Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry

The transfer of multivariate calibration models is investigated between a primary (A) and two secondary Fourier transform near-infrared (near-IR) spectrometers (B, C). The application studied in this work is the use of bands in the near-IR combination region of 5000-4000 cm(-)(1) to determine physiological levels of glucose in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, triacetin, and urea. The three spectrometers are used to measure 80 samples produced through a randomized experimental design that minimizes correlations between the component concentrations and between the concentrations of glucose and water. Direct standardization (DS), piecewise direct standardization (PDS), and guided model reoptimization (GMR) are evaluated for use in transferring partial least-squares calibration models developed with the spectra of 64 samples from the primary instrument to the prediction of glucose concentrations in 16 prediction samples measured with each secondary spectrometer. The three algorithms are evaluated as a function of the number of standardization samples used in transferring the calibration models. Performance criteria for judging the success of the calibration transfer are established as the standard error of prediction (SEP) for internal calibration models built with the spectra of the 64 calibration samples collected with each secondary spectrometer. These SEP values are 1.51 and 1.14 mM for spectrometers B and C, respectively. When calibration standardization is applied, the GMR algorithm is observed to outperform DS and PDS. With spectrometer C, the calibration transfer is highly successful, producing an SEP value of 1.07 mM. However, an SEP of 2.96 mM indicates unsuccessful calibration standardization with spectrometer B. This failure is attributed to differences in the variance structure of the spectra collected with spectrometers A and B. Diagnostic procedures are presented for use with the GMR algorithm that forecasts the successful calibration transfer with spectrometer C and the unsatisfactory results with spectrometer B.     

Calibration standardization algorithm for partial least-squares regression: application to the determination of physiological levels of glucose by near-infrared spectroscopy

Calibration standardization methodology for near-infrared (near-IR) spectroscopy is described for updating a partial least-squares calibration model to take into account changes in instrumental response. The guided model reoptimization (GMR) algorithm uses a transfer set of eight samples to characterize the new response and a database of previously acquired spectra used to develop the original calibration model. The samples in the transfer set need not have been measured under the old instrumental conditions, making the algorithm compatible with samples that change over time. The spectra comprising the transfer set are used to guide an iterative optimization procedure that (1) finds an optimal subset of samples from the original database to use in computing the updated model and (2) finds an optimal set of weights to apply to the spectral resolution elements in order to minimize the effects of instrumental changes on the computed model. The optimization relies on an alternating grid search and stepwise addition/deletion steps. The algorithm is evaluated through the use of combination region near-IR spectra to determine physiological levels of glucose in a synthetic biological matrix containing bovine serum albumin and triacetin in phosphate buffer. The ability to update a calibration to account for changes in the response of a Fourier transform spectrometer over four to six years is examined in this study. Separate spectral databases collected in 1994 and 1996 are used with a transfer set and separate test set of spectra collected in 2000. With the 1994 database, the standardization algorithm achieves a standard error of prediction (SEP) of 0.69 mM for the 2000 test set. This compares favorably to SEP values > 2 mM when the original 1994 calibration model is used without standardization. A similar improvement in the prediction performance of the 2000 test set is obtained after standardization with the 1996 database (SEP = 0.70 mM).     

Blank augmentation protocol for improving the robustness of multivariate calibrations

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.     

On-line near-infrared spectrometer to monitor urea removal in real time during hemodialysis

The ex vivo removal of urea during hemodialysis treatments is monitored in real time with a noninvasive near-infrared spectrometer. The spectrometer uses a temperature-controlled acousto optical tunable filter (AOFT) in conjunction with a thermoelectrically cooled extended wavelength InGaAs detector to provide spectra with a 20 cm(-1) resolution over the combination region (4000-5000 cm(-1)) of the near-infrared spectrum. Spectra are signal averaged over 15 seconds to provide root mean square noise levels of 24 micro-absorbance units for 100% lines generated over the 4600-4500 cm(-1) spectral range. Combination spectra of the spent dialysate stream are collected in real-time as a portion of this stream passes through a sample holder constructed from a 1.1 mm inner diameter tube of Teflon. Real-time spectra are collected during 17 individual dialysis sessions over a period of 10 days. Reference samples were extracted periodically during each session to generate 87 unique samples with corresponding reference concentrations for urea, glucose, lactate, and creatinine. A series of calibration models are generated for urea by using the partial least squares (PLS) algorithm and each model is optimized in terms of number of factors and spectral range. The best calibration model gives a standard error of prediction (SEP) of 0.30 mM based on a random splitting of spectra generated from all 87 reference samples collected across the 17 dialysis sessions. PLS models were also developed by using spectra collected in early sessions to predict urea concentrations from spectra collected in subsequent sessions. SEP values for these prospective models range from 0.37 mM to 0.52 mM. Although higher than when spectra are pooled from all 17 sessions, these prospective SEP values are acceptable for monitoring the hemodialysis process. Selectivity for urea is demonstrated and the selectivity properties of the PLS calibration models are characterized with a pure component selectivity analysis.     

