Noninvasive in vivo measurement of venous blood pH during exercise using near-infrared reflectance spectroscopy

Blood pH is an important indicator of anaerobic metabolism in exercising muscle. This paper demonstrates multivariate calibration techniques that can be used to produce a general pH model that can be applied to spectra from any new subject without significant prediction error. Tissue spectra (725 approximately 880 nm) were acquired through the skin overlying the flexor digitorum profundus muscle on the forearms of eight healthy subjects during repetitive hand-grip exercise and referenced to the pH of venous blood drawn from a catheter placed in a vein close to the muscle. Calibration models were developed using multi-subject partial least squares (PLS) and validated using subject-out cross-validation after the subject-to-subject spectral variations were corrected by mathematical preprocessing methods. A combination of standard normal variate (SNV) scaling and principal component analysis loading correction (PCALC) successfully removed most of the subject-to-subject variations and provided the most accurate prediction results.     

Skin color correction for tissue spectroscopy: demonstration of a novel approach with tissue-mimicking phantoms

The application of partial least squares (PLS) regression to visible-near-infrared (VIS-NIR) spectroscopy for modeling important blood and tissue parameters is generally complicated by the variation in skin pigmentation (melanin) across the human population. An orthogonal correction method for removing the influence of skin pigmentation has been demonstrated in diffuse reflectance spectra from two-layer tissue-mimicking phantoms. The absorption properties of the phantoms were defined by lyophilized human hemoglobin (bottom layer) and synthetic melanin (top layer). Tissue-like scattering was simulated in both layers with intralipid. The approach uses principal components analysis (PCA) loading vectors from a separate set of phantom spectra that encode the unwanted melanin variation to remove the effect of melanin from the test phantoms. The preprocessing of phantom spectra using this orthogonal correction method resulted in PLS models with reduced complexity and enhanced prediction performance. Preliminary results from a separate study that evaluates the feasibility of defining skin color variation in an experiment with a single human subject are also presented.     

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters, such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) of 0.9451 and root-mean-square error of prediction (RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.     

Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction

Six popular approaches of «NIR spectrum–property» calibration model building are compared in this work on the basis of a gasoline spectral data. These approaches are: multiple linear regression (MLR), principal component regression (PCR), linear partial least squares regression (PLS), polynomial partial least squares regression (Poly-PLS), spline partial least squares regression (Spline-PLS) and artificial neural networks (ANN). The best preprocessing technique is found for each method. Optimal calibration parameters (number of principal components, ANN structure, etc.) are also found. Accuracy, computational complexity and application simplicity of different methods are compared on an example of prediction of six important gasoline properties (density and fractional composition). Errors of calibration using different approaches are found. An advantage of neural network approach to solution of «NIR spectrum–gasoline property» problem is illustrated. An effective model for gasoline properties prediction based on NIR data is built.     

Hemoglobin correction for near-infrared pH determination in lysed blood solutions

The near-infrared (NIR) measurement of blood pH relies on the spectral signature of histidine residing on the hemoglobin molecule. If the amount of hemoglobin in solution varies, the size of the histidine signal can vary depending on changes in either the pH or hemoglobin concentration. Multivariate calibration models developed using the NIR spectra collected from blood at a single hemoglobin concentration are shown to predict data from different hemoglobin levels with a bias and slope. A simple, scalar path length correction of the spectral data does not correct this problem. However, global partial least-square (PLS) models built with data encompassing a range of hemoglobin concentration have a cross-validated standard error of prediction (CVSEP) similar to the CVSEP of data obtained from a single hemoglobin level. It will be shown that the prediction of pH of an unknown sample using a global PLS model requires that the unknown have a hemoglobin concentration falling within the range encompassed by the global model. An alternative method for correcting the predicted pH for hemoglobin levels is also presented. The alternative method updates the single-hemoglobin-level models with slope and intercept estimates from the pH predictions of data collected at alternate hemoglobin levels. The slope and intercept correction method gave SEP values averaging to 0.034 pH units. Since both methods require some knowledge of the hemoglobin concentration in order for a pH prediction to be made, a model for hemoglobin concentration is developed using spectral data and is used for pH correction.     

