Three-Mode Abstracts, Part N

INDEX

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• Nahorniak, M. L., & Booksh, K. S. (2003).
  Optimizing the implementation of the PARAFAC method for near-real time calibration of excitation-emission fluorescence analysis. Journal of Chemometrics, 17, 608-617.
  A field-portable, single-exposure excitation-emission matrix (EEM) fluorometer is used in conjunction with parallel factor analysis (PARAFAC) for sub-ppb polycyclic aromatic hydrocarbon (PAH) determinations in the presence of spectral interferents. Several strategies for bringing multiway calibration methods such as PARAFAC into the field were explored. It was shown that automated methods of PARAFAC model selection can be as effective as manual selection. In addition, it was found that there is not always a single best model to employ for prediction. Second, the effect that reducing data density by systematically decreasing calibration set size and spectral resolution has on PARAFAC speed and prediction accuracy was investigated. By decreasing data density, the computational intensity of the PARAFAC algorithm can be reduced to increase the plausibility of on-the-fly data analysis. It was found that reducing eight sample PAH calibration sets to two or three calibration standards significantly decreased computation intensity yet generated adequate predictions. It was also found that spectral resolution can be decreased to reach an optimal compromise between calibration accuracy and analysis speed while minimizing instrumental requirements.

• Nannerup, L. D., Jakobsen, M, Van Den Berg, F., Jensen, J. S., Moller, J. K. S., & Bertelsen, G. (2004).
  Optimizing colour quality of modified atmosphere packed sliced meat products by control of critical packaging parameters. Meat Science, 68, 577-585.
  To study the influence of different packaging and storage parameters on the colour stability of modified atmosphere packed, cured, cooked ham, a multiplicative analysis of variance model (GEMANOVA) was developed. The critical parameters investigated were % residual-O-2, product to headspace volume ratio (P/H volume ratio), temperature, light intensity and oxygen transmission rate (OTR). The model illustrated that all the investigated parameters interacted, but especially % residual-O-2 and P/H volume ratio - i.e., the absolute O-2 content, influenced the degree of discoloration. The complex interactions of the parameters justified the selected model, as it emphasised the necessity of evaluating the parameters simultaneously instead of considering them individually. The importance of absolute O-2 content was further validated through an industrial experiment including three different kinds of sliced meat products.

• Navea, S., De Juan, A., & Tauler, R. (2001).
  Three-way data analysis applied to multispectroscopic monitoring of protein folding. Analytical Chimica Acta, 446, 187-197.
  Multivariate curve resolution-alternating least squares (MCR-ALS) is proposed as a three-way analysis method to deal with multispectroscopic monitoring of protein folding. MCR-ALS provides the concentration profiles associated with the different protein conformations occurring during the process and their related spectra. The concentration profiles describe the folding mechanism and the spectra provide the structural information of the conformations involved. Analysis either of the protein folding process monitored with different techniques (i.e. a row-wise augmented data matrix) or of several experiments done in different conditions using the same technique (i.e. a column-wise augmented matrix) or both possibilities at the same time (i.e. a row- and column-wise augmented matrix), can be performed. Thermal unfolding and refolding of alpha -lactalbumin, monitored using far- and near-UV circular dichroism, fluorescence and UV spectrometry, is shown as example. Information related to changes in the tertiary and the secondary structure of the protein, to the presence of intermediates along the protein folding process and to the reversibility of the thermal process can be obtained.

• Navea, S., De Juan, A., & Tauler, R. (2002).
  Detection and resolution of intermediate species in protein folding processes using fluorescence and circular dichroism spectroscopies and multivariate curve resolution. Analytical Chemistry, 74, 6031-6039.
  Thermally induced protein unfolding/folding processes have been studied on alpha-lactalbumin and alpha-apolactalbumin. Experiments monitored by fluorescence and circular dichroism spectroscopic techniques on alpha-apolactalbumin showed the formation of an intermediate species, whereas in the case of a-lactalbumin, this intermediate species was not detected. The presence and resolution of this intermediate species, its spectrum, and the evolution of all conformations during protein unfolding/folding processes were estimated using the multivariate curve resolution-alternating least-squares method. Elucidation of the nature and contribution of the different secondary structure motifs in each of the resolved protein conformations, including the intermediate, was also carried out. Multivariate resolution has shown to be an excellent tool for the complete characterization of all protein conformations involved in folding processes, including intermediate species that cannot be isolated by physical or chemical means. Indeed, it is in the determination and modeling of these intermediates that this chemometric approach outperforms in power and reliability previous methodologies based on simpler measurements and data treatments and fills the void linked to the elucidation and interpretation of complex mechanisms in protein folding processes.