Comparison of combination and first overtone spectral regions for near-infrared calibration models for glucose and other biomolecules in aqueous solutions

Partial least squares calibration models are compared for the measurement of glucose, lactate, urea, ascorbate, triacetin, and alanine in aqueous solutions from single-beam spectra collected over the first overtone (6500-5500 cm(-1)) and the combination (5000-4000 cm(-1)) regions of the near-infrared spectrum. Spectra are collected under two sets of conditions with one designed for combination spectra and the other designed for first overtone spectra. As part of the optimization of conditions, an exponential function is presented that accurately characterizes the strong dependency between spectral quality and sample thickness. Sample thickness set for the first overtone and combination spectra are 7.5 and 1.5 mm, respectively. Independent calibration models are established for each solute from both combination and first overtone spectra. Direct comparison reveals superior performance by models generated from combination spectra, particularly for glucose and urea. Standard error of prediction (SEP) values are 1.12 and 0.45 mM for glucose models generated from first overtone and combination spectra, respectively. SEP values for urea are 7.33 and 0.10 mM for first overtone and combination spectra, respectively. Such high SEP values for urea with first overtone spectra correspond to an inability to quantify urea from these spectra because of a lack urea-specific molecular absorption features in this spectral region. Net analyte signal (NAS) is used to quantify the degree of selectivity provided within the first overtone and combination spectral regions. The superior selectivity of combination spectra is confirmed by comparing the length of the NAS vectors for each matrix component.     

Scattering and absorption effects in the determination of glucose in whole blood by near-infrared spectroscopy

Optical properties of whole bovine blood are examined under conditions of different glucose loadings. A strong dependency is established between the scattering properties of the whole blood matrix and the concentration of glucose. This dependency is explained in terms of variations in the refractive index mismatch between the scattering bodies (predominately red blood cells) and the surrounding plasma. Measurements in the presence of a well-known glucose transport inhibitor indicate that variations in refractive index mismatch are related to the penetration of glucose into the red blood cells and demonstrate that increased scattering involves the uptake of glucose by red blood cells. Finally, multivariate calibration models are presented for the measurement of glucose in a whole blood matrix. These models are based on near-infrared spectral data collected from 80 different samples prepared from a single whole blood matrix. Calibration studies are performed over the combination, first-overtone, and short-wavelength spectral regions. The best calibration model is generated from combination region spectra, providing a standard error of prediction (SEP) of less than 1 mM over the concentration range of 3-30 mM. The model based on the first-overtone region is slightly degraded but still provides acceptable performance (SEP = 1.20 mM). The model based on the short-wavelength region is further degraded (SEP = 2.53 mM). To rationalize these results, an analysis of the selectivity of the calibration models is performed by computing the glucose net analyte signal. It is established that the models based on the combination and first-overtone regions are dominated by glucose absorption information, while the model computed from the short-wavelength region is based primarily on scattering information. This result provides evidence that absorption information is needed in order to obtain a glucose calibration model with acceptable performance.     

Low-cost approaches to robust temperature compensation in near-infrared calibration and prediction situations

The traditional way of handling temperature shifts and other perturbations in calibration situations is to incorporate the non-relevant spectral variation in the calibration set by measuring the samples at various conditions. The present paper proposes two low-cost approaches based on simulation and prior knowledge about the perturbations, and these are compared to traditional methods. The first approach is based on augmentation of the calibration matrix through adding simulated noise on the spectra. The second approach is a correction method that removes the non-relevant variation from new spectra. Neither method demands exact knowledge of the perturbation levels. Using the augmentation method it was found that a few, in this case four, selected samples run under different conditions gave approximately the same robustness as running all the calibration samples under different conditions. For the carbohydrate data set, all robustification methods investigated worked well, including the use of pure water spectra for temperature compensation. For the more complex meat data set, only the augmentation method gave comparable results to the full global model.     

