Genetic Algorithm-partial Least Squares Research Articles

Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210–400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red–Green–Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

Spider chart, greenness and whiteness assessment of experimentally designed multivariate models for simultaneous determination of three drugs used as a combinatory antibiotic regimen in critical care units: Comparative study

In this study, five earth-friendly spectrophotometric methods using multivariate techniques were developed to analyze levofloxacin, linezolid, and meropenem, which are utilized in critical care units as combination therapies. These techniques were used to determine the mentioned medications in laboratory-prepared mixtures, pharmaceutical products and spiked human plasma that had not been separated before handling. These methods were named classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm partial least squares (GA-PLS), and artificial neural network (ANN). The methods used a five-level, three-factor experimental design to make different concentrations of the antibiotics mentioned (based on how much of them are found in the plasma of critical care patients and their linearity ranges). The approaches used for levofloxacin, linezolid, and meropenem were in the ranges of 3–15, 8–20, and 5–25 µg/mL, respectively. Several analytical tools were used to test the proposed methods' performance. These included the root mean square error of prediction, the root mean square error of cross-validation, percentage recoveries, standard deviations, and correlation coefficients. The outcome was highly satisfactory. The study found that the root mean square errors of prediction for levofloxacin were 0.090, 0.079, 0.065, 0.027, and 0.001 for the CLS, PCR, PLS, GA-PLS, and ANN models, respectively. The corresponding values for linezolid were 0.127, 0.122, 0.108, 0.05, and 0.114, respectively. For meropenem, the values were 0.230, 0.222, 0.179, 0.097, and 0.099 for the same models, respectively. These results indicate that the developed models were highly accurate and precise. This study compared the efficiency of artificial neural networks and classical chemometric models in enhancing spectral data selectivity for quickly identifying three antimicrobials. The results from these five models were subjected to statistical analysis and compared with each other and with the previously published ones. Finally, the whiteness of the methods was assessed by the recently published white analytical chemistry (WAC) RGB 12, and the greenness of the proposed methods was assessed using AGREE, GAPI, NEMI, Raynie and Driver, and eco-scale, which showed that the suggested approaches had the least negative environmental impact. Furthermore, to demonstrate solvent sustainability, a greenness index using a spider chart methodology was employed.

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

Advanced chemometric methods for simultaneous quantitation of caffeine, codeine, paracetamol, and p-aminophenol in their quaternary mixture

Two different multivariate techniques have been applied for the quantitative analysis of caffeine, codeine, paracetamol and p-aminophenol (PAP) in quaternary mixture, namely, Partial Least Squares (PLS-1) and Artificial Neural Networks (ANN). For suitable analysis, a calibration set of 25 mixtures with various ratios of the drugs and PAP impurity were established using a 4-factor 5-level experimental design. The most meaningful wavelengths for the chemometric models were chosen using Genetic Algorithm (GA) as a variable selection technique. By using an independent validation set, the validity of the proposed methods was evaluated. A comparative study was established between the three multivariate models (PLS-1, GA–PLS and GA–ANN). The comparison between the various models revealed that the GA–ANN model was superior at resolving the highly overlapped spectra of this quaternary combination. The drugs were successfully quantified in their pharmaceutical dosage form utilizing the GA–ANN models.

Estimating low concentration heavy metals in water through hyperspectral analysis and genetic algorithm-partial least squares regression

Hyperspectral spectrum enables assessment of heavy metal content, but research on low concentration in water is limited. This study employed in situ hyperspectral data from Dalian Lake, Shanghai to develop a machine learning model for accurately determining heavy metal concentrations. Initially, we employed a combination of empirical analysis and algorithm-based analysis to identify the optimal features for retrieving Cu and Fe ions. Based on the correlation coefficients between heavy metals and water quality, the feature bands for TOC, Chl-a and TP were selected as empirical features. Algorithm-based feature selection was conducted by employing the random forest (RF) approach with the original spectrum (OR), first-order derivative reflectance (FDR), and second-order derivative reflectance (SDR). For the development of a prediction model, we utilized the Genetic Algorithm-Partial Least Squares Regression (GA-PLSR) approach for Cu and Fe ions inversion. Our findings demonstrated that the integration of both empirical features and algorithm-selected features resulted in superior performance compared to using algorithm-selected features alone. Importantly, the crucial wavelength data primarily located at 497, 665, 686, 831 and 935 nm showed superior results for Cu retrieval, while wavelengths of 700, 746, 801, 948, and 993 nm demonstrated better results for Fe retrieval. These results also displayed that the GA-PLSR model outperformed both the PLSR and RF models, exhibiting an R2 of 0.75, RMSE of 0.004, and MRE of 0.382 for Cu inversion. For Fe inversion, the GA-PLSR model outperformed other models with an R2 of 0.73, RMSE of 0.036, and MRE of 0.464. This research provides a scientific basis and data support for monitoring low concentrations of heavy metals in water bodies using hyperspectral remote sensing techniques.

