground Beef Composition
Authors: Westad Frank , Nilsen Bjørg Narum , Wahlgren Nils Magnus , Hildrum Kjell Ivar
Published: May 2009

When monitoring the proximal composition of ground beef on a conveyor belt with a near infrared reflectance sensor, interference by signals from the belt itself proved to be a serious problem. A soft independent modelling of class analogies classification method proved useful in identifying spectra with belt components which, consequently, were removed to yield pure meat spectra. Partial lease squares regression on the basis of the corrected spectra yielded improved models for proximal analysis of the ground beef.
Precise near Infrared Spectral Acquisition of Intact Tomatoes in Interactance Mode
Authors: Khuriyati Nafis , Matsuoka Takahisa , Kawano Sumio
Published: May 2009

To apply near infrared (NIR) spectroscopy for non-destructive quality control of intact hydroponic tomatoes, a search for precise spectral acquisition was performed. Along the equator of each fruit, six NIR spectra in the short wavelength region were acquired with a portable NIR instrument equipped with interactance fibres. Four types of spectra, which were averaged from 1, 2, 3 and 6 position(s), were calculated to be used as input vectors for dry matter calibration. The calibration results suggested that the use of 1-position spectra was not always sufficiently accurate while the 2-position spectra was acceptable. To obtain a highly efficient and stable calibration equation, the 3- or 6-position spectra should be used. By using the 6-position averaged spectra, the calibration equation with the ratio of standard deviation of reference data in validation set to standard error of prediction ( RPD) of 5.05 could be obtained.
Changes in Carbohydrate Content during Wheat Maturation—What is Measured by near Infrared Spectroscopy?
Authors: Gergely Szilveszter , Salgó András
Published: May 2009

The role of bread, pasta and related products produced from milled wheat seeds is important to the human diet, so monitoring changes of starch content in developing grain is essential. Immature wheat grains are also used as a functional food, particularly as a source of water-soluble carbohydrates. The amount and variation in content of different carbohydrates changes considerably during maturation and these changes were non-destructively monitored in developing grain using near infrared (NIR) spectroscopy. Characteristic changes in three carbohydrate absorption bands [1585–1595 nm (Carbohydrate I), 2270–2280 nm (Carbohydrate II) and 2325–2335 nm (Carbohydrate III)] were identified and it was concluded that the different dynamics of carbohydrates (starch accumulation as well as synthesis/decomposition of water-soluble carbohydrates) could be followed sensitively by monitoring these three different regions of NIR spectra. Carbohydrate I represents the effect of starch accumulation during maturation based on the vibrations of intermolecular hydrogen bonded O–H groups in polysaccharides. Carbohydrate II is the manifestation of O–H stretching and C–C stretching vibrations existing unengaged in water-soluble carbohydrates while Carbohydrate III describes the changes in C–H stretching and deformation band of poly- and mono-oligosaccharides. NIR spectroscopic techniques are shown to be effective in monitoring plant physiological processes and the spectra have hidden information for predicting the stage of growth in wheat seed.
Success in Using near Infrared Spectroscopy to Estimate Wood Properties of Pinus Taeda Radial Strips Not Due to Autocorrelation
Authors: Schimleck Laurence R. , Jones P. David , Peter Gary F. , Daniels Richard F. , Clark Alexander
Published: May 2009

Near infrared (NIR) spectroscopy provides a rapid method for estimating several important wood properties of 10 mm sections of radial wooden strips. Successful calibrations have been obtained with NIR spectra collected from 3 to 16 consecutive 10 mm sections of the same wood core. The success of these calibrations might be due to an autocorrelation that exists between the adjacent sections of a core. In this study, we compared calibrations with spectra collected from consecutive 10 mm sections to calibrations obtained with spectra collected from unrelated 10 mm sections. Very similar calibration statistics were obtained with both sets of spectra, demonstrating that existing calibration success is not due to an autocorrelation.
Temperature Robust Multivariate Calibration: An Overview of Methods for Dealing with Temperature Influences on near Infrared Spectra
Authors: Hageman J.A. , Westerhuis J.A. , Smilde A.K.
Published: May 2009

