Calibration Of Near-infrared Spectra Research Articles

Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets

Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as “meets standard,” “needs HPLC assay,” or “fails standard.” Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.

A new ensemble modeling method for multivariate calibration of near infrared spectra.

Ensemble modeling has gained increasing attention for improving the performance of quantitative models in near infrared (NIR) spectral analysis. Based on Monte Carlo (MC) resampling, least absolute shrinkage and selection operator (LASSO) and partial least squares (PLS), a new ensemble strategy named MC-LASSO-PLS is proposed for NIR spectral multivariate calibration. In this method, the training subsets for building the sub-models are generated by sampling from both samples and variables to ensure the diversity of the models. In detail, a certain number of samples as sample subsets are randomly selected from training set. Then, LASSO is used to shrink the variables of the sample subset to form the training subset, which is used to build the PLS sub-model. This process is repeated N times and N sub-models are obtained. Finally, the predictions of these sub-models are used to produce the final prediction by simple average. The prediction ability of the proposed method was compared with those of LASSO-PLS, MC-PLS and PLS models on the NIR spectra of corn, blend oil and orange juice samples. The superiority of MC-LASSO-PLS in prediction ability is demonstrated.

The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals.

Basing on the wavelengths with consistent and stable spectral signals between spectrometers, wavelength combinations were screened by different methods to obtain robust and simple near infrared spectra (NIR) calibration models that can be shared by slave spectrometers directly. Firstly, the wavelength set of Usc, at which the spectral signals between spectrometers are consistent and stable, was obtained by the method of screening the wavelengths with consistent and stable signals between spectrometers (SWCSS for short). Then, the wavelength set of Uscr whose spectral responses are correlated with dependent variables strongly was selected from Usc. Basing on Uscr, the methods of uninformative variable elimination (UVE), variable importance in projection (VIP) and selectivity ratio (SR) were applied to further screen optimal wavelength sets to obtain better NIR calibration models. These sets were recorded as UscrUVE, UscrVIP and UscrSR, respectively. The NIR partial least squares (PLS) models for predicting total alkaloids content of tobacco leaves were built on the three optimal wavelength sets, and named as UscrUVE-PLS, UscrVIP-PLS, UscrSR-PLS, respectively. Both UscrUVE-PLS and UscrVIP-PLS give satisfactory prediction errors for master and slave samples, and work better than the PLS model built on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelength combinations based on consistent and stable spectral information cannot only simplify PLS models and improve the models' efficiency, but also ensure the models' accuracy when they are transferred to slave spectrometers. Wavelength selection based on the whole wavelengths without considering spectra consistency between spectrometers can improve the performance of the calibration models on the master spectrometer but cannot ensure the prediction accuracy of the slave samples.

Three-step hybrid strategy towards efficiently selecting variables in multivariate calibration of near-infrared spectra.

Variable (feature or wavelength) selection is a critical step in multivariate calibration of near-infrared (NIR) spectra. The high-resolution NIR or its imaging instruments usually generate hundreds or thousands of wavelengths, which make the variable selection methods tend to appear a high risk of overfitting, low efficiency, or requiring large computational abilities. Thus, it is a great challenge to efficiently select informative variables and obtain an optimal variable combination in a huge variable space. We propose a hybrid strategy for efficiently selecting variables based on three steps including rough selection, fine selection and optimal selection. The strong interpretability method like wavelength interval selection method (interval partial least squares, iPLS) was first used to roughly select informative intervals and shrink the variable space. Wavelength point selection methods such as variable importance in projection (VIP) and modified variable combination population analysis (mVCPA) were used to continuingly shrink the variable space from large to small in order to remain the very important variables. In the third step, applying some optimization methods such as iteratively retaining informative variables (IRIV) and genetic algorithm (GA) is to find an optimal variable combination from the remaining variables. It makes full use of the advantages of various involved methods and makes up for their disadvantages when facing high dimensional data. Two NIR datasets were employed to investigate the performance of the three-step hybrid strategy. It can significantly improve the prediction performance of the models built when compared with other single or hybrid methods (iPLS, VIP, iPLS-VIP, iPLS-VCPA, iPLS-mVCPA, VIP-GA, VIP-IRIV, mVCPA-GA, mVCPA-IRIV), indicating that the three-step hybrid strategy, including iPLS-VIP-IRIV, iPLS-VIP-GA, iPLS-mVCPA-GA and iPLS-mVCPA-IRIV, could efficiently select informative variables. Therefore, the three-step hybrid strategy is a good alternative for variable selection methods in the face of high dimensional NIR spectral data.

Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Study of an adaptable calibration model of near-infrared spectra based on KF-PLS

The key to the quantitative analysis of near-infrared spectra is the establishment of an accurate and adaptable model. In this paper, a calibration method of a dynamic evolutionary model combined with Kalman filter (KF) and partial least squares (PLS) algorithms is proposed. First, an initial quantitative calibration model is built by applying the method of PLS regression to calculate the spectral data samples and their chemical constituent concentrations. Second, inspired by the dynamic evolution characteristics of the KF, the main PLS factor coefficient iterative method is set up based on the KF, by which the calibration model is dynamically corrected. Finally, to prove the validity of the proposed method, a near-infrared spectral dataset from a real corn sample is tested, and the standard PLS method and KF-PLS method are compared. For the 40 external samples (2015), the root mean square error of prediction (RMSEP) of the PLS model was 0.7046% (moisture) and 0.9352% (protein), which is obviously worse than the effects of the KF-PLS model (moisture, 0.3726%, and protein, 0.5451%). Moreover, the R2val (validation decision coefficient) of the KF-PLS model was 0.9035 (moisture) and 0.8565 (protein), which is better than the result of the PLS model (moisture, 0.5659, and protein, 0.6654). Regarding the ability to adapt to equipment (disturbance factor equal to 15%), KF–PLS (RMSEP: moisture, 0.1942%, and protein, 0.3299%; R2val: moisture, 0.9221, and protein, 0.8936) had less prediction error than PLS (RMSEP: moisture, 0.8546%, and protein, 1.0881%; R2val: moisture, 0.5077, and protein, 0.4946). The proposed method has satisfactory accuracy in tracking the evolution of the near-infrared spectrometry measurement state. The novel algorithm can effectively detect the moisture and protein concentration in corn and is also more compatible with outdated equipment, environmental changes and external samples than the PLS algorithm. Thus, this study explores a new approach towards a highly adaptive near-infrared spectrum calibration model.

Multivariate calibration of near-infrared spectra by using influential variables

Near-infrared (NIR) spectral analysis usually needs to take advantage of multivariate calibration. However, not all the variables in the spectra have equal contributions to a calibration model. Identification of informative variables is a key step to build a high performance model. According to the influence of a variable on the calibration model, influential variable (IV) is defined and a method for identification of IVs is proposed in this work. In the method, a set of partial least squares (PLS) models are built using a subset of variables selected randomly by Monte Carlo re-sampling, and then the clustering of these models are investigated by means of principal component analysis. The variables that make the models grouping can be identified as the IVs. Finally, the PLS model built with the selected IVs is adopted as the calibration model. Five NIR spectral datasets are used to test the performance and applicability of the method. The results show that the identified IVs are reasonable and the calibration model is efficient enough to produce accurate and reliable predictions.