Effect of Combined Non-Wood and Wood Spectra of Biomass Chips on Rapid Prediction of Ultimate Analysis Parameters Using near Infrared Spectroscopy

The ultimate analysis parameters, including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass, were rarely found to be predicted by non-destructive tests to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near-infrared (NIR) raw spectra of non-wood and wood samples from fast-growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5-range, and MP 3-range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples were non-wood samples of 65–67% and wood samples of 33–35%, and the model performance was evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA–PLSR, yielding R2P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR–5 range method with R2P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR-3 range method, resulting in R2P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of the H model provided R2P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in the calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for a robust model, but also must provide their trend line characteristics in the scatter plot, i.e., correlation coefficient (R), slope, and intercept (same slope and slope approached to 1, and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope, and intercept to obtain the better-optimized model was studied. The results show that the different species affected the model performance of each parameter prediction in a different manner, and by scatter plot analysis, which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time the effect has been studied by the principle of a scatter plot.

Multicomponent prediction of Sudan dye adulteration in crude palm oil using SERS – Based bimetallic nanoflower combined with genetic algorithm

Crude palm oil (CPO) is an important edible vegetable oil used globally, recently subjected to Sudan dye adulteration. This study explored the feasibility of a novel SERS-based Au@Ag substrate to detect four Sudan dyes (I - IV) in CPO. When mixed with spiked CPO, the SERS substrate produced strong signals that increased with increasing concentrations from 0.001 to 4.0 ppm. The genetic algorithm partial least square (GA-PLS) model outperformed the partial least square (PLS) and the ant colony optimization – PLS (ACO-PLS) models with Rc values of 0.9844, 0.9865, 0.9884, and 0.9888 0.9846. The calculated LOD were 0.00088 ppm, 0.00092 ppm, 0.00095 ppm, and 0.00097 and real sample recovery rates of 88.0–113.0%, 92.0–103.0%, 91.2–98.5% and 97.0–109.5% for Sudan I, II, III, and IV, respectively. The findings affirmed the SERS sensor’s considerable potential for rapid and selective detection of Sudan dyes in CPO when combined with chemometrics.

Principal component analysis–multivariate adaptive regression splines (PCA-MARS) and back propagation-artificial neural network (BP-ANN) methods for predicting the efficiency of oxidative desulfurization systems using ATR-FTIR spectroscopy

Oxidative desulfurization (ODS) of diesel fuels has received attention in recent years due to mild working conditions and effective removal of the aromatic sulfur compounds. There is a need for rapid, accurate, and reproducible analytical tools to monitor the performance of ODS systems. During the ODS process, sulfur compounds are oxidized to their corresponding sulfones which are easily removed by extraction in polar solvents. The amount of extracted sulfones is a reliable indicator of ODS performance, showing both oxidation and extraction efficiency. This article studies the ability of a non-parametric regression algorithm, principal component analysis-multivariate adaptive regression splines (PCA-MARS) as an alternative to back propagation artificial neural network (BP-ANN) to predict the concentration of sulfone removed during the ODS process. Using PCA, variables were compressed to identify principal components (PCs) that best described the data matrix, and the scores of such PCs were used as input variables for the MARS and ANN algorithms. Thecoefficientofdeterminationincalibration (R2c), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were calculated for PCA-BP-ANN (R2c = 0.9913, RMSEC = 2.4206 and RMSEP = 5.7124) and PCA-MARS (R2c = 0.9841, RMSEC = 2.7934 and RMSEP = 5.8476) models and were compared with the genetic algorithm partial least squares (GA-PLS) (R2c = 0.9472, RMSEC = 5.5226 and RMSEP = 9.6417) and as the results reveal, both methods are better than GA-PLS in terms of prediction accuracy. The proposed PCA-MARS and PCA-BP-ANN models are robust models that provide similar predictions and can be effectively used to predict sulfone containing samples. The MARS algorithm builds a flexible model using simpler linear regression and is computationally more efficient than BPNN due to data-driven stepwise search, addition, and pruning.

