Wavelength Selection Procedure Research Articles

Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.)

With the passage of the 2018 Farm Bill, industrial hemp (Cannabis sativa L.) has become a legal and economically promising crop commodity for U.S. farmers. There has been a surge of interest in growing industrial hemp for producing cannabinoids, such as cannabidiol (CBD), because of their medical potential. Quantitative determination of cannabinoids in harvested materials (primarily floral tissues) is critical for cannabinoid production and compliance testing. The concentrations of cannabinoids in hemp materials are conventionally determined using wet-chemistry chromatographic methods, which require destructive sampling, and are time-consuming, costly, and thus not suitable for on-site rapid testing. This study presents a novel effort to utilize hyperspectral imaging technology for non-destructive quantification of major cannabinoids, including CBD, THC (tetrahydrocannabinol), CBG (cannabigerol) and their acid forms in fresh floral and leaf materials of industrial hemp on a dry weight basis. Hyperspectral images in the wavelength range of 400–1000 nm were acquired from floral and leaf tissues immediately after harvest from a total of 100 industrial hemp plants of five cultivars at varied growth stages. Linear discriminant analysis showed hyperspectral imaging could identify CBD-rich/poor and THC-legal/illegal flower samples with accuracies of 99% and 97%, respectively. Quantitative models based on full-spectrum PLS (partial least squares) achieved prediction accuracies of RPD (ratio of prediction to deviation) = 2.5 (corresponding R2 = 0.84) for CBD and THC in floral tissues. Similar accuracies were obtained for their acid forms in flower samples. The predictions for CBG and its acid form in floral tissues and all six cannabinoids in leaf tissues were unsatisfactory with noticeably lower RPD values. Consistently improved accuracies were obtained by parsimonious PLS models based on a wavelength selection procedure for minimized variable collinearity. The best RPD values of approximately 2.6 (corresponding R2 = 0.85) were obtained for CBD and THC in floral materials. This study demonstrates the utility of hyperspectral imaging as a potential valuable tool for rapid quantification of cannabinoids in industrial hemp.

Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data.

The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in and , simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.

Full spectrum and selected spectrum based multivariate calibration methods for simultaneous determination of betamethasone dipropionate, clotrimazole and benzyl alcohol: Development, validation and application on commercial dosage form

Five different chemometric methods were developed for the simultaneous determination of betamethasone dipropionate (BMD), clotrimazole (CT) and benzyl alcohol (BA) in their combined dosage form (Lotriderm® cream). The applied methods included three full spectrum based chemometric techniques; namely principal component regression (PCR), Partial Least Squares (PLS) and Artificial Neural Networks (ANN), while the other two methods were PLS and ANN preceded by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. A multilevel multifactor experimental design was adopted for proper construction of the models. A validation set composed of 12 mixtures containing different ratios of the three analytes was used to evaluate the predictive power of the suggested models. All the proposed methods except ANN, were successfully applied for the analysis of their pharmaceutical formulation (Lotriderm® cream). Results demonstrated the efficiency of the four methods as quantitative tool for analysis of the three analytes without prior separation procedures and without any interference from the co-formulated excipient. Additionally, the work highlighted the effect of GA on increasing the predictive power of PLS and ANN models.

Resolution of overlapped quaternary spectral bands by net analyte signal based methods; an application to different combinations in tablets and capsules

Net analyte signal (NAS) based methods (NBMs); namely NAS preprocessing (NAP) and hybrid linear analysis (HLA) were applied as multivariate calibration methods for resolving quaternary spectrally overlapped bands of Chlorzoxazone (CXZ), Paracetamol (PAR), Ibuprofen (IBU) and Ketoprofen (KTO) which are commonly co-formulated. The calibration set was composed of 17 mixtures within the concentrations ranges of 8.80–13.20, 10.56–15.84, 7.04–10.56 and 1.76–2.64 μg/mL for CXZ, PAR, IBU and KTO, respectively. A wavelength selection procedure was presented for the introduced NBMs. Figures of merit as limit of detection, selectivity and analytical sensitivity were also calculated based on the NAS concept. The developed NBMs models used smaller number of wavelengths as well as smaller number of latent variables which make NAP and HLA models more simple and interpretable than the conventional partial least squares (PLS). The models were successfully applied to tablets and capsules, and ANOVA was applied in comparison with an HPLC method.

Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry

A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800–1500 cm − 1 spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample preparation step.

Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy

An improved genetic algorithms (GAs) is used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression. The methods also allow the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. Studies are performed to characterize the signal and noise characteristics of the spectral data, and optimal configurations for the GAs are found for each data set through experimental design techniques. The experiments tested in this method were sugar content (SC), titratable acidity (TA) and valid acidity (pH). Despite the complexity of the spectral data, the GAs procedure were found to perform well (RMSEP = 0.395, 0.0195, 0.0087 for SC, TA and pH respectively), leading to calibration models that significantly outperform those based on full-spectrum analyses (RMSEP = 0.512, 0.0198, 0.0111 for SC, TA and pH respectively). In addition, a significant reduction in the number of spectral points required to build the models is realized and all of the numbers of wavelengths for building the calibration models can reduce by 84.4%. This work proved that the GA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization.

Selection of Wavelength Range and Number of Factors to be Used in the PLS Treatment of Spectrophotometric Data

A procedure for selection of wavelength range and number of factors to be used in partial least square calibration that involves the calculation of prediction residual sum of squares (PRESS) in different conditions is proposed. The best model takes into account the minimum PRESS value that does not show significant differences with respect to the corresponding model with fewer factors. The ability of the proposed method to minimize errors in partial least squares (PLS) prediction is demonstrated by applying it to the resolution of phenytoine (DPH) and phenobarbital (PB) binary mixtures with errors less than 2.8%; the results are compared with those obtained using another wavelength selection procedure. The ensuing method, which was validated by high performance liquid chromatography (HPLC), also gives good results with real samples (pharmaceutical preparations).

Fermentation process tracking through enhanced spectral calibration modeling

The FDA process analytical technology (PAT) initiative will materialize in a significant increase in the number of installations of spectroscopic instrumentation. However, to attain the greatest benefit from the data generated, there is a need for calibration procedures that extract the maximum information content. For example, in fermentation processes, the interpretation of the resulting spectra is challenging as a consequence of the large number of wavelengths recorded, the underlying correlation structure that is evident between the wavelengths and the impact of the measurement environment. Approaches to the development of calibration models have been based on the application of partial least squares (PLS) either to the full spectral signature or to a subset of wavelengths. This paper presents a new approach to calibration modeling that combines a wavelength selection procedure, spectral window selection (SWS), where windows of wavelengths are automatically selected which are subsequently used as the basis of the calibration model. However, due to the non-uniqueness of the windows selected when the algorithm is executed repeatedly, multiple models are constructed and these are then combined using stacking thereby increasing the robustness of the final calibration model. The methodology is applied to data generated during the monitoring of broth concentrations in an industrial fermentation process from on-line near-infrared (NIR) and mid-infrared (MIR) spectrometers. It is shown that the proposed calibration modeling procedure outperforms traditional calibration procedures, as well as enabling the identification of the critical regions of the spectra with regard to the fermentation process.

Spectrophotometric resolution of ternary mixtures of pseudoephedrine hydrochloride, dextromethorphan hydrobromide, and sodium benzoate in syrups using wavelength selection by net analyte signals calculated with hybrid linear analysis

