Influence of environmental factors on the detection of blood in sheep faeces using visible-near-infrared spectroscopy as a measure of Haemonchus contortus infection

BACKGROUND

Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible-near-infrared (Vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis-NIR spectroscopy in quantifying blood in faeces.

METHODS

Visible-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387-609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores.

RESULTS

Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated 'healthy' SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57-94%, specificity 44-79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood.

CONCLUSION

This study demonstrates the potential of Vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.

A rapid analysis method of safflower (Carthamus tinctorius L.) using combination of computer vision and near-infrared

The quality of safflower (Carthamus tinctorius L.) in the market is uneven due to the problems of dyeing and adulteration of safflower, and there is no perfect standard for the classification of quality grade of safflower at present. In this study, computer vision (CV) and near-infrared (NIR) were combined to realize the rapid and nondestructive analysis of safflower. First, the partial least squares discrimination analysis (PLS-DA) model was used to qualitatively identify the dyed safflower from 150 samples. Then the partial least squares (PLS) model was used for quantitative analysis of the hydroxy safflower yellow pigment A (HSYA) and water extract of undyed safflower. Herein, the discrimination rate of PLS-DA model reached 100%, and the residual predictive deviation (RPD) values of the PLS models for HSYA and water extract were 2.5046 and 5.6195, respectively. It indicated that the rapid analysis method combining CV and NIR was reliable, and its results can provide important reference for the formulation of safflower quality classification standards in the market.

Quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae by near infrared spectroscopy coupled with chemometrics

OBJECTIVE

To establish a fast, simple and reliable method for quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae (referred as BZ below) by near infrared spectroscopy coupled with chemometrics.

METHOD

Twelve batches of raw medicinal materials of BZ were collected from three main producing location in China. According to the Pharmacopoeia of the People's Republic of China, these raw decoction pieces were stir-fried in wheat bran using a stir-frying machine for 3, 6, 9, 12 and 15 min, respectively. The resulted 60 samples were categorized into three classes (i.e., light, moderate and dark) by experienced pharmacists according to their surface color. After that, these slices were smashed to acquire near infrared spectra and to determine the contents of atractylenolide I, II and III by HPLC method. Qualitative and quantitative models were constructed to relate the spectra to the color labels and to the contents of three atractylenolides. Various chemometrics methods, including calibration methods like principal component analysis, partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), spectra pretreatment methods like standard normal variate, multiplicative scatter correction, derivation and smoothing, feature selection methods like particle swarm optimization, genetic algorithm (GA) and other fourteen methods were compared in detail. The PLS-DA models were evaluated by jackknife tests with calculating parameters such as error rate (ERR), true positive rate (TPR), true negative rate (TNR) and F1 score, meanwhile the PLSR models were evaluated by five fold cross-validation tests with calculating parameters such as coefficients of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD).

RESULTS

The PLS-DA models with spectra pretreated by 1D5S or 1D9S and wavelengths selected by InfFS, Relief-F, MutInfFS, fisher or CFS performed best, yielding 0.00 of ERR, 1.00 of TPR, 1.00 of TNR, and 1.00 of F1 for all three classes. As for quantitative models, the PLSR models by 1D5S spectra pretreatment and GA wavelengths selection performed best, where R²_C and R²_P were all >0.95, RMSE_C and RMSE_P were all <0.04%, MAE_C and MAE_P were all <0.04%, and RPD were all >5.

CONCLUSION

The present qualitative and quantitative models can be successfully used to distinguish the degree of suitability of processed BZ, and to determine the contents of three atractylenolides, which thus are of great help for quality evaluation and control of processed BZ and other decoction pieces.

Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis

The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO₂) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO₂ levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO₂ level leaves and 100% of the current-CO₂ level leaves. The spectral bands accounting for the discrimination of the elevated-CO₂ leaves were at 1657 and 1698 nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO₂, returning to current-CO₂ levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO₂ levels. The fingerprints suggest that after suspension of elevated-CO₂, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.

Express detection of expired drugs based on near-infrared spectroscopy and chemometrics: A feasibility study

Levofloxacin is a third-generation fluoroquinolone antimicrobials drug that inhibits bacterial DNA replication. Driven by huge profit, one kind of particular counterfeit, e.g., repackaged expired tablets, becomes very common especially in developing countries. The feasibility of identifying expired levofloxacin tablets by combining NIR spectroscopy with chemometrics was investigated. Five kinds of levofloxacin samples from different manufacturers were collected for experiment. Two types of expired mode were considered and a simple model-independent algorithm was used for feature selection. Principal component analysis (PCA) was used for exploratory analysis and simple discriminant analysis. Taking seventy samples as the target class, a final one-class model based on Data Driven Soft Independent Modeling by Class Analogy with abbreviation DD-SIMCA was constructed, which achieved 97% sensitivity and 100% specificity on the independent set composed of 34 unexpired and 128 expired tablets. These results confirm that the combination of NIR spectroscopy, feature selection and class-modeling is feasible for identifying the expired levofloxacin tablets. Such a method can be extended to the analysis of similar drugs.