Cyclic subspace regression with analysis of wavelength-selection criteria

Common methods of building linear calibration models are principal component regression (PCR), partial least squares (PLS), and least squares (LS). Recently, the method of cyclic subspace regression (CSR) has been presented and shown to provide PCR, PLS, LS and other related intermediate regressions with one algorithm. When forming a linear model with spectral data for quantitative analysis, prediction results can be adversely affected by responses that do not conform well to the linear model proposed. Wavelength selection can be used to eliminate wavelengths where such problem responses occur. It has recently been reported that CSR regression vectors can be formed by summing weighted eigenvectors where weights are determined from the hat matrix, singular values, and eigenvectors characterizing the sample space. Investigation of these weights shows that wavelength selection based on loading vectors can be misleading. Specifically, by using CSR it is shown that a small weight for an eigenvector can annihilate a large peak in a loading vector. In this study, correlograms are used with CSR regression vectors and eigenvector weights as wavelength-selection criteria. It is demonstrated that even though a model generated by LS for a wavelength subset produces substantially reduced prediction errors relative to PCR and PLS, CSR weight plots show that the LS model overfits and should not be used. Simulated situations containing spectral regions with excess noise or nonlinear responses are examined to study the effectiveness of wavelength selection based on the previously listed criteria. Near infrared spectra of gasoline samples with several known properties are also studied.     

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

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

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

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

基于可见/近红外光谱分析技术的水性油墨黏度预测模型

针对水性油墨黏度测量方法存在操作复杂、主观性强等问题,利用可见/近红外光谱分析技术结合化学计量学方法,建立水性油墨黏度预测模型,实现水性油墨黏度的快速无损检测。首先,利用微型光纤光谱仪采集水性油墨样本的反射光谱;再通过比较不同预处理方法对原始光谱数据的预处理效果,分别基于原始全光谱及预处理后的光谱数据构建水性油墨黏度的偏最小二乘回归(PLSR)和主成分回归(PCR)预测模型;最后,将预处理后的光谱数据采用连续投影算法(SPA)和竞争性自适应重加权算法(CARS)提取特征波长,并基于特征波长的光谱数据建立水性油墨黏度的PLS预测回归模型。结果表明,采用SPA算法从全光谱中只提取了4个特征波长,不仅显著简化了模型,提升了模型的运算效率,建立的SNV-SPA-PLS模型还具有最佳的预测性能(R_p²=0.9992,RMSEP=0.0732)。该研究结果表明应用光谱分析技术实现对水性油墨黏度检测是有效可行的,为进一步通过光谱分析技术进行水性油墨在线黏度检测提供了新方法,为提高印刷品质量稳定性提供了技术基础。     

New indicator for optimal preprocessing and wavelength selection of near-infrared spectra

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal preprocessing. The SE indicator performs better than the two other indicators, and it is also applicable to other situations where the Beer-Lambert law is valid.     

单基发射药生产过程中驱水棉水分含量的快速检测方法?

由于驱水棉的水分含量对单基发射药成型工艺有着较大的影响,采用近红外光谱分析技术对驱水棉水分含量进行快速检测。通过对比分析纯水、硝化棉(NC)、乙醇以及驱水棉的光谱图,确定了水分含量检测建模区域为5 015.6~5 224.8 cm~(-1)和6 525.9~7 008.7 cm~(-1)。比较不同光谱预处理方法,发现标准正态变量校正(SNV)、一阶导数和平滑的组合方法对驱水棉的光谱进行预处理效果最好。采用偏最小二乘法对水分含量建立定量校正模型,并对预测集样本进行预测和对模型进行重复性验证。试验结果表明:校正集和交互验证相关系数R2分别为0.995 4和0.994 4,预测均方根误差RMSEP值为0.039 0,对预测集的药料样本预测的平均相对误差为0.997%,模型的重复性良好,检测时间小于20 s,能满足单基发射药连续自动化生产工艺的要求。     

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.     

Potential of airborne hyperspectral imagery to estimate fruit yield in citrus

Alternate bearing is a well-marked yield variability phenomenon that occurs in almost all tree-fruit crops. The potential benefits of applying various alternate bearing control measures on alternate bearing crops can only be realized when yield information on individual trees of particular crops is obtained. The objective of this study was to examine the potential of airborne hyperspectral imagery to estimate the fruit yield in citrus. Hyperspectral images in 72 visible and near-infrared (NIR) wavelengths (from 407 to 898 nm) were acquired over a citrus orchard in Japan by an Airborne Imaging Spectrometer for Applications (AISA) Eagle system. The canopy features of individual trees were identified using pixel-based average spectral reflectance values at various wavelengths from the acquired images, which were then used to develop yield prediction models. Yield prediction models were developed using five different techniques — (i) several vegetation indices (VIs), (ii) key wavelengths determined by simple correlation analysis (SCA), (iii) principal components (PCs) based on principal component regression (PCR), and (iv) PLS factors as well as (v) important wavelengths determined by B-matrix based on partial least squares (PLS) regression. The results indicated that the VIs used in this study were poorly correlated with fruit yield on individual trees. The key or important wavelengths determined by the two methods proposed in this study could provide reasonable prediction of fruit yield. Comparatively, the B-matrix method based on the PLS regression was superior to the simple correlation analysis in determining the key or importance wavelengths that are correlated to the fruit yield. However, the PCs extracted from the hyperspectral data were weak predictors of citrus yield. Greater prediction accuracy was obtained with the model based on PLS factors than with the models based on the key or important wavelengths. These results confirmed the hypothesized correlation between canopy features and citrus yield. The methods proposed in this study have considerable promise in estimating fruit yield on individual citrus trees. The yield information is valuable for planning harvest schedules and developing programs for application of tree-specific alternate bearing control measures and other management practices.     