Multi-spectroscopic assay methods for concurrent determination of recent anti-gout combination, a comparative study

Recently, Chemometric calibration methods in spectrophotometric analysis are achieving significant attention in the quality control of resolving drug mixtures and pharmaceutical formulations containing two or more drugs with overlapping spectra. The simple univariate methods have been used over the last few decades and has proven to be highly efficient and easy to apply. In this study, a comparative study was performed between some univariate and multivariate methods to determine if chemometric methods can substitute univariate methods in pharmaceutical analysis. In this study, three chemometric techniques were compared to seven univariate techniques to resolve a mixture of mefenamic acid and febuxostat in their raw materials, dosage forms and spiked human plasma. Mefenamic acid and febuxostat were used together for treatment of gout. The applied chemometric methods are partial least squares (PLS), artificial neural network (ANN) and genetic algorithm partial least squares (GA-PLS), while the used univariate methods include first derivative, second derivative, ratio spectra, derivative ratio spectra, ratio subtraction, Q-Absorbance ratio and mean centering spectrophotometric methods. The ten proposed methods were found to be green, sensitive, and rapid. They are simple and did not require any pre-separation steps. The results of both univariate and multivariate approaches were statistically compared with the reported spectrophotometric methods using student’s t test and ratio variance F-test. They were also compared with each other, using one-way analysis of variance (ANOVA). These methods were assessed and validated according to ICH guidelines. The studied drugs were analyzed in their pharmaceutical dosage forms and spiked human plasma with good recoveries using the developed methods, which qualify them for routine quality control of the studied drugs.

Green Chemometric Determination of Cefotaxime Sodium in the Presence of Its Degradation Impurities Using Different Multivariate Data Processing Tools; GAPI and AGREE Greenness Evaluation

Four eco-friendly, cost-effective, and fast stability-indicating UV-VIS spectrophotometric methods were validated for cefotaxime sodium (CFX) determination either in the presence of its acidic or alkaline degradation products. The applied methods used multivariate chemometry, namely, classical least square (CLS), principal component regression (PCR), partial least square (PLS), and genetic algorithm-partial least square (GA-PLS), to resolve the analytes' spectral overlap. The spectral zone for the studied mixtures was within the range from 220 to 320 nm at a 1 nm interval. The selected region showed severe overlap in the UV spectra of cefotaxime sodium and its acidic or alkaline degradation products. Seventeen mixtures were used for the models' construction, and eight were used as an external validation set. For the PLS and GA-PLS models, a number of latent factors were determined as a pre-step before the models' construction and found to be three for the (CFX/acidic degradants) mixture and two for the (CFX/alkaline degradants) mixture. For GA-PLS, spectral points were minimized to around 45% of the PLS models. The root mean square errors of prediction were found to be (0.19, 0.29, 0.47, and 0.20) for the (CFX/acidic degradants) mixture and (0.21, 0.21, 0.21, and 0.22) for the (CFX/alkaline degradants) mixture for CLS, PCR, PLS, and GA-PLS, respectively, indicating the excellent accuracy and precision of the developed models. The linear concentration range was studied within 12-20 μg mL-1 for CFX in both mixtures. The validity of the developed models was also judged using other different calculated tools such as root mean square error of cross validation, percentage recoveries, standard deviations, and correlation coefficients, which indicated excellent results. The developed methods were also applied to the determination of cefotaxime sodium in marketed vials, with satisfactory results. The results were statistically compared to the reported method, revealing no significant differences. Furthermore, the greenness profiles of the proposed methods were assessed using the GAPI and AGREE metrics.

