Feasibility Of Near-infrared Spectroscopy Research Articles

On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R2) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.

On the feasibility of near infrared spectroscopy to detect contaminants in water using single salt solutions as model systems

This research work evaluates the feasibility of NIRS to detect contaminants in water using single salt solutions as model systems. Previous research has indicated the potential of near infrared spectroscopy (NIRS) for detecting solutes in water; however, a comprehensive investigation of the limit of detection of this technique has not been carried out. Near infrared transmittance spectra of aqueous salt solutions in the concentration range 0.002–0.1molL−1 (equivalent to 117–13,334ppm or 0.0001–0.01% mass/mass) were investigated. The first overtone region of the near infrared spectrum (1300–1600nm) was found to be the most effective wavelength range for prediction of salt concentration in aqueous solutions. Calibration models built using this wavelength range and employing the extended multiplicative scatter spectral pre-treatment resulted in root mean squared error of prediction values ranging from 0.004 to 0.01molL−1. The limit of detection (LOD) was estimated to be of the order of 0.1% (mass/mass) or 1000ppm. Within the framework of Aquaphotomics, it was possible to examine the effect of different salts on the NIR spectra of water in the first overtone range. Our results were confirmed through test experiments at various geographical locations employing dispersive and Fourier transform type NIRS instruments.

Lautering Performance Prediction from Malt by Combining Whole Near-Infrared Spectral Information with Lautering Process Evaluation as Reference Values

The aim of this study is to predict lautering performance from malt batches. It pursues an innovative approach by combining the total near-infrared (NIR) spectral information content with lautering process evaluation data. For this, multivariate data analysis is applied to combine the information content of whole NIR spectra of malt with lautering process data. Three NIR spectra were taken for each of the 11 single-malt batches of 100% pilsner malt. Additionally, standard laboratory analyses were performed. Standardized lautering processes were carried out, evaluated, and categorized in “good,” “semi,” and “bad.” The 51 lautering process evaluation values, each with three associated NIR spectra, add up to a total of 153 objects employed in building the prediction model. Partial least squares discriminant analysis (PLS-DA) was used for classification and calculating prediction accuracy. The prediction accuracy of the calibration set was 92.7% with only three failed lautering performance predictions. In the test set validation, only one lautering process prediction failed. This results in a prediction accuracy of 90.6% for the calibration model. With this study, the feasibility of NIR spectroscopy in combination with multivariate statistics to predict lautering performance by analysis of malt was proven.

Non-invasive quantification of 5 fluorouracil and gemcitabine in aqueous matrix by direct measurement through glass vials using near-infrared spectroscopy

Fourier transform near infrared spectroscopy (NIRS) was used for quantitative analysis of two cytotoxic drugs used in pharmaceutical infusion, 5-fluorouracil (5FU) and gemcitabine (GEM), at therapeutic concentrations in aqueous matrix. Spectra were collected from 4000 cm(-1) to 13,000 cm(-1) by direct measurement through standard glass vials and calibration models were developed for 5FU and GEM using partial least-squares regression. NIR determination coefficient (R(2)) greater than 0.9992, root-mean-square-error of cross-validation (RMESCV) of 0.483 mg/ml for 5FU and 0.139 mg/ml for GEM and the root mean square error of prediction (RMSEP) of 0.519 for 5FU and 0.108 mg/ml for GEM show a good prediction ability of NIR spectroscopy to predict 5FU and GEM concentrations directly through a glass packaging. According to accuracy profile, the linearity was validated from 7 to 50mg/ml and 2 to 40 mg/ml for 5-fluorouracil and gemcitabine respectively. This new approach for cytotoxic drugs control at hospital has shown the feasibility of near infrared spectroscopy to quantify antineoplastic drugs in aqueous matrix by a direct measurement through glass vial in less than 1 min and by non-invasive measurement perfect to limit exposure of operator to cytotoxic drugs.