Direct solar spectral irradiance and transmittance measurements from 350 to 2500 nm

A radiometrically stable, commercially available spectroradiometer was used in conjunction with a simple, custom-designed telescope to make spectrally continuous measurements of solar spectral transmittance and directly transmitted solar spectral irradiance. The wavelength range of the instrument is 350-2500 nm and the resolution is 3-11.7 nm. Laboratory radiometric calibrations show the instrument to be stable to better than 1.0% over a nine-month period. The instrument and telescope are highly portable, can be set up in a matter of minutes, and can be operated by one person. A method of absolute radiometric calibration that can be tied to published top-of-the-atmosphere (TOA) solar spectra in valid Langley channels as well as regions of strong molecular absorption is also presented. High-altitude Langley plot calibration experiments indicate that this technique is limited ultimately by the current uncertainties in the TOA solar spectra, approximately 2-3%. Example comparisons of measured and modtran-modeled direct solar irradiance show that the model can be parameterized to agree with measurements over the large majority of the wavelength range to the 3% level for the two example cases shown. Side-by-side comparisons with a filter-based solar radiometer are in excellent agreement, with a mean absolute difference of tau = 0.0036 for eight overlapping wavelengths over three experiment days.     

Prediction of ethylene content in melt-state random and block polypropylene by near-infrared spectroscopy and chemometrics: comparison of a new calibration transfer method with a slope/bias correction method

This paper reports the prediction of the ethylene content (C2 content) in random polypropylene (RPP) and block polypropylene (BPP) in the melt state by near-infrared (NIR) spectroscopy and chemometrics. NIR spectra of RPP and BPP in the melt states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system. The NIR spectra of RPP and BPP were compared. Partial least-squares (PLS) regression calibration models predicting the ethylene (C2) content that were developed by using each RPP or BPP spectra set separately yielded good results (SECV (standard error of cross validation): RPP, 0.16%; BPP, 0.31%; correlation coefficient: RPP, 0.998; BPP, 0.996). We also built a common PLS calibration model by using both the RPP and the BPP spectra set. The results showed that the common calibration model has larger SECV values than the models based on the RPP or the BPP spectra sets individually and is not practical for the prediction of the C2 content. We further investigated whether a calibration model developed by using the BPP spectra set can predict the C2 contents in the RPP sample set. If this is possible, it can save a significant amount of work and cost. The results showed that the use of the BPP model for the RPP sample set is difficult, and vice versa, because there are some differences in the molar absorption coefficients between the RPP and BPP spectra. To solve this problem, a transfer method from one sample spectra (BPP) set to the other spectra (RPP) set was studied. A difference spectrum between an RPP spectrum and a BPP spectrum was used to transfer from the BPP calibration set to the RPP calibration set. The prediction result (SEP (standard error of prediction), 0.23%, correlation coefficient, 0.994) of RPP samples by the transferred calibration set and model showed that it is possible to transfer from the BPP calibration set to the RPP calibration set. We also studied the transfer from the RPP calibration set (the range of C2 content: 0-4.3%) to the BPP calibration set. The prediction result of C2 content (the range of C2 contents: 0-7.7%) in BPP by use of the calibration model based on the transferred BPP spectra from the RPP spectra showed that the transfer method is only effective for the interpolation of the C2 content range by the nonlinear change in the peak intensities with the C2 content.     

Transfer of multivariate classification models between laboratory and process near-infrared spectrometers for the discrimination of green Arabica and Robusta coffee beans

Analogous to the situation found in calibration, a classification model constructed from spectra measured on one instrument may not be valid for prediction of class from spectra measured on a second instrument. In this paper, the transfer of multivariate classification models between laboratory and process near-infrared spectrometers is investigated for the discrimination of whole, green Coffea arabica (Arabica) and Coffea canefora (Robusta) coffee beans. A modified version of slope/bias correction, orthogonal signal correction trained on a vector of discrete class identities, and model updating were found to perform well in the preprocessing of data to permit the transfer of a classification model developed on data from one instrument to be used on another instrument. These techniques permitted development of robust models for the discrimination of green coffee beans on both spectrometers and resulted in misclassification errors for the transfer process in the range of 5-10%.     