• Navea, S., De Juan, A., & Tauler, R. (2003).
  Modeling temperature-dependent protein structural transitions by combined near-IR and mid-IR spectroscopies and multivariate curve resolution. Analytical Chemistry, 75, 5592-5601.
  The combination of near- and midinfrared spectroscopies (NIR and MIR) is proposed to monitor temperature-dependent transitions of proteins. These techniques offer a high discriminating power to distinguish among protein structural conformations but, in temperature-dependent processes, present the drawback associated with the intense and evolving absorption of the deuterium oxide, used as a solvent in the protein solutions. Multivariate curve resolution-alternating least squares (MCR-ALS) is chosen as the data analysis technique able to unravel the contributions of the pure protein and deuterium oxide species from the mixed raw experimental measurements. To do so, MCR-ALS works by analyzing simultaneously experiments from MIR and NIR on pure deuterium oxide solutions and protein solutions in D2O. This strategy has proven to be effective for modeling the protein process in the presence of D2O and, therefore, for avoiding the inclusion of artifacts in the data stemming from inadequate baseline corrections. The use of MIR and NIR and MCR-ALS has been tested in the study of the temperatare-dependent evolution of beta-lactoglobulin. Only the combined use of these two infrared techniques has allowed for the distinction of the three pure conformations involved in the process in the working thermal range: native, R-type state, and molten globule.

         

• Neal, R.J., Snyder Jr, C.W., & Kroonenberg, P.M. (1991).
  Individual differences and segment interactions in throwing. Human Movement Journal, 10, 653-676.
  Arm segment velocities of 12 athletes throwing three differently weighted balls were analyzed by three-mode principal component analysis. Individual differences were characterized in terms of the combined influences of the phases of the throwing motion and the arm segment velocity relationships established in those phases. Using three individual differences components, 75% of the variance was described. The arm segment velocity relationships were described by two main components identified as directional velocity and proximal versus distal velocity. The time periods components distinguished between relationships among the arm segment velocities that occur in the windup versus those of the release phase. Three individual differences components are identified and appeared to be related to a general throwing style, the influence of skill level on technique, and the differential effect of the varying ball weights, respectively. Each athlete's throws are weighted combinations of these three components. The timing of segment involvement is investigated and the results indicate sequential patterns from proximal to distal as the throw unfolds. However, the summation of speed principle should not be applied universally to explain segment motion and interaction.

• Neuenschwander, B.E., & Flury, B.D. (1995).
  Common canonical variates. Biometrika, 82, 553-560.
  Canonical correlation analysis measures the linear relationship between two random vectors X₁ and X₂ as the maximum correlation between linear combinations of X₁ and linear combinations of X₂. Several generalizations of canonical correlation analysis to k > 2 random vectors X₁,..., X_k have been proposed in the literature (Kettenring, 1971, 1985), based on the principle of maximising some generalised measure of correlation. In this paper we propose an alternative generalisation, called common canonical variates, based on the assumption that the canonical variates have the same coefficients in all k sets of variables. This generalisation is applicable in situations where all X_i have the same dimension. The authors present normal theory maximum likelihood estimation of common canonical variates, and illustrate their use on a morphometric data set.