Attention based residual network for medicinal fungi near infrared spectroscopy analysis

As an effective technology, near infrared spectroscopy (NIRS) can be widely applied to analysis of active ingredients in medicinal fungi. Multiple regression methods are used to compute the relationship between spectral vectors and ingredient contents. In this paper, an autonomous feature extraction method by using attention based residual network (ABRN) to model original NIRS vectors is introduced. Attention module in ABRN is employed to enhance feature wave bands and to decay noise. Different from traditional NIRS analysis methods, ABRN does not require any preprocessing of artificial feature selections which rely on expert experience. The experiments test ABRN by analyzing original spectrums of medicinal fungi (Antrodia Camphorata and Matsutake), which are from 800 nm to 2500 nm, and predicting active ingredients within them. We compare ABRN with other popular NIRS analysis methods. The root mean square error of Antrodia Camphorata training set (RMSET) and validation set (RMSEV) are 0.0229 g⋅g⁻¹ and 0.0349 g⋅g⁻¹ for polysaccharide, and 0.0173 g⋅g⁻¹ and 0.0189 g⋅g⁻¹ for triterpene. The RMSET and RMSEV of Matsutake are 0.1343 g⋅g⁻¹ and 0.2472 g⋅g⁻¹ for polysaccharide, and 0.0328 g⋅g⁻¹ and 0.0445 g⋅g⁻¹ for ergosterol. The (coefficient of determination) of these four ingredients are 0.711, 0.753, 0.847 and 0.807. The results indicate that ABRN has better performance in autonomously extracting feature wave bands from original NIRS vectors, which can decrease the loss of tiny feature peaks.

Rapid determination of phytosterols by NIRS and chemometric methods

Phytosterols have been extensively studied because it plays essential roles in the physiology of plants and can be used as nutritional supplement to promote human health. We use a rapid method by coupling near-infrared spectroscopy (NIRS) and chemometric techniques to quickly and efficiently determine three essential phytosterols (β-sitosterol, campesterol and stigmasterol) in vegetable oils. Continuous wavelet transform (CWT) method was adopted to remove the baseline shift in the spectra. The quantitative analysis models were constructed by partial least squares (PLS) regression and randomization test (RT) method was used to further improve the models. The optimized models were used to calculate the phytosterol contents in prediction set in order to evaluate their predictability. We have found that the phytosterol contents obtained by the optimized models and Gas Chromatography/Mass Spectrometry (GC/MS) analysis are almost consistent. The root mean square error of prediction (RMSEP) and ratio of prediction to deviation (RPD) for the three phytosterols are 525.7590, 212.2245, 65.1611 and 4.0060, 4.7195 and 3.5441, respectively. The results have proved the feasibility of the proposed method for rapid and non-destructive analysis of phytosterols in edible oils.

Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration

The authentication of traditional Chinese medicine (TCM) is critically important for public-health and economic terms. Notoginseng, a classical TCM of high economic and medical value, could be easily adulterated with Sophora flavescens powder (SFP), corn flour (CF) or other analogues of low-grade (ALG) because of their similar tastes, appearances and much lower cost. The main objective of this study was to evaluate the feasibility of applying of near-infrared (NIR) spectroscopy and multivariate calibration for identifying and quantifying several common adulterants in notoginseng powder. Two datasets were prepared for experiment. The competitive adaptive reweighted sampling (CARS) was used to select informative variables. Two different schemes were used for sample set partition. Model population analysis (MPA) was made. The results showed that, the constructed partial least squares (PLS) model using a reduced set of variables from CARS can provide superior performance to the full-spectrum PLS model. Also, the sample set partition is very of great importance. It seems that the combination of NIR spectroscopy, CARS and PLS is feasible to quantify common adulterants in notoginseng powder.

Tea types classification with data fusion of UV-Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis

The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF + UV-Vis, SF + NIR, NIR + UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.

Rapid Authentication and Quality Evaluation of Cinnamomum verum Powder Using Near-Infrared Spectroscopy and Multivariate Analyses

Parallel to the growing global interest in alternative medical therapies, high measures of counterfeit pharmaceuticals enter the global market and, therefore, detection of such marketed products is essential. This article throws an illuminating spot on the adulteration of Cinnamomum verum (Cinnamomum zeylanicum) with Cinnamomum cassia and exhaustively extracted C. verum. A speedy and nondestructive near-infrared method in conjunction with the mathematical tools of chemometrics was used to distinguish between genuine cinnamon and its common adulterants. The principal component analysis and the hierarchical cluster analysis models successfully discriminated between unadulterated and adulterated samples. In the second part of the work, soft independent modeling of class analogy was implemented to construct a chemometric model to authenticate C. verum samples. The constructed model could successfully predict and judge the quality of C. verum powder without any misleading predictions. Finally, partial least squares regression was approached to establish the correlation for adulterated samples regarding their cassia and exhausted cinnamon content. The R2 of calibration and validation were all higher than 0.9, while the root mean square errors were all lower than 0.05, indicating that the established models were successful. Overall, the developed models were shown to have significant potential as time-saving and accurate methods for identification of true cinnamon powder, which can help guarantee both quality aspects of identity and purity of the herbal drug by avoiding its adulteration and could be implemented as a routine screening in its quality control with no need for any sample preparation.

An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data

Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation.

Strategic development of a multivariate calibration model for the uniformity testing of tablets by transmission NIR analysis

The use of transmission near infrared spectroscopy (TNIRS) is of particular interest in the pharmaceutical industry. This is because TNIRS does not require sample preparation and can analyze several tens of tablet samples in an hour. It has the capability to measure all relevant information from a tablet, while still on the production line. However, TNIRS has a narrow spectrum range and overtone vibrations often overlap. To perform content uniformity testing in tablets by TNIRS, various properties in the tableting process need to be analyzed by a multivariate prediction model, such as a Partial Least Square Regression modeling. One issue is that typical approaches require several hundred reference samples to act as the basis of the method rather than a strategically designed method. This means that many batches are needed to prepare the reference samples; this requires time and is not cost effective. Our group investigated the concentration dependence of the calibration model with a strategic design. Consequently, we developed a more effective approach to the TNIRS calibration model than the existing methodology.

