Chemometrics and Intelligent Laboratory Systems最新文献

Guar gum is a non-ionic polysaccharide found in abundance in nature. It may be used as a thickening agent, stabilizer, or emulsifier in pharmaceutical formulations, food products, or cosmetics. Its ability to form viscous solutions makes it useful in drug delivery systems, controlled release formulations, and as a matrix for oral drug delivery. The investigation of chemical structures through graph invariants is of great concern. Topological descriptors are numerical numbers associated with the molecular structure and have the ability to predict certain physical and chemical properties of the underlying structure. In this paper, we have calculated the harmonic index, the inverse sum indeg index, the third Zagreb index, the Hyper Zagreb index, the sigma index, the reformulated first Zagreb index, the reformulated multiplicative first Zagreb index, the Harmonic–arithmetic index, and the Atom Bond sum connectivity indices of guar gum and its chemical derivatives. Finally, the chemical applicability of these topological descriptors is checked for different carbohydrates (monosaccharides, disaccharides, and polysaccharides) by using straight-line, parabolic and logarithmic regression models. It has been observed that these topological descriptors are useful to predict two physical properties, namely density and molecular weight.

Definitive screening design (DSD) has become a widely used type of Design of Experiments for chemical, pharmaceutical and biopharmaceutical processes and product development due to its optimization properties with an estimation of main, interaction, and squared variable effects with a minimum number of experiments. These high dimensional DOEs with more variables than samples, and with partly correlated variables, make the statistical interpretation frequently challenging. The purpose of the study was to test bootstrap PLSR using a heredity procedure to select the variable subset to be finally evaluated by MLR. The heredity selection was used on bootstrap T values given by original PLSR coefficients (B) divided on the bootstrap estimated standard deviation. The investigated fractional weighted and non-parametric bootstrap PLSR resulted in same variable selection outcome and final models in this study.

A simulation study with 7 main variables and 12 tested literature real data DSDs with 4, 5, 7 and 8 main variables showed improved model performance for small and particularly for large DSDs for the bootstrap PLSR MLR methods compared to two common DSD reference methods; DSD fit definitive screening and AICc forward stepwise regression (AICc FSR). Variable selection accuracy and predictive ability were significantly improved by the investigated method in 6 out of 13 DSDs compared to the best model from either of the two reference methods. The remaining 7 DSDs gave the same model as best reference model. Strong heredity was found to provide the best models for all real data in this study. The use of the heredity procedure on the percent non-zero SVEM FSR variable effects followed by MLR showed promising results. AICc Lasso regression was among other methods partially tested and was found to set almost all variables to zero effect when tested on three large minimum DSDs. While the DSD fit definitive screening method may often be the first choice for DSD, the heredity bootstrap PLSR MLR and heredity SVEM FSR MLR may be alternative methods to improve the variable selection and model precision.

The determination of total nicotine, total sugar, reducing sugar and total nitrogen contents in tobacco is of great significance to tobacco quality evaluation and formulation design. To quickly detect the content of 4 components of tobacco, using near-infrared (NIR) and mid-infrared (MIR) spectral data from 129 solid samples of tobacco powder provided by Shanghai Tobacco Group Co., Ltd., Two NIR-MIR spectral fusion techniques are studied, that is, fusion technology 1 is to establish a model by fusing feature variables after variable selection of each spectrum. The fusion technology 2 is to first fuse the NIR-MIR spectral data and then select the variables to establish the model. Both fusion technologies use successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), backward interval PLS (biPLS), forward interval PLS (fiPLS), synergy interval PLS (siPLS), and interval interaction moving window partial least squares (iMWPLS) algorithms to filter wavelength variables. The results showed that for total nicotine and total sugar, the PLSR model established by fusion technology method 2 combined with iMWPLS algorithm is the best, and its RMSEP decreases from 0.2314 to 1.3225 to 0.0821 and 0.8079 respectively compared with the full spectrum fusion method, which is superior to the single NIR and MIR models and NIR-MIR fusion technology 1. For reducing sugars, the simple full-spectrum fusion model has the best analytical ability and the lowest RMSEP, which is superior to the single NIR-MIR models and all models established by two spectral fusion techniques combined with six wavelength selection algorithms. For total nitrogen, the prediction effect of fusion technology 1 combined with iMWPLS algorithm model was significantly improved compared with single NIR and MIR models and NIR-MIR fusion technology 2, and its RMSEP was 0.0634. The results showed that the two NIR-MIR spectral fusion techniques made full use of the complementary information provided by NIR and MIR spectroscopy, and successfully applied them to the rapid detection of total nicotine, total sugar, reducing sugar and total nitrogen content in tobacco, which provided a new method and idea for the rapid detection of tobacco components.

This paper proposes a joint state and unknown inputs (UIs) discrete-time estimation method for industrial processes, represented by a state-space model. To cope with the outliers in process data, the measurement noise is characterized by the Student’s t-distribution. The identification of UIs is accomplished through the recursive expectation–maximization (REM) approach. Specifically, in the E-step, a recursively calculated Q-function is formulated by the maximum likelihood criterion, and the states and the variance scale factor are estimated iteratively. In the M-step, UIs are updated analytically together with the degree of freedom is updated approximately. The effectiveness of the proposed algorithm is validated using a quadruple water tank process and a continuous stirred tank reactor. It shows that the proposed method significantly enhances the robustness and estimation accuracy of state and UIs in industrial processes, effectively handling outliers and reducing computational demands for real-time applications.

The research work shows the potentiality of advanced linear and nonlinear learning algorithm techniques in the prediction of apples texture sensory attributes as “hardness”, “crunchiness”, “flouriness”, “fibrousness”, and “graininess”. Starting from the information contained in the entire mechanical and acoustic curves acquired during samples compression test, the prediction performances of five different statistical tools as Partial Least Squares regression (PLS), Multilayer Perceptron (MLP), Support Vector Regression (SVR) and Gaussian Process Regression (GPR) are shown and discussed.