• Neuhold, Y. M. & Maeder M. (2002).
  Hard-modelled trilinear decomposition (HTD) for an enhanced kinetic multicomponent analysis. Journal of Chemometrics, 16, 218-227.
  We present a novel approach for kinetic, spectral and chromatographic resolution of trilinear data sets acquired from slow chemical reaction processes via repeated chromatographic analysis with diode array detection. The method is based on fitting rate constants of distinct chemical model reactions (hard-modelled, integrated rate laws) by a Newton-Gauss-Levenberg/Marquardt (NGL/M) optimization in combination with principal component analysis (PCA) and/or evolving factor analysis (EFA), both known as powerful methods from bilinear data analysis. We call our method hard-modelled trilinear decomposition (HTD). Compared with classical bilinear hard-modelled kinetic data analysis, the additional chromatographic resolution leads to two major advantages: (1) the differentiation of indistinguishable rate laws, as they can occur in consecutive first-order reactions; and (2) the circumvention of many problems due to rank deficiencies in the kinetic concentration profiles. In this paper we present the theoretical background of the algorithm and discuss selected chemical rate laws. Copyright (C) 2002 John Wiley Sons, Ltd.

     

• Nguyen, N., Hoole, P., & Marchal, A. (1994).
  Regenerating the spectral shapes of s-phone and sh-phone from a limited set of articulatory parameters. Journal of the Acoustical Society of America, 9, 9.**
  This work was aimed at exploring articulatory-acoustic relationships in the production of French fricatives. More precisely, an attempt was made to find out whether the spectral shapes of /s/ and /sh/ can be regenerated from the x and y coordinates of three electromagnetic transducers affixed to the tongue in the midsagittal plane. The corpus was composed of the two fricatives /s/ and /sh/ combined with the vowels /a/ and /i/ in sequences of the type /vs sh v/ and /v sh sv/, and was read by one male native speaker of French, the spectrum regeneration was based on a statistical procedure which consisted of estimating the factors explaining the main part of the acoustic variance from the position of the transducers, by means of multiple linear regression. The articulatory-acoustic correlations were high and allowed us to regenerate the fricative spectra with a good accuracy. The way in which the acoustic parameters varied as a function of the articulatory ones in the statistical model was in good agreement with data reported in previous works. The results support the idea that the tongue has relatively few degrees of freedom in the production of /s/ and /sh/.

     

• Ni, Y. N., Huang, C. F., & Kokot, S. (2004).
  Application of multivariate calibration and artificial neural networks to simultaneous kinetic-spectrophotometric determination of carbamate pesticides. Chemometrics and Intelligent Laboratory Systems, 71, 177-193.
  A method for the simultaneous determination of the pesticides, carbofuran, isoprocarb and propoxur in fruit and vegetable samples has been investigated and developed. It is based on reaction kinetics and spectrophotometry, and results are interpreted with the aid of chemometrics. The analytical method relies on the differential rates of coupling reactions between the hydrolysis product of each carbamate and 4-aminophenol in the presence of potassium periodate in an alkaline solution. The optimized method was successfully tested by analyzing each of the carbamates independently, and linear calibration models are described. For the simultaneous determinations of the carbamates found in ternary mixtures, kinetic and spectral data were processed either by three-way data unfolding method or decomposed by trilinear modeling. Subsequently, 10 different RBF-ANN, PARAFAC and NPLS calibration models were constructed with the use of synthetic ternary mixtures of the three carbamates, and were validated with a separate set of mixtures. The performance of the calibration models was then ranked on the basis of several different figures of merit with the aid of the multi-criteria decision making approach, PROMETHEE and GAIA. RBF-ANN and PC-RBF-ANN were the best performing methods with %Relative Prediction Errors (R-PE) in the 3-4% range and Recovery of about 97%. When compared with other recent studies, it was also noted that RBF-ANN has consistently outperformed the more common prediction methods such as PLS and PCR as well as BP-ANN. The successful RBF-ANN method was then applied for the determination of the three carbamate pesticides in purchased vegetable and fruit samples.

• Nianyi, C., Wencong, L., Ruiliang, C., Chonghe, L., & Pei, Q. (1999).
  Chemometrics methods applied to industrial optimization and materials optimal design. Chemometrics and Intelligent Laboratory Systems, 45, 329-333.
  Based on the methods of multivariate data processing of the data records for complicated chemical reaction systems, computer software named ‘process analyzer’ and ‘materials research advisor’ has been built. The methods used include some new methods of pattern recognition, such as back-mapping methods, local-structure view and data transformation methods. These new computational techniques have been widely used in the chemical, petrochemical and metallurgical industries for industrial optimization and materials design with good results.