Intravascular-ultrasound assisted localization and revascularization of an ostial chronic total occlusion: utility of near-field and far-field imaging

Percutaneous coronary interventions (PCI) of chronic total occlusions (CTOs) can be technically challenging, but are valuable in patients with severe angina. Recently, algorithms for CTO-PCI have been proposed to facilitate the selection of the PCI approach that can best achieve procedural success. When the ostium of the occluded vessel is ambiguous or not well visualized, the success rate of antegrade approaches is significantly diminished. In our case, we demonstrate the utility of intravascular ultrasound imaging in the localization of an ambiguous or "stump-less" ostial CTO in addition to providing real-time far-field imaging, which is helpful in tracking wire progress through the occluded segment. Adjunctive imaging was instrumental in achieving procedural success using the antegrade approach.

Imagery use and affective responses during exercise: an examination of cerebral hemodynamics using near-infrared spectroscopy

Imagery, as a cognitive strategy, can improve affective responses during moderate-intensity exercise. The effects of imagery at higher intensities of exercise have not been examined. Further, the effect of imagery use and activity in the frontal cortex during exercise is unknown. Using a crossover design (imagery and control), activity of the frontal cortex (reflected by changes in cerebral hemodynamics using near-infrared spectroscopy) and affective responses were measured during exercise at intensities 5% above the ventilatory threshold (VT) and the respiratory compensation point (RCP). Results indicated that imagery use influenced activity of the frontal cortex and was associated with a more positive affective response at intensities above VT, but not RCP to exhaustion (p < .05). These findings provide direct neurophysiological evidence of imagery use and activity in the frontal cortex during exercise at intensities above VT that positively impact affective responses.

Studying social cognition using near-infrared spectroscopy: the case of social Simon effect

In order to understand the so-called "social brain," we need to monitor social interactions in face-to-face paradigms. Near-infrared spectroscopy (NIRS) is a promising technique to achieve this goal. We investigate the neuronal underpinnings of sharing a task in a proper social context. We record cortical activity by means of NIRS, while participants perform a joint Simon task. Different from other hemodynamic techniques, NIRS allows us to have both participants sit comfortably close to each other in a realistic and ecological environment. We found higher activation in the sensorimotor cortex while processing compatible trials as compared to incompatible ones referring to one's own action alternative. Strikingly, when the participant was not responding because it was the turn of the other member of the pair, the inferior parietal was activated. This study provides twofold findings: first, they suggest that the joint Simon effect relies more on shared attentional mechanisms than a proper mapping of the other's motor response. Second, they highlight the invaluable contribution NIRS can afford to social neuroscience in order to preserve ecological and naturalistic settings.

Detection of cerebral autoregulation by near-infrared spectroscopy in neonates: performance analysis of measurement methods

Cerebral Autoregulation, in clinical practice, is assessed by means of correlation or coherence analysis between mean arterial blood pressure (MABP) and cerebral blood flow (CBF). However, even though there is evidence linking cerebral autoregulation assessment with clinical outcome in preterm infants, available methods lack precision for clinical use. Classical methods, used for cerebral autoregulation, are influenced by the choice of parameters such as the length of the epoch under analysis and the choice of suitable frequency bands. The influence of these parameters, in the derived measurements for cerebral autoregulation, has not yet been evaluated. In this study, cerebral autoregulation was assessed using correlation, coherence, a modified version of coherence and transfer function gain, and phase. The influence of the extra-parameters on the final scores was evaluated by means of sensitivity analysis. The methods were applied to a database of 18 neonates with measurements of MABP and tissue oxygenation index (TOI). TOI reflects changes in CBF and was measured by means of near-infrared spectroscopy.

Isolating the sources of widespread physiological fluctuations in functional near-infrared spectroscopy signals

Physiological fluctuations at low frequency (<0.1 Hz) are prominent in functional near-infrared spectroscopy (fNIRS) measurements in both resting state and functional task studies. In this study, we used the high spatial resolution and full brain coverage of functional magnetic resonance imaging (fMRI) to understand the origins and commonalities of these fluctuations. Specifically, we applied a newly developed method, regressor interpolation at progressive time delays, to analyze concurrently recorded fNIRS and fMRI data acquired both in a resting state study and in a finger tapping study. The method calculates the voxelwise correlations between blood oxygen level dependent (BOLD) fMRI and fNIRS signals with different time shifts and localizes the areas in the brain that highly correlate with the fNIRS signal recorded at the surface of the head. The results show the wide spatial distribution of this physiological fluctuation in BOLD data, both in task and resting states. The brain areas that are highly correlated with global physiological fluctuations observed by fNIRS have a pattern that resembles the venous system of the brain, indicating the blood fluctuation from veins on the brain surface might strongly contribute to the overall fNIRS signal.

Method for depth-resolved quantitation of optical properties in layered media using spatially modulated quantitative spectroscopy

We have demonstrated that spatially modulated quantitative spectroscopy (SMoQS) is capable of extracting absolute optical properties from homogeneous tissue simulating phantoms that span both the visible and near-infrared wavelength regimes. However, biological tissue, such as skin, is highly structured, presenting challenges to quantitative spectroscopic techniques based on homogeneous models. In order to more accurately address the challenges associated with skin, we present a method for depth-resolved optical property quantitation based on a two layer model. Layered Monte Carlo simulations and layered tissue simulating phantoms are used to determine the efficacy and accuracy of SMoQS to quantify layer specific optical properties of layered media. Initial results from both the simulation and experiment show that this empirical method is capable of determining top layer thickness within tens of microns across a physiological range for skin. Layer specific chromophore concentration can be determined to <±10% the actual values, on average, whereas bulk quantitation in either visible or near infrared spectroscopic regimes significantly underestimates the layer specific chromophore concentration and can be confounded by top layer thickness.

