Prediction of ethylene content in melt-state random and block polypropylene by near-infrared spectroscopy and chemometrics: influence of a change in sample temperature and its compensation method

This paper reports on the influence of a change in sample temperature, and a method for its compensation, for the prediction of ethylene (C2) content in melt-state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. Near-infrared (NIR) spectra of RPP in the melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and an FT-NIR laboratory system. There are some significant differences between the solid and melt-state RPP spectra. Moreover, we investigated the predicted values of the C2 content from the RPP or BPP spectra measured at 190 degrees C and 250 degrees C using the calibration model for the C2 content developed using the RPP or BPP spectra measured at 230 degrees C. The errors in the predicted values of the C2 content depend on the pretreatment methods for each calibration model. It was found that multiplicative signal correction (MSC) is very effective in compensating for the influence of the change of temperature for the RPP or BPP samples on the predicted C2 content. From the suggestion of principal component analysis (PCA) and difference spectrum analysis, we propose a new compensation method for the temperature change that uses the difference spectra between two spectra sets measured at different temperatures. We achieved good results using the difference spectra between the RPP/BPP spectra sets measured at 190 degrees C and 250 degrees C after correction and the calibration model developed with the spectra measured at 230 degrees C. The comparison between the method using MSC and the proposed method showed that the predicted error in the latter is slightly better than those in the former.     

Practical calibration correction method for the maintenance of an on-line near-infrared monitoring system for molten polymers

The present study has investigated a practical calibration correction method for an on-line monitoring system for molten polymers using a near-infrared (NIR) spectrometer. A partial least squares (PLS) calibration model for the ethylene (C2) content in melt polypropylene (PP) was developed for the investigation of changes in the performance of the on-line system before and after maintenance necessitated by the relocation. The predicted values for the C2 content from the spectra measured after maintenance by using the calibration model developed from the spectra collected before maintenance showed that there were some differences between the spectra obtained by the NIR spectrometer system before and after maintenance. The loadings from factor analysis suggested that the main cause for the differences in the system performance before and after maintenance was wavenumber shifts in the NIR spectra of PP in the melt state. Six popular standardization or calibration transfer methods (direct standardization (DS), piecewise direct standardization (PDS), additive correction (AD), multiplicative correction (MP), slope and bias (SB), and difference spectrum with interpolation (DSI)) were evaluated for the calibration correction of the on-line NIR monitoring system. However, the results of the evaluation showed that these standardization methods need more than two samples to obtain the high accuracy for the nonlinearity contained in the spectra set. From the standpoint of practical calibration in a real plant, the acceptable number of samples for the calibration is one or two. Moreover, recalibration using transferred spectra is not preferable because of the traceability for a calibration model. As a practical solution for a calibration correction in a real plant, a method considering wavenumber shift and path-length correction has been proposed in this study. The predicted results for the C2 content in the melt-state PP from the spectra measured after maintenance by using the proposed method have shown that the proposed method is useful for calibration correction in a real plant in spite of using only one sample.     

Correction of temperature-induced spectral variation by continuous piecewise direct standardization

In process analytical applications it is not always possible to keep the measurement conditions constant. However, fluctuations in external variables such as temperature can have a strong influence on measurement results. For example, nonlinear temperature effects on near-infrared (NIR) spectra may lead to a strongly biased prediction result from multivariate calibration models such as PLS. A new method, called Continuous Piecewise Direct Standardization (CPDS) has been developed for the correction of such external influences. It represents a generalization of the discrete PDS calibration transfer method and is able to adjust for continuous nonlinear influences such as the temperature effects on spectra. It was applied to shortwave NIR spectra of ethanol/water/2-propanol mixtures measured at different temperatures in the range 30-70 degrees C. The method was able to remove, almost completely, the temperature effects on the spectra, and prediction of the mole fractions of the chemical components was close to the results obtained at constant temperature.     

Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment

The present study has aimed at providing new insight into short-wave near-infrared (NIR) spectroscopy of biological fluids. To do that, we analyzed NIR spectra in the 800-1,100-nm region of 100 raw milk samples. The contents of fat, proteins, and lactose were predicted by partial least-squares (PLS) regression and band assignment in that region was investigated based upon PLS loading plots and regression coefficients. For the fat prediction, the whole set of samples was divided into two groups and the fat concentration was predicted for the samples that were not included in the calibration procedures. The correlation coefficient and root-mean-square error of prediction (RM-SEP) in the better prediction run were found to be 0.996 and 0.087 wt %, respectively. Assignment of the bands due to fat was proposed based upon the regression coefficients and PLS loading weights, and the importance of a pretreatment in the prediction was discussed. Milk proteins also yielded sufficient correlation coefficients and RMSEP although the contributions of protein bands to the milk spectra were much smaller than those of the fat bands. The sizes of the calibration models for protein prediction were considered. This is the first time that good correlation coefficients and RMSEP of proteins have ever been obtained for the short-wave NIR spectra of milk. For lactose, noisy regression coefficients with limited prediction ability were obtained. Band assignment was investigated also for bands due to proteins and lactose. We propose the detailed band assignment for the short-wave NIR region useful for various biological fluids. The results presented here demonstrate that the short-wave NIR region is promising for the fast and reliable determination of major components in biological and biomedical fluids.     

Evaluation of visible and near-infrared spectroscopy as a tool for assessing fiber fineness during mechanical preparation of dew-retted flax

Flax fiber must be mechanically prepared to improve fineness and homogeneity of the sliver before chemical processing and wet-spinning. The changes in fiber characteristics are monitored by an airflow method, which is labor intensive and requires 90 minutes to process one sample. This investigation was carried out to develop robust visible and near-infrared calibrations that can be used as a rapid tool for quality assessment of input fibers and changes in fineness at the doubling (blending), first, second, third, and fourth drawing frames, and at the roving stage. The partial least squares (PLS) and principal component regression (PCR) methods were employed to generate models from different segments of the spectra (400-1100, 1100-1700, 1100-2498, 1700-2498, and 400-2498 nm) and a calibration set consisting of 462 samples obtained from the six processing stages. The calibrations were successfully validated with an independent set of 97 samples, and standard errors of prediction of 2.32 and 2.62 dtex were achieved with the best PLS (400-2498 nm) and PCR (1100-2498 nm) models, respectively. An optimized PLS model of the visible-near-infrared (vis-NIR) spectra explained 97% of the variation (R(2) = 0.97) in the sample set with a standard error of calibration (SEC) of 2.45 dtex and a standard error of cross-validation (SECV) of 2.51 dtex R(2) = 0.96). The mean error of the reference airflow method was 1.56 dtex, which is more accurate than the NIR calibration. The improvement in fiber fineness of the validation set obtained from the six production lines was predicted with an error range of -6.47 to +7.19 dtex for input fibers, -1.44 to +5.77 dtex for blended fibers at the doubling, and -4.72 to +3.59 dtex at the drawing frame stages. This level of precision is adequate for wet-spinners to monitor fiber fineness of input fibers and during the preparation of fibers. The advantage of visNIR spectroscopy is the potential capability of the technique to assess fineness and other important quality characteristics of a fiber sample simultaneously in less than 30 minutes; the disadvantages are the expensive instrumentation and the expertise required for operating the instrument compared to the reference method. These factors need to be considered by the industry before installing an off-line NIR system for predicting quality parameters of input materials and changes in fiber characteristics during mechanical processing.     

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.     

Determining water content in human nails with a portable near-infrared spectrometer

The water content of human nail plates was determined using a portable near-infrared (NIR) spectrometer with an InGaAs photodiode array detector. NIR diffuse reflectance (DR) spectra were collected from 108 cut nail plates with different relative humidity and in vivo from fingernails. Partial least-squares (PLS) regression was applied to the NIR spectra in the 1115-1645 nm region to develop calibration models that determine the water content in the cut nail plates and fingernails. A good correlation was obtained between the NIR spectra and the water content measured by nuclear magnetic resonance (NMR) for the NIR measurement of both cut nail plates and fingernails. The results indicate that the water content in the nails can be determined very rapidly (1 s) by means of the portable NIR spectrometer and PLS regression.     

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set, 100% accuracy is obtained by the BPNN. Thus it is concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy.     

Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods

Savitzky-Golay (SG) smoothing and moving window partial least square (MWPLS) methods were applied to the model optimization and the waveband selection for near-infrared (NIR) spectroscopy analysis of soil organic matter. The optimal single wavelength prediction bias (OSWPB) was used to evaluate the similarity of calibration set and prediction set, and a new division method for calibration set and prediction set was proposed. SG smoothing modes were expanded to 540 kinds. The specific computer algorithm platforms for optimization of SG smoothing mode combined with PLS factor and for MWPLS method with changeable parameters were built up. The optimal waveband for soil organic matter was 1926-2032 nm, the optimal smoothing mode was the 2nd order derivative, 6th degree polynomial, 45 smoothing points, the PLS factor, RMSEP and R_P were 8, 0.260 (%) and 0.877 respectively. The prediction effect was obviously better than that in the whole spectral collecting region. To get stable results, all the optimization processes were based on the average prediction effect on 50 different divisions of calibration set and prediction set.     

