bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Both Homogenisation and Storage on the Spectra of Red Grapes and on the Measurement of Total Anthocyanins, Total Soluble Solids and pH by Visual near Infrared Spectroscopy
Authors: Cozzolino D. , Cynkar W.U. , Dambergs R.G. , Janik L. , Gishen M.
Published: May 2009

The effect of homogenisation and storage on both the visible (Vis) and near infrared (NIR) spectra as well as on the calibration performance for the measurement of total anthocyanins (colour), total soluble solids (TSS) and pH in red grapes were investigated. Homogenates obtained from fresh and previously frozen red grapes (overnight freezing for 24 hours, and after one, three, six and 12 months frozen storage) were scanned in reflectance mode (400–2500 nm) using a monochromator instrument (Foss NIRSystems 6500). Principal component analysis was used to examine the influence of homogeniser type and frozen storage on the spectra. Neither the homogeniser type used nor overnight freezing had visual effects on the spectra. Spectra and analytical data were used to develop partial least squares calibrations to quantify the influence of storage on the measurement of total anthocyanins (colour), TSS and pH. When cross-predicting between fresh and frozen samples, the standard error of prediction was slightly increased when compared with calibrations developed on fresh samples alone. On the other, hand frozen samples were predicted well with the fresh sample calibration for the chemical parameters evaluated. However, although there may be some increases in error, these results suggest that it might be possible to use Vis-NIR calibrations developed on frozen samples alone to measure the concentration of total anthocyanins, TSS and pH in either fresh or frozen samples after appropriate slope and bias correction.
Forensic Classification of Paper with Infrared Spectroscopy and Principal Components Analysis
Authors: Kher Ashwini , Stewart Samantha , Mulholland Mary
Published: May 2009

Fourier transform infrared (FT-IR) spectra of six document papers were classified by the chemometric technique of soft independent modelling of class analogy (SIMCA) using principal component analysis (PCA). The data were split into two regions, mid infrared (MIR, 2500–4000 cm−1) and the near infrared region (NIR, 4000–9000 cm−1). The raw data failed to produce an appreciable separation; hence it was pre-processed using derivatives and the multiple wavenumber ratios technique. The aim of this research was to determine the spectral region with the best discriminating power and evaluate the impact of data pre-processing techniques on the classification of paper. It was found that MIR (both raw and ratios) had higher discriminating ability than the NIR. The first derivatives and two ratios produced the best classifications. These results indicate that IR spectroscopy coupled with chemometric analyses can be effectively employed for the forensic classification of paper.
Classification of Soil Texture Classes by Using Soil Visual near Infrared Spectroscopy and Factorial Discriminant Analysis Techniques
Authors: Mouazen A.M. , Karoui R. , De Baerdemaeker J. , Ramon H.
Published: May 2009

Texture is one of the main properties affecting the accuracy of visible (vis) and near infrared (NIR) spectroscopy during on-the-go measurement of soil properties. Classification of soil spectra into predefined texture classes is expected to increase the accuracy of measurement of other soil properties using separate groups of calibration models, each developed for one texture class. A mobile, fibre-type, vis-NIR spectrophotometer (Zeiss Corona 1.7 vis-NIR fibre), with a light reflectance measurement range of 306.5–1710.9 nm was used to measure the light reflectance from fresh soil samples collected from many fields in Belgium and northern France. A total of 365 soil samples were classified into four different texture classes, namely, coarse sandy, fine sandy, loamy and clayey soils. The factorial discriminant analysis (FDA) was applied on the first five principal components obtained from the principal component analysis performed on the vis-NIR spectra in order to classify soils into the four assigned groups. Correct classification (CC) of 85.7% and 81.8% was observed for the calibration and validation data sets, respectively. However, validation of the vis-NIR-FDA technique on the validation set showed poor discrimination between the coarse sandy and fine sandy soil groups, with a great deal of overlapping. Therefore, the soil groups were reduced to three groups by combining the coarse sandy and fine sandy soil groups into one group and FDA was applied again. A better classification was obtained with CC of 89.9 and 85.1% for the calibration and validation data sets, respectively. However, the CC for the sand group in the validation set was rather small (46.7%), which was attributed to the small sample number and poor correlation between sand fraction and vis-NIR spectroscopy. It was concluded that vis-NIR-FDA is an efficient technique to classify soil into three main groups of sandy (light soils), loamy (medium soils) and clayey (heavy soils). Additional samples from the sandy and clayey groups should be included to improve the accuracy of the vis-NIR-FDA classification models to be used for an on-the-go vis-NIR measurement system of soil properties.
A New Method for Multivariate Calibration
Authors: Marbach Ralf
Published: May 2009