New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring

This paper reports new methodology to obtain a calibration model for noninvasive blood glucose monitoring using diffuse reflectance near-infrared (NIR) spectroscopy. Conventional studies of noninvasive blood glucose monitoring with NIR spectroscopy use a calibration model developed by in vivo experimental data sets. In order to create a calibration model, we have used a numerical simulation of light propagation in skin tissue to obtain simulated NIR diffuse reflectance spectra. The numerical simulation method enables us to design parameters affecting the prediction of blood glucose levels and their variation ranges for a data set to create a calibration model using multivariate analysis without any in vivo experiments in advance. By designing the parameters and their variation ranges appropriately, we can prevent a calibration model from chance temporal correlations that are often observed in conventional studies using NIR spectroscopy. The calibration model (regression coefficient vector) obtained by the numerical simulation has a characteristic positive peak at the wavelength around 1600 nm. This characteristic feature of the regression coefficient vector is very similar to those obtained by our previous in vitro and in vivo experimental studies. This positive peak at around 1600 nm also corresponds to the characteristic absorption band of glucose. The present study has reinforced that the characteristic absorbance of glucose at around 1600 nm is useful to predict the blood glucose level by diffuse reflectance NIR spectroscopy. We have validated this new calibration methodology using in vivo experiments. As a result, we obtained a coefficient of determination, r2, of 0.87 and a standard error of prediction (SEP) of 12.3 mg/dL between the predicted blood glucose levels and the reference blood glucose levels for all the experiments we have conducted. These results of in vivo experiments indicate that if the parameters and their vibration ranges are appropriately taken into account in a numerical simulation, the new calibration methodology provides us with a very good calibration model that can predict blood glucose levels with small errors without conducting any experiments in advance to create a calibration model for each individual patient. This new calibration methodology using numerical simulation has promising potential for NIR spectroscopy, especially for noninvasive blood glucose monitoring.     

Pure component selectivity analysis of multivariate calibration models from near-infrared spectra

A novel procedure is proposed as a method to characterize the chemical basis of selectivity for multivariate calibration models. This procedure involves submitting pure component spectra of both the target analyte and suspected interferences to the calibration model in question. The resulting model output is analyzed and interpreted in terms of the relative contribution of each component to the predicted analyte concentration. The utility of this method is illustrated by an analysis of calibration models for glucose, sucrose, and maltose. Near-infrared spectra are collected over the 5000-4000-cm(-)(1) spectral range for a set of ternary mixtures of these sugars. Partial least-squares (PLS) calibration models are generated for each component, and these models provide selective responses for the targeted analytes with standard errors of prediction ranging from 0.2 to 0.7 mM over the concentration range of 0.5-50 mM. The concept of the proposed pure component selectivity analysis is illustrated with these models. Results indicate that the net analyte signal is solely responsible for the selectivity of each individual model. Despite strong spectral overlap for these simple carbohydrates, calibration models based on the PLS algorithm provide sufficient selectivity to distinguish these commonly used sugars. The proposed procedure demonstrates conclusively that no component of the sucrose or maltose spectrum contributes to the selective measurement of glucose. Analogous conclusions are possible for the sucrose and maltose calibration models.     

Near-infrared spectroscopic measurement of urea in dialysate samples collected during hemodialysis treatments

Single-beam spectra were collected over the combination region of the near-infrared spectrum for 80 samples collected from 15 people over a two-week period. Partial least-squares (PLS) regression was used to generate an optimized calibration model for urea. PLS calibration models accurately measure urea in the spent dialysate matrix. Prediction errors are on the order of 0.15 mM, which is sufficient for the clinical assessment of the dialysis process. In addition, the feasibility of a global calibration model is demonstrated by generating a calibration model from samples and spectra obtained from 12 people to predict the level of urea in samples collected from 3 different people. In this case, the standard error of prediction is 0.09 mM. Spectra were modified in order to systematically examine the impact of resolution and noise. Little impact is observed by altering the spectral resolution from 4 to 32 cm-1. Spectral noise, however, plays an important role in the accuracy of these calibration models. Increasing the magnitude of the spectral noise increases the prediction errors and increases the width of the spectral range necessary for extracting the analytical information. The utility of the method is demonstrated by analyzing dialysate samples collected during actual dialysis treatments. In addition, the necessary resolution and spectral quality necessary for reliable on-line urea monitoring is identified. These findings indicate that a dedicated, on-line urea spectrometer must posses a resolution of 16 cm-1 coupled with a sample thickness of 1.5 mm and spectral noise levels on the order of 25 micro-absorbance units when measured as the root-mean-square (RMS) noise of 100% lines.     

Transfer of Near-Infrared Multivariate Calibrations without Standards

A novel approach to the transfer of multivariate calibration is proposed. This method is based on the finite impulse response (FIR) filtering of a set of spectra to be transferred, using a spectrum on the target instrument to direct the filtering process. Often, the target spectrum is the mean of a calibration set. The method is compared against direct transfer and piecewise direct transfer on near-infrared reflectance spectra in two representative data sets. Results from these studies suggest that FIR transfer compares favorably with piecewise direct transfer in terms of accuracy and precision of the match of transferred spectra to the predictive calibration models developed on the target instrument. Unlike piecewise direct transfer, FIR transfer requires no measurement of standard samples on both the source and target spectrometers. Details and current limitations of the FIR transfer method are presented.     