Application of MIA-QSAR in Designing New Protein P38 MAP Kinase Compounds Using a Genetic Algorithm

Multivariate image analysis quantitative structure-activity relationship (MIA-QSAR) study aims to obtain information from a descriptor set, which are image pixels of two-dimensional molecule structures. In the QSAR study of protein P38 mitogen-activated protein (MAP) kinase compounds, the genetic algorithm application for pixel selection and image processing is investigated. There is a quantitative relationship between the structure and the pIC50 based on the information obtained. (The pIC50 is the negative logarithm of the half-maximal inhibitory concentration ( IC50 ), so pIC50 = −log IC50 .) Protein P38 MAP kinase inhibitors are used in the treatment of malignant tumors. The development of a model to predict the pIC50 of these compounds was performed in this study. To accomplish this, the molecules were first plotted and fixed in the same coordinates in ChemSketch. Then, the images were processed in the MATLAB program. Partial least squares (PLS) model, orthogonal signal correction partial least squares (OSC-PLS) model, and genetic algorithm partial least squares (GA-PLS) model methods are used to generate quantitative models, and pIC50 prediction is performed. The GA-PLS model has the highest predictive power for a series of statistical parameters such as root mean square error of prediction (RMSEP) and relative standard errors of prediction (RSEP). Finally, the molecular junction (docking) was done for predicted molecules in quantitative structure activity relationship (QSAR) with an appropriate receptor and acceptable results were obtained. These results are good and proper for the prediction of compounds with better properties.

Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms.

Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.

Origins classification of egg with different storage durations using FT-NIR: A characteristic wavelength selection approach based on information entropy

The origin of fresh eggs is an essential determinant of their quality. This study's main goal was to classify fresh eggs' origins, after the characteristic wavelength from the near-infrared reflectance spectral of the eggs, using a support vector machine (SVM) for classification. To improve classification accuracy, eight categories of eggs were treated as classification targets. A Fourier transform near-infrared (FT-NIR) spectrometer was used to acquire spectral data of 800 eggs. A characteristic wavelength selection approach was presented based on information entropy (CWSABIE). Genetic algorithm-partial least squares (GA-PLS), interval partial least squares (iPLS), and competitive adaptive reweighting sampling (CARS) were compared. Standard normal variable transformation (SNV), Savitzky–Golay filtering, and Centralisation were applied to preprocess spectral data. The results indicate that when using CWSABIE with SNV, the model had the highest accuracy (93.8%) and can be used to classify the data of eggs after 15 days of storage (91.4%). The model show potential for application to online inspection for eggs from different storage days.

SERS-based sensor coupled with multivariate models for rapid detection of palm oil adulteration with Sudan II and IV dyes

Recent reports of adulteration using Sudan dyes have raised consumer and regulatory concerns regarding the safety of edible crude palm oil (CPO). This research developed a novel Hollow Au@Ag Nanoflower (NF) to detect Sudan II and IV dyes in CPO. Different concentrations of the Sudan dyes were combined with a Surface-enhanced Raman spectroscopy (SERS) sensor that yielded a strong SERS signal logarithmically with increasing concentrations of the dyes ranging from 0.005 to 4.0 ppm. Competitive Adaptive Reweighted Sampling (CARS- PLS), Genetic algorithm – PLS (GA-PLS), and bootstrapping soft shrinkage-PLS (BOSS-PLS) were used to develop quantitative models for Sudan dyes prediction. The CARS-PLS model performed best for Sudan II and IV, with Rc values of 0.9921 and 0.9846, respectively, and real sample recovery rates of 98.79–104.49% and 94.37–109.59%. The results show that the SERS sensor combined with chemometrics has great potential to detect trace Sudan dyes in CPO rapidly.

Reducing collinearity by reforming spectral lines with two-dimensional variable selection method

In the spectral quantitative analysis of complex solutions, the broad width of the spectral peak comprises the multi-component spectra, which mix up with neighboring characteristic bands and cause collinearity of spectra and seriously affecting the accuracy of the analysis. A two-dimensional variable selection method is proposed to solve this problem by reforming the component spectrum in this paper. The useful data is selected from the spectral dataset with two dimensions of wavelength and pathlength which together represent the chemical and physical properties of the component. The new spectra of multiple components are formed with these data, and they are different from each other to eliminate the collinearity. The method's effectiveness is studied through the quantitative analysis of four components in human blood. According to one-dimensional and two-dimensional variable selection methods, interval partial least squares (iPLS), genetic algorithm PLS (GA-PLS), and ant colony optimization PLS (ACO-PLS) were used to select the effective datasets of four components, respectively. The experimental results show that the model's prediction accuracy based on the two-dimensional variable selection method is much better than that based on one-dimensional variables.