A new method for multivariate calibration is described that combines the best features of “classical” (also called “physical” or “K-matrix”) calibration and “inverse” (or “statistical” or “P-matrix”) calibration. By estimating the spectral signal in the physical way and the spectral noise in the statistical way, so to speak, the prediction accuracy of the inverse model can be combined with the low cost and ease of interpretability of the classical model, including “built-in” proof of specificity of response. The cost of calibration is significantly reduced compared to today's standard practice of statistical calibration using partial least squares or principal component regression, because the need for lab-reference values is virtually eliminated. The method is demonstrated on a data set of near-infrared spectra from pharmaceutical tablets, which is available on the web (so-called Chambersburg Shoot-out 2002 data set). Another benefit is that the correct definitions of the “limits of multivariate detection” become obvious. The sensitivity of multivariate measurements is shown to be limited by the so-called “spectral noise,” and the specificity is shown to be limited by potentially existing “unspecific correlations.” Both limits are testable from first principles, i.e. from measurable pieces of data and without the need to perform any calibration.
Near Infrared Reflectance Spectroscopy of Oil in Intact Canola Seed (Brassica Napus L.). II. Association between Principal Components and oil Content
Authors: Golebiowski T. , Leong A.S. , Panozzo J.F.
Published: May 2009

This work reports that the measurements of the likeness or the uniqueness of the 1100–2500 nm reflectance spectra of intact canola seed determined from principal component analysis (PCA) approximated spectra with global H or neighbourhood H statistics were not associated with oil concentration within the seed. The absence of stability in association between the H measurements and oil content was related to inconsistency in the amount and distribution (between principal components) of the spectral variation correlated to the oil content within and between different batches of canola seed. PCA was used to approximate variation in the 1100–2500 nm, second order derivative, reflectance spectra of intact canola seed, acquired from 15 batches of seed samples. The first eight principal components (PCs) captured 97.14% to 99.35% of the total variance in the spectra. The amount of variation captured by individual components was independent of the number of samples in the batch and oil content within the seed. The pattern of variance distribution among principal components was inconsistent and highlighted the uniqueness of the origin of the spectral variation in each batch of canola seed. In this study, the strength of correlation between oil content and principal components was used as a measure of component significance to the analysis of oil in the intact canola seed. In the examined sets of spectra, oil content was correlated to the low order components, PC1 to PC4. In the 15 files of spectra, oil content showed the strongest correlation to PC2 in eight sets of data, to PC3 in four sets of data and to PC1 in three sets of data. The strength of association between oil and the individual components varied considerably in magnitude among examined files of spectra; r2 = 0.28–0.81 for the first strongly correlated component, r2 = 0.05–0.29 for the second and r2 = 0.02–0.19 for the third. The position of the PCs in the correlation sequence was inconsistent and underlined differences of oil signal/spectral data interactions in the individual sets of data. Examination of principal component loadings showed that in the reported files of spectra, principal components correlated to the oil content frequently captured variance at segments, which denote absorptions specific and accidental to canola oil. The outline of the loadings did not conform to a single, regular pattern common to all sets of data. The reported results are in disagreement with rationale of the methodology, which involves the spectra matching techniques for validating the predictive efficiency of near infrared (NIR) calibrations. The reported results highlighted that the reliable NIR quantification of oil content from reflectance spectra of intact canola seed would require an independent validation for every acquired set of spectra.
Using near Infrared Spectroscopy for Predicting Process Conditions. A Laboratory Study from Pulp Production
Authors: Henriksen Heidi , Næs Tormod , Segtnan Vegard , Aastveit Are
Published: May 2009

Most industries face a growing challenge concerning data handling due to the large data storage capacity available today. In many cases, it is difficult to navigate through these amounts of data in search of relevant information. An important tool in this context is statistical process control (SPC), which enables the discovery of possible process drift or other problems as early as possible. In this work the potential of using near infrared (NIR) spectroscopy as a multifunction tool for SPC in the context of process monitoring has been investigated. Both principal component analysis (PCA) and partial least squares regression (PLS) are tested as tools for extracting useful information from NIR spectra. The two methods have been compared based on interpretation of score plots and explained variance. We have also tested classification tools for prediction of classes and various types of validation, since these data came from designed experiments. It has been demonstrated that PLS is a useful tool both for forward and backward predictions. Another topic considered is discovery of instrument drift and outlier detection. It has been demonstrated that PLS is a useful tool in both contexts. The robustness of PLS predictions has been investigated and it was found that PLS score plots can reveal useful information early in the process. This study was a feasibility study and the models can not be used directly in any large scale installations. This work has, however, demonstrated the usefulness of multivariate techniques in such processes and found a good basis for further model development.
Detection of Tenderness, Post-Rigor Age and Water Status Changes in Sheep Meat Using near Infrared Spectroscopy
Authors: McGlone V. Andrew , Devine Carrick E. , Wells Robyn W.
Published: May 2009

The tenderness (objective measure is shear force) of sheep meat ( longissimus thoracis et lumborum) during post-rigor ageing at 15°C can be predicted using near infrared (NIR) analysis ( r2 = 0.85). Measurements ( n = 260 from 65 lambs) were made at 0, 8, 24 and 72 h post-rigor using an interactance system operating over the spectral range from 400–1700 nm. There was no predictive power in the NIR models to segregate tenderness measurements made at any one particular post-rigor time. NIR measurements could segregate the post-rigor ages with good accuracy ( r2 = 0.91). The OH combination band at 840 nm is the dominant feature of the predictive models and suggests that the underlying chemistry is related to the post-rigor changes in the water status of the meat. Observations on another similar data set ( n = 48 from 12 lambs), involving NIR and water content measurements, confirmed there are significant water changes in meat during aging. We speculate that the NIR models may be sensitive to the accumulation of free water arising from the degradation of cytoskeletal proteins during ageing, releasing the water normally tightly bound up in the tertiary protein structures.
Near Infrared Reflectance as a Rapid and Inexpensive Surrogate Measure for Fatty Acid Composition and Oil Content of Peanuts (Arachis Hypogaea L.)
Authors: Fox Glen , Cruickshank Alan
Published: May 2009