Comparison of derivative preprocessing and automated polynomial baseline correction method for classification and quantification of narcotics in solid mixtures

This work offers a real-world comparison of derivative preprocessing and a new polynomial method described by Lieber and Mahadevan-Jansen (LMJ) for baseline correction of Raman spectra with widely varying backgrounds. This comparison is based on their outcomes in factor analysis, analyte discrimination, and quantification. Both correction methods are applied to a Raman spectra data set taken from 85 solid samples of illegal narcotics diluted with various materials. It is found that neither approach outperforms the other, as they give similar principal component analysis (PCA) models and quantification errors: cocaine and heroin show cross-validation errors of approximately 8%, while MDMA is quantified to a cross-validation error of approximately 3-4%. The LMJ method does offer several other advantages, the most significant being the retention of original peak shapes after the correction, which simplifies the interpretation of the preprocessed spectra. The LMJ method is therefore recommended for use as a baseline correction method in future research with Raman spectroscopy.     

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

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

Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis

Due to their heterogeneous structure and variability in form, individual corn (Zea mays L.) kernels present an optical challenge for nondestructive spectroscopic determination of their chemical composition. Increasing demand in agricultural science for knowledge of specific traits in kernels is driving the need to find high-throughput methods of examination. In this study macroscopic near-infrared (NIR) reflectance hyperspectral imaging was used to measure small sets of kernels in the spectroscopic range of 950 nm to 1700 nm. Image analysis and principal component analysis (PCA) were used to determine kernel germ from endosperm regions as well as to define individual kernels as objects out of sets of kernels. Partial least squares (PLS) analysis was used to predict oil or oleic acid concentrations derived from germ or full kernel spectra. The relative precision of the minimum cross-validated root mean square error (RMSECV) and root mean square error of prediction (RMSEP) for oil and oleic acid concentration were compared for two sets of two hundred kernels. An optimal statistical prediction method was determined using a limited set of wavelengths selected by a genetic algorithm. Given these parameters, oil content was predicted with an RMSEP of 0.7% and oleic acid content with an RMSEP of 14% for a given corn kernel.     

Calibration of multiplexed fiber-optic spectroscopy

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

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

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

预浸料生产中智能控制系统的应用

用近红外光谱技术对无碱布/酚醛预浸料的树脂含量、可溶树脂含量和挥发分含量进行在线检测,通过偏最小二乘方法分别建立标准模型,选择光谱预处理方法和PLS的因子数.用近红外方法和标准方法对未知样品进行分析,通过t检验结果显示两种方法没有显著性的差别,利用该方法可以同时预测三项指标,1分钟之内就可以分析一个样品,没有破坏性.如果质量指标不合格,通过自动控制系统发出指令,及时调节工艺参数.研究表明近红外光谱方法能够十分有效和准确对分析预浸料质量.     

Wavelength selection for multivariate calibration using tikhonov regularization

Prediction of sample properties using spectroscopic data with multivariate calibration is often enhanced by wavelength selection. This paper reports on a built-in wavelength selection method in which the estimated regression vector contains zero to near-zero coefficients for undesirable wavelengths. The method is based on Tikhonov regularization with the model 1-norm (TR1) and is applied to simulated and near-infrared (NIR) spectral data. Models are also formed from wavelength subsets determined by the standard method of stepwise regression (SWR). Harmonious (bias/variance tradeoff) and parsimonious considerations are compared with and without wavelength selection for principal component regression (PCR), ridge regression (RR), partial least squares (PLS), and multiple linear regression (MLR). Results show that TR1 models generally contain large baseline regions of near-zero coefficients, thereby essentially achieving built-in wavelength selection. For example, wavelengths with spectral interferences and/or poor signal-to-noise ratios obtain near zero regression coefficients. Results often improve with TR1 models, compared to full wavelength PCR, RR, and PLS models. The SWR subset results are similar to those for the TR1 models using the NIR data and worse with the simulated spectral situations. In general, wavelength selection improves prediction accuracy at a sacrifice to a potential increase in variance and the parsimony remains nearly equivalent compared to full wavelength models. New insights gained from the reported studies provide useful guidelines on when to use full wavelengths or use wavelength selection methods. Specifically, when a small number of large wavelength effects (good sensitivity and selectivity) exist, subset selection by SWR (with caution) and TR1 do well. With a small to moderate number of large to moderate sized wavelength effects, TR1 is better. Lastly, when a large number of small effects are present, full wavelengths with the methods of PCR, RR, or PLS are best.