Number Of Partial Least Squares Factors Research Articles

Prediction of glucose and sucrose values of black tea samples using NIR spectroscopy and chemometrics

FT-NIR and chemometrics are considered one of the most important analytical tools for the quality control of tea. The present study focused on developing and validating a rapid quantification method for the quantification of sugar, specifically sucrose and glucose in black tea using FT-NIR spectroscopy. Calibration models were established by partial least squares (PLS) regression analysis. Fructose, glucose, and sucrose concentrations in black tea were analyzed using high-performance liquid chromatography. The spectral range and the number of PLS factors were optimized for the Residual Predictive Deviation (RPD) and correlation coefficient (r2) of determination. The NIR spectral region between 5878 and 3996 cm−1 (1701–2500 nm) was selected as the best range for predicting glucose, sucrose, and fructose concentrations in black tea. The calibration model developed for sucrose has high RPD (4.29) and r2 (0.95) indicating the potential for predicting the inherent sucrose content in black tea with high accuracy. The calibration model developed for glucose has RPD of 2.73 and r2 (0.87) showing the potential for predicting glucose content in black tea with reasonable accuracy. Both calibration models could be useful for quality assurance purposes in black tea, and easier and faster than the wet chemical HPLC method. FT-NIR spectroscopy coupled with chemometrics has been demonstrated for the first time to quantify the inherent glucose and sucrose concentrations in black tea. Calibration models could be used to determine adulteration of black tea with sucrose and glucose.

Analytical methodology to elemental quantification of weathered terrestrial analogues to meteorites using a portable Laser-Induced Breakdown Spectroscopy (LIBS) instrument and Partial Least Squares (PLS) as multivariate calibration technique

The present work is focused on the in situ quantitative analysis of Si, Al, Mg, Ca, Ba, Na, and Fe, present in weathered terrestrial analogues to meteorites (black steel slag and impact glasses), using a portable Laser Induced Breakdown Spectroscopy (LIBS) instrument. For that purpose, several standards pellets of known elemental concentrations were manufactured. The elemental and molecular homogeneity of the pellets was studied by means of Scanning Electron Microscopy coupled to Energy Dispersive X-ray spectroscopy (SEM-EDS) and Raman spectroscopy. This check was always made before the LIBS analysis. Univariate and multivariate (Partial Least Squares (PLS) regression) calibration approaches on LIBS spectra were selected as initial calibration models. After a comparison between both approaches, the former was discarded due to the poor linearity of the calibration curves, and PLS regression was chosen to treat the LIBS spectra as the multivariate calibration approach (in the ultraviolet (UV) and infrared (IR) spectral ranges). Predictive capabilities of each calibration model were evaluated by calculating regression coefficient (r), number of PLS factors (rank), relative errors of cross validation (RMSECV), residual predictive deviation (RPD) and the Bias value. At the end, the simultaneous use of both ranges of wavelengths was demonstrated to be more fruitful rather than using the individual ones, probably due to the higher number of emission lines, number of spectral variables and the PLS latent variables for each element. Moreover, a Reference Material was used as external validation, obtaining satisfactory results in the determination of elements. The predictive ability of the PLS models was evaluated on samples of Darwin Glasses (Australia), Libyan Desert Glasses (Western Desert of Egypt) and black steel slag residues (steelworks of Basque Country). The obtained results were in concordance with the range of composition measured also by X-ray Fluorescence Spectrometer (ED-XRF). Our methodology is a good, rapid, simple and cost-effective alternative for in situ analysis of these terrestrial analogues over other techniques.

Exploration of attenuated total reflectance mid-infrared spectroscopy and multivariate calibration to measure immunoglobulin G in human sera

Immunoglobulin G (IgG) is crucial for the protection of the host from invasive pathogens. Due to its importance for human health, tools that enable the monitoring of IgG levels are highly desired. Consequently there is a need for methods to determine the IgG concentration that are simple, rapid, and inexpensive. This work explored the potential of attenuated total reflectance (ATR) infrared spectroscopy as a method to determine IgG concentrations in human serum samples. Venous blood samples were collected from adults and children, and from the umbilical cord of newborns. The serum was harvested and tested using ATR infrared spectroscopy. Partial least squares (PLS) regression provided the basis to develop the new analytical methods. Three PLS calibrations were determined: one for the combined set of the venous and umbilical cord serum samples, the second for only the umbilical cord samples, and the third for only the venous samples. The number of PLS factors was chosen by critical evaluation of Monte Carlo-based cross validation results. The predictive performance for each PLS calibration was evaluated using the Pearson correlation coefficient, scatter plot and Bland–Altman plot, and percent deviations for independent prediction sets. The repeatability was evaluated by standard deviation and relative standard deviation. The results showed that ATR infrared spectroscopy is potentially a simple, quick, and inexpensive method to measure IgG concentrations in human serum samples. The results also showed that it is possible to build a united calibration curve for the umbilical cord and the venous samples.