The fatty acid composition of ground nuts ( Arachis hypogaea L.), commonly known as peanuts, is an important consideration when a new variety is being released. The composition impacts on nutrition and, importantly, shelf-life of peanut products. To select for suitable breeding material, it was necessary to develop a rapid, non-destructive and cost efficient method. Near infrared spectroscopy was chosen as that methodology. Calibrations were developed for two major fatty acid components, oleic and linoleic acids and two minor components, palmitic and stearic acids, as well as total oil content. Partial least squares models indicated a high level of precision with a squared multiple correlation coefficient greater than 0.90 for each constituent. Standard errors of prediction for oleic, linoleic, palmitic, stearic acids and total oil content were 6.4%, 4.5%, 0.8%, 0.9% and 1.3%, respectively. The results demonstrated that suitable calibrations could be developed to predict the oil composition and content of peanuts for a breeding programme.
A Preliminary Study Using Fourier Transform near Infrared Spectroscopy to Monitor the Shelf-Life of Packed Industrial Ricotta Cheese
Authors: Sinelli N. , Barzaghi S. , Giardina C. , Cattaneo T.M.P.
Published: May 2009

Ricotta cheese is a dairy product characterised by a short shelf life. The aim of this study was to monitor changes occurring during storage of packed industrial ricotta cheese using a simple and fast method. Several samples of manufactured ricotta cheese were monitored during storage at three different temperatures (3, 10 and 20°C). Spectral data were collected over the range 12000 to 4400 cm−1 using a Fourier transform near infrared (FT-NIR) spectrometer fitted with an optic fibre working in diffuse reflectance. Some chemical and rheological parameters were also measured. Principal component analysis (PCA) was applied as an exploratory chemometric technique to each one of the three sets of data (FT-NIR, chemical and rheological data) and a calibration model between the NIR data set and chemical and rheological indices was developed by means of partial least squares regression. The PCA results showed in all cases the influence of the storage temperature on the shelf life trend and were able to identify a critical day of shelf life for each storage temperature.
Nutritive Evaluation of Forages by near Infrared Reflectance Spectroscopy
Authors: Andrés Sonia , Murray Ian , Calleja Alfredo , Giráldez F. Javier
Published: May 2009

Since the potential of near infrared (NIR) spectroscopy for forage evaluation was discovered three decades ago, it has become clear that it is a powerful tool for the estimation of chemical components in these feed-stuffs. In addition, it has been successfully applied for the estimation of digestibility and degradability parameters, thus facilitating livestock ration formulation. The present review deals with the main reference methods that have been used in order to achieve proper calibration of NIR spectroscopy. Special attention will be focused on the weak points of these procedures with the intention of increasing NIR spectroscopy potential in this area.
Qualitative Identification of Tea by near Infrared Spectroscopy Based on Soft Independent Modelling of Class Analogy Pattern Recognition
Authors: Chen Quansheng , Zhao Jiewen , Zhang Haidong , Muhua Liu , Fang Ming
Published: May 2009

Near-infrared (NIR) spectroscopy has been successfully utilised for the rapid identification of tea varieties. The spectral features of each tea category are reasonably differentiated in the NIR region and the spectral differences provided enough qualitative spectral information for identification. Soft independent modelling of class analogy (SIMCA) as the pattern recognition was applied in this paper. In this study, both α-error (i.e. the rejection of correct samples from their class) and β-error (i.e. the acceptance of objects that do not belong to that class) are focused on. Four tea classes from Longjing tea, Biluochun tea, Qihong tea and Tieguanyin tea were modelled separately by principal component analysis (PCA). The results showed that at the 99% confidence level, the α-errors were equal to 0.1 only for the Longjing tea class when training and 0.2 only for the Biluochun tea class when testing, while the remaining α-errors and all β-errors were equal to zero. The study demonstrated that NIR spectroscopy technology with a SIMCA pattern recognition method can be successfully applied as a rapid method to identify the class of tea.
The Challenge of Single Estimates in near Infrared Calibration and Prediction: The Measurement of Durum Vitreousness Using Receival Instruments
Authors: Wesley I.J. , Ruggiero K. , Osborne B.G. , Anderssen R.S.
Published: May 2009