Hybrid linear analysis (HLA), as a recent factor-based multivariate calibration technique, was applied for the spectrophotometric determination of ternary mixtures of pseudoephedrine hydrochloride (PSU), dextromethorphan hydrobromide (DXT), and sodium benzoate (BNZ). The utilized HLA was assisted by a wavelength selection procedure which was based on the calculation of the net analyte signal (NAS) regression plot in any considered wavelengths window for each test sample, in addition to a moving window strategy for searching the region with maximum linearity of NAS regression plot (minimum error indicator (EI)). HLA was applied because it was simpler to adapt to the NAS regression plot methodology, and also used less factors than partial least squares (PLS). An orthogonal array design was applied for formation of calibration and prediction sets in the concentration ranges 0–7500 μmol L −1 for PSU, 0–300 μmol L −1 for DXT, and 0–1400 μmol L −1 for BNZ. The method had the ability to select wavelength regions that minimize the effect of non-linearity of the spectral data, in addition to that of non-modeled interferences. The application of the selected wavelength regions improved the obtained relative standard error of predictions for PSU, DXT, and BNZ, respectively, from 5.24, 8.67, and 5.48% to 2.19, 5.21, and 3.62% (using lower number of factors). To check the ability of the proposed method in selection of linear regions of spectra, a test for detecting non-linear regions of spectral data in multivariate spectroscopic assays was also described. Additives in the commercial syrup samples did not interfere with their determinations. The method was successfully applied for the determination of pseudoephedrine HCl, dextromethorphan HBr, and sodium benzoate in cough suppressant syrup samples.

Wavelength selection by net analyte signals calculated with multivariate factor-based hybrid linear analysis (HLA). A theoretical and experimental comparison with partial least-squares (PLS)

A wavelength selection procedure is presented for the recently introduced factor-based multivariate calibration technique of hybrid linear analysis (HLA). It involves the calculation of the net analyte signal regression plot (NASRP) for each test sample, combined with a moving window strategy. A search for the minimum error indicator (EI) is performed using the first significant factors of a data matrix from which the contribution of the analyte of interest has been removed. HLA uses less factors than both principal component regression (PCR) and partial least-squares (PLS), and is simpler to adapt to the NASRP methodology. The ability of the method to select wavelength regions that minimize the effect of non-modeled interferences is illustrated both with simulated examples and with experimental bromhexine-containing cough suppressant syrup samples.

Genetic algorithm-based wavelength selection for the near-infrared determination of glucose in biological matrixes: initialization strategies and effects of spectral resolution.

An improved genetic algorithm (GA)-based wavelength selection procedure is developed to optimize both the near-infrared wavelengths used and the number of latent variables employed in building partial least-squares (PLS) calibration models. This GA-based wavelength selection algorithm is applied to the determination of glucose in two different biological matrixes. With random selection of a small number of initial wavelengths, a dramatic reduction in the number of wavelengths required for building the PLS calibration models is observed. The fitness function used to guide the GA, the method of recombination used, and the effect of spectral resolution on the wavelength selection are also studied. In the resolution study, the original data with a point spacing of 2 cm-1 are deresolved to 4-, 8-, and 16-cm-1 point spacings by truncating the collected interferograms before applying the Fourier processing step. The use of lower resolution spectra is found to reduce further the number of final wavelengths selected by the GA, and the performance of the optimal calibration models obtained with the original spectra is maintained with the lower resolution spectra of both 4- and 8-cm-1 point spacing. Degradation in performance is observed with the spectra computed with a point spacing of 16 cm-1, however.

Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy.

Genetic algorithms (GAs) are used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least-squares regression. The method also allows the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. The data used to test this methodology are derived from the determination of aqueous organic species by near-infrared spectroscopy. The three data sets employed focus on the determination of (1) methyl isobutyl ketone in water over the range of 1-160 ppm, (2) physiological levels of glucose in a phosphate buffer matrix containing bovine serum albumin and triacetin, and (3) glucose in a human serum matrix. These data sets feature analyte signals near the limit of detection and the presence of significant spectral interferences. Studies are performed to characterize the signal and noise characteristics of the spectral data, and optimal configurations for the GA are found for each data set through experimental design techniques. Despite the complexity of the spectral data, the GA procedure is found to perform well, leading to calibration models that significantly outperform those based on full spectrum analyses. In addition, a significant reduction in the number of spectral points required to build the models is realized.