Multivariate calibration is a powerful tool for establishing a relationship between spectral variables and properties of interest. Usually, changes in spectral variables are ascribed to changes in the chemical composition of the sample. However, spectral intensities that are measured at varying temperatures do not only change because of changes in sample composition but also respond to the change in temperature. In these cases, multivariate calibration can be (severely) hindered, resulting in a loss of prediction capabilities. This paper provides an overview of the characteristics and possibilities of (most) methods for temperature robust multivariate calibration. The methods are discussed by using two data sets.
The Use of Visible and near Infrared Spectroscopy to Classify the Floral Origin of Honey Samples Produced in Uruguay
Authors: Corbella E. , Cozzolino D.
Published: May 2009

This study reports the use of visible (vis) and near infrared (NIR) spectroscopy as a tool to classify honey samples from Uruguay, according to their floral origin. Classification models were developed using principal component analysis, discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Honey samples ( n = 50) from two floral origins, namely Eucalyptus spp. and pasture, were split randomly into even calibration ( n = 25) and validation sets ( n = 25). Both LDA and DPLS models correctly classified, on average, more than 75% of the honey samples belonging to pasture and more than 85% of the honey samples belonging to Eucalyptus spp. These results showed that vis-NIR might be a suitable and alternative method that can easily be implemented by both the industry and retailers to classify samples according their floral origin. Vis-NIR analysis requires little sample preparation and is rapid. However, the relatively limited number of samples involved in the present work led us to be cautious in terms of extrapolating the results of this work to other floral types.
Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding
Authors: Welle Roland , Greten Willi , Müller Thomas , Weber Gary , Wehrmann Hartwig
Published: May 2009

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.
Predicting Intramuscular Fat Content in Pork and Beef by near Infrared Spectroscopy
Authors: Prevolnik M. , Čandek-Potokar M. , Škorjanc D. , Velikonja-Bolta Š. , Škrlep M. , Žnidaršic T. , Babnik D.
Published: May 2009

Prediction ability of near infrared (NIR) spectroscopy for intramuscular fat content (IMF) determination was studied. The material comprised 126 muscle samples; 46 pig longissimus dorsi and semitendinosus and 34 beef longissimus dorsi muscle samples. The IMF content was chemically determined in duplicate using two different chemical methods; fat extraction according to Folch et al. and Soxhlet extraction with hydrolysis according to SIST ISO 1443. Folch extraction underestimated IMF content compared to Soxhlet extraction with hydrolysis (-0.32%, P < 0.0001). Similar repeatability was obtained for Folch and Soxhlet extraction with hydrolysis (0.17% and 0.18%, respectively, P < 0.0001). Sample spectra were scanned from 400–2500 nm by the NIR Systems model 6500 spectrophotometer (Silver Spring, MD, USA) and analysed by WinISI II on minced and intact (pork only) samples. Modified partial least squares regression was used to develop models and to obtain calibration statistics: coefficient of determination in calibration( R2 C) and cross-validation ( R2 CV) and standard error in calibration ( SEC) and cross-validation ( SECV). We prepared different models (for a single muscle/common, by applying NIR spectrum or the whole spectrum, on intact and minced samples). Obtained models proved the remarkable prediction ability of NIR spectroscopy to determine IMF content ( R2 CV between 0.84 and 0.99; SECV between 0.14% and 0.53%) and confirms the potential of NIR spectroscopy to replace laborious chemical procedures. Regarding the factors studied, calibrations were less accurate for intact than for minced samples; the use of an NIR spectrum compared to the whole spectrum had no important effect on the prediction ability. According to calibration statistics, the prediction using a common equation for several muscles seems more reliable than the equations within the muscle, but the latter showed lower bias.
Near Infrared Spectroscopy for Agricultural Materials: An Instrument Comparison
Authors: Mouazen A.M. , Saeys W. , Xing J. , De Baerdemaeker J. , Ramon H.
Published: May 2009