The development of a near infrared (NIR) calibration for the estimation of percentage hard vitreous kernel (%HVK) in whole durum at grain receival points has been driven by the needs of international trade. This application presented some special challenges due to the nature of the available reference data, which were obtained using the Farinator method. The Farinator (transverse grain cutter) method is subjective and is thus subject to large errors, leading to single estimates being very poor reference data. This problem can be overcome by making a number of replicate measurements and using the average of these as a more accurate estimate of the true value. However, this is at the expense of substantially increased analysis time, hence cost. In addition, the HVK methodology involves a grain by grain assessment in which each grain can have only one of two possible classifications, i.e. “vitreous” and “not fully vitreous”. A binomial test such as this introduces an additional complication arising from sampling error, thus making the accuracy of the method sensitive to the number of grains examined. The accuracy also varies across the range of %HVK values so that the greatest errors are observed in the middle of the range. Fortunately, however, the high end of the range corresponding to the lowest errors is also the most commercially relevant. This paper describes a procedure in which, by restricting the calibration range to the optimum for a binomial test, an NIR model suitable for use at receival points could be obtained. The compromise was to calibrate using single Farinator estimates on a large number of samples but to validate using average Farinator estimates on a small number of samples. The results have confirmed that the NIR model yields a more reliable estimate of the true value of %HVK than would a single Farinator estimate on which the calibration was based.
Maturity, Variety and Origin Determination in White Grapes (Vitis Vinifera L.) Using near Infrared Reflectance Technology
Authors: Arana Ignacio , Jarén Carmen , Arazuri Silvia
Published: May 2009

Soluble solids content, variety and origin of grapes are three relevant features to determine wine quality. Soluble solids content is directly related to ripeness. The capacity to certify the origin or variety of a grape is of major interest for wine makers. The aim of this paper was to study whether near infrared reflectance (NIR) spectroscopy could be used to determine soluble solids content and to identify different varieties and origins of grapes. Samples of Viura and Chardonnay grapes were collected in two locations (Cadreita and Villamayor de Monjardin) which varied in environmental conditions. Mass and sugars content measured by a refractometer and NIR (800–500 nm) reflectance spectra were obtained. A partial least squares regression (PLS) was made to calibrate the NIR spectra. The regression models for Chardonnay grapes were more robust than for Viura grapes, showing coefficients of determination for calibration and validation of 0.75 and 0.70 and a standard error of cross-validation of 1.27. Therefore, NIR technology is suitable to determine the soluble solids content although it requires a calibration model for each variety, using a large number of data points and variables. The combined variety model showed a residual predictive deviation of 1.33, while Viura and Chardonnay models showed 1.54 and 1.88, respectively. The results of discriminant analysis using variables from the NIR spectrum showed a percentage of well-classified grapes according to the variety to which they belonged of 97.2% and a 79.2% of well-classified Chardonnay grapes according to their origin. In conclusion, NIR technology is a useful alternative for identifying the variety and origin of grapes.
The Application of near Infrared Spectroscopy for the Detection of Secondary Uranium Minerals Using a Fibre-Optic Probe
Authors: Weier Matt L. , Frost Ray L. , Reddy B. Jagannadha
Published: May 2009

The proposed increase in usage of nuclear power implies increased nuclear waste resulting in the formation of many uranyl secondary minerals. These minerals can translocate and a method of easy identification is required. Because many of the minerals contain water and/or OH units, the minerals lend themselves to near infrared spectroscopy. Spectra of a range of uranyl carbonate, sulphate and silicate minerals were obtained using a fibre optic-probe with an attenuated total reflectance cell in reflectance mode. The minerals include andersonite, liebigite, rutherfordine, uranopilite, zippeite, boltwoodite, soddyite, sklodowskite, uranophane and weeksite. These uranyl minerals are characterised by intense bands around 7000 cm−1 resulting from the second harmonic of the normal mode of the OH groups. Uranyl silicates show intense bands in the 4200 to 5400 cm−1 region. Two sets of bands, which are electronic in nature, are observed: one centred upon 10100 cm−1 and a second set around 8500 cm−1 in the high wavenumber region.
Application of near Infrared Spectroscopy to Assessments of Colour Change in Plantation-Grown Eucalyptus Grandis Wood Subjected to Heat and Steaming Treatments
Authors: Wu Yi-Qiang , Tsuchikawa Satoru , Hayashi Kazuo
Published: May 2009

The relationships between colour parameters and chemical components of Eucalyptus grandis wood under a steaming and a heating process were examined by near infrared (NIR) spectroscopy to assess the best drying conditions. The results indicated that: 1) under steaming conditions, the second derivatives of absorption bands due to OH in the crystalline region of cellulose and CH in hemicellulose presented statistically significant positive correlation values for L* or b* and a decrease in lignin might be the reason leading to the colour changes; 2) under heat-treated conditions, the colour darkening process in wood was often accompanied by a severe collapse phenomena, especially at the 100°C condition. It was found spectroscopically that the concentrations of cellulose and hemicellulose were increased relatively by the collapse occurring and that lignin decreased with the elevated drying temperature. The crystalline regions in cellulose and hemicellulose were related to “blueness” (partial darkness) of E. grandis caused by the drying process.
Determination of Cephalexin Crystallinity and Investigation of Formation of its Amorphous Solid by Chemoinformetrical near Infrared Spectroscopy
Authors: Otsuka Makoto , Fukui Yuya , Otsuka Kuniko , Kim Hyo-Jin , Ozaki Yukihiro
Published: May 2009

