An innovative nanoparticle-modified carbon paste sensor for ultrasensitive detection of lignocaine and its extremely carcinogenic metabolite residues in bovine food samples: Application of NEMI, ESA, AGREE, ComplexGAPI, and RGB12 algorithms

Direct extraction of ten estrogens from milk samples with DVB/NVP-modified magnetic solid-phase extraction adsorbent followed by pre-column derivatization-UHPLC-MS/MS

Estrogens and their analogues can cause harm to human health through the food chain. Ten estrogens in different milk samples were directly extracted by amphiphilic divinylbenzene/N-vinyl-2-pyrrolidone (DVB/NVP)-Fe3O4@SiO2-based magnetic solid-phase extraction (MSPE) followed by pre-column derivatization and ultra-high performance liquid chromatography tandem mass-spectrometry (UHPLC-MS/MS) detection. Under the optimal conditions, the limits of detection for ten analytes were in the range of 0.05-0.38 ng mL-1 in whole liquid milk matrix and 0.04-3.00 ng g-1 in milk powder matrix. The intra-/inter-day accuracy ranged in 83.4-113.8%, with RSDs in 2.5-15.0%. A total of 15 brands of liquid milk and milk powder samples were analyzed, and only estradiol was detected in three brands of boxed liquid milk within safe range. The proposed sample pretreatment eliminated the common protein precipitation process, improved the sample throughput, and has the potential for routine testing of estrogens and their analogues in market-sale milk samples.

A technique based on infrared spectroscopy for determining sulfanilamide levels sustainably: Progress and comparisons of greenness and whiteness using ComplexGAPI, AGREE, and RGB

The study aimed to determine the potential of the infrared (IR) spectrophotometric technique for measuring the content of sulphanilamide with the sulfonamide group. The study aimed to obtain the IR spectra of sulfanilamide and use the -SO2 band at 1114.37 for the quantitative assay, determining its area under the curve (AUC). The study gives an alternative approach to existing analytical techniques that require vast amounts of organic solvents, which are costly and can be toxic, thus impacting the environment and increasing the analysis cost. The study evaluated the method's whiteness and greenness by utilizing the Complex green analytical procedure index, analytical GREEness calculator and Red Green Blue algorithm tool. The linierity was found to be 5 to 30 µg/ml. The present study has developed an infrared (IR) spectroscopic method that employs a straightforward sample preparation technique in methanol. The IR spectroscopic method's linearity range was determined to be 5-30 µg/ml. The p-value was 0.001 at 95 % confidence level assuring better recovery. This method is evaluated according to the Q2R1 ICH guideline. It is applicable to routine quality control analysis without pre-extraction using green IR spectroscopy. In conclusion, the study demonstrated that IR spectrophotometric techniques can quantify sulfanilamide while reducing the use of organic solvents, contributing to the green-and-white analytical chemistry approach. The developed methods are reliable, accurate, and cost-effective and have the potential to be implemented in routine analysis of sulfanilamide.

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.

Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry

Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.

Voltammetric analysis of pholcodine on graphene-modified GNPs/PTs with green assessment

Pholcodine, an anti-tussive medication widely used as an over-the-counter, OTC drug, has recently faced restrictions in several countries. This paper presents a sensitive electrochemical approach for pholcodine detection. The electrochemical method involved fabricating a graphene nanoplatelets electrode, incorporating polythiophene nanospheres polymer to promote electron transfer and increase the activated surface area. Characterization of the fabricated electrode was performed using transmission electron microscopy, ATR-Fourier-transform infrared spectroscopy, X-ray crystallography, X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy. The electrochemical behavior of pholcodine with the fabricated electrode was investigated using cyclic voltammetry, chronoamperometry, square wave voltammetry (SWV), and differential pulse voltammetry (DPV). The developed electrode led to a linear response for pholcodine ranging from 10 to 45 mg/L with detection limits of 1.41 and 1.51 mg/mL for SWV and DPV, respectively and quantification limits of 4.27 and 4.57 mg/L for SWV and DPV, respectively. The proposed method has accurately recovered pholcodine in spiked serum samples with a recovery percentage ranging from 1.2 to 2.9%. The optimized method is found to be accurate, precise, and robust by applying validation parameters provided by International Council for Harmonization. Two green metrics were computed to assess the method's greenness, the findings showed that the developed method is environmentally friendly with minimum sample preparation steps.