The selection of a spectrophotometer for the measurement of constituents of agricultural materials with acceptable accuracy and cost effectiveness requires a comparative study of the performance of different spectrophotometers. Four commercially available spectrophotometers were evaluated, based on measurements performed on three agricultural materials. These spectrophotometers, differing mainly in wavelength range and measurement principles, comprised a diode array (DA) of 300–1700 nm, a combination of diode array and scanning monochromator (DASM) of 350–2500 nm, a Fourier transform (FT) of 750–2500 nm and a scanning monochromator (SM) of 400–2500 nm spectrophotometers. They were used to measure the moisture content of soil, the chemical constituents of hog manure and to detect bruising in apples. Three spectral pre-treatments were considered. Calibrations were developed using partial least squares (PLS) regression with the leave-one-out cross-validation technique for soil and manure and principal component analysis (PCA) for apple. The four instruments provided good predictions for soil moisture content, with the largest coefficient of determination ( r2) values between 0.84–0.86 and with the largest ratio of prediction to deviation ( RPD) of standard deviation ( SD) to root mean square error of cross-validation ( RMSECV) ranging from 2.53 to 2.75. The DASM and SM were comparable and slightly better than the DA and FT. For hog manure, total nitrogen was predicted more accurately with the four instruments ( r2 = 0.83–0.89 and RPD = 2.43–3.01) than the phosphorus ( r2 = 0.66–0.85 and RPD = 1.72–2.61) and potassium ( r2 = 0.70–0.84 and RPD = 1.83–2.50). The order of accuracy of the four spectrophotometers for the measurement of total nitrogen was: FT–SM–DA–DASM. For phosphorus and potassium the order was: DA–DASM–FT–SM and SM–FT–DASM–DA, respectively. The DA performed better than the DASM and FT for discrimination of bi-colour and single-colour bruised apple, respectively. Therefore, selection of a spectrophotometer depends mainly on the type of material analysed and the constituent to be measured. Wavelengths above 1700 nm were found unnecessary for the applications considered and the DA spectrophotometer was of sufficient accuracy, as it is robust, significantly cheaper and can be used in the field for on-line measurements.
Comparison of Transflectance and Reflectance to Analyse Hog Manures
Authors: Saeys W. , Xing J. , De Baerdemaeker J. , Ramon H.
Published: May 2009

In this study, the reflectance and transflectance sample presentation mode were compared for the analysis of the nutrient content of hog ( Sus domesticus) manure using visible and near infrared (vis-NIR) spectroscopy. A total of 194 hog manures, which were collected in the spring of 2004 from farms in the northern part of Belgium, were assayed by conventional wet chemical analysis and spectroscopy for the following constituents: dry matter content (DM), organic matter content (OM), pH, total Kjeldahl nitrogen (Ntot), ammonium nitrogen (NH4-N), phosphorus (P), potash (K), calcium (Ca), sodium (Na) and magnesium (Mg). Samples were scanned with a Foss NIRSystems Model 6500 scanning monochromator in reflectance and transflectance mode, respectively. A ceramic reference was measured in between the two modes. The monochromator was equipped with a DCFA sample presentation unit and ranges from 400 to 2498 nm. Partial least squares regression was employed to relate the spectral information to the nutrient content. The PLS models were calibrated for both sample presentation modes using leave-one-out cross-validation. The results of this study showed that the transflectance mode performed better than the reflectance mode. From the transflectance measurements, very good quantitative predictions for total N, good quantitative predictions for K, DM and OM, approximate predictions for NH4-N, P and Mg, very approximate predictions for Ca and a discrimination between high and low values for Na were obtained. pH was not predictable. The reflectance measurements were able to provide good quantitative predictions for total N and K, approximate quantitative predictions for NH4-N, very approximate predictions for DM, OM, P and Mg and discrimination between high and low values for Ca. Na was even less predictable and pH might be unpredictable.
Analysis of Betaines Using near Infrared Spectroscopy
Authors: Ibarra Emma , Valencia Omar , Pérez Héctor
Published: May 2009

Cocoamidopropyl betaines are becoming increasingly important in cosmetic formulations because of their mild and effective surface active properties. A simple and accurate method is needed for quality control during the production of betaines. The aim of the present study was to determine if near infrared spectroscopy can replace wet methods for routine analysis of betaine. The calibration curve was obtained by partial least squares. The optimisation of calibration factors was guided by coefficient of determination ( R2) and the root mean square error of evaluation ( RMSEE). R2 was 0.99 or higher and RMSEE 0.025, 0.071, 0.03% and 0.007 units for active matter, sodium chloride, solids and pH, respectively. The method was validated with independent samples in the same manner on a different day and true values were obtained with R2 of 0.99 or higher and root mean standard error of prediction of 0.060, 0.074, 0.075% and 0.035 units for active matter, sodium chloride, solids and pH, respectively.
Automated Fourier Transform near Infrared Determination of Buprofezin in Pesticide Formulations
Authors: Armenta Sergio , Moros Javier , Garrigues Salvador , de la Guardia Miguel
Published: May 2009