Rapid determination of immunoglobulin G concentration in cold ethanol precipitation process of raw plasma with near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is known to be a powerful analytical tool in process monitoring. The feasibility of NIRS was investigated for determination of immunoglobulin G (IgG) in raw plasma cold ethanol precipitation process. Partial least squares (PLS) was used to develop regression model for 63 samples between spectra and reference data measured with a UV spectrophotometer. Three different variable selection methods, including correlation coefficient method, interval partial least squares (iPLS) and successive projection algorithm (SPA), were performed and compared with models based on all the variables. The values of Rc and root mean square error of cross validation (RMSECV) produced by the best model for the calibration set were 0.9599 and 0.6135g/L, respectively. While for the validation set, the values of Rp and root mean square error of prediction (RMSEP) were 0.9577 and 0.4913g/L, respectively. The results of this paper demonstrated that NIRS could be a feasible alternative approach for rapid determination of IgG in the cold ethanol precipitation process and can be used as a PAT tool in the future.

Prediction of dry veneer stiffness using near infrared spectra from transverse section of green log

This study examined the feasibility of near infrared spectroscopy as a novel technique for log assessment on the basis of wood property. Near infrared (NIR) spectra were obtained from the transverse section of green log and multivariate regression analysis was carried out to predict the stiffness of veneer processed from the log. The stiffness of the veneer was dynamic modulus of elasticity measured using ultrasonic method. The calibrations of veneer stiffness had moderate relationships between measured and NIR-predicted values, with regression coefficients ranging from 0.84 to 0.88. The calibration equations were applied to the test set and it was found that predictions were also well fitted, with regression coefficients ranging from 0.67 to 0.89. The results indicate that the variation of wood stiffness within the logs could be assessed using the NIR spectra from the cross-section of logs. The spectra were obtained from green condition of the log and the stiffness of veneer was measured after kiln drying. Thus, the results imply that the wood stiffness in dry condition could be predicted using the spectra collected from green logs. If the models obtained in this study put into the imaging system, the two-dimensional map of the stiffness would be visualized on the cross-section of logs. The NIR spectroscopy coupled with imaging system could compensate the weak point of the traditional methods for log assessment.

Validation of a NIR quantification method for the determination of chlorogenic acid in Lonicera japonica solution in ethanol precipitation process

The feasibility of near-infrared spectroscopy (NIRS) for chlorogenic acid content analysis in ethanol precipitation process of water extract of Lonicera japonica was verified in this work. A calibration and validation set was designed for the conception and evaluation of the method adequacy. An experimental protocol was then followed, involving two different NIR instruments for data acquisition. On the basis of this protocol, the model was developed based on partial least squares regression (PLS) and the determination coefficient (R2cal and R2val), standard error of calibration and prediction (SEC and SEP) were 0.9962, 0.9955, 111.1μg/mL and 107.1μg/mL for Holographic Grating NIR instrument, and 0.9984, 0.9971, 53.6μg/mL and 83.3μg/mL for Fourier Transform NIR instrument. However, such above criteria did not clearly demonstrate the model's prediction error over each analyzed content range. Consequently, a novel approach based on accuracy profile which allowed the acquisition of the lower limit of quantification (LLOQ) was used to validate the robustness and accuracy of PLS model. The resulting accuracy profile showed that PLS model was able to determine chlorogenic acid content by two NIR systems, whose LLOQ was about 1550μg/mL. It was concluded that the two NIR systems were suitable for use as Process Analytical Technology (PAT) to understand ethanol precipitation process of water extract of Lonicera japonica.

Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels

The current US corn grading system accounts for the portion of damaged kernels, measured by time-consuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and frost damage. Four classification algorithms were utilized: Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), k-nearest neighbors (K-NN), and least-squares support vector machines (LS-SVM). The feasibility of NIRS for discriminating normal or viable-germinating corn kernels and soybean seeds from abnormal or dead seeds was also tested. This application could be highly valuable for seed breeders and germplasm-preservation managers because current viability tests are based on a destructive method where the seed is germinated. Heat-damaged corn kernels were best discriminated by PLS-DA, with 99% accuracy. The discrimination of frost-damaged corn kernels was not possible. Discrimination of non-viable seeds from viable also was not possible. Since previous results in the literature contradict the current damage-discrimination results, the threshold of seed damage necessary for NIRS detection should be analyzed in the future. NIRS may accurately classify seeds based on changes due to damage, without any correlation with germination.

