Calibration of multiplexed fiber-optic spectroscopy

Large-scale commercial bioprocesses that manufacture biopharmaceutical products such as monoclonal antibodies generally involve multiple bioreactors operated in parallel. Spectra recorded during in situ monitoring of multiple bioreactors by multiplexed fiber-optic spectroscopies contain not only spectral information of the chemical constituents but also contributions resulting from differences in the optical properties of the probes. Spectra with variations induced by probe differences cannot be efficiently modeled by the commonly used multivariate linear calibration models or effectively removed by popular empirical preprocessing methods. In this study, for the first time, a calibration model is proposed for the analysis of complex spectral data sets arising from multiplexed probes. In the proposed calibration model, the spectral variations introduced by probe differences are explicitly modeled by introducing a multiplicative parameter for each optical probe, and then their detrimental effects are effectively mitigated through a "dual calibration" strategy. The performance of the proposed multiplex calibration model has been tested on two multiplexed spectral data sets (i.e., MIR data of ternary mixtures and NIR data of bioprocesses). Experimental results suggest that the proposed calibration model can effectively mitigate the detrimental effects of probe differences and hence provide much more accurate predictions than commonly used multivariate linear calibration models (such as PLS) with and without empirical data preprocessing methods such as orthogonal signal correction, standard normal variate, or multiplicative signal correction.     

Quantitative spectroscopic analysis of heterogeneous mixtures: the correction of multiplicative effects caused by variations in physical properties of samples

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g., particle size and shape, sample packing, and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum and, hence, mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical components in the same sample mixture. On the basis of this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e., near-infrared total diffuse transmittance spectra of four-component suspension samples and near-infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology.     

Multivariate calibration models for lysozyme from near-infrared transmission spectra in scattering solutions of monodisperse microspheres

The ability to quantify lysozyme is demonstrated for a series of aqueous samples with different degrees of scattering. Near-infrared spectra are collected for two sets of lysozyme/scattering solutions. In both sets of samples, the solutions are composed of lysozyme dissolved in acetate buffer with suspended monodisperse latex microspheres of polystyrene. The diameter of the microspheres is 6.4 microm for the first set and 0.6 microm for the second. For each set, the amount of microspheres range from 0.005 to 0.998 wt %, the lysozyme concentrations range from 0.834 to 28.6 mg/mL, and solution compositions are designed to minimize correlations between the concentration of lysozyme and percentage of microspheres. Near-infrared spectra are collected individually for each set of solutions. Single-beam spectra are collected over the combination spectral range (5000-4000 cm(-1), 2.0-2.5 microm) by transmitting the incident radiation through a 1.5-mm-thick sample that is maintained at 21 degrees C. Partial least-squares calibration models are evaluated individually for each data set both with and without wavelength optimization. Results indicate that models from raw, nonmodified, single-beam spectra are incapable of extracting lysozyme concentration from these highly scattering solutions. Accurate concentration measurements are possible, however, by implementing either a multiplicative scatter correction to the single-beam spectra or by taking the ratio of these single-beam spectra to an appropriate reference spectrum. In addition, digital Fourier filtering of these spectra enhances model performance. The best calibration model in the presence of 6.4-microm microspheres is obtained from multiplicative scatter corrected single-beam spectra over the 4550-4190-cm(-1) spectral range. The mean percent error of prediction (MPEP) and standard error of prediction (SEP) for this model are 2.2% and 0.28 mg/mL, respectively. Likewise, the multiplicative scatter corrected spectra with wavelength optimization provided the best calibration model for the 0.6-microm data set. In this case, the MPEP and SEP are 2.3% and 0.44 mg/mL, respectively. In addition, the ability to predict lysozyme concentrations is evaluated for the situation where the degree of scattering is greater in the predication samples compared to the calibration samples. Differences in the prediction ability are noted between the 6.4- and 0.6-microm data sets.     