Removal of analyte-irrelevant variations in near-infrared tissue spectra

This paper describes mathematical techniques to correct for analyte-irrelevant optical variability in tissue spectra by combining multiple preprocessing techniques to address variability in spectral properties of tissue overlying and within the muscle. A mathematical preprocessing method called principal component analysis (PCA) loading correction is discussed for removal of inter-subject, analyte-irrelevant variations in muscle scattering from continuous-wave diffuse reflectance near-infrared (NIR) spectra. The correction is completed by orthogonalizing spectra to a set of loading vectors of the principal components obtained from principal component analysis of spectra with the same analyte value, across different subjects in the calibration set. Once the loading vectors are obtained, no knowledge of analyte values is required for future spectral correction. The method was tested on tissue-like, three-layer phantoms using partial least squares (PLS) regression to predict the absorber concentration in the phantom muscle layer from the NIR spectra. Two other mathematical methods, short-distance correction to remove spectral interference from skin and fat layers and standard normal variate scaling, were also applied and/or combined with the proposed method prior to the PLS analysis. Each of the preprocessing methods improved model prediction and/or reduced model complexity. The combination of the three preprocessing methods provided the most accurate prediction results. We also performed a preliminary validation on in vivo human tissue spectra.     

Development of a method for the determination of human skin moisture using a portable near-infrared system.

In this study, a portable near-infrared (NIR) system was newly integrated with a photodiode array detector that has no moving parts, and this system has been successfully applied for the evaluation of human skin moisture. The good correlation between NIR absorbance and the absolute water content of separated hairless mouse skin, in vitro, was showed, depending on the water content (7.4-84.9%) using this portable NIR system. Partial least squares (PLS) regression was used for calibration with the 1150-1650-nm wavelength range. For practical use for the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo using the portable NIR system on the basis of the relative water content values of stratum corneum from the conventional capacitance method. The PLS model showed a good correlation. This study indicated that the portable NIR system, as compared to conventional methods, could be a powerful tool for human skin moisture, which may be much more stable to environmental conditions, such as temperature and humidity. Furthermore, to confirm the performance of the newly integrated portable NIR system, a scanning-type conventional NIR spectrometer was used in the same experiments, and the results were compared.     

Non-destructive prediction of the drug content of an acetaminophen suppository by near-infrared spectroscopy and X-ray computed tomography

The purpose of this study is to develop non-destructive methods to determine the drug content of suppositories using near-infrared (NIR) spectrometry and X-ray computed tomography (XCT). The suppository samples (acetaminophen content: 0, 100, 200, 300, 400 and 500?mg/suppository) consisted of acetaminophen powder and hard fat. NIR spectra of 18 standard suppository samples were recorded, and the data were divided into two wave number ranges, 4000–10?000?cm^?1 (LR), and 4280–6650?cm^?1 (SR). The best calibration model was determined to minimize the standard error of cross-validation (SECV) by the leave-one-out method in the partial least squares regression (PLS). Sliced XCT images of the suppositories were measured, and apparent density (AD) was evaluated using the image of the sample. The NIR models gave the best correlation coefficient constant (R) values, since the results for LR and SR gave straight lines with R of 0.9274 and 0.9707, respectively. The AD of the suppositories by XCT increased with increasing drug content, and the relationship between the AD and drug content had a straight line with R of 0.9958. Both NIR and X-ray CT performed accurate measurements of suppository samples through plastic packaging.     