New Approaches for Field Analyses of Cotton Quality by Means of Near-IR Spectroscopy Supported by Chemometrics

There is a strong economic interest in routine measurements of cotton quality as production processes and final products depend on it. An important cotton property is “micronaire,” a parameter that is indicative of the fibers' maturity and its fineness. Currently, micronaire is normally measured in laboratories with equipment that prohibits routine field analyses. The goal of this study is a proof-of-principle demonstrating that cotton quality as determined via fiber micronaire is correlated to fiber properties and that these properties can be determined by near-infrared (NIR) reflection spectroscopy using portable instrumentation in conjunction with Principal Component Regression for micronaire prediction. A set of 191 cotton samples was acquired from over 100 different upland cotton varieties, and initial spectroscopic studies confirmed the feasibility of NIR spectroscopy to measure cotton micronaire in the laboratory with portable NIR instrumentation. Sample reproducibility was an issue which has been resolved with two approaches, that is, model spectral artifacts, mainly baseline shifts, by means of chemometric calibrations or application of second derivative spectroscopy to suppress baseline drifts. Results from this study demonstrated in up to 90% of the test samples that the micronaire values fall into the acceptance range. Thus, a promising new approach for field analyses is on the horizon and has been assessed in this study. Further, the acceptance range could be reduced to ±0.2 m.u. and still ≥70% of the samples fell inside the restricted acceptance range. Up to 60% of the samples fell inside an acceptance range of ±0.1 m.u.

Optical coefficient‐based multivariate calibration on near‐infrared spectroscopy

AbstractThe time and expense of calibration development limit the feasibility of NIR spectroscopy for many industrial applications, with a major portion of the costs being related to creation of a sufficient set of calibration samples. Net analyte signal (NAS) and generalized least squares (GLS) pre‐processing have been proposed in the literature as methods to simplify multivariate calibration by reducing the quantity of calibration samples by orthogonalizing or shrinking interference signals. Synthetic calibration has also been reported as a method to combine interference signals with pure component spectra to generate virtual calibration models, thereby reducing the number of real calibration samples required. The goals of this paper were to (1) compare theoretical and practical differences between NAS and GLS pre‐processing and (2) explore the potential of simplified NIR calibrations, both empirical and synthetic, constructed using optical coefficient‐based signal processing on predicting chemical compositions of pharmaceutical powder mixtures. A reduced calibration dataset including only one pharmaceutical powder mixture composition and pure component spectra was used for both empirical and synthetic calibrations. Absorption and reduced scattering coefficients, obtained from spatially‐resolved spectroscopy, were used herein as interference signals in NAS/GLS pre‐processing for both calibrations. As a result, NAS and GLS were shown to be equivalent in both theoretical and practical senses. After optical coefficient‐based signal processing, simplified calibrations, both empirical and synthetic, were demonstrated to have similar model performance as generic pre‐processing methods such as SNV and derivative, while requiring fewer principal components and achieving a lower prediction error. Copyright © 2010 John Wiley & Sons, Ltd.

Feasibility of Near-Infrared Spectroscopy for On-Line Grading of Sawn Lumber

Near-infrared (NIR) spectroscopy, coupled with multivariate analysis, has been used to evaluate the wood properties of sawn lumber of Japanese larch (Larix kaempferi), whose diffuse reflection spectra were acquired under static and moving conditions. Prediction models of the dynamic modulus of elasticity (E(fr)), the modulus of elasticity in bending tests (E(b)), the bending strength (F(b)), the wood density (DEN), and the moisture content (MC) were developed using partial least squares (PLS) analysis. For all wood properties, models obtained from data collected under the moving condition as an analogue of on-line measurement were superior to those from the static condition data. The regression coefficients for the PLS models predicting the mechanical properties in both static and moving conditions showed clear peaks at the absorption bands due to the three major polymers of wood, i.e., cellulose, hemicellulose, and lignin. NIR spectroscopy has high potential for the on-line grading of sawn lumber.