Application of FT-NIR Spectroscopy to Apple Wine for Rapid Simultaneous Determination of Soluble Solids Content, pH, Total Acidity, and Total Ester Content

The feasibility of rapid simultaneous analysis of soluble solid content (SSC), PH, total acidity, and total ester content in apple wine by Fourier transform near-infrared (FT-NIR) spectroscopy together with partial least squares (PLS) models was studied in this work. A set of 380 apple wine samples were scanned in a quartz cuvette with a 1-mm path length in the NIR region (4,000–12,000 cm−1) in transmission mode. Calibration models were established by PLS with full cross-validation. The NIR spectra were pretreated with different methods, and the effective wave number range together with the number of PLS factors used in the model calibration was optimized. The optimal models yielded the determination coefficients of 0.91, 0.93, 0.98, and 0.92 and the root mean standard error of prediction (RMSEP) of 0.601, 0.077, 0.021, and 0.096 for SSC, pH, total acidity, and total ester content, respectively. This work demonstrated that NIR spectroscopy was a rapid and convenient technique for analyzing and predicting SSC, pH, total acidity, and total ester content simultaneously during apple wine fermentation process.

Rapid Analysis of Zinc in Soil with Long-NIR Spectroscopy

Near-infrared (NIR) spectroscopy combined with the partial least squares (PLS) regression was successfully applied for the rapid quantitative analysis of Zn2+in soil. The models were established using an approach based on randomness and similarity to obtain objective and practical models. Sixty-three samples were randomly selected from a total of 148 samples as the validation set. The remaining 85 samples were used as the modeling set, and it was divided into similar calibration (50 samples) and prediction (35 samples) sets. The results show that the long-NIR region at 1100 nm to 2498 nm could be considered as the information waveband of Zn2+in soil. The optimal number of PLS factors was 10, and the validation root mean square error (V-SEP) and validation correlation coefficients of prediction (V-RP) were 21.817 mg kg-1and 0.849, respectively. The Zn2+prediction values of the validation samples are close to the measured values. The results provided valuable reference for designing the dedicated spectrometers.

On-line near infrared spectroscopy as a Process Analytical Technology (PAT) tool to control an industrial seeded API crystallization

The final step of an active pharmaceutical ingredient (API) manufacturing synthesis process consists of a crystallization during which the API and residual solvent contents have to be quantified precisely in order to reach a predefined seeding point. A feasibility study was conducted to demonstrate the suitability of on-line NIR spectroscopy to control this step in line with new version of the European Medicines Agency (EMA) guideline [1]. A quantitative method was developed at laboratory scale using statistical design of experiments (DOE) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. NIR models were built to quantify the API in the range of 9–12% (w/w) and to quantify the residual methanol in the range of 0–3% (w/w). To improve the predictive ability of the models, the development procedure encompassed: outliers elimination, optimum model rank definition, spectral range and spectral pre-treatment selection. Conventional criteria such as, number of PLS factors, R2, root mean square errors of calibration, cross-validation and prediction (RMSEC, RMSECV, RMSEP) enabled the selection of three model candidates. These models were tested in the industrial pilot plant during three technical campaigns. Results of the most suitable models were evaluated against to the chromatographic reference methods. Maximum relative bias of 2.88% was obtained about API target content. Absolute bias of 0.01 and 0.02% (w/w) respectively were achieved at methanol content levels of 0.10 and 0.13% (w/w). The repeatability was assessed as sufficient for the on-line monitoring of the 2 analytes. The present feasibility study confirmed the possibility to use on-line NIR spectroscopy as a PAT tool to monitor in real-time both the API and the residual methanol contents, in order to control the seeding of an API crystallization at industrial scale. Furthermore, the successful scale-up of the method proved its capability to be implemented in the manufacturing plant with the launch of the new API process.