Biophotonic in situ sensor for plant leaves

Knowledge of the water concentration of plants can be helpful in several environmental and agricultural domains. There are many methods for the determination of water content in plant leaves; however, most of them give a relative moisture level or an analytical measure after a previous calibration procedure. Even for other biochemical compounds such as dry matter or chlorophyll, the measurement techniques could be destructive. For this reason, a nondestructive method has been developed to measure the biochemical compounds of a plant leaf, using an infrared spectroscopy technique. One important advantage is the simplicity of the device (RAdiomètre portatif de Mesure In Situ, RAMIS) and its capability to perform measurements in situ. The prototype is a leaf-clip configuration and is made of LEDs at five wavelengths (656, 721, 843, 937, and 1550 nm), and a silicon/germanium photosensor. To compute the water content of vegetative leaves, the radiative transfer model PROSPECT was implemented. This model can accurately predict spectral transmittances in the 400 nm to 2500 nm spectral region as a function of the principal leaf biochemical contents: water, dry matter, and chlorophyll. Using the transmittance measured by RAMIS into an inversion procedure of

PROSPECT

A Model of Leaf Optical Properties Spectra, we are able to compute the values of water contents that show an agreement with the water contents measured directly using dry weight procedures. This method is presented as a possibility to estimate other leaf biochemical compounds using appropriate wavelengths.

Signal-to-noise contribution of principal component loads in reconstructed near-infrared Raman tissue spectra

The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can reliably be used in the low SNR data set (set B) compared to the high SNR data set (set A). Despite the fact that no definitive threshold could be found, this method may help to determine the cutoff for the number of principal components used in discriminant analysis. Future analysis of a selection of spectral databases using this technique will allow optimum thresholds to be selected for different applications and spectral data quality levels.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

Two-dimensional near-infrared correlation spectroscopy study the methanol in acidic pH region

The effect of pH on the structure of methanol was investigated by FT-NIR (Fourier transform near-infrared) spectroscopy and generalized two-dimensional (2D) correlation spectroscopy. pH perturbed 2D correlation spectra are calculated for the spectra in the 5,500-4,000, 7,500-5,500 cm(-1) regions at different pH values. We observed that the stretching of CH(3) was shifted because of the direct interaction of the CH(3) group of methanol with the OH group of water, the change of free OH is more sensitive to pH than the cyclic dimmer and CH.O.

Temperature measurements of turbid aqueous solutions using near-infrared spectroscopy

We report a method that uses near-infrared spectroscopy and multivariate analysis to measure the temperature of turbid aqueous solutions. The measurement principle is based on the fact that the peak wavelength of the water absorption band, with its center near 1440 nm, shifts with changes in temperature. This principle was used to measure the temperatures of 1 mm thick samples of aqueous solutions containing Intralipid (2%), which are often used as optical phantoms for biological tissues due to similar scattering characteristics. Temperatures of pure water and aqueous solutions containing glucose (100 mg/ml and 200 mg/ml) were also measured for comparison. For the turbid Intralipid solutions, the absorbance spectrum varied irregularly with time due to the change in scattering characteristics. However, by making use of the difference between the absorbance at 1412 nm and the temperature-independent absorbance at 1440 nm, we obtained SEPs (standard error of prediction) of 0.3 degrees C and 0.2 degrees C by univariate linear regression and partial least squares regression, respectively. These accuracies were almost the same as those for the transparent samples (pure water and glucose solution).

Usefulness of spectroscopy for biomedical engineering

Modifications of phenylalanine amino acid after its exposure to near-infrared (NIR) radiation have been investigated using ATRFTIR (Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy). The process of amino acid aggregation after its exposure to NIR has been observed. A possible mechanism of amino acid dimer formation has been proposed with the help of theoretical calculations of quantum mechanics (MP2 and B3LYP/6-31 G* level) using the GAUSSIAN 03 package. The usefulness of spectroscopy for biomedical engineering is discussed. ATR-FTIR appears to be a powerful tool for measuring tissue damage in aqueous environments.

Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis

We investigate whether the functional near-infrared spectroscopic (fNIRS) signal includes a signal from the changing skin blood flow. During a locomotor task on a treadmill, changes in the hemodynamic response in the front-parietal area of healthy human subjects are simultaneously recorded using an fNIRS imaging system and a laser Doppler tissue blood flow meter. Independent component analysis (ICA) for fNIRS signals is performed. The skin blood flow changes during locomotor tasks on a treadmill. The activated spatial distribution of one of the components separated by ICA reveals an overall increase in fNIRS channels. To evaluate the uniformity of the activated spatial distribution, we define a new statistical value-the coefficient of spatial uniformity (CSU). The CSU value is a highly discriminating value (e.g., 2.82) compared with values of other components (e.g., 1.41, 1.10, 0.96, 0.61, and 0.58). In addition, the independent component signal corresponding to the activated spatial distribution is similar to changes in skin blood flow measured with the laser Doppler tissue blood flow meter. The coefficient of correlation indicates strong correlation. Localized activation areas around the premotor and medial somatosensory cortices are shown more clearly by eliminating the extracted component.

Determination of uncertainty in parameters extracted from single spectroscopic measurements

The ability to quantify uncertainty in information extracted from spectroscopic measurements is important in numerous fields. The traditional approach of repetitive measurements may be impractical or impossible in some measurements scenarios, while chi-squared analysis does not provide insight into the sources of uncertainty. As such, a need exists for analytical expressions for estimating uncertainty and, by extension, minimum detectable concentrations or diagnostic parameters, that can be applied to a single noisy measurement. This work builds on established concepts from estimation theory, such as the Cramer-Rao lower bound on estimator covariance, to present an analytical formula for estimating uncertainty expressed as a simple function of measurement noise, signal strength, and spectral overlap. This formalism can be used to evaluate and improve instrument performance, particularly important for rapid-acquisition biomedical spectroscopy systems. We demonstrate the experimental utility of this expression in assessing concentration uncertainties from spectral measurements of aqueous solutions and diagnostic parameter uncertainties extracted from spectral measurements of human artery tissue. The measured uncertainty, calculated from many independent measurements, is found to be in good agreement with the analytical formula applied to a single spectrum. These results are intended to encourage the widespread use of uncertainty analysis in the biomedical optics community.