Determination of Total Ash and Acid-insoluble Ash of Chinese Herbal Medicine Prunellae Spica by Near Infrared Spectroscopy

Rapid and accurate determination of total ash and acid-insoluble ash of Chinese herbal medicine would be valuable for the quality illustration because current approaches are laborious and time-consuming. This study investigated the feasibility of near infrared spectroscopy in predicting simultaneously the total ash and acid-insoluble ash contents of Prunellae Spica (one of the most widely used Chinese herbal medicine). Samples were collected from ten different geographic regions and scanned under the near-infrared spectroscopy region (4000-12000 cm(-1)). Models between the spectral data and the results of reference analysis were developed, and subsequently validated. Calibration models based on partial least squares were accurate for the prediction of total ash (R2=0.914; standard errors of prediction=0.373), and acid-insoluble ash (R2=0.905; standard errors of prediction=0.452).

Activated sludge process monitoring through in situ near-infrared spectral analysis

The application of near infrared (NIR) spectroscopy for industrial process monitoring is achieving increasing importance over the last twenty years. In fact, the real time monitoring capacity of NIR spectroscopy is a very important feature for process monitoring, prediction and control as it allows a fast evaluation of the state of the process. However, the application of NIR spectroscopy in wastewater treatment processes is still to be explored. Although some applications of the technique for wastewater monitoring have been reported in the literature, there is still a need for more investigation related with applications, limitations and advantages of the technique when compared with other methods. An activated sludge reactor for aerobic treatment of a complex medium was monitored in situ with a NIR transflectance probe and traditional chemical parameters analysed off-line. NIR spectrophotometric data measured at the feed, reactor and settler were coupled to principal component analysis (PCA) to infer about the ability of this monitoring system to detect changes in the feed influent. The analysis of the score plots resulting from PCA permitted to identify the moments at which the perturbations occurred and to follow the consequent instability induced in the reactor till the day where the system is recuperated. The promising results obtained, suggest the interest in more detailed studies on the feasibility of NIR spectroscopy as an alternative method for monitoring and control of wastewater treatment processes.

Quantitative determination of acyclovir in plasma by near infrared spectroscopy

The aim of this study was to assess the feasibility of near infrared spectroscopy (NIRS) for analysis of acyclovir in plasma. This methodology was based on the direct measurement of the transmission spectra of liquid samples and a multivariate calibration model (partial least squares, PLS) to determine the acyclovir concentration in plasma sample. The PLS calibration set was built on using the spiked samples by mixing different amounts of acyclovir. Concentration of acyclovir in the plasma samples was calculated employing a 6-factors PLS calibration using the spectral information in the range of 6102–5450 cm − 1 . The root mean square errors of prediction (RMSEP) found was 1.21 for acyclovir. The developed PLS-NIRS procedure allows the determination of 120 samples/h does not require any sample pretreatment and avoids waste generation.

Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS

The feasibility of near infrared spectroscopy (NIRS) for discrimination between Chinese rice wine of dif- ferent geographical origins (Shaoxing and Jiashan, China) is presented in this research. NIR spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Qualitative analysis models were developed based on par- tial least squares regression (PLSR). The prediction perfor- mance of calibration models in different wavelength range was also investigated. The best models gave a 100% clas- sification of wines of the two geographical origins in the range of 1300-1650 nm. The content of trace metals (potas- sium, magnesium, zinc, and iron) was also investigated to classify wines of the two categories by atomic absorption spectroscopy (AAS). The AAS results were in agreement with NIRS, with 100% classification for wines of the two categories. In addition, the correlation between NIRS and AAS was also investigated by PLSR. Potassium and mag- nesium were well predicted by quantitative models based on NIR spectra and AAS data. The correlation coefficient of calibration (Rcal) for potassium and magnesium were 0.958 and 0.885, respectively, and the correlation coeffi- cient of validation (Rval) were 0.861 and 0.700, respec- tively. The results demonstrated that NIRS technique could be used as a rapid method for classification of geograph- ical origin of Chinese rice wine, and AAS could be used as an alternative technique or to validate the discrimination results.