• Nielsen, J. P., Bertrand, D., Micklander, E., Courcoux, P., & Munck, L. (2001).
  Study of NIR spectra, particle size distributions and chemical parameters of wheat flours: a multi-way approach. Journal of Near Infrared Spectroscopy, 9, 275-285.
  Near infrared (NIR) reflectance spectra contain information about both physical and chemical characteristics of flour samples and have great potential for on-line/at-line quality control in a flour mill. The addition of physical characteristics such as particle size distribution data to the NIR spectra and chemical composition data of wheat flour samples was anticipated to provide a better understanding and translation of multivariate measurements into the operational routines and experiences of mill operators. This was studied using a multi-way model called "Analysis of Common Dimensions and Specific Weights" (COMDIM). By this method the underlying dimensions across several data tables with different numbers of variables are defined and the scores and loadings are interpretable in the same way as in a classical Principal Component Analysis. The method was applied on raw NIR spectra as well as after correcting the NIR spectra using the Standard Normal Variate (SNV). The model output in terms of weights, scores and loadings were highly interpretable and in agreement with common characteristics of wheat flour samples. Four underlying dimensions explained 99.4% of the total variation, both when analysing raw and SNV-corrected spectra. A comparison of the two analyses clearly shows that correcting the spectra puts more emphasis on the chemical information in the spectra. However, even corrected NIR spectra contain considerable information about the particle size properties of the flour samples. It is suggested that the COMDIM model can be a useful tool in the process control in a flour mill and it can be used on a wide range of multi-way data problems to assure a high degree of interpretability.

• Nierop, A.F.M. (1993).
  The INDRES model: An INDSCAL model with residuals orthogonal to INDSCAL dimensions. In R. Steyer, K.F. Wender, & K.F. Widaman (Eds.), Psychometric Methodology. Proceedings of the 7th European Meeting of the Psychometric Society in Trier, (pp. 366-370). Stuttgart and New York: Gustav Fischer Verlag.
  The main purpose of this paper is to develop a model that improves the recovery of the true INDSCAL dimensions. This purpose is attained by making the residuals as orthogonal as possible to the recovered dimensions. A simulation study assesses the results.

• Nikolajsen, R. P. H., Hansen, A. M. & Bro, R. (2001).
  Attempt to separate the fluorescence spectra of adrenaline and noradrenaline using chemometrics. Luminescence, 16, 91-101.
  An investigation was conducted on whether the fluorescence spectra of the very similar catecholamines adrenaline and noradrenaline could be separated using chemometric methods. The fluorescence landscapes (several excitation and emission spectra were measured) of two data sets with respectively 16 and 6 samples were measured, the smaller data set with higher resolution and i.e. precision. The samples were artificial urine (pH similar or equal to 3) spiked with the catecholamines in the concentration ranges 40-1200 nmol/L and 5.5-18 mu mol/L, respectively. Unfold partial least squares regression (Unfold-PLSR) on the larger data set and parallel factor analysis (PARAFAC) of the six samples of the smaller set showed that there was no difference between the fluorescence landscapes of adrenaline and noradrenaline. It can be concluded that chemometric separation of adrenaline and noradrenaline is not obtainable using this type of fluorescence measurement. Raman scatter. which overlaps the catecholamine spectra, was shown not to have any influence on the models calculated.