Functional near-infrared spectroscopy: current status and future prospects

Near-infrared spectroscopy (NIRS), which was originally designed for clinical monitoring of tissue oxygenation, has been developing into a useful tool for neuroimaging studies (functional near-infrared spectroscopy). This technique, which is completely noninvasive, does not require strict motion restriction and can be used in a daily life environment. It is expected that NIRS will provide a new direction for cognitive neuroscience research, more so than other neuroimaging techniques, although several problems with NIRS remain to be explored. This review demonstrates the strengths and the advantages of NIRS, clarifies the problems, and identifies the limitations of NIRS measurements. Finally, its future prospects are described.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition. It does this by mapping the signal into a standard functional neuroimaging analysis software, statistical parametric mapping (SPM), and forms, in effect, a new SPM toolbox specifically designed for NIRS in an OT configuration. The validity of the program has been tested using synthetic data, and its applicability is demonstrated with experimental data.

Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study

Following previous Monte Carlo simulations, we describe in detail an example of detecting evoked visual hemodynamic responses in a human subject as a preliminary demonstration of the novel global interference cancellation technology. The raw time series of oxyhemoglobin (O(2)Hb) and deoxyhemoglobin (HHb) changes, their block averaged results before and after adaptive filtering, together with the power spectral density analysis are presented. Simultaneous respiration and EKG recordings suggested that spontaneous low-frequency oscillation and cardiac activity were the major sources of global interference in our test. When global interference dominates (e.g., for O(2)Hb in our data, judged by power spectral density analysis), adaptive filtering effectively reduced this interference, doubling the contrast-to-noise ratio (CNR) for evoked visual response detection. When global interference is not obvious (e.g., in our HHb data), adaptive filtering provided no CNR improvement. The results also showed that the hemodynamic changes in the superficial layers and the estimated total global interference in the target measurement were highly correlated (r=0.96), which explains why this global interference cancellation method should work well when global interference is dominating. In addition, the results suggested that association between the superficial layer hemodynamics and the total global interference is time-varying.

Clinical evaluation of time-resolved spectroscopy by measuring cerebral hemodynamics during cardiopulmonary bypass surgery

We developed a three-wavelength time-resolved spectroscopy (TRS) system, which allows quantitative measurement of hemodynamics within relatively large living tissue. We clinically evaluated this TRS system by monitoring cerebral circulation during cardiopulmonary bypass surgery. Oxyhemoglobin, deoxyhemoglobin, total hemoglobin and oxygen saturation (SO(2)) were determined by TRS on the left forehead attached with an optode spacing of 4 cm. We also simultaneously monitored jugular venous oxygen saturation (SjvO(2)) and arterial blood hematocrit (Hct) using conventional methods. The validity and usefulness of the TRS system were assessed by comparing parameters obtained with the TRS and conventional methods. Although the changes in SO(2) were lower than those in SjvO(2), SO(2) obtained by TRS paralleled the fluctuations in SjvO(2), and a good correlation between these values was observed. The only exceptions occurred during the perfusion period. Moreover, there was a good correlation between tHb and Hct values (r(2)=0.63). We concluded that time-resolved spectroscopy reflected the conditions of cerebral hemodynamics of patients during surgical operations.

Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy

Practical implementation of multivariate calibration models has been limited in several areas due to the requirement of appropriate development and validation to prove their performance to standardization agencies. Herein, a detailed description of the application of multivariate calibration based on partial least-squares regression models (PLSR) for the determination of soluble solids (BRIX), polarizable sugars (POL), and reducing sugars (RS) in sugar cane juice, based on near infrared spectroscopy (NIR), for the alcohol industries is presented. The development of the models, including variable selection and outlier elimination, and their validation by determination of figures of merit, such as accuracy, precision, sensitivity, analytical sensitivity, prediction intervals, and limits of detection and quantification, are described for a representative data set of 1381 sugar cane samples. Values estimated by PLSR are compared with appropriate reference methods, where the results indicated that the PLSR models can be used in the alcohol industry as an alternative to refractometry and lead clarification before polarization measurements (standard methods for BRIX and POL, respectively). For RS, the results of a titration reference method were compared with the PLSR estimates and also with an estimate based on BRIX and POL values, as actually used in the alcohol industry. The PLSR method presented a better agreement with the titration method. However, the results indicated that the RS estimates from both PLSR and those based on the BRIX and POL values, actually used, should be improved to a safe determination of RS.

Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans

This paper proposes a novel wavelet denoising method, which exploits the statistics of individual scans acquired in the course of a coaveraging process. The proposed method consists of shrinking the wavelet coefficients of the noisy signal by a factor that minimizes the expected square error with respect to the true signal. Since the true signal is not known, a sub-optimal estimate of the shrinking factor is calculated by using the sample statistics of the acquired scans. It is shown that such an estimate can be generated as the limit value of a recursive formulation. In a simulated example, the performance of the proposed method is seen to be equivalent to the best choice between hard and soft thresholding for different signal-to-noise ratios. Such a conclusion is also supported by an experimental investigation involving near-infrared (NIR) scans of a diesel sample. It is worth emphasizing that this experimental example concerns the removal of actual instrumental noise, in contrast to other case studies in the denoising literature, which usually present simulations with artificial noise. The simulated and experimental cases indicate that, in classic denoising based on wavelet coefficient thresholding, choosing between the hard and soft options is not straightforward and may lead to considerably different outcomes. By resorting to the proposed method, the analyst is not required to make such a critical decision in order to achieve appropriate results.