Weighted partial least squares regression by variable grouping strategy for multivariate calibration of near infrared spectra

A weighted partial least squares (PLS) regression method for multivariate calibration of near infrared (NIR) spectra is proposed. In the method, the spectra are split into groups of variables according to the statistic values of variables, i.e., the stability, which has been used to evaluate the importance of variables in a calibration model. Because the stability reflects the relative importance of the variables for modeling, these groups present different spectral information for construction of PLS models. Therefore, if a weight which is proportional to the stability is assigned to each sub-model built with different group variables, a combined model can be built by a weighted combination of the sub-models. This method is different from the commonly used variable selection strategies, making full use of the variables according to their importance, instead of only the important ones. To validate the performance of the proposed method, it was applied to two different NIR spectral data sets. Results show that the proposed method can effectively utilize all variables in the spectra and enhance the prediction ability of the PLS model.

The natural abundance of 13C with different agricultural management by NIRS with fibre optic probe technology

In the present study the natural abundance of 13C is quantified in agricultural soils in Mexico which have been submitted to different agronomic practices, zero and conventional tillage, retention of crop residues (with and without) and rotation of crops (wheat and maize) for 17 years, which have influenced the physical, chemical and biological characteristics of the soil. The natural abundance of C13 is quantified by near infrared spectra (NIRS) with a remote reflectance fibre optic probe, applying the probe directly to the soil samples. Discriminate partial least squares analysis of the near infrared spectra allowed to classify soils with and without residues, regardless of the type of tillage or rotation systems used with a prediction rate of 90% in the internal validation and 94% in the external validation. The NIRS calibration model using a modified partial least squares regression allowed to determine the δ 13C in soils with or without residues, with multiple correlation coefficients 0.81 and standard error prediction 0.5‰ in soils with residues and 0.92 and 0.2‰ in soils without residues. The ratio performance deviation for the quantification of δ 13C in soil was 2.5 in soil with residues and 3.8 without residues. This indicated that the model was adequate to determine the δ 13C of unknown soils in the −16.2‰ to −20.4‰ range. The development of the NIR calibration permits analytic determinations of the values of δ 13C in unknown agricultural soils in less time, employing a non-destructive method, by the application of the fibre optic probe of remote reflectance to the soil sample.

A weighted multiscale regression for multivariate calibration of near infrared spectra

A weighted multiscale regression for building a combined model in multivariate calibration of near infrared spectra is proposed. In the approach, the spectra are decomposed into different scale blocks (or frequency components) by wavelet transform (WT) at first, then partial least squares (PLS) models are built with the decomposed components, and at last a combined model is built by a weighted averaging. The weight of each model is determined by the prediction residual error sum of squares (PRESS) value obtained with Monte Carlo cross validation (MCCV). The underlying philosophy of the strategy is that useful information may be embedded in all the components obtained by WT, although the higher and lower frequency components mainly represent noise and background, respectively. To validate the effectiveness and universality of the proposed method, it was applied to two different sets of near-infrared (NIR) spectra of tobacco lamina. Compared with the results obtained with commonly used PLS methods, the proposed method is proved to be a high-performance tool for multivariate calibration of complex NIR spectra.

New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.

A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A modified method of uninformative variable elimination (UVE) was proposed for variable selection in NIR spectral modeling based on the principle of Monte Carlo (MC) and UVE. The method builds a large number of models with randomly selected calibration samples at first, and then each variable is evaluated with a stability of the corresponding coefficients in these models. Variables with poor stability are known as uninformative variable and eliminated. The performance of the proposed method is compared with UVE-PLS and conventional PLS for modeling the NIR data sets of tobacco samples. Results show that the proposed method is able to select important wavelengths from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis. Furthermore, if wavelet compression is combined with the method, more parsimonious and efficient model can be obtained.

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.