Physics-based multiplicative scatter correction approaches for improving the performance of calibration models

Light scattering effects pose a major problem in the estimation of chemical properties of particulate systems such as blood, tissue, and pharmaceutical solids. Recently, Martens et al. proposed an extended multiplicative signal correction (EMSC) approach where light-scattering effects were taken into account in an empirical manner. It is possible to include causal, first-principles mathematical models based on the physics of light scattering into the EMSC framework. This could lead to significant improvements in the separation of absorption and scattering effects. A preconditioning step prior to application of EMSC, whereby a transformation based on the physics of light scattering is used to convert the spectra into a form where the absorption and scattering effects are separable (an underlying assumption of EMSC), is proposed. Results indicate that the transformation followed by EMSC gives better calibration models than the direct application of EMSC to the absorbance spectra.     

Raman spectra of biological samples: A study of preprocessing methods

In this study preprocessing of Raman spectra of different biological samples has been studied, and their effect on the ability to extract robust and quantitative information has been evaluated. Four data sets of Raman spectra were chosen in order to cover different aspects of biological Raman spectra, and the samples constituted salmon oils, juice samples, salmon meat, and mixtures of fat, protein, and water. A range of frequently used preprocessing methods, as well as combinations of different methods, was evaluated. Different aspects of regression results obtained from partial least squares regression (PLSR) were used as indicators for comparing the effect of different preprocessing methods. The results, as expected, suggest that baseline correction methods should be performed in advance of normalization methods. By performing total intensity normalization after adequate baseline correction, robust calibration models were obtained for all data sets. Combination methods like standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) in their basic form were not able to handle the baseline features present in several of the data sets, and these methods thus provide no additional benefits compared to the approach of baseline correction in advance of total intensity normalization. EMSC provides additional possibilities that require further investigation.     

Application of partial least-squares regression to near-infrared reflectance spectroscopic determination of shive content in flax

Shive, the nonfiberous core portion of the stem, in flax fiber after retting is related to fiber quality. The objective of this study is to develop a standard calibration model for determining shive content in retted flax by using near-infrared reflectance spectroscopy. Calibration samples were prepared by manually mixing pure, ground shive and pure, ground fiber from flax retted by three different methods (water, dew, and enzyme retting) to provide a wide range of shive content from 0 to 100%. Partial least-squares (PLS) regression was used to generate a calibration model, and spectral data were processed using various pretreatments such as a multiplicative scatter correction (MSC), normalization, derivatives, and Martens' Uncertainty option to improve the calibration model. The calibration model developed with a single sample set resulted in a standard error of 1.8% with one factor. The best algorithm was produced from first-derivative processing of the spectral data. MSC was not effective processing for this model. However, a big bias was observed when independent sample sets were applied to this calibration model to predict shive content in flax fiber. The calibration model developed using a combination sample set showed a slightly higher standard error and number of factors compared to the model for a single sample set, but this model was sufficiently accurate to apply to each sample set. The best algorithm for the combination sample set was generated from second derivatives followed by MSC processing of spectral data and from Martens' Uncertainty option; it resulted in a standard error of 2.3% with 2 factors. The value of the digital second derivative centered at 1674 nm for these spectral data was highly correlated to shive content of flax and could form the basis for a simple, low-cost sensor for the shive or fiber content in retted flax.     

Comparison of methods for transfer of calibration models in near-infared spectroscopy: a case study based on correcting path length differences using fiber-optic transmittance probes in in-line near-infrared spectroscopy

This article addresses problems related to transfer of calibration models due to variations in distance between the transmittance fiber-optic probes. The data have been generated using a mixture design and measured at five different probe distances. A number of techniques reported in the literature have been compared. These include multiplicative scatter correction (MSC), path length correction (PLC), finite impulse response (FIR), orthogonal signal correction (OSC), piecewise direct standardization (PDS), and robust calibration. The quality of the predictions was expressed in terms of root mean square error of prediction (RMSEP). Robust calibration gave good calibration transfer results, while the other methods did not give acceptable results.     