• Nikolajsen, R. P. H., Booksh, K. S., Hansen, A. M. & Bro, R. (2003).
  Quantifying catecholamines using multi-way kinetic modelling Analytica Chimica Acta, 475, 137-150. A new method for quantifying adrenaline and noradrenaline concentrations from mixtures of catecholamine standards is described. The method derives selectivity from the different rates, at which the fluorescing 3,5,6-trihydroxyindole derivatives (lutines) of the catecholamines are formed and degraded for adrenaline and noradrenaline. The standards used had the concentration ranges 50-1200 nmol/l for adrenaline and 30-1400 nmol/l for noradrenaline. Fluorescence landscapes were measured at consecutive time points for every sample hereby creating a four-way data array. It is shown that the raw dataset can be dramatically reduced in size without loosing significant information hereby making calculations much faster and lessening instrumental performance requirements. The data follow a two-component four-way parallel factor analysis model (PARAFAC), from which quantitative information is also obtained. Two-component multilinear partial least squares regression (N-PLSR) was also employed for the quantification of the catecholamines. The results for PARAFAC and N-PLSR were very similar with root mean squared errors of cross-validation (RMSECV) being in the range 24-30 nmol/l. Several improvements of the method are suggested, and it is expected that the method will be suitable for determination of catecholamines in urine from healthy subjects. (C) 2002 Elsevier Science B:V. All rights reserved.

• Nilsson, J., Homan, E. J., Smilde, A. K., Grol, C. J., & Wikstrom, H. (1998).
  A multiway 3D QSAR analysis of a series of(S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides. Journal of Computer-Aided Molecular Design, 12, 81-93.
  Recently, the multilinear PLS algorithm was presented by Bro and later implemented as a regression method in 3D QSAR by Nilsson et al. In the present article a well-known set of (S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6- methoxybenzamides, with affinity towards the dopamine D2 receptor subtype, was utilised for the validation of the multilinear PLS method. After exhaustive conformational analyses on the ligands, the active analogue approach was employed to align them in their presumed pharmacologically active conformations, using (–)-piquindone as a template. Descriptors were then generated in the GRID program, and 40 calibration compounds and 18 test compounds were selected by means of a principal component analysis in the descriptor space. The final model was validated with different types of cross-validation experiments, e.g. leave-one-out, leave-three-out and leave-fiveout. The cross-validated Q2 was 62% for all experiments, confirming the stability of the model. The prediction of the test set with a predicted Q2 of 62% also established the predictive ability. Finally, the conformations and the alignment of the ligands in combination with multilinear PLS, obviously, played an important role for the success of our model.

• Nilsson, J., De Jong, S., & Smilde, A.K. (1997).
  Multiway calibration in 3D QSAR. Journal of Chemometrics, 11, 511-524.
  We have introduced multilinear PLS in 3D QSAR and applied it to GRID descriptors from a set of benzamides with affinity to the dopamine D₃ receptor subtype, synthesized as potential drugs against schizophrenia. The key issue in 3D QSAR modelling is to obtain a predictive model that is easy to interpret. Each component in the multilinear PLS model explains clearly defined details, e.g. substituent positions, while the bilinear PLS solution is general and more difficult to interpret. The best models were obtained after four components with multilinear PLS (Q² = 51%) and after only one component with bilinear PLS (Q² = 50%). The external test set was predicted better with multilinear PLS (Q² = 31%) than with bilinear PLS (Q² = 25%). With multilinear PLS one loses in fit and gains in stability and simplicity owing to the fewer parameters that need to be estimated as compared with bilinear PLS. Finally, multilinear PLS is also less influenced by insignificant variation in the descriptor block, which is an advantage in 3D QSAR modelling.

• Nix, D.A., Papcun, G., Hogden, J., & Zlokarnik, I. (1996).
  Two cross-linguistic factors underlying tongue shapes for vowels. Journal of the Acoustical Society of America, 99, 3707-3717.
  Desirable characteristics of a vocal-tract parametrization include accuracy, low dimensionality, and generalizability across speakers and languages. A low-dimensional, speaker-independent linear parametrization of vowel tongue shapes can be obtained using the Parafac three-mode factor analysis procedure [Harshman et al., J. Acoust. Soc. Am. 62, 693-707 (1977)]. Harshman et al. applied Parafac to midsagittal x-ray vowel data from five English speakers, reporting that two speaker-independent factors are required to accurately represent the tongue shape measured along anatomically normalized vocal-tract diameter grid lines. Subsequently, the cross-linguistic generality of this parametrization was brought into question by the application of Parafac to Icelandic vowel data, where three nonorthogonal factors were reported [Jackson, J. Acoust. Soc. Am. 84, 124-143 (1988)]. This solution is shown to be degenerate; a reanalysis of Jackson's Icelandic data produces two factors that match Harshman et al.'s factors for English vowels, contradicting Jackson's distinction between English and Icelandic language-specific "articulatory primes." To obtain vowel factors not constrained by artificial measurement grid lines, x-ray tongue shape traces of six English speakers were marked with 13 equally spaced points. Parafac analysis of this unconstrained (x,y) coordinate data results :In two factors that are clearly interpretable in terms of the traditional vowel quality dimensions front/back, high/low.