FT‐NIR spectroscopy: a rapid method for estimation of moisture content in bael pulp

PurposeThe purpose of this paper is to develop FT‐NIR technique for determination of moisture content in bael pulp.Design/methodology/approachCalibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 70 to 95 per cent (wb). The prediction models based on partial least squares (PLS) regression, were developed in the near‐infrared region (4,000‐2,500cm‐1). Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre‐processing (vector normalization, minimum‐maximum normalization and multiplicative scatter correction) methods.FindingsThe best calibration model was developed with min‐max normalization (MMN) spectral pre‐processing (R2=99.3). The MMN pre‐processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.993 was obtained for the calibration model developed. The developed results indicated that FTNIR spectroscopy could be used for rapid detection of moisture content in bael pulp samples without any sample destruction.Originality/valueThe research in this paper is useful for the quick detection of moisture content of bael fruit pulp during processing.

Application of near infrared spectroscopy for rapid analysis of intermediates of Tanreqing injection

A method for rapid quantitative analysis of four kinds of Tanreqing injection intermediates was developed based on Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS) algorithm. The NIR spectra of 120 samples were collected in transflective mode. The concentrations of chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid (UDCA), and chenodeoxycholic acid (CDCA) were determined with the HPLC–DAD/ELSD as reference method. In the PLS calibration, the NIR spectra were pretreated with different methods and the number of PLS factors used in the model calibration was optimized by leave-one-out cross-validation. The performance of the final PLS models was evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and correlation coefficients ( R). The R values in the prediction sets were all higher than 0.93, and the SEPs for the 6 compounds are 1.18, 6.02, 2.71, 155, 126, 30.0 mg/l, respectively. The established models were used for the liquid preparation process analysis of Tanreqing injection in three batches, and a model updating method was proposed for the long-term usage of the established models. This work demonstrated that NIR spectroscopy is more rapid and convenient than the conventional methods to analyze the intermediates of Tanreqing injection, and the presented method is helpful to the implementation of process analytical technology (PAT) in pharmaceutical industry of Chinese Medicines Injections.

Rapid quantification of phenolic acids in Radix Salvia Miltrorrhiza extract solutions by FT-NIR spectroscopy in transflective mode

A rapid method for simultaneous determination of main phenolic acids in Radix Salvia Miltrorrhiza extract solutions was developed using Fourier transform near infrared spectroscopy in transflective mode and multivariate calibration and HPLC-UV as the reference method. Partial least squares (PLS) algorithm was conducted on the calibration of regression models. The multiplicative scatter correction, Norris derivative and second derivative were adopted for the spectral pre-processing, and the number of PLS factors were optimized by leave-one-out cross-validation. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient ( R). The R values achieved in the prediction set were above 0.93. The developed models were used for analysis of unknown samples and routine monitoring with satisfactory results. This work demonstrated that NIR spectroscopy combined with PLS algorithm could be used for the rapid determination of the main phenolic acids of Salvia Miltrorrhiza extract solutions.

Combining orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping spectra of three kinds of metal ions

A novel method named OSC-WPT-PLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. OSC is used to remove information in the response matrix D by subtracting the structured noise that is orthogonal to the concentration matrix C. Wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. PLS was applied for multivariate calibration and noise reduction by eliminating the less important latent variables. In this case, using trials, the kind of wavelet function, the decomposition level, the number of OSC components and the number of PLS factors for the OSC-WPT-PLS method were selected as Daubechies 4, 3, 2 and 3, respectively. A program (POSCWPTPLS) was designed to perform the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). The relative standard errors of prediction (RSEP) obtained for total elements using OSC-WPT-PLS, WPT-PLS and PLS were compared. Experimental results demonstrated that the OSC-WPT-PLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.