Near-infrared spectra of Penicillium camemberti strains separated by extended multiplicative signal correction improved prediction of physical and chemical variations

Different methods for spectral preprocessing were compared in relation to the ability to distinguish between fungal isolates and growth stages for Penicillium camemberti grown on cheese substrate. The best classification results were obtained by temperatureand wavelength-extended multivariate signal correction (TWEMSC) preprocessing, whereby three patterns of variation in nearinfrared (NIR) log(1/R) spectra of fungal colonies could be separated mathematically: (1) physical light scattering and its wavelength dependency, (2) differences in light absorption of water due to varying sample temperature, etc., and (3) differences in light absorption between different fungal isolates. With this preprocessing, discriminant partial least squares (PLS) regression yielded 100% correct classification of three isolates, both within the cross-validated calibration set and in two independent test sets of samples.     

Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing

In this study, a novel chemometric algorithm for improved evaluation of analytical data is presented and applied to three spectroscopic data sets obtained by different analytical methods. This so-called secured principal component regression (sPCR) was developed for detecting and correcting uncalibrated spectral features newly emerging in spectra after finalizing the PCR calibration, which may result in major concentration errors. Hence, detection and correction of uncalibrated features is essential. Furthermore, detected uncalibrated features provide qualitative information for sensing and process monitoring applications indicating problems in the process flow. After conventional PCR calibration, sPCR analyzes measurement data in two steps: The first step investigates whether the obtained data set is consistent with the calibration model or not. If spectroscopic features are found that cannot be modeled by the principal components, they are extracted from the measurement spectrum. This corrected spectrum is then evaluated by conventional PCR. In the Experimental Section, sPCR was successfully applied to three data sets obtained by different spectroscopic measurements in order to corroborate general applicability of the proposed concept. For each data set, one of several substances was excluded from the calibration acting in the sPCR assessment as uncalibrated absorber. The test sets consisted of disturbed and undisturbed samples. A total of 109 out of 110 test samples were correctly classified as disturbed or undisturbed by an uncalibrated absorber. It was confirmed that the extracted disturbance spectra are in accordance with the spectra of the uncalibrated analytes. The concentration results obtained with sPCR were found to be equivalent to conventional PCR results in the case of undisturbed samples and more precise for disturbed samples.     

Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii

Hyperspectral instruments provide the spectral detail necessary for extracting multiple layers of information from inherently complex coastal environments. We evaluate the performance of a semi-analytical optimization model for deriving bathymetry, benthic reflectance, and water optical properties using hyperspectral AVIRIS imagery of Kaneohe Bay, Hawaii. We examine the relative impacts on model performance using two different atmospheric correction algorithms and two different methods for reducing the effects of sunglint. We also examine the impact of varying view and illumination geometry, changing the default bottom reflectance, and using a kernel processing scheme to normalize water properties over small areas. Results indicate robust model performance for most model formulations, with the most significant impact on model output being generated by differences in the atmospheric and deglint algorithms used for preprocessing.     

Advanced nonlinear approaches for predicting the ingredient composition in compound feedingstuffs by near-infrared reflection spectroscopy

For quantitative applications, the most common usage of near-infrared reflection spectroscopy (NIRS) technology, calibration involves establishing a mathematical relationship between spectral data and data provided by the reference. This model may be fairly complex, since the near-infrared spectrum is highly variable and contains physical/chemical information for the sample that may be redundant, and multivariate calibration is usually required. When the relationship to be modeled is nonlinear, classical regression methods are inadequate, and more complex strategies and algorithms must be sought in order to model this nonlinearity. The development of NIRS calibrations to predict the ingredient composition, i.e., the inclusion percentage of each ingredient, in compound feeds is a complex task, due to the nature of the parameters to be predicted and to the heterogeneous nature of the matrices/formulas in which each ingredient participates. The present paper evaluates the use of least squares support vector machines (LSSVM) and two local calibration methods, CARNAC and locally biased regression, for developing NIRS models to predict two of the most representative ingredients in compound feed formulations, wheat and sunflower meal, using a large spectral library of 7523 commercial compound feed samples. For both ingredients, the best results were obtained using CARNAC, with standard errors of prediction (SEP) of 1.7% and 0.60% for wheat and sunflower meal, respectively, and even better results when the algorithm was allowed to refuse to predict 10% of the unknowns. Meanwhile, LSSVM performed less well on wheat (SEP 2.6%) but comparably on sunflower meal (SEP 0.60%), giving results very similar to those reported previously for artificial neural networks. Locally biased regression was the least successful of the three methods, with SEPs of 3.3% for wheat and 0.72% for sunflower meal. All the nonlinear methods improved on the standard approach using partial least squares (PLS), which gave SEPs of 5.3% for wheat and 0.81% for sunflower meal.     

