Journal of Chemometrics最新文献

Rapid and accurate quantification of ciprofloxacin hydrochloride (CIP) and tylosin tartrate (TYZ) in veterinary formulations is crucial for ensuring product quality and therapeutic efficacy. This study introduces a green and cost-effective analytical method that combines the simplicity of UV spectrophotometry with the optimization power of nature-inspired algorithms for the simultaneous determination of CIP and TYZ in a tablet veterinary formulation. Fourteen nature-inspired algorithms were comparatively assessed using root average squared error (RASE), average absolute error (AAE), and the coefficient of determination (R²). The Corona virus optimization (CVO) algorithm and the Bat algorithm demonstrated superior performance for CIP and TYZ, respectively. The CVO algorithm, optimized for CIP, exhibited RASE, AAE, and R² values of 0.37, 0.27, and 0.998, respectively, for the calibration set, while the bat algorithm, tailored for TYZ, yielded RASE, AAE, and R² values of 0.54, 0.41, and 0.984. Test sets yielded RASE, AAE, and R² values of 0.55, 0.46, and 0.991 for CIP and 0.20, 0.15, and 0.995 for TYZ, respectively, confirming the algorithms predictive ability. Validation was performed using the accuracy profile approach. The limits of detection (LODs) were determined to be 0.86 μg mL⁻¹ for CIP and 0.36 μg mL⁻¹ for TYZ, while the limits of quantification (LOQs) were calculated as 2.88 μg mL⁻¹ for CIP and 1.21 μg mL⁻¹ for TYZ. The method environmental impact was comprehensively assessed using The Green Solvent Selection Tool (GSST), The National Environmental Methods Index (NEMI), a modified Eco-Scale, the Modified GAPI (MoGAPI), and a complementary whiteness evaluation via the RGBfast algorithm, confirming its eco-friendly profile. The proposed method demonstrated superior greenness, as reflected in its elevated GSST scores and favorable NEMI assessment. Specifically, the method achieved a modified Eco-Scale score of 84, a MoGAPI score of 81, and a whiteness index of 61, as determined by the RGBfast algorithm. These results confirm the method environmentally sustainable profile, reinforcing its suitability for green analytical applications. This novel approach offers significant advantages in terms of cost, speed, and environmental sustainability compared to conventional chromatographic techniques, paving the way for more efficient and greener analytical methods in pharmaceutical quality control. Furthermore, this study highlights the innovative integration of UV spectroscopy with nature-inspired algorithms, demonstrating significant advancements over conventional UV methodologies for pharmaceutical analysis.

In this work, we compared the chromatographic properties of 27 homemade monomer- and polymer-modified stationary phases synthesized via the Ugi reaction for hydrophilic interaction liquid chromatography (HILIC). These stationary phases along with the unmodified substrate were characterized by retention factors of 33 polar biologically active compounds belonging to various classes (nucleobases/nucleosides, sugars, carboxylic acids, and water-soluble vitamins). Additionally, the widely used Tanaka HILIC test was performed. The experimental data from both characterization approaches were processed using several chemometric techniques, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means algorithm. It was initially expected that polymer-modified phases would differ significantly from monomer-modified ones due to their mixed-mode properties. It was confirmed by the clear separation of these two types of stationary phases on the PCA score plot obtained for binary logarithms of selectivities (calculated from all 33 retention factors). Dissimilarities observed among some monomer-modified stationary phases resulted in insights into Ugi reaction conditions suitable for obtaining adsorbents with distinct chromatographic properties. Each class of test compounds required specific mobile phase composition to achieve reasonable chromatographic characteristics, such as retention times and peak shapes. To exclude the long-lasting re-equilibration stage associated with mobile phase changes, a smaller set of only three test compounds was proposed, yielding nearly the same clustering results as the complete dataset. This simplified procedure can facilitate the rapid characterization of newly synthesized stationary phases and allow for comparison with previously studied phases.

Conformal predictions transform a measurable, heuristic notion of uncertainty into statistically valid confidence intervals such that, for a future sample, the true class prediction will be included in the conformal prediction set at a predetermined confidence. In a Bayesian perspective, common estimates of uncertainty in multivariate classification, namely p-values, only provide the probability that the data fits the presumed class model, P(D|M). Conformal predictions, on the other hand, address the more meaningful probability that a model fits the data, P(M|D). Herein, two methods to perform inductive conformal predictions are investigated—the traditional Split Conformal Prediction that uses an external calibration set and a novel Bagged Conformal Prediction, closely related to Cross Conformal Predictions, that utilizes bagging to calibrate the heuristic notions of uncertainty. Methods for preprocessing the conformal prediction scores to improve performance are discussed and investigated. These conformal prediction strategies are applied to identifying four non-steroidal anti-inflammatory drugs (NSAIDs) from hyperspectral Raman imaging data. In addition to assigning meaningful confidence intervals on the model results, we herein demonstrate how conformal predictions can add additional diagnostics for model quality and method stability.

Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is an important medicinal plant and flavoring spice. A. tsao-ko dried at different drying temperatures has different nutritional and medicinal values, leading to the phenomenon of substandard products in the market from time to time. In this study, attenuated total reflection–Fourier transform infrared spectroscopy (ATR-FTIR) data were pre-processed with SD, normalization, EWMA, SNV to compare their effects on the recognition ability of SVM, RF, XGBoost, and CatBoost models. Meanwhile, full-band and local-band 2DCOS profiles were obtained to characterize the differences in chemical features of A. tsao-ko dried by different drying temperatures and classified in conjunction with the ResNet model. The results show that although traditional machine learning can obtain better classification results, the classification efficiency is very unsatisfactory, and the correct classification rate is improved to 97% after derivative (SD) preprocessing. The 2DCOS atlas is able to visualize the feature information in the samples, which is further combined with the ResNet model to obtain 100% classification correctness with excellent generalization ability and convergence effect. The above study was able to provide new ideas for quality evaluation of A. tsao-ko.

The paper discusses methods of using chemometrics methods for processing the output data of sensors with polycomposite coatings for analyzing the gas phase of raw milk and obtaining analytical information about its total microbiological contamination, the content of yeast and mold, and the presence of pathogenic microorganisms. To predict microbiological indicators of milk quality, the partial least squares regression and quadratic discriminant analysis were used. The initial data matrix included both an optimized set of sensor output data and calculated parameters at various data fusion levels. It is shown that multidimensional patterns of sensor output data differ depending on the task. A model for predicting the microbiological contamination of milk (QMAFAnM) with an error of 0.342 log CFU was obtained. It was shown that the sensitivity of classification of milk samples by the presence or absence of pathogenic microorganisms using discriminant analysis is 67%, and the specificity is 100% when using the calculated parameters of the sensor array. The proposed approaches can be applicable for processing data from various types of sensors when analyzing real objects with complex compositions.

The discussions in the QSAR community and the steps that led to the definition of the OECD Principles for the validation of QSAR models are illustrated here, framing the process in the general framework of QSAR modeling. The individual OECD Principles are presented, commenting on them in the light of significant publications that have appeared over the years, with particular attention to the aspects of statistical validation according to the chemometric approach. It will be highlighted how and to what extent the OECD Principles have influenced the subsequent work of all QSAR modelers and have led to a significant improvement in validated QSAR modeling applicable in the regulatory field and beyond.