             

• Nomikos, P., & MacGregor, J.F. (1994).
  Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40, 1361-1375.
  Multivariate statistical procedures for monitoring the progress of batch processes are developed. The only information needed to exploit the procedures is a historical database of past successful batches. Multiway principal component analysis is used to extract the information in the multivariate trajectory data by projecting them onto low-dimensional spaces defined by the latent variables or principal components. This leads to simple monitoring charts, consistent with the philosophy of statistical process control, which are capable of tracking the progress of new batch runs and detecting the occurrence of observable upsets. The approach is contrasted with other approaches which use theoretical or knowledge-based models, and its potential is illustrated using a detailed simulation study of a semibatch reactor for the production of styrene-butadiene latex.

• Nomikos, P., & MacGregor, J.F. (1995a).
  Multivariate SPC charts for monitoring batch processes. Technometrics, 37, 41-59.
  The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.

• Nomikos, P., & MacGregor, J.F. (1995b).
  Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems, 30, 97-108.
  Multivariate statistical procedures for monitoring the progress of batch processes are developed. Multi-way partial least squares (MPLS) is used to extract the information from the process measurement variable trajectories that is more relevant to the final quality variables of the product. The only information needed is a historical database of past successful batches. New batches can be monitored through simple monitoring charts which are consistent with the philosophy of statistical process control. These charts monitor the batch operation and provide on-line predictions of the final product qualities. Approximate confidence intervals for the predictions from PLS models are developed. The approach is illustrated using a simulation study of a styrene-butadiene batch reactor.

• Nørgaard, L. (1995a).
  A multivariate chemometric approach to fluorescence spectroscopy. Talanta, 42, 1305-1324.
  A multivariate approach to the solution of problems often encountered in the spectrofluorometry of natural samples, utilising information from whole spectra is presented. (a) Piecewise direct standardisation is implemented and employed to transfer emission spectra measured with two different xenon lamps of different ages as if the spectra were measured with the same lamp. (b) It has been shown using a multivariate analysis approach that it is possible to use the raw data points instead of the smoothed data based on an algorithm included in the instrument software by the manufacturer. (c) It is documented that Raman scattering does not hamper the performance of multivariate calibration; on the contrary, in an experiment with sugar samples the concentration prediction errors become about five times lower by including the whole emission spectrum in the analysis instead of using a univariate calibration based on an emission wavelength that only reflects the analyte of interest. (d) An algorithm for variable selection is implemented and employed in the selection of optimal excitation wavelengths. Among 13 emission spectra recorded for a sugar sample at different excitation wavelengths, four of these are chosen that describe 98.51% of the total variance in the original data. (e) Finally the combination of fluorescence spectroscopy and multivariate calibration with conventional chemical data according to the near-infrared black box model is presented. The refined sugar quality parameter, the ash content and the fluorescence emission spectra are correlated by a partial least-squares regression model. Five experiments employing different monochromator slit widths and sugar concentrations are performed, and the best correlation. obtained by full cross-validation of the 15 sugar samples is R = 0.98.