A Green-and-White Integrative Analytical Strategy Combining Univariate and Chemometric Techniques for Quantifying Recently Approved Multi-Drug Eye Solution and Potentially Cancer-Causing Impurities: Application to the Aqueous Humor

BACKGROUND

Drug impurities are now seen as a major threat to the production of pharmaceuticals around the world and a major part of the global contamination problem, especially when it comes to carcinogenic impurities.

OBJECTIVE

We present the first spectrophotometric strategy based on a combination of univariate and multivariate methods as impurity profiling methods for the estimation of lignocaine (LIG) and fluorescein (FLS) with their carcinogenic impurities: 2,6-xylidine (XYL) and benzene-1,3-diol (BZD).

METHOD

The data processing strategy depends on overcoming unresolved bands by employing five affordable, accurate, selective, and sensitive methods. The methods applied were a direct UV univariate spectrophotometric analysis (D0) and four multivariate chemometric methods, including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm (GA-PLS). FLS analysis (1-16 μg/mL) was performed using the D0 method at 478 nm; then, the application of the ratio subtraction method (RSM) allowed the removal of interference caused by the FLS spectrum. From the resulting ratio spectra, LIG, XYL, and BZD can be efficiently determined by chemometrics. The calibration set was carefully selected at five concentration levels using a partial factorial training design, resulting in 25 mixtures with central levels of 160, 40, and 3 μg/mL for LIG, XYL, and BZD, respectively. Another 13 samples were applied to validate the predictive ability.

RESULTS

The statistical parameters demonstrated exceptional recoveries and smaller prediction errors, confirming the experimental model's predictive power.

CONCLUSIONS

The proposed approach was effectively tested using newly FDA-approved LIG and FLS pharmaceutical preparation and aqueous humor. Additionally, it was effectively assessed for whiteness, greenness, and sustainability using five assessment tools.

HIGHLIGHTS

With its remarkable analytical performance, sustainability, affordability, simplicity, and cost-efficiency, the proposed strategy is an indispensable tool for quality control and in situ analysis in little-equipped laboratories, increasing the proposed approach's surveillance ability.

Environmentally sustainable DRS-FTIR probe assisted by chemometric tools for quality control analysis of cinnarizine and piracetam having diverged concentration ranges: Validation, greenness, and whiteness studies

A novel diffuse reflectance fourier transform infrared spectroscopic method accompanied by chemometrics was optimized to fulfill the white analytical chemistry and green analytical chemistry principles for the quantification of cinnarizine and piracetam for the first time without any prior separation in their challenging pharmaceutical preparation, which has a pretty substantial difference in the concentration of cinnarizine/piracetam (1:16). Furthermore, the suggested method was used for cinnarizine/piracetam dissolution testing as an effective alternative to traditional methods. For the cinnarizine/piracetam dissolution tests, we used a dissolution vessel with 900 mL of phosphate buffer pH 2.5 at 37 °C ± 0.5 °C, then the sampling was carried out by frequent withdrawal of 20 µl samples from the dissolution vessel at a one-minute interval, over one hour, then representative fourier transform infrared spectra were recorded. To create a partial-least-squares regression model, a fractional factorial design with 5 different levels and 2 factors was used. This led to the creation of 25 mixtures, 15 as a calibration set and 10 as a validation set, with varying concentration ranges: 1-75 and 16-1000 μg/mL for cinnarizine/piracetam, respectively. Upon optimization of the partial-least-squares regression model, in terms of latent variables and spectral region, root mean square error of cross-validation of 0.477 and 0.270, for cinnarizine/piracetam respectively, were obtained. The optimized partial-least-squares regression model was further validated, providing good results in terms of recovery% (around 98 to 102 %), root mean square error of prediction (0.436 and 3.329), relative root mean square error of prediction (1.210 and 1.245), bias-corrected mean square error of prediction (0.059 and 0.081), and limit of detection (0.125 and 2.786) for cinnarizine/piracetam respectively. Ultimately, the developed method was assessed for whiteness, greenness, and sustainability using five assessment tools. the developed method achieved a greener national environmental method index and complementary green analytical procedure index quadrants with higher eco-scale assessment scores (91), analytical greenness metric scores (0.87), and red-greenblue 12 algorithm scores (89.7) than the reported methods, showing high practical and environmental acceptance for quality control of cinnarizine/piracetam.