Atmospheric correction over case 2 waters with an iterative fitting algorithm: relative humidity effects

In algorithms for the atmospheric correction of visible and near-IR satellite observations of the Earth's surface, it is generally assumed that the spectral variation of aerosol optical depth is characterized by an Angstr?m power law or similar dependence. In an iterative fitting algorithm for atmospheric correction of ocean color imagery over case 2 waters, this assumption leads to an inability to retrieve the aerosol type and to the attribution to aerosol spectral variations of spectral effects actually caused by the water contents. An improvement to this algorithm is described in which the spectral variation of optical depth is calculated as a function of aerosol type and relative humidity, and an attempt is made to retrieve the relative humidity in addition to aerosol type. The aerosol is treated as a mixture of aerosol components (e.g., soot), rather than of aerosol types (e.g., urban). We demonstrate the improvement over the previous method by using simulated case 1 and case 2 sea-viewing wide field-of-view sensor data, although the retrieval of relative humidity was not successful.     

Practical Aspects of the Spectral Analysis of Irregularly Sampled Data With Time-Series Models

Several algorithms for the spectral analysis of irregularly sampled random processes can estimate the spectral density for a low frequency range. A new time-series method extended that frequency range with a factor of thousand or more. The new algorithm has two requirements to give useful results. First, at least ten closest pairs of neighboring irregular observations should have a distance less than the minimum resampling distance for the chosen discrete-time frequency range. Second, a low-order time-series model should be appropriate to describe the global character of the data. The consequences and importance of this second demand are studied for irregular turbulence observations with narrow spectral details. Models of low orders are estimated from equidistant hot-wire observations and from irregularly sampled laser Doppler anemometer (LDA) data, which are obtained from the same turbulence process. The irregular data are resampled with the nearest neighbor method, both with and without slotting. Apart from the usual bias contributions of resampling irregular data, LDA data can give an additional spectral bias if the instantaneous sampling rate is correlated to the actual magnitude of the turbulent velocity. Making histograms of the amplitudes and the interarrival times provides useful information about irregularly sampled data.     

Development of 2D band-target entropy minimization and application to the deconvolution of multicomponent 2D nuclear magnetic resonance spectra

Spectral reconstruction from multicomponent spectroscopic data is the frequent primary goal in chemical system identification and exploratory chemometric studies. Various methods and techniques have been reported in the literature. However, few algorithms/methods have been devised for spectral recovery without the use of any a priori information. In the present studies, a higher dimensional entropy minimization method based on the BTEM algorithm (Widjaja, E.; Li, C.; Garland, M. Organometallics 2002, 21, 1991-1997.) and related techniques were extended to large-scale arrays, namely, 2D NMR spectroscopy. The performance of this novel method had been successfully verified on various real experimental mixture spectra from a series of randomized 2D NMR mixtures (COSY NMR and HSQC NMR). With the new algorithm and raw multicomponent NMR alone, it was possible to reconstruct the pure spectroscopic patterns and calculate the relative concentration of each species without recourse to any libraries or any other a priori information. The potential advantages of this novel algorithm and its implications for general chemical system identification of unknown mixtures are discussed.     