The crystallinity of cephalexin (CEX) was evaluated by chemoinformetrical near infrared (NIR) spectroscopy. The molecular interaction of ground amorphous solid of CEX was investigated by the method. Six kinds of standard material with various degree of crystallinity were prepared by the physical mixing of crystalline and ground amorphous CEX. X-ray powder diffraction profiles and NIR spectra were recorded for the standard samples. Chemoinformetric analysis was performed on the NIR spectral data sets by principal component regression (PCR). The correlation between the actual and the predicted crystallinity of CEX using the conventional X-ray diffraction method showed a straight line relationship with a slope of 1.000, an intercept of −5.77 × 10−4 and a correlation coefficient of determination ( R2) of 0.986. A NIR spectrum of amorphous CEX showed significantly different peaks at 1530 and 1620 nm due to the NH2 group from those of crystalline CEX. PCR was performed on various kinds of pre-transformed NIR spectral data sets of standard samples of CEX. To minimise the standard error of cross-validation ( SECV), the spectral data sets were subjected to the leave-one-out method. The combination of normalization and multiplicative scatter correlation treatments yielded the lowest SECV values. Based on a three-components model, a plot of the calibration data between the actual and CEX crystallinity predicted by the NIR method was obtained. The plot showed a straight line ( Y = 0.993X + 0.262; R2 = 0.992; n = 18). The mean bias for the NIR and X-ray powder diffraction methods were calculated to be 3.40 and −1.58% and their mean accuracy were 7.70 and 5.76%, respectively. NIR spectral changes of crystalline CEX during grinding suggested the intermolecular hydrogen bonds between the amino and the carboxyl groups are destroyed and the crystals are transformed into the amorphous CEX.
Quality Determination of Chinese Rice Wine Based on Fourier Transform near Infrared Spectroscopy
Authors: Yu Haiyan , Ying Yibin , Fu Xiaping , Lu Huishan
Published: May 2009

To evaluate the applicability of near infrared (NIR) spectroscopy for the determination of five enological parameters (alcoholic degree, pH value, total acid, amino acid nitrogen and °Brix) in Chinese rice wine, transmission spectra were collected in the spectral range from 800 nm to 2500 nm in a 1 mm path length rectangular quartz cuvette with air as the reference at room temperature. Five calibration equations for the enological parameters were established between the reference data and NIR spectra by partial least squares (PLS) regression, separately. The best calibration results were achieved for the determination of alcoholic degree and °Brix. The coefficients of determination in calibration ( R2cal) for alcoholic degree and °Brix were 0.93 and 0.96, respectively. The predictive deviation ratio ( RPD) value of the calibration for alcoholic degree was higher than 3 (3.95), which demonstrated the robustness of the calibration model. The RPD value for °Brix was 2.34. The performance of the calibration models for pH, total acid and amino acid nitrogen were not as good as that of alcoholic degree and °Brix. The RPD values for the three parameters were 1.41, 1.38 and 1.27, respectively and R2cal values were 0.97, 0.83 and 0.90, respectively. In the validation step, r2val values for alcoholic degree and °Brix were all higher than 0.9. The performance of pH and total acid were acceptable [the coefficients of determination in validation ( r2val) for pH and total acid were 0.82 and 0.77, respectively]. The performance of amino acid nitrogen model was the worst, with an r2val value of 0.62. The results demonstrated that the NIR spectroscopic technique could be used to predict the concentrations of these five enological parameters in Chinese rice wine.
Non-Contact Transflectance near Infrared Imaging for Representative on-Line Sampling of Dried Salted Coalfish (Bacalao)
Authors: Wold Jens Petter , Johansen Ib-Rune , Haugholt Karl Henrik , Tschudi Jon , Thielemann Jens , Segtnan Vegard H. , Narum Bjørg , Wold Erik
Published: May 2009

This paper describes a multi-spectral imaging near infrared (NIR) transflectance system developed for on-line determination of crude chemical composition of highly heterogeneous foods and other bio-materials. The system was evaluated for moisture determination in 70 dried salted coalfish (bacalao), an extremely heterogeneous product. A spectral image cube was obtained for each fish and different sub-sampling approaches for spectral extraction and partial least squares calibration were evaluated. The best prediction models obtained correlation R2 values around 0.92 and root mean square error of cross-validation of 0.70%, which is much more accurate than today's traditional manual grading. The combination of non-contact NIR transflectance measurements with spectral imaging allows rather deep penetrating optical sampling as well as large flexibility in spatial sampling patterns and calibration approaches. The technique works well for moisture determination in heterogeneous foods and should, in principle, work for other NIR absorbing compounds such as fat and protein. A part of this study compares the principles of reflectance, contact transflectance and non-contact transflectance with regard to water determination in a set of 20 well-defined dried salted cod samples. Transflectance and non-contact transflectance performed equally well and were superior to reflectance measurements, since the measured light penetrated deeper into the sample.
The Determination of Red Grape Quality Parameters Using the LOCAL Algorithm
Authors: Dambergs R.G. , Cozzolino D. , Cynkar W.U. , Janik L. , Gishen M.
Published: May 2009