Quantitative solid-state analysis of three solid forms of ranitidine hydrochloride in ternary mixtures using Raman spectroscopy and X-ray powder diffraction

The aim of the study was to develop a reliable quantification procedure for mixtures of three solid forms of ranitidine hydrochloride using X-ray powder diffraction (XRPD) and Raman spectroscopy combined with multivariate analysis. The effect of mixing methods of the calibration samples on the calibration model quality was also investigated. Thirteen ternary samples of form 1, form 2 and the amorphous form of ranitidine hydrochloride were prepared in triplicate to build a calibration model. The ternary samples were prepared by three mixing methods (a) manual mixing (MM) and ball mill mixing (BM) using two (b) 5 mm (BM5) or (c) 12 mm (BM12) balls for 1 min. The samples were analyzed with XRPD and Raman spectroscopy. Principal component analysis (PCA) was used to study the effect of mixing method, while partial least squares (PLS) regression was used to build the quantification models. PCA score plots showed that, in general, BM12 resulted in the narrowest sample clustering indicating better sample homogeneity. In the quantification models, the number of PLS factors was determined using cross-validation and the models were validated using independent test samples with known concentrations. Multiplicative scattering correction (MSC) without scaling gave the best PLS regression model for XPRD, and standard normal variate (SNV) transformation with centering gave the best model for Raman spectroscopy. Using PLS regression, the root mean square error of prediction (RMSEP) values of the best models were 5.0–6.9% for XRPD and 2.5–4.5% for Raman spectroscopy. XRPD and Raman spectroscopy in combination with PLS regression can be used to quantify the amount of single components in ternary mixtures of ranitidine hydrochloride solid forms. Raman spectroscopy gave better PLS regression models than XRPD, allowing a more accurate quantification.

Simultaneous analysis of main catechins contents in green tea ( Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy

This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient ( R). The correlations coefficients ( R) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1100 to 2500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient ( R 2). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R 2 value of 0.996–0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.

Comparison of the HPLC Method and FT-NIR Analysis for Quantification of Glucose, Fructose, and Sucrose in Intact Apple Fruits

A rapid quantification method was developed and validated for simultaneous and nondestructive quantifying the constituent sugar concentrations of intact apples using Fourier transform near-infrared (FT-NIR) spectroscopy in diffuse reflectance mode. Multiplicative scatter correction (MSC), the second derivative of Savitsky-Golay, and mean centering were used as spectral preprocessing options. Calibration models were established by the partial least squares (PLS) regression analysis, and validation of the method was performed according to the high-performance liquid chromatography (HPLC) chromatographic method. Spectral range and the number of PLS factors were optimized for the lowest root-mean-square error of prediction (RMSEP) and correlation coefficient of determination (r). The best models showed satisfactory predictions as measured by the RMSEP and r values: glucose, 0.201 and 0.950; fructose, 0.298 and 0.968; sucrose, 0.335 and 0.969, respectively. FT-NIR analysis of constituent sugar concentrations in the intact apple form was found to be more flexible and much faster than performed with the HPLC method.

Moisture determination in hygroscopic drug substances by near infrared spectroscopy

The moisture level in a hygroscopic drug substance was successfully determined by near infrared spectroscopy using coulometric Karl Fischer titration as the reference method. The importance of sample handling and proper application of the reference technique are stressed for this difficult sample type. Samples were prepared with moisture levels from 0.5 to 11.4% (w/w) and reflectance spectra were collected over the spectral range 1100–2500 nm. Calibration models were built using partial least squares (PLS) regression analysis. Optimum models were found by choosing proper spectral ranges and number of PLS factors. The best calibration models were built using first derivative spectra, a spectral range of 1850–1936 nm and 5 PLS factors. The corresponding standard error of prediction was 0.11% (w/w) water.

Temperature-Insensitive Near-Infrared Spectroscopic Measurement of Glucose in Aqueous Solutions

A digital Fourier filter is combined with partial least-squares (PLS) regression to generate a calibration model for glucose that is insensitive to sample temperature. This model is initially created by using spectra collected over the 5000 to 4000 cm−1 spectral range with samples maintained at 37°C. The analytical utility of the model is evaluated by judging the ability to determine glucose concentrations from a set of prediction spectra. Absorption spectra in this prediction set are obtained by ratioing single-beam spectra collected from solutions at temperatures ranging from 32 to 41°C to reference spectra collected at 37°C. The temperature sensitivity of the underlying water absorption bands creates large baseline variations in prediction spectra that are effectively eliminated by the Fourier filtering step. The best model provides a mean standard error of prediction across temperatures of 0.14 mM (2.52 mg/dL). The benefits of the Fourier filtering step are established, and critical experimental parameters, such as number of PLS factors, mean and standard deviation for the Gaussian shaped Fourier filter, and spectral range, are considered.