A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A modified method of uninformative variable elimination (UVE) was proposed for variable selection in NIR spectral modeling based on the principle of Monte Carlo (MC) and UVE. The method builds a large number of models with randomly selected calibration samples at first, and then each variable is evaluated with a stability of the corresponding coefficients in these models. Variables with poor stability are known as uninformative variable and eliminated. The performance of the proposed method is compared with UVE-PLS and conventional PLS for modeling the NIR data sets of tobacco samples. Results show that the proposed method is able to select important wavelengths from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis. Furthermore, if wavelet compression is combined with the method, more parsimonious and efficient model can be obtained.     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

Near-infrared spectroscopic analysis of the breaking force of extended-release matrix tablets prepared by roller-compaction: influence of plasticizer levels and sintering temperature

The aim of this study was to investigate the feasibility of near-infrared (NIR) spectroscopy for the determination of the influence of sintering temperature and plasticizer levels on the breaking force of extended-release matrix tablets prepared via roller-compaction. Six formulations using theophylline as a model drug, Eudragit® RL PO or Eudragit® RS PO as a matrix former and three levels of TEC (triethyl citrate) as a plasticizer were prepared. The powder blend was roller compacted using a fixed roll-gap of 1.5?mm, feed screw speed to roller speed ratio of 5:1 and roll pressure of 4?MPa. The granules, after removing fines, were compacted into tablets on a Stokes B2 rotary tablet press at a compression force of 7?kN. The tablets were thermally treated at different temperatures (Room Temperature, 50, 75 and 100?°C) for 5?h. These tablets were scanned in reflectance mode in the wavelength range of 400–2500?nm and were evaluated for breaking force. Tablet breaking force significantly increased with increasing plasticizer levels and with increases in the sintering temperature. An increase in tablet hardness produced an upward shift (increase in absorbance) in the NIR spectra. The principle component analysis (PCA) of the spectra was able to distinguish samples with different plasticizer levels and sintering temperatures. In addition, a 9-factor partial least squares (PLS) regression model for tablets containing Eudragit® RL PO had an r² of 0.9797, a standard error of calibration of 0.6255 and a standard error of cross validation (SECV) of 0.7594. Similar analysis of tablets containing Eudragit® RS PO showed an r² of 0.9831, a standard error of calibration of 0.9711 and an SECV of 1.192.     

Calibration transfer of partial least squares jet fuel property models using a segmented virtual standards slope-bias correction method

Fifteen pure molecular chemicals were used to transfer near-IR partial least squares (PLS) models of jet fuel properties between two dispersive near-IR instruments by a novel calibration transfer, standardization, method. PLS was applied to establish models for quantitative analysis of jet fuels properties. The modeled jet fuel properties include: API gravity; %aromatics; cetane index; density; distillation temperatures for 10%, 20%, 50% and 90% recovered volume; flashpoint; freeze point, %hydrogen content; %saturates; and viscosity. The transfer of the PLS models requires that spectra of only 15 pure chemicals be acquired on the primary and secondary instruments. The spectra of the chemicals are then segmented into distinct spectral regions which are subsequently used to digitally construct spectra of virtual standards which mimic jet fuel spectra in the training set. The resulting virtual standards for the primary and secondary instruments are then predicted using the PLS models, and the prediction values are regressed to provide a simple but effective slope and bias correction for transfer. SVSSB calibration transfer of 7 jet fuels properties shows better performance than PDS, for example, in the case of cetane index Root Mean Square Error of Prediction (RMSEP_c) of SVSSB and PDS corrected secondary instrument relative to primary instrument prediction are 0.19 and 0.27 respectively. SVSSB and PDS show comparable performance of the other 6 jet fuel properties. For example, RMSEP_c of SVSSB and PDS corrected secondary of % hydrogen content of secondary instrument relative to the primary instrument prediction are 0.015 and 0.014 respectively. The Segmented Virtual Standards Slope and Bias Method (SVSSB) performs as well as using real jet fuel standards to generate a slope and bias correction, and also as well as conventional Piecewise Direct Standardization (PDS), while eliminating the need to maintain either the complex fuel standards or the primary instrument.     

On-line monitoring of the density of linear low-density polyethylene in a real plant by near-infrared spectroscopy and chemometrics

This paper reports on-line monitoring of the density of linear low-density polyethylene (LLDPE) by near-infrared (NIR) spectroscopy and chemometrics. The on-line monitoring was carried out not only in a laboratory but also in a real plant. We composed an on-line monitoring system for molten polymers consisting of a Fourier transform near-infrared (FT-NIR) spectrometer, input/output (I/O) module, a personal computer, and a sampling cell that we developed. We first compared NIR spectra of LLDPE in the solid and melt states and then developed calibration models that predict the density using partial least squares regression (PLS). The sample sets for developing prediction models were collected for three months at the plant, and the density of LLDPE was continuously monitored on-line for another three months using the model. The standard error of prediction (SEP) for the on-line monitoring of the density of LLDPE at the plant was +/-2.1 mg/cm(3) (range: 0.91-0.95 g/cm(3)).     