In a study of visible (vis) and near infrared (NIR) spectra collected from grape samples representing many growing vintages, varieties and regions, WinISI LOCAL performed better than modified partial least squares in the prediction of anthocyanin and pH. However, both algorithms performed equally well for the prediction of total soluble solids (TSS). Calibrations for anthocyanins and pH relied mainly on the visible wavelengths, but pH calibrations may result from pH effects on the spectral properties of anthocyanin. On the other hand, calibrations for TSS relied mainly on NIR wavelengths. An examination of the LOCAL match sets showed that, in addition to spectral matching, there appeared to be concomitant compositional matching, possibly due to dominant spectral features related to the relevant analyte. Observations imply that calibrations for anthocyanins, pH and TSS in grapes are not restrained by vintage, variety and region. A more important factor in determining a robust, global application may be to ensure that the full concentration range of the analyte is well represented in the calibration database.
Applications of near Infrared Spectroscopy in Quality Screening of Early-Generation Material in Cereal Breeding Programmes
Authors: Osborne B.G.
Published: May 2009

In a breeding programme for quality improvement of cereals, crossbred lines are propagated and selected from generation to generation until desirable properties are obtained. The lines promoted to the next generation need to be the ones that have properties as close as possible to some specified quality target. Efficiency in selection of these lines from thousands of candidates requires the provision to the breeder of quality information at the earliest generation, in the time between harvest and sowing of the next generation and at the lowest cost. Near infrared (NIR) spectroscopy of whole grain has a number of well-known advantages (speed of analysis, no sample preparation required, low cost per test and concurrent analysis of multiple constituents) that make it particularly valuable for this purpose. This paper reviews the development over the last thirty years and the current status of NIR in the quality testing of wheat, barley, rice, durum, maize and oat breeding material.
Determination of Ethenzamide and Acetoaminophen in Quaternary Powdered Samples by near Infrared Spectroscopy and Searching Combination Moving Window Partial Least Squares Algorithm: Effects of Spectral Resolution and Signal-to-Noise Ratio
Authors: Hu Yun , Li Boyan , Wang Haishui , Osaki Kazuo , Ozaki Yukihiro
Published: May 2009

The present study reports an application of the searching combination moving window partial least squares (SCMWPLS) algorithm to the determination of ethenzamide and acetoaminophen in quaternary powdered samples by near infrared (NIR) spectroscopy. Another purpose of the study was to examine the instrumentation effects of spectral resolution and signal-to-noise ratio of the Büchi NIRLab N-200 FT-NIR spectrometer equipped with an InGaAs detector. The informative spectral intervals of NIR spectra of a series of quaternary powdered mixture samples were first located for ethenzamide and acetoaminophen by use of moving window partial least squares regression (MWPLSR). Then, these located spectral intervals were further optimised by SCMWPLS for subsequent partial least squares (PLS) model development. The improved results are attributed to both the less complex PLS models and to higher accuracy of predicted concentrations of ethenzamide and acetoaminophen in the optimised informative spectral intervals that are featured by NIR bands. At the same time, SCMWPLS is also demonstrated as a viable route for wavelength selection. The systematic comparison of different spectral resolutions and signal-to-noise ratios of NIR spectra has shown that the spectral resolution 8 cm−1 and 64 co-added scans can lead to better calibration models and satisfactory modelling performance.
Comparison of Fourier Transform near Infrared Spectroscopy Partial Least Square Regression Models for South African Extra Virgin Olive Oil Using Spectra Collected on Two Spectrophotometers at Different Resolutions and Path Lengths
Authors: Manley Marena , Eberle Kerstin
Published: May 2009

Near infrared (NIR) partial least square (PLS) regression models for the determination of several extra virgin olive oil quality parameters (peroxide value, free fatty acid content, specific extinction coefficients K232 and K270, linoleic, oleic and saturated acid content, α:β ratios of linoleic and oleic acid, pigment and total polyphenol content, thiobarbituric acid reactive substances value) were developed from spectra collected on two different Fourier transform near infrared (FT-NIR) spectrophotometers using different sample holders, path lengths and resolutions. Spectra were recorded on the PerkinElmer IdentiCheck spectrophotometer (9091–4000 cm−1) in transmittance mode at resolutions of 64, 32, 16 and 8 cm−1 at each of two path lengths (0.2 and 0.5 mm) and on the Büchi NIRLab N-200 instrument (10 224–4000 cm−1) in transflectance mode at a fixed resolution of 8 cm−1 and a path length of 0.6 mm. The PLS regression models, for each respective parameter, were statistically compared to evaluate the effect of different sample scanning conditions on model performance. The comparison of the regression models developed from PerkinElmer spectra revealed that spectra recorded at the lowest resolution of 64 cm−1 produced equally accurate models when compared to higher resolution spectra and that the two path lengths resulted in no significant differences. Comparisons between PLS models developed from Büchi and PerkinElmer spectra, respectively, showed significant differences for only a few parameters. In general, reliable prediction results were obtained for the peroxide value [standard error of prediction ( SEP) = 4.15 meq O2 kg−1, r2 = 0.87], K232 ( SEP = 0.94, r2 = 0.94), linoleic acid ( SEP = 0.83%, r2 = 0.90) and saturated acid content ( SEP = 0.91%, r2 = 0.88) PLS regression models.
The near Infrared Window—Glass or Quartz? A Study in Precision
Authors: Williams Phil
Published: May 2009