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.     

Spectral variable selection for partial least squares calibration applied to authentication and quantification of extra virgin olive oils using Fourier transform Raman spectroscopy

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.     

Effects of nonlinearities and uncorrelated or correlated errors in realistic simulated data on the prediction abilities of augmented classical least squares and partial least squares

Comparisons of prediction models from the new augmented classical least squares (ACLS) and partial least squares (PLS) multivariate spectral analysis methods were conducted using simulated data containing deviations from the idealized model. The simulated data were based on pure spectral components derived from real near-infrared spectra of multicomponent dilute aqueous solutions. Simulated uncorrelated concentration errors, uncorrelated and correlated spectral noise, and nonlinear spectral responses were included to evaluate the methods on situations representative of experimental data. The statistical significance of differences in prediction ability was evaluated using the Wilcoxon signed rank test. The prediction differences were found to be dependent on the type of noise added, the numbers of calibration samples, and the component being predicted. For analyses applied to simulated spectra with noise-free nonlinear response, PLS was shown to be statistically superior to ACLS for most of the cases. With added uncorrelated spectral noise, both methods performed comparably. Using 50 calibration samples with simulated correlated spectral noise, PLS showed an advantage in 3 out of 9 cases, but the advantage dropped to 1 out of 9 cases with 25 calibration samples. For cases with different noise distributions between calibration and validation, ACLS predictions were statistically better than PLS for two of the four components. Also, when experimentally derived correlated spectral error was added, ACLS gave better predictions that were statistically significant in 15 out of 24 cases simulated. On data sets with nonuniform noise, neither method was statistically better, although ACLS usually had smaller standard errors of prediction (SEPs). The varying results emphasize the need to use realistic simulations when making comparisons between various multivariate calibration methods. Even when the differences between the standard error of predictions were statistically significant, in most cases the differences in SEP were small. This study demonstrated that unlike CLS, ACLS is competitive with PLS in modeling nonlinearities in spectra without knowledge of all the component concentrations. This competitiveness is important when maintaining and transferring models for system drift, spectrometer differences, and unmodeled components, since ACLS models can be rapidly updated during prediction when used in conjunction with the prediction augmented classical least squares (PACLS) method, while PLS requires full recalibration.     

Long-term analysis of GOME in-flight calibration parameters and instrument degradation

Since 1995, the Global Ozone Monitoring Experiment (GOME) has measured solar and backscattered spectra in the ultraviolet and visible wavelength range. Now, the extensive data set of the most important calibration parameters has been investigated thoroughly in order to analyze the long-term stability and performance of the instrument. This study focuses on GOME in-flight calibration and degradation for the solar path. Monitoring the sensor degradation yields an intensity decrease of 70% to 90% in 240-316 nm and 35% to 65% in 311-415 nm. The spectral calibration is very stable over the whole period, although a very complex interaction between predisperser temperature and wavelength was found. The leakage current and the pixel-to-pixel gain increased significantly during the mission, which requires an accurate correction of the measured radiance and irradiance signals using proper calibration parameters. Finally, several outliers in the data sets can be directly assigned to instrument and satellite anomalies.     

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.     

Solar actinic flux spectroradiometry: a technique for measuring photolysis frequencies in the atmosphere.

A spectroradiometer has been developed for direct measurement of the solar actinic UV flux (scalar intensity) and determination of photolysis frequencies in the atmosphere. The instrument is based on a scanning double monochromator with an entrance optic that exhibits an isotropic angular response over a solid angle of 2pi sr. Actinic flux spectra are measured at a resolution of 1 nm across a range of 280-420 nm, which is relevant for most tropospheric photolysis processes. The photolysis frequencies are derived from the measured radiation spectra by use of published absorption cross sections and quantum yields. The advantage of this technique compared with the traditional chemical actinometry is its versatility. It is possible to determine the photolysis frequency for any photochemical reaction of interest provided that the respective molecular photodissociation parameters are known and the absorption cross section falls within a wavelength range that is accessible by the spectroradiometer. The instrument and the calibration procedures are described in detail, and problems specific to measurement of the actinic radiation are discussed. An error analysis is presented together with a discussion of the spectral requirements of the instrument for accurate measurements of important tropospheric photolysis frequencies (J(O(1))(D), J(NO(2)), J(HCHO)). An example of measurements from previous atmospheric chemistry field campaigns are presented and discussed.