Illuminating the BOLD signal: combined fMRI–fNIRS studies

Functional magnetic resonance imaging (fMRI) is currently combined with electrophysiological methods to identify the relationship between neuronal activity and the blood oxygenation level-dependent (BOLD) signal. Several processes like neuronal activity, synaptic activity, vascular dilation, blood volume and oxygenation changes underlie both response modalities, that is, the electrophysiological signal and the vascular response. However, accessing single process relationships is absolutely mandatory when aiming at a deeper understanding of neurovascular coupling and necessitates studies on the individual building blocks of the vascular response. Combined fMRI and functional near-infrared spectroscopy studies have been performed to validate the correlation of the BOLD signal to the hemodynamic changes in the brain. Here we review the current status of the integration of both technologies and judge these studies in the light of recent findings on neurovascular coupling.

Integration of fMRI, NIROT and ERP for studies of human brain function

Different methods of assessing human brain function possess specific advantages and disadvantages compared to others, but it is believed that combining different approaches will provide greater information than can be obtained from each alone. For example, functional magnetic resonance imaging (fMRI) has good spatial resolution but poor temporal resolution, whereas the converse is true for electrophysiological recordings (event-related potentials or ERPs). In this review of recent work, we highlight a novel approach to combining these modalities in a manner designed to increase information on the origins and locations of the generators of specific ERPs and the relationship between fMRI and ERP signals. Near infrared imaging techniques have also been studied as alternatives to fMRI and can be readily integrated with simultaneous electrophysiological recordings. Each of these modalities may in principle be also used in so-called steady-state acquisitions in which the correlational structure of signals from the brain may be analyzed to provide new insights into brain function.

Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6).

Investigating the post-stimulus undershoot of the BOLD signal--a simultaneous fMRI and fNIRS study

Measuring the hemodynamic response with functional magnetic resonance imaging (fMRI) together with functional near-infrared spectroscopy (fNIRS) may overcome limitations of single-method approaches. Accordingly, we measured the event-related hemodynamic response with both imaging methods simultaneously in young subjects during visual stimulation. An intertrial interval of 60 s was chosen to include the prolonged post-stimulus undershoot of the blood oxygenation level dependent (BOLD) signal. During visual stimulation, the BOLD signal, oxy-, and total hemoglobin (Hb) increased, whereas deoxy-Hb decreased. The post-stimulus period was characterized by an undershoot of the BOLD signal, oxy-Hb, and an overshoot of deoxy-Hb. Total Hb as measured by fNIRS returned to baseline immediately after the end of stimulation. Results suggest that the post-stimulus events as measured by fNIRS are dominated by a prolonged high-level oxygen consumption in the microvasculature. The contribution of a delayed return of blood volume to the BOLD post-stimulus undershoot in post-capillary veins as suggested by the Balloon and Windkessel models remains ambiguous. Temporal changes in the BOLD signal were highly correlated with deoxy-Hb, with lower correlation values for oxy- and total Hb. Furthermore, data show that fNIRS covers the outer 1 cm of the brain cortex. These results were confirmed by simultaneous fMRI/fNIRS measurements during rest. In conclusion, multimodal imaging approaches may contribute to the understanding of neurovascular coupling.

The fast optical signal--robust or elusive when non-invasively measured in the human adult?

Near infrared spectroscopy (NIRS) can detect vascular changes in cerebral cortical tissue elicited by functional stimulation. For some 10 years, another optical signal has been reported to be accessible by NIRS. This signal has been reported to correlate to the electrophysiological response rendering NIRS an exquisite non-invasive approach to investigate neurovascular coupling in the human adult. Due to their typical latency of up to hundreds of milliseconds, these signals have been termed "fast" optical signals and have been postulated to stem from scatter changes in neuronal tissue, as a fingerprint of the electrophysiological response. Here, we utter a less optimistic view on the non-invasive detectability of these changes in the human, motivated by an upper limit signal size estimation, predicting a signal size by orders of magnitude smaller than those previously reported. Also, we discuss the influence of small stimulus correlated movement artifacts potentially mimicking a fast optical signal. Based on invasive studies, we perform an upper limit estimation for changes in intensity and mean time of flight, which can be expected assuming a scatter change in the cerebral cortex while measuring on the surface of the head of an adult subject. Since the resulting numbers are far below those previously reported, we constructed a simple system, which minimizes technical noise. The system allows us to detect rather small intensity changes (2 x 10(-3)%) when averaging over approximately 3000 stimuli. Despite this outstandingly low noise level of the system, we find a reliable change in response to a sub-motor-threshold steady state median nerve stimulation in just one single subject (8 subjects examined, 4 subjects twice). Exceeding the motor threshold leads to large stimulus related artifacts, on a similar time scale and with comparable amplitude as previously reported signals. To check for potential modality specific problems, we next performed a visual stimulation study, avoiding potential motor artifacts. For the steady state visually evoked response, no subject yielded a reliable result (11 subjects examined, 4 subjects twice). The paper discusses these findings by a review of the literature on fast optical signals and their being accessible in the adult human.

Optimal determination of detector placement in cerebral NIR spectroscopy of neonates using chemometric techniques

This paper investigates the optimal placement of NIRS optodes in order to maximise the detection of haemoglobin changes in cortical grey matter resulting from an evoked response in neonates. The analysis is based upon predictions of optical signal at the surface of the head, using a Finite Element based model of light diffusion in tissue. Using the generated intensity data, the combination of optode positions, which maximise the signal from cortical grey matter whilst minimising that from surface tissue or cerebral white matter, is determined using a Chemometric statistical analysis. The neonatal head is modelled as a 2 dimensional circle with 3 layers corresponding to the skin/scalp, and grey and white matter. A wide range of absorption coefficients for each layer is simulated, based upon physiologically reasonable values for parameters. Surface intensity at 10 different optode positions have been generated for a total of 31,250 combinations of these variables for the 3 layers. It was found that with 3 optodes at 5, 15, and 50 mm apart from the source, the smallest root-mean-square error between the estimated and modelled values can be obtained. Increasing the number of optodes further does not improve the performance.