All near infrared instruments are equipped with a “window” to protect the detectors from the atmosphere. Many types of sample cells are also fitted with a window between the sample surface and the detector. These windows may be composed of special optical glass or quartz. This paper describes variability of wheat flour spectra when using a series of glass and quartz windows. Differences among the windows of both glass and quartz exhibited differences of up to respectively 40% and 14% in the spectral data when they were used in the development of calibration models for the prediction of protein content in wheat flour. The differences among the windows did not affect the precision of spectral data when the same window was used for replicate testing, but induced absolute biases of up to over 3% (glass) and 1% (quartz) to the predicted protein results. Biases could be countered by use of derivatives or by combining spectral data from flours by using the different windows in the development of the calibration models. An anomaly was noted in the flour spectra obtained using quartz window number 4. The anomaly was found to cause significant biases in the prediction of protein content. The biases could be removed if the calibration model was developed using a wavelength range that avoided the anomaly.
Near Infrared Analysis of Whole Blood and Plasma in Blood-Collecting Tubes
Authors: Turza Sándor , Kurihara Mitsunori , Kawano Sumio
Published: May 2009

An easy-to-use measurement method requiring no cleaning between samples, was developed and applied for the determination of cows' plasma constituents. Whole blood and plasma samples were measured with blood-collecting tubes as sample cells, providing easy sample handling and presentation. For whole blood, near infrared (NIR) spectra in the short wavelength region from 600 to 1000 nm were recorded with a portable NIR instrument (Fruit Tester 20: FT20). Plasma samples were measured in both the short wavelength region with the FT20 and in the long wavelength region from 1100 to 2500 nm with an NIRSystems 6500. Both instruments were equipped with specially designed sample holders for blood-collecting tubes and fibre optics. Partial least squares (PLS) regression was applied to derive calibrations for total protein, total cholesterol and glucose. Whole blood showed inferior performance to plasma because of scattering interference and absorption by red blood cells. Plasma results heavily depended on the constituent analysed. Total protein was determined accurately in both wavelength regions due to its high concentration and high absorptivity. For total cholesterol, the long wavelength region provided much better results because of its higher absorptivity. Results for glucose were only mediocre in both regions as its concentration and absorptivity were low. Overall, results indicate that blood-collecting tubes can be easily and effectively used for quantitative analysis of major constituents in blood plasma.
Sulphate Efflorescent Minerals from the El Jaroso Ravine, Sierra Almagrera, Spain—A Scanning Electron Microscopic and Infrared Spectroscopic Study
Authors: Frost Ray L. , Wain Daria L. , Reddy B. Jagannadha , Martens Wayde , Martinez-Frias Jesus , Rull Fernando
Published: May 2009

Near infrared (NIR) spectroscopy is a technique that can be utilised to analyse minerals remotely through a fibre-optic cable. A suite of sulphate efflorescent minerals from the El Jaroso ravine, Sierra Almagrera, Spain have been analysed by ultraviolet, visible, NIR and mid infrared (IR) spectroscopy. Such a suite of minerals may be used to model the types of mineral deposits on Mars. The minerals were analysed by scanning electron microscopy and X-ray microanalysis (EDX) before spectroscopic analysis. Halotrichite was observed and the EDX analyses showed the mineral to be a mixture of heinrichite and pickingerite. The NIR spectrum of the eflorescencia sample showed the sulphate deposit to be composed of a mixture of jarosite and halotrichites. The 7400 to 6400 cm−1 (1351 to 1562 nm) NIR spectral region of the efflorescent sulphate minerals can be used to characterise the various sulphate deposits. The detection of a band at 8338 cm−1 (1199 nm) reveals the presence of ferrous iron in jarosite which is unusual for the jarosite minerals. This can be caused by ferric iron reduction by thermophilic iron-oxidising bacteria. Mid IR spectra showed the characteristic OH stretching bands of the jarosites and the efflorescent mixtures. The position of water stretching vibrations at 2900 cm−1 and lower shows strong hydrogen bonding as is also evidenced by water bending modes at around 1682 cm−1.
Detecting Core-Shell Structure Formation Using near Infrared Spectroscopy
Authors: Lenzi Marcelo K. , Lima Enrique L. , Pinto José Carlos
Published: May 2009

It is well known that the final end-use properties of polymer resins depend on several variables. Among them, the morphological aspects of the polymer resin have received considerable attention over the last few years. Particularly, efforts have been devoted towards the study and production of core-shell polymer particles, which find many applications as supports; chromatographic packing among others. Recently, Lenzi et al.1 developed a new process, that combines typical suspension and emulsion polymerisation recipes, to produce polystyrene core-shell particles. In this work, it is shown that NIR spectroscopy can be used as an alternative tool as an in-line system (for in situ detection) of the formation of the core-shell structure in polystyrene core-shell polymer particles produced during simultaneous suspension/emulsion polymerisations.
The Degumming of Silk Fabrics: A Preliminary near Infrared Spectroscopy Study
Authors: Mossotti R. , Innocenti R. , Zoccola M. , Anghileri A. , Freddi G.
Published: May 2009