Analytical Performance and Greenness Evaluation of Five Multi-Level Design Models Utilized for Impurity Profiling of Favipiravir, a Promising COVID-19 Antiviral Drug.

In 2018, the discovery of carcinogenic nitrosamine process related impurities (PRIs) in a group of widely used drugs led to the recall and complete withdrawal of several medications that were consumed for a long time, unaware of the presence of these genotoxic PRIs. Since then, PRIs that arise during the manufacturing process of the active pharmaceutical ingredients (APIs), together with their degradation impurities, have gained the attention of analytical chemistry researchers. In 2020, favipiravir (FVR) was found to have an effective antiviral activity against the SARS-COVID-19 virus. Therefore, it was included in the COVID-19 treatment protocols and was consequently globally manufactured at large-scales during the pandemic. There is information indigence about FVR impurity profiling, and until now, no method has been reported for the simultaneous determination of FVR together with its PRIs. In this study, five advanced multi-level design models were developed and validated for the simultaneous determination of FVR and two PRIs, namely; (6-chloro-3-hydroxypyrazine-2-carboxamide) and (3,6-dichloro-pyrazine-2-carbonitrile). The five developed models were classical least square (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm-partial least squares (GA-PLS), and artificial neural networks (ANN). Five concentration levels of each compound, chosen according to the linearity range of the target analytes, were used to construct a five-level, three-factor chemometric design, giving rise to twenty-five mixtures. The models resolved the strong spectral overlap in the UV-spectra of the FVR and its PRIs. The PCR and PLS models exhibited the best performances, while PLS proved the highest sensitivity relative to the other models.

Comparison of Genetic and Non-genetic Algorithm Partial Least Squares for Sugar Quantification in Philippine Honeys

Quantification of sugars is considered to be a key factor in the assessment of honey quality. Standard chromatographic methods for sugar quantification in honey may be unsuitable for routine analysis due to the associated time, cost, and complexity. Herein, a comparison between non-genetic and genetic algorithm–partial least squares (GA-PLS) chemometric and infrared spectroscopic approaches is reported for the direct quantification of glucose, fructose, and sucrose in Philippine honeys. A full factorial set (n = 61) was designed to predict Box-Behnken test set (n = 16) sugar concentrations. First derivative (FD) signal processing followed by PLS of the 1500–800 cm−1 spectral region harboring the analytes provided optimum results with R 2 average = 0.943 in the training set and a mean root mean square error of prediction (RMSEPaverage) of 0.049 across fructose, glucose, and sucrose. Applying GA-PLS after FD processing across the spectral region from 3700 to 700 cm−1 did not further improve the results (R2 average = 0.926, RMSEPaverage = 0.077). FD followed by PLS (1500–800 cm−1) provided minimal differences between the actual and predicted sugar content in training and testing values, implying that the probe produced developmental avenues to directly quantify these sugars. When applied to real samples (n = 26), the method potentially differentiated real honey from non-honey and adulterated samples.

Label-free Au NRs-based SERS coupled with chemometrics for rapid quantitative detection of thiabendazole residues in citrus

Citrus is a highly consumed fruit worldwide. However, the excessive use of thiabendazole (TBZ) pesticides during citrus cultivation poses a health risk to people. Hence, a rapid and quantitative method has been established for TBZ determination in citrus by coupling gold nanorods (Au NRs) based surface enhanced Raman scattering (SERS) coupling chemometrics. The results show that support vector machine (SVM) can distinguish TBZ residues of different orders of magnitude with 99.1667% accuracy and that genetic algorithm-partial least squares (GA-PLS) had the best performance in the quantitative prediction of TBZ residues (Rp2 = 0.9737, RMSEP = 0.1179 and RPD = 5.85) in citrus. The limit of detection (LOD) was 0.33 μg mL−1. Furthermore, the proposed method was validated by a standard HPLC method using t-test with no significant difference. Therefore, the proposed Au NRs-based SERS technique can be used for the rapid quantitative analysis of TBZ residues in citrus.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models’ performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation

Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.