Validation of the CAS neonatal NIRS system by monitoring vv-ECMO patients: preliminary results

The CAS neonatal NIRS system determines absolute regional brain tissue oxygen saturation (SnO2) and brain true venous oxygen saturation (SnvO2) non-invasively. Since NIRS-interrogated tissue contains both arterial and venous blood from arterioles, venules, and capillaries, SnO2 is a mixed oxygen saturation parameter, having values between arterial oxygen saturation (SaO2) and cerebral venous oxygen saturation (SvO2). To determine a reference for SnO2, the relative contribution of SvO2 to SaO2 drawn from a brain venous site vs. systemic SaO2 is approximately 70:30 (SvO2:SaO2). If the relationship of the relative average contribution of SvO2 and SaO2 is known and does not change to a large degree, then NIRS true venous oxygen saturation, SnvO2, can be determined non-invasively using SnO2 along with SaO2 from a pulse oximeter.

Dynamic cortical activity during spasms in three patients with West syndrome: a multichannel near-infrared spectroscopic topography study

PURPOSE

To investigate spatial and temporal cortical activity during clusters of naturally occurring epileptic spasms in patients with West syndrome (WS) by using multichannel near-infrared spectroscopy (mNIRS).

METHODS

Conventional magnetic resonance imaging (MRI) and interictal and ictal single-photon emission computed tomography (SPECT) were carried out in three patients with WS. Thereafter, cortical hemodynamics during naturally occurring epileptic spasms were measured by mNIRS with simultaneous video/electroencephalographic (EEG) monitoring.

RESULTS

Ictal SPECT revealed multiple hyperperfused areas within the cortex. With the use of mNIRS, an increase in regional cerebral blood volume (CBV) was observed in these areas, which is representative of cortical activation. The increase in CBV was accompanied by an increase in the concentrations of both oxy- and deoxyhemoglobin. The following heterogeneous regional changes in CBV during ictus were observed: (a) transient increases that were synchronized with spasms; (b) a gradual increase during an ictal event that fluctuated in synchrony with spasms; and (c) a combination of transient and gradual increases. An increase in regional CBV occurred in multiple areas that were activated either simultaneously or sequentially during an ictal event. Topographic changes in CBV were closely correlated with the phenotype of the spasm.

CONCLUSIONS

During ictal events, multiple cortical areas were activated simultaneously or sequentially. The pattern of cortical activation closely affected the phenotype of the spasm, which suggested that the cortex was involved in the generation of spasms.

Measurements of cavity ringdown spectroscopy of acetone in the ultraviolet and near-infrared spectral regions: potential for development of a breath analyzer

We report a study on the cavity ringdown spectroscopy of acetone in both the ultraviolet (UV) and the near-infrared (NIR) spectral regions to explore the potential for development of a breath analyzer for disease diagnostics. The ringdown spectrum of acetone in the UV (282.4-285.0 nm) region is recorded and the spectrum is in good agreement with those obtained by other spectral techniques reported in the literature. The absorption cross-section of the C-H stretching overtone of acetone in the NIR (1632.7-1672.2 nm) is reported for the first time and the maximum absorption cross-section located at 1666.7 nm is 1.2 x 10(-21) cm(2). A novel, compact, atmospheric cavity with a cavity length of 10 cm has been constructed and implemented to investigate the technical feasibility of the potential instrument size, optical configuration, and detection sensitivity. The detection limit of such a mini cavity employing ringdown mirrors of reflectivity of 99.85% at 266 nm, where acetone has the strongest absorption, is approximately 1.5 ppmv based on the standard 3 criteria. No real breath gas samples are used in the present study. Discussions on the detection sensitivity and background spectral interferences for the instrument development are presented. This study demonstrates the potential of developing a portable, sensitive breath analyzer for medical applications using the cavity ringdown spectral technique.

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.

Chemometric evaluation of pharmaceutical properties of antipyrine granules by near-infrared spectroscopy

The purpose of this research was to apply near-infrared (NIR) spectroscopy with chemometrics to predict the change of pharmaceutical properties of antipyrine granules during granulation by regulation of the amount of water added. The various kinds of granules (mean particle size, 70-750 microm) were obtained from the powder mixture (1 g of antipyrine, 6 g of hydroxypropylcellulose, 140 g of lactose, and 60 g of potato starch) by regulation of the added water amount (11-19 wt/wt%) in a high-speed mixer. The granules were characterized by mean particle size, angle of repose, compressibility, tablet porosity, and tablet hardness as parameters of pharmaceutical properties. To predict the pharmaceutical properties, NIR spectra of the granules were measured and analyzed by principal component regression (PCR) analysis. The mean particle size of the granules increased from 81 micro m to 650 micro m with an increase in the amount of water, and it was possible to make larger spherical granules with narrow particle size distribution using a high-speed mixer. Angle of repose, compressibility, and porosity of the tablets decreased with an increase of added water, but tablet hardness increased. The independent calibration models to evaluate particle size, angle of repose, and tablet porosity and hardness were established by using PCR based on NIR spectra of granules, respectively. The correlation coefficient constants of calibration curves for prediction of mean particle size, angle of repose, tablet porosity, and tablet hardness were 0.9109, 0.8912, 0.7437, and 0.8064, respectively. It is possible that the pharmaceutical properties of the granule, such as mean particle size, angle of repose, tablet porosity, and tablet hardness, could be predicted by an NIR-chemometric method.