An automated procedure has been developed for Fourier transform near infrared (FT-NIR) determination of buprofezin in pesticide formulations. This methodology is based on on-line pesticide extraction with acetonitrile from solid samples and its determination by using peak area absorbance measurements between 2147 and 2132 nm, corrected with a horizontal baseline established at 2091 nm. The repeatability, as a relative standard deviation of five independent analyses of 18.9 mg g−1 of buprofezin, was 0.06% and the limit of detection 5 mg L−1. The reagent consumption was clearly reduced compared with a chromatographic reference procedure from 40.4 mL acetonitrile per sample, required by high performance liquid chromatography (HPLC), to 2 mL acetonitrile consumed for FT-NIR. The sample measurement throughput obtained by the FT-NIR methodology was 30 h−1, five times higher than that obtained by HPLC (6 h−1). It can be concluded that the proposed vibrational spectroscopic method is appropriate for the quality control of commercial pesticide formulations. The on-line sample treatment avoids contact by the operator with toxic products and this method is an environmentally friendly alternative to the measurement in the mid infrared which requires the use of CHCl3.
Rapid Determination of Fungicide Contaminated on Tomato Surfaces Using the DESIR-NIR: A System for ppm-order Concentration
Authors: Saranwong Innapa , Kawano Sumio
Published: May 2009

A system for rapid fungicide determination using near infrared spectroscopy was developed using the dry-extract system for infrared (DESIR) technique. Glass microfibre filter paper was used as the substrate. Fungicide solutions were prepared by dissolving the commercial dichlofluanid-carrying fungicide, Euparen, into acetone at concentrations of 2 ppm to 90 ppm of the active ingredient. Some techniques for adding the fungicide solutions onto the filter paper were examined. It was found that by adding 2 mL of the fungicide solution to the filter paper held in an identical size Petri dish made of polysyrene, a sufficiently accurate calibration result with SEP of 6.58 ppm could be obtained. In the following step, the technique was applied to the determination of fungicide on tomato surfaces. The fungicide was extracted from the fruit by washing with acetone. Then, the acetone solution was used for the DESIR embedding instead of the fungicide solution in the first experiment. A satisfactory result, with an SEP of 7.89 ppm for the washed solution that was equivalent to approximately 1.58 ppm per gram fruit weight, could be obtained.
Sugar “Imaging” of Fruit Using a Low Cost Charge-Coupled Device Camera
Authors: Long Robert L. , Walsh Kerry B. , Greensill Colin V.
Published: May 2009

Sugar “imaging” of fruit has previously been reported using NIR filters and relatively expensive (high signal-to-noise) charge-coupled device (CCD) instrumentation. In a bid to use lower cost CCD instrumentation (criterion of less than AU $5,000 total component costs), the signal-to-noise constraint on calibration model performance was investigated by artificially degrading spectra from a 15-bit AtoD system. A low cost 8-bit CCD camera was then used in conjunction with a filter wheel in a transmittance configuration employing three 50 W halogen lamps. Multiple linear regregression calibrations were developed based on absorbance data of five wavelengths (830, 850, 870, 905 and 930 nm) relevant to sugar and water. Calibration models for the sucrose concentration of solutions on a cellulose matrix were poor ( R2 = 0.4) when based on individual pixel data, but acceptable ( R2 = 0.98, RMSECV = 1.1) ( n = 20, mean = 13.9% total soluble sugars (TSS), SD = 6.04) when based on an average of a 23 × 23 pixel block (i.e. 529 pixels). For a calibration based on melon tissue TSS, using spectral data averaged over groups of 529 pixels, results were poorer than expected ( R2 = 0.4, RMSEP = 1.74 ( n = 163, mean = 9.45, SD = 2.07% TSS). Predicted TSS output for all pixel blocks from an image was used to generate a false colour image. We conclude that this application requires a higher level of signal-to-noise (for example, 10-bit, > 60 dB CCD).