Calibration of near infrared spectra for measuring decomposing cellulose and green manure in soils

Plant residues are the major organic input to soils. Therefore, a method for monitoring the degradation of cellulose as a major component of plant residues is expected to be a useful aid in studying their turnover in soil. In order to examine whether the decay of cellulose in soil can be monitored by near infrared spectroscopy (NIRS), we analysed soil samples from an incubation experiment using this rapid and inexpensive method. A soil amended with and without cellulose (2 g cellulose kg −1 soil) was incubated under aerobic conditions in the dark at 15 °C for 70 days. The soil samples, which were taken at the start and twelve times within the incubation period were spectrally analysed with a NIR-spectrometer. The decay of cellulose was simulated using negative exponential functions. These simulated cellulose concentration in the soil was used for the calibration of NIRS-equations. Although the cellulose comprises only a very small part of the total organic carbon in the soil, the decay of cellulose could be clearly monitored by NIRS. Ninety-five percent of the variation in the soil cellulose concentration as simulated by the negative exponential function could be explained by the NIRS-equation ( r 2=0.95), when using appropriate rate constants in the exponential decay. The spectral signature, represented by this NIRS-equation could be proved to represent cellulose by comparing it with the spectrum of pure powdered cellulose not mixed with soil. We applied the NIRS-equation from the cellulose treatment to soil samples of a green manure treatment. The coefficient of determination for residual green manure in soil, predicted by this NIRS-equation versus residual green manure contents as simulated by a negative exponential function, was r 2=0.84 and 0.94 for a sandy and a clay soil, respectively. Our results confirm that NIRS provides a useful tool for keeping track of a specific and relatively small organic fraction among the background of a large amount of total soil organic matter.

A Background and noise elimination method for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of near infrared (NIR) spectral signals. The method is based on the use of multi-resolution, which is one of the main advantages provided by wavelet transform. The signals are firstly split into different frequency components, which keep the same data points of the original signals. In conjunction with a modified uninformative variable elimination (mUVE) criterion, the new method can be used to remove the low-frequency varying background and the high-frequency noise simultaneously. The method is successfully applied to simulated spectral data set and experimental NIR spectral data, resulting in more parsimonious multivariate models with higher precision. In addition, the proposed strategy can be applied to other spectral signals as well.

Factor selection strategies for orthogonal signal correction applied to calibration of near-infrared spectra

Orthogonal signal correction (OSC) is a preprocessing technique used for correction of instrumental drift, bias and scatter in near-infrared spectra. OSC separates the variation into orthogonal factors, where the factors contain the variation within the X matrix data that is not correlated with the y matrix vector data. The aim of this study is to investigate different orthogonal factor selection methods, which will enhance the performance of the OSC routine for quantitative analysis of near-infrared spectra. In order for factor selection methods to be applied to OSC, an implementation of an existing OSC algorithm is used; this method computes OSC factors in a principal component manner. A binarized weighting matrix is then applied to the OSC factors for the purpose of OSC factor subset selection/nonselection. The optimization strategies used for subset selection of OSC factors were a genetic algorithm and stepwise selection. Three data sets were formed: (1) no preprocessing, (2) preprocessing by removal of sequential OSC factors and (3) preprocessing by removal of an OSC factor subset. Combinations of spectral predictors were selected from these data sets by hill climbing, feature selection, genetic algorithm and full-spectrum modeling. Partial least squares regression was undertaken to form calibration models. It was found that selection of OSC factor subsets produced better standard errors of prediction relative to data preprocessed by sequential selection of OSC factors.

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.

Analysis of film coating thickness and surface area of pharmaceutical pellets using fluorescence microscopy and image analysis

A method is presented which enables geometrical characterisation of pharmaceutical pellets and their film coating. It provides a high level of details on the single pellet level. Image analysis was used to determine the coating thickness ( h) applied on the pellets and the surface area ( A) of the pellet cores. Different definitions of A and h are evaluated. Hierarchical analysis of variance was used to resolve different sources contributing to the total variance. The variance within pellets and the variance between pellets were found as significant sources of variation. Special emphasis was put on evaluation of A/ h due to its influence on the release rate of an active drug substance from the pellet core. The pellet images were thus used to predict variations in the release rate using a mathematical model as a link between the image data and the release rate. General aspects of image analysis are discussed. The method would be useful in calibration of near infrared spectra to h in process analytical chemistry.