Calibration of near infrared spectroscopy for solid fat content of fat blends analysis using nuclear magnetic resonance data

The functional properties of fats are determined by the distribution pattern of fatty acid residues in their acylglycerols, which may be modified by ester interchange (transesterification). In the margarine industry, the time course of the transesterification of fat blends is monitored by assaying for the amount of the solid fraction at different temperatures (SFC-solid fat content) currently measured by nuclear magnetic resonance (NMR). The aim of this study was to evaluate the feasibility of near infrared spectroscopy (NIRS) to quantify the SFC of different fat blends using NMR data for calibration. SFC values of 128 samples, consisting of different blends of palm stearin, palm kernel oil and concentrates of triglycerides enriched with ω-3 polyunsaturated fatty acids were assayed by NMR prior to (64 samples) and following inorganic (10 samples) or lipase-catalysed transesterification (54 samples). Prior to SFC measurement by NMR, sample preparation takes about 90 min. With NIRS technique, a faster determination is achieved since NIR spectra for SFC estimations are directly obtained on the sample at room temperature. High correlations were obtained for cross-validation of the data estimated by NIRS models and NMR for SFC assays at 10 °C ( R 2 = 0.91, RMSECV = 2.4), 20 °C ( R 2 = 0.96, RMSECV = 1.7), 30 °C ( R 2 = 0.96, RMSECV = 1.3) and 35 °C ( R 2 = 0.96, RMSECV = 1.3) of the different blends tested. The obtained results show that NIRS is a reliable technique to replace NMR for SFC estimation.

Near-Infrared Reflectance Spectroscopic Analysis of Moisture, Fat, Protein, and Physiological Activity in Buckwheat Flour for Breeding Selection

We examined the feasibility of near-infrared (NIR) reflectance spectroscopy to estimate the contents of moisture, fat, and protein in the flours of common and tartary buckwheat harvested in 1996 and 1997. Multiple linear -regression analysis for NIR spectral and chemical data was carried out. The standard error of the prediction (SEP) for the samples harvested in 1996 was 0.22% for moisture, 0.06% for fat, and 0.21% for protein, when the calibration equations obtained from the analysis of common and tartary buckwheat were used. When the same calibration equations for the buckwheat flour harvested in 1997 were used, the correlation graphs between the reference values and the calculated values were skewed and biased. However, the relative values were sufficient for the selection based on the contents of the constituents, and the bias-corrected SEPs were 0.15% for moisture, 0.10% for fat, and 0.44% for protein. We also examined the feasibility of NIR spectroscopy to estimate the total polyphenol content and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging activity in the flours of common buckwheat harvested in 1998. The estimation error was acceptable for use in the selection of varieties with a higher activity. Using the NIR method, the contents of moisture, fat and protein, and physiological activity (total polyphenol content and DPPH radical-scavenging activity) in the buckwheat flours could be successfully estimated for a simple and rapid-breeding selection.

Feasibility of Rapid and Nondestructive Determination of Ash Content in Cornu Cervi Parvum Using Near-Infrared Spectroscopy

Near-infrared (NIR) spectroscopy was applied to the nondestructive and rapid determination of ash content in Cornu Cervi Parvum, a dried horn of Cervus nippon or Cervus elaphus, which serves as a natural drug for general health care in Asian countries. Cervine horns are dried for use as a drug and are classified according to their age as either Cornu Cervi Parvum (new horn) or Cervi Cornu (old horn). Ash content is generally used to determine the quality of cervine horns. A traditional method to determine ash content is an ignition method that measures ash residue after incineration. However, more than 4 h is required for incineration and the destruction of organic compounds. NIR spectroscopy was studied as an alternative analytical method to decrease analysis time and increase laboratory efficiency. By use of partial least-squares regression, relevant information related to ash in the NIR region was successfully extracted. NIR prediction results showed good correlation with those from the ignition method. The overall results showed the feasibility of NIR spectroscopy for the determination of ash content in Cornu Cervi Parvum.