All Predictive models validations evidence best accuracies for texture sensory attributes “hardness” and “crunchiness” and in general for GPR learning algorithm. By combining mechanical and acoustic profiles, 5-fold cross validations produce values of coefficient of determination R² up to 0.885 (GPR) and 0.840 (GPR), respectively for “hardness” and “crunchiness”. These results, comparable to those obtained by considering a large number of mechanical and acoustic parameters extracted from acquired profiles as predictive factors, evidence a new and reliable way for the prediction of texture sensory attributes of apples. The proposed approach can overcome the necessity to define, in advance, number and type of features to be calculated from instrumental texture profiles and can be easily implemented in an automatic process.

A comprehensive multi-scale computational strategy was developed in this study based on mass transfer and machine learning for simulation of drug concentration distribution in a biomaterial matrix. The controlled release was modeled and validated via the hybrid model. Mass transfer equations along with kinetics models were solved numerically and the results were then used for machine learning models. We investigated the performance of three regression models, namely Decision Tree (DT), Random Forest (RF), and Extra Tree (ET) in predicting medicine concentration (C) based on r and z data. Hyper-parameter optimization is conducted using Glowworm Swarm Optimization (GSO). Results revealed high predictive accuracy across all models, with ET demonstrating superior performance, achieving a coefficient of determination value (R²) of 0.99854, an RMSE of 1.1446E-05, and a maximum error of 6.49087E-05. DT and RF also exhibit notable performance, with coefficients of determination equal to 0.99571 and 0.99655, respectively. These results highlight the effectiveness of ensemble tree-based methods in accurately predicting chemical concentrations, with Extra Tree (ET) Regression emerging as the most promising model for this specific dataset.

Accurate baseline correction is a fundamental requirement for extracting meaningful spectral information and enabling precise quantitative analysis using Raman spectroscopy. Although numerous baseline correction techniques have been developed, they often require meticulous parameter adjustments and yield inconsistent results. To address these challenges, we have introduced a novel approach, namely constrained Gaussian radial basis function fitting (CGF). Our method involves solving a curve-fitting problem using Gaussian radial basis functions under specific constraints. To ensure stability and efficiency, we developed a linear programming algorithm for the proposed approach. We evaluated the performance of CGF using simulated Raman spectra and demonstrated its robustness across various scenarios, including changes in data length and noise levels. In contrast to standard methods, which frequently require complicated parameter adjustments and may exhibit varying errors, our approach provides a simple parameter search and consistently achieves low errors. We further assessed CGF using real Raman spectra, leading to enhanced accuracy in the quantitative analysis of the Raman spectra of chemical warfare agents. Our results emphasize the potential of CGF as a valuable tool for Raman spectroscopy data analysis, significantly advancing sophisticated analytical techniques.

Spectroscopic measurements can show distorted spectral shapes arising from a mixture of absorbing and scattering contributions. These distortions (or baselines) often manifest themselves as non-constant offsets or low-frequency oscillations. As a result, these baselines can adversely affect analytical and quantitative results. Baseline correction is an umbrella term where one applies pre-processing methods to obtain baseline spectra (the unwanted distortions) and then remove the distortions by differencing. However, current state-of-the art baseline correction methods do not utilize analyte concentrations even if they are available, or even if they contribute significantly to the observed spectral variability. We modify a class of state-of-the-art methods (penalized baseline correction) that easily admit the incorporation of a priori analyte concentrations such that predictions can be enhanced. This modified approach will be deemed supervised and penalized baseline correction (SPBC). Performance will be assessed on two near infrared data sets across both classical penalized baseline correction methods (without analyte information) and modified penalized baseline correction methods (leveraging analyte information). There are cases of SPBC that provide useful baseline-corrected signals such that they outperform state-of-the-art penalized baseline correction algorithms such as AIRPLS. In particular, we observe that performance is conditional on the correlation between separate analytes: the analyte used for baseline correlation and the analyte used for prediction—the greater the correlation between the analyte used for baseline correlation and the analyte used for prediction, the better the prediction performance.

This study illustrates the effective control of COVID-19 infection through the adsorption of safranal (SAF) on B₁₆N₁₆ and Al₁₆N₁₆ fullerene-like cages. The SAF adsorption onto the B₁₆N₁₆ and Al₁₆N₁₆ surfaces in gas, water (H₂O), and chloroform (CHCl₃) environments were assessed using density functional theory (DFT) and time-dependent (TD) density functional theory methods, analyzing the substrates and their complexes. The Al₁₆N₁₆/SAF complex exhibited the most negative binding energy and structural stability in the water phase compared to the B₁₆N₁₆/SAF complex at the PBE0-D3 level. The thermodynamic parameters indicated that the adsorption of SAF onto the fullerene-like cages is exothermic, particularly for the Al₁₆N₁₆/SAF complex. Additionally, the interaction of SAF with the fullerene-like cages in the water phase is more pronounced than in gas and chloroform environments. The complexes' energy gap (Eg) decreases in all three environments compared to the perfect systems, with a significant reduction of over 21 % in all phases. This substantial decrease in the energy gap suggests that the complexes have increased reactivity and sensitivity to SAF, likely due to a significant change in electronic conductivity. The results of molecular docking indicate that the Al₁₆N₁₆/SAF complex in the water phase exhibited a strong binding affinity compared to the other compounds studied. These findings suggest that the Al₁₆N₁₆/SAF complex holds promise as a potential inhibitor for COVID-19 and as a valuable material for biomedical applications and drug delivery systems.