Two redox-active beta-carotene molecules in photosystem II

Photosystem II (PS II) contains secondary electron-transfer paths involving cytochrome b(559) (Cyt b(559)), chlorophyll (Chl), and beta-carotene (Car) that are active under conditions when oxygen evolution is blocked such as in inhibited samples or at low temperature. Intermediates of the secondary electron-transfer pathways of PS II core complexes from Synechocystis PCC 6803 and Synechococcus sp. and spinach PS II membranes have been investigated using low temperature near-IR spectroscopy and electron paramagnetic resonance (EPR) spectroscopy. We present evidence that two spectroscopically distinct redox-active carotenoids are formed upon low-temperature illumination. The Car(+) near-IR absorption peak varies in wavelength and width as a function of illumination temperature. Also, the rate of decay during dark incubation of the Car(+) peak varies as a function of wavelength. Factor analysis indicates that there are two spectral forms of Car(+) (Car(A)(+) has an absorbance maximum of 982 nm, and Car(B)(+) has an absorbance maximum of 1027 nm) that decay at different rates. In Synechocystis PS II, we observe a shift of the Car(+) peak to shorter wavelength when oxidized tyrosine D (Y(D)*) is present in the sample that is explained by an electrostatic interaction between Y(D)* and a nearby beta-carotene that disfavors oxidation of Car(B). The sequence of electron-transfer reactions in the secondary electron-transfer pathways of PS II is discussed in terms of a hole-hopping mechanism to attain the equilibrated state of the charge separation at low temperatures.

Liquid light guides versus fiber light guides in clinical near-infrared spectroscopy

New commercial liquid light guides have an advantage over fiberoptic bundles regarding breakage during clinical handling. We investigate the quality of clinical data collection using liquid versus fiber bundles as receivers. A four-wavelength NIRO-500 near-IR spectrophotometer is used with single-terminal fiber bundles, multiterminal fiber bundles, or a single-terminal liquid light guide as receivers. Repeated 3-min trials are done using a stable phantom, an unstable phantom, and the human forearm. A least-squares linear best-fit line and its root mean square error (RMSE), a measure of signal noise, are derived for each wavelength of each trial. The mean and standard deviations for the RMSEs of the single-terminal fiber optic receiving cable are derived for comparison standards. The liquid light guides have 51 to 174% greater signal noise with RMSEs 2 to 12 standard deviations above the mean of the single-terminal fiber bundle. The multiterminal fiber bundles have 49% less to 32% greater signal noise and had RMSEs within 1 to 4 standard deviations above the mean of the single-terminal fiber bundle. These comparisons suggest fiber optic bundles are preferable for clinical near-IR spectroscopy (NIRS) applications requiring low signal noise.

Analysis on performance and optimization of frequency-domain near-infrared instruments

Frequency-domain near-infrared techniques have been widely used to detect the optical properties of biological tissues noninvasively. In this paper we propose an analytical model to evaluate the performance of frequency-domain instruments. Based on the diffusion equation and the transfer properties of optoelectronic components, we treat all parts, including the medium, as two-port networks and apply systematic methods to answer questions concerning frequency-domain instruments. Experiments show that this method can reasonably reflect the properties of the instrument within an accuracy of 7%. This kind of method can be used to design suitable instruments for various applications. We also analyze the selection of the instrument parameters to achieve optimal performance at an efficient cost using this analytical model.

Charge-coupled-device based scanner for tomography of fluorescent near-infrared probes in turbid media

We present a novel tomographer for three-dimensional reconstructions of fluorochromes in diffuse media. Photon detection is based on charge-coupled device technology that allows the implementation of a large parallel array of detection channels with high sensitivity. Using this instrument we studied the response and detection limits of near-infrared fluorochromes in diffuse media as a function of light intensity and for a wide range of biologically relevant concentrations. We further examined the resolution of the scanner and the reconstruction linearity achieved. We demonstrate that the instrument attains better than 3 mm resolution, is linear within more than two orders of magnitude of fluorochrome concentration, and can detect fluorescent objects at femto-mole quantities in small animal-like geometries. These measurements delineate detection and reconstruction characteristics associated with imaging of novel classes of fluorescent probes developed for in vivo molecular and functional probing of tissues.

A study of the crystallisation of amorphous salbutamol sulphate using water vapour sorption and near infrared spectroscopy

The crystallisation of amorphous salbutamol sulphate prepared by spray drying was monitored using a humidity controlled microbalance (Dynamic Vapour Sorption apparatus, Surface Measurement Systems) combined with a near-infrared probe. Amorphous salbutamol sulphate was prepared by spray drying from a solution in water. The particles were then analysed using scanning electron microscopy, thermogravimetric analysis, differential scanning calorimetry, powder X-ray diffraction, isothermal microcalorimetry and water vapour sorption analysis combined with near-infrared spectroscopy (NIR). Isothermal microcalorimetry and water vapour sorption combined with NIR spectroscopy were able to detect the transition from the amorphous to crystalline state. However while the isothermal microcalorimeter showed only a classic crystallisation exotherm when the material was exposed at 75% RH, the DVS-NIR results at the same humidity highlighted a more complex process. When exposed at 75% RH, the uptake of water was followed by crystallisation that was detected using NIR. The expulsion of water after crystallisation was very slow and at a constant rate whether the material was exposed to 75 or 0% RH. The NIR and DVS studies indicated that the material had crystallised very soon after exposure to high RH. The water that was expelled during crystallisation was not displaced from the particles and remained associated with the particles for many days. This study showed that the use of gravimetric analysis together with NIR spectroscopy provided valuable information on the dynamics of the crystallisation of salbutamol sulphate. The retention of water within recently crystallised salbutamol is potentially important to the behaviour of dosage forms containing the amorphous (or partially amorphous) form of this drug.