The silk filament spun by the silkworm ( Bombyx mori) is composed of two fibroin filaments held together by a cementing layer of sericin. Fibroin and sericin account for about 75 w% and 25 w% of the raw silk, respectively. The degumming process involves removing the sericin layer prior to dyeing. Generally, it makes use of a solution containing soap, synthetic detergents or proteolytic enzymes. A set of 50 protease degummed silk fabrics (crêpe) with different weight losses were analysed by means of near infrared (NIR) spectroscopy. A correlation between degumming loss ( SE = 0.312%) and NIR spectra was obtained using the principal component regression (PCR) algorithm. The correlation obtained had a coefficient of determination ( R2) of 0.97 and a standard error of calibration ( SEC) of 1.6% for a mean value of 14.6%. In the second part of the study, NIR spectroscopy was used to determine the concentration of sericin in solution. A set of sericin aqueous solutions ranging from 20 g L−1 to 200 g L−1 was prepared and the spectra were acquired in transflectance mode between 10000–3700 cm−1 and correlated with corresponding sericin concentration values. The correlation obtained between specified sericin concentration values and values estimated by NIR spectra had the statistics R2 of 0.99 and SEC of 4.9 g L−1 for an average sericin concentration of 103.8 g L−1. The assignment of characteristic NIR spectral bands of sericin and fibroin was made.
Near- and Mid Infrared Spectroscopy of the Lithium-Bearing Amphibole Holmquistite from Barraute, Canada
Authors: Kloprogge J. Theo , Duong Loc V. , Martens Wayde N. , Frost Ray L. , Visser Diederik
Published: May 2009

The mid- and near infrared spectra of holmquistite, a lithium-containing orthorhombic amphibole, from Barraute (Quebec, Canada) has been measured. The OH stretching region is characterised by bands around 3660, 3645, 3629 and 3613 cm−1 assigned to (3Mg)–OH, (2Mg + Fe2+)–OH, (Mg + 2Fe2+)–OH and (3Fe2+)–OH, respectively. Based on the ratio between the 3645 and 3660 cm−1 bands Fe2+/(Fe2+ + Mg2+) ratios in the M1 and M3 sites were determined to be in the range between 0.3 (mid infrared) and 0.4 (Raman). In the near infrared region, two times four bands were observed associated with the four possible combination modes of the four M–OH stretching and bending modes around 4029, 4193, 4323 and 4400 cm−1 and with the four overtones of M–OH stretching modes around 7055, 7091, 7123 and 7155 cm−1.
Quantification of the Major Phenolic Compounds, Soluble Solid Content and Total Antioxidant Activity of Green Rooibos (Aspalathus Linearis) by Means of near Infrared Spectroscopy
Authors: Manley Marena , Joubert Elizabeth , Botha Mariza
Published: May 2009

Near infrared (NIR) spectroscopy was used to develop calibration models to predict aspalathin, nothofagin and dihydrochalcone contents of dried, green rooibos plant material, as well as to predict the aspalathin content of the respective water extracts. Calibration models were also developed to predict the total polyphenol content (TPC), soluble solid content (SSC) and total antioxidant activity (TAA) of the water extracts. Results showed that NIR spectroscopy can effectively predict the aspalathin content of dried, green rooibos with a standard error of prediction ( SEP) and coefficient of determination ( r2) of 0.45 g 100 g−1 and 0.85, respectively and the dihydrochalcone content of rooibos with an SEP of 0.49 g 100 g−1 and r2 of 0.87. After extending the aspalathin content range of the sample set, by adding varying amounts of a dried rooibos extract powder with high concentrations of aspalathin (15.95 g 100 −1) and nothofagin (1.94 g 100 g−1) to some samples, slightly less accurate, but more robust, models were obtained for the aspalathin ( SEP = 0.53 g 100 g−1; r 2 = 0.87) and dihydrochalcone ( SEP = 0.57 g 100 g−1; r 2 = 0.88) contents. The NIR calibration model developed for nothofagin content ( SEP = 0.10 g 100 g−1; r 2 = 0.71) (extended range: SEP = 0.10 g 100 g−1; r 2 = 0.77) of dried, green rooibos is suitable for screening purposes. The current transmittance NIR calibration models developed for aspalathin content, TPC, SSC and the TAA of green rooibos water extracts did not give acceptable results.
Partial Least Squares Regression of Hyperspectral Images for Contaminant Detection on Poultry Carcasses
Authors: Lawrence Kurt C. , Windham William R. , Park Bosoon , Heitschmidt Gerald W. , Smith Douglas P. , Feldner Peggy
Published: May 2009

The US Department of Agriculture has developed multispectral and hyperspectral imaging systems to detect faecal contaminants. Until recently, the hyperspectral imaging system has been used as a research tool to detect a few optimum wavelengths for use in a multispectral imaging system. However, with the development of complementary metal oxide semiconductor cameras, discrete wavelengths or subsets of the full-detector range can be used to greatly increase the speed of hyperspectral imaging systems. This paper reports on the use of a broad-spectrum multivariate statistical analysis technique for detecting contaminants with hyperspectral imaging. Partial least squares regression was used for model development. Calibration models from spatially averaged region of interest data were developed with and without smoothing, with and without scatter correction and with and without first derivative (difference) pre-processing. Results indicate that using the full spectral range with scatter correction was needed for good model development. Furthermore, validation of the various calibration models indicated that pre-processing with scatter correction, nine-point boxcar smoothing and first derivative pre-processing resulted in the best validation with about 95% of the over 400 contaminants detected with only 26 false positives (errors of commission). About one-third of the false positives were from bruised wingtips which would not be visible during in-plant commercial processing.