近红外光谱法测定茶多酚中总儿茶素含量

以高效液相色谱(HPLC)分析结果为参考值,建立了快速测量茶多酚中总儿茶素含量的近红外光谱定标模型.将48份茶多酚样品组成定标样品集,在1000～2500nm(4000～10000cm-1)的近红外漫反射光谱为定标波长范围内,光谱经一阶导数(Firstderivative)、二阶导数(Secondderivative)、标准归一化(Stan-dardnormalvariate,SNV)和多元散射校正(multiplicativesignalcorrection,MSC)处理后结合偏最小二乘回归(PLS)定标.经内部交叉验证表明,光谱经SNV处理后建模结果最佳.模型的相关系数Corr.Coeff=0.997,校正均方根RMSEC=1.71%.比较了经典最小二乘法(CLS)、偏最小二乘法(PLS)和主成分回归(PCR)等方法建模结果,以偏最小二乘回归建模效果最好.     

Examination of spectral pretreatments for partial least-squares calibrations for chemical and physical properties of wheat

Use of near-infrared (NIR) diffuse reflectance on ground wheat meal for prediction of protein content is a well-accepted practice. Although protein content has a strong bearing on the suitability of wheat (Triticum aestivum L.) for processed foods, wheat quality, as largely influenced by the configuration and conformation of the monomeric and polymeric endosperm storage proteins, is also of great importance to the food industry. The measurement of quality by NIR, however, has been much less successful. The present study examines the effects and trends of applying mathematical transformations (pretreatments) to NIR spectral data before partial least-squares (PLS) regression. Running mean smooths, Savitzky-Golay second derivatives, multiplicative scatter correction, and standard normal variate transformation, with and without detrending, were systematically applied to an extensive set of hard red winter wheat and hard white wheat grown over two seasons. The studied properties were protein content, sodium dodecyl sulfate (SDS) sedimentation volume, number of hours during grain fill at temperature <24 degrees C, and number of hours during grain fill at temperature >32 degrees C. The size of the convolution window used to perform a smooth or second derivative was also examined. The results indicate that for easily modeled properties such as protein content, the importance of pretreatment was lessened, whereas for the more difficult-to-model properties, such as SDS sedimentation volume, wide-window (>20 points) smooth or derivative convolutions were important in maximizing calibration performance. By averaging 30 PLS cross-validation trial statistics (standard error) for each property, we were able to ascertain the inherent modeling ability of each wheat property.     

Nutritive evaluation of olive tree leaves by near-infrared spectroscopy: effect of soil contamination and correction with spectral pretreatments

Olive leaves obtained as a byproduct in the Mediterranean region could play an important role in the nutrition of extensive ruminant systems. However, the reported variation in their nutritive value, among other reasons due to discrepancies in mineral content, is considered an important obstacle for their common use. Near-infrared spectroscopy (NIRS) could fulfill the requirements of these productive systems, providing analytical information in a rapid and economic way. In this work, the effect of soil contamination on NIR spectra has been studied, as well as its correction with some of the most commonly used spectral pretreatments (derivatives, multiplicative scatter correction, auto scaling, detrending, and a combination of the last two transforms). Effects were evaluated by visual inspection of the transformed spectra and comparison of the calibration statistics obtained to estimate acid insoluble ash and total ash contents and in vitro pepsin cellulase digestibility of organic and dry matter. The incidence of spectral curvature effects caused by soil contamination that can be conveniently corrected with pretreatments such as derivatives was confirmed.     

基于谱线漂移校正模型的实验室光谱定标修正

针对色散型高光谱成像仪实验室光谱定标方法进行了研究,在实验室光谱采集过程中仪器内部产热导致波段中心波长的漂移,由于高光谱带宽较窄,波段内中心波长的偏移会对光学遥感器的辐射定标精度产生影响。鉴于此,提出了谱线漂移校正模型来校正光谱定标结果。在文章最后分析了模型的精度并分别根据校正前后的光谱定标结果反算出积分球出射口处的辐亮度,与真实积分球数据对比,结果证明应用谱线漂移校正模型可以很好地校正谱线温漂的现象。