• Nørgaard, L. (1995b).
  Classification and prediction of quality and process parameters of thick juice and beet sugar by fluorescence spectroscopy and chemometrics. Zuckerindustrie, 120, 970-981.
  Full spectrum fluorescence spectroscopy in combination with multivariate statistical methods (chemometrics) is used to withdraw information from white sugar solutions and thick juice samples from 6 different sugar factories within the same region in Northern Europe with respect to classification and prediction of quality and process parameters. The weekly samples are collected during the 1993 campaign. Classification of the sugar samples according to factory was possible by explorative data analysis (soft independent modelling of class analogy, SIMCA) with 6% misclassification. The factory classification was not so distinct, probably due to sampling problems, when based on thick juice samples diluted 976 times, where circa 30% of the samples were misclassified. These results demonstrate that the fluorescence spectra recorded reflect the quality of the raw material superimposed by the influence of the process, resulting in a characteristic spectral pattern of each factory. By multivariate calibration (partial least squares, PLS) the quality parameters ash, color, alpha-amino-N, and SO2 content of sugar samples were predicted from the fluorescence emission spectra. The test set correlation coefficients (R) between laboratory analysed values and the values predicted by the calibration model based on the fluorescence spectra are 0.91, 0.94, 0.98, and 0.85, respectively. In an analogue way the parameters ash and color were predicted in thick juice samples with correlation coefficients of 0.89 and 0.90. When the pH value of the sugar samples was corrected to pH = 7.0, the misclassification percentage of the factories increased to circa 10%, and the correlation coefficients to ash, color, alpha-amino-N, and SO2 content became 0.88, 0.93, 0.98, and 0.84, indicating that some information is lost. Five pairs of excitation-emission wavelengths were selected by a chemometric algorithm (principal variables, PV), giving reasonable correlations results compared to full spectrum models..

• Nørgaard, L. (1996).
  Spectral resolution and prediction of slit widths in fluorescence spectroscopy by two- and three-way methods. Journal of Chemometrics, 10, 615-630.
  Modem spectrofluorometers have several instrumental settings to be adjusted and decided on before sample measurement, e.g. excitation and emission slit widths, emission scan velocity and spectral ranges to be recorded. The influences of these settings on the recorded spectra are crucial, particularly when applying full fluorescence spectra in the analysis of a given problem. The effect on the fluorescence emission spectra when changing the slit widths is studied in detail by recording the emission spectra of an ovalene standard block at all possible excitation (3-15 nm) and emission (3-20 nm) slit width combinations. By the two-way curve resolution method alternating regression (AR) it is possible to resolve the emission spectra into three hidden spectra describing the coarse, medium coarse/fine and fine structure of the recorded spectra. By the three-way methods Parafac and Tucker it is possible to separate the effects of both the excitation and emission slit widths on the recorded spectra. An analogous analysis of a natural sugar sample results in a one factor Parafac solution, probably because of the large amount of different substances found in a table sugar sample resulting in rather featureless emission spectra not so susceptible to influence by the instrumental settings. Finally, it is demonstrated that two-way unfold PLS, Parafac and Tucker regression models are able to predict the excitation and emission slit widths from the recorded emission spectra. The root mean square errors of prediction (RMSEP) are about 0.5 nm (R approximate to 0.990) for the excitation slit and 0.3 nm (R approximate to 0.999) for the emission slit.

• Norup, L. R., Hansen, P. W., Ingvartsen, K. L., & Friggens, N. C. (2001).
  An attempt to detect oestrus from changes in Fourier transform infrared spectra of milk from dairy heifers. Animal Reproduction Science, 65, 43-50.
  This study was carried out to investigate if there were systematic changes in milk Fourier transform infrared (FT-IR) spectra relative to stage of the oestrous cycle in cattle. Oestrous cycles of 22 lactating heifers were synchronized with two injections of prostaglandin F2 alpha (PGF) administered 11 days apart. The heifers were milked twice daily, and milk samples were collected from each heifer at each milking for a period of 70 days, starting on the day of the second PGF injection. Oestrus was diagnosed by visual detection in conjunction with monitoring rectal temperature. Milk samples were analyzed by FT-IR spectroscopy and the spectra data were analyzed using partial least squares (PLS) methods in relation to time of observed oestrus in heifers. In this investigation, it was not possible to identify reliable changes in milk FT-IR spectra in relation to oestrus on a single heifer basis, though there was a weak correlation between FT IR spectra and expected time of oestrus when the analysis was carried out across all the heifers.

                       

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