On-line predictions of the aspen fibre and birch bark content in unbleached hardwood pulp, using NIR spectroscopy and multivariate data analysis

An on-line fibre-based near-infrared (NIR) spectrometric analyser was adapted for on-site process analysis at an integrated paperboard mill. The analyser uses multivariate techniques for the quantitative predication of the aspen fibre (aspen) and the birch bark contents of sheets of unbleached hardwood pulp. The NIR analyser is a prototype constructed from standard NIR components. The spectroscopic data was processed by using principal component analysis (PCA) and partial least square (PLS) regression. Three sample sets were collected from three experimental designs, each composed of known pulp contents of birch, aspen and birch bark. Sets 1 and 2 were used for model calibration and set 3 was used to validate the models. The PLS model that produced the best predictions gave an error of prediction (RMSEP) of 13% for aspen and less than 2% for birch bark. Eight components resulted in an R²X of 99.3%, R²Y of 99.6%, and Q² of 95.3%. For additional validation of aspen, three unbleached hardwood samples from the mill's production were calculated to lie between − 7% and + 6%, regarding to the PLS model. When vessel cells were counted under a light microscope a value for the aspen content of 4.7% was obtained. The predictive models evaluated were suitable for quality assessments rather than quantitative determination.     

Calibration models for the vinyl acetate concentration in ethylene-vinyl acetate copolymers and its on-line monitoring by near-infrared spectroscopy and chemometrics: use of band shifts associated with variations in the vinyl acetate concentration to improve the models

The present study investigates calibration models for the vinyl acetate (VA) concentration in ethylene-vinyl acetate (EVA) copolymers and its on-line monitoring by near-infrared (NIR) spectroscopy and chemometrics. The key point in the present study is to make use of band shifts associated with concentration changes in the vinyl acetate (VA) for the improvement of the models. NIR spectra of EVA in melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and a FT-NIR laboratory system. Some of the bands in the NIR spectra for both states show significant shifts with the variations in the VA concentration. The peak shifts induced by the VA concentration changes are larger in the solid-state EVA than those in the melt-state EVA. We have developed calibration models for the VA concentration in the solid-state EVA and investigated how to improve the calibration models. The factor analysis of partial least squares (PLS) regression has suggested that the wavenumber shifts caused by the VA concentration changes affect the calibration models for the VA concentration in EVA. From the analysis, it has been proposed that the wavenumbers in the spectrum of one sample in nine EVA samples (VA concentration range: 0-41.1%) are shifted for the improvement of the calibration models, and the effects of the proposed method have been confirmed by using the PLS calibration models for the VA concentration in the solid EVA samples. As the next step, the effects of the wavenumber shift method have been explored for the calibration models for the VA concentration in the melt-state EVA. After that, the discrimination method using the score plots of PLS and the application sequence for the on-line monitoring to use the proposed wavenumber shift method were studied. The simulation results using the discrimination and wavenumber shift methods have shown that those methods are very effective to improve the predicted values of the calibration models for the on-line monitoring of the VA concentration in the melt-state EVA.     

Determination of lycopene and beta-carotene content in tomato fruits and related products: Comparison of FT-Raman, ATR-IR, and NIR spectroscopy

Tomatoes and various products derived from thermally processed tomatoes are major sources of lycopene, but apart from this micronutrient, other carotenoids such as beta-carotene also are present in the fruit. They occur in tomato fruits and various tomato products in amounts of 2.62-629.00 (lycopene) and 0.23-2.83 mg/100 g (beta-carotene). Standard methods for determining the carotenoid content require the extraction of the analyte as well as other cleanup steps. In this work, FT-Raman, ATR-IR, and NIR spectroscopy are applied in order to establish new, fast, and nondestructive calibration methods for quantification of lycopene and beta-carotene content in tomato fruits and related products. The best prediction quality was achieved using a model based on IR spectroscopy (R2 = 0.98 and 0.97, SECV = 33.20 and 0.16 for lycopene and beta-carotene, respectively). In spite of the fact that Raman spectra of tomato products show characteristic key bands of the investigated carotenoids, this method gives slightly lower reliability (R2 = 0.91 and 0.89, SECV = 74.34 and 0.34 for lycopene and beta-carotene, respectively). NIR spectroscopy, which has been used for quantification purposes in the agricultural sector for several decades, in this study shows the worse prediction quality (R2 = 0.85 and 0.80, SECV = 91.19 and 0.41 for lycopene and beta-carotene, respectively).