Chemometrics and Intelligent Laboratory Systems最新文献

Calcined clays and calcium carbonates can be used to reduce clinker factor in blended cements, offering significant economic and environmental benefits. Limestone calcined clay cements (LC3) combine them as supplementary cementitious materials (SCMs) for delivering a sustainable alternative to conventional Ordinary Portland Cement products, with envisioned applications in construction and rehabilitation of historical buildings.

This study reports a chemometric approach to the development of LC3 mortars, targeting to minimize clinker content while maintaining or improving the technical characteristics. To evaluate their performance, apparent density, modulus of elasticity, open porosity, water absorption, flexural and compressive strength have been considered as responses. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were employed in a three-step mixture-process design allowing to obtain LC3 mixtures with a 21 wt% reduction in clinker while achieving notable enhancements in the physical properties (open porosity −9% and water absorption −10 %), along with commendable increases in compressive strength (+17 %) when compared to benchmark mortars produced without SCMs. The successful integration of multivariate techniques in designing sustainable building materials is showcased, highlighting the potential of chemometric methodologies to reduce the environmental impact as well as to increase the performance of building materials.

In chemical processes with distributed outputs, characteristics of products are influenced by their distributions and significantly correlated with process variables. It is crucial for an accurate distribution characteristic prediction to adequately describe variable relationships and their temporal variations. For this purpose, a temporal graph convolutional network (TGCN) soft sensor is developed to describe the distribution of outputs. First, the variable relationships are represented in a topology subgraph based on prior knowledge. Then, the graph is supplemented based on variable screening results with the maximal information coefficient (MIC) as standard. Finally, the graph convolutional mechanism is used to model variable relationships, the gated recurrent unit to capture temporal dependencies, and GNNexplainer to provide a comprehensive explanation for the prediction. Results suggest that prediction accuracy and explainability is improved by the proposed TGCN soft sensor on the basis of prior knowledge, and verified in the case of molecular weight distribution (MWD) modeling.

The prediction of analyte concentration by spectral responses using a calibration model is a commonly used method in chemical analysis. However, insufficient modeling samples will limit the performance of the calibration model. Artificial generation of spectral samples with analyte concentration values is an effective way to address the shortage of modeling samples. However, traditional methods for generating spectral samples with concentration values still have problems in terms of diversity and accuracy. We proposed a method for generating spectral samples with analyte concentration values based on an adversarial autoencoder (AAE). The proposed method combined spectral responses and analyte concentration as the inputs and fitted the extracted latent variables into a prior distribution. By decoding the random sampling points of the prior distribution, the spectral samples with analyte concentration values were generated. Four spectral datasets were used to validate the effectiveness of the proposed method. Two traditional spectral generation methods were used to evaluate the performance of the proposed methods. It was found that the proposed method performed significantly better than traditional ones. The spectral responses generated by the proposed method had good diversity and similarity to the real ones. In addition, the generated spectral samples could also accurately simulate the actual relationship between spectral responses and analyte properties. The proposed method is an effective solution to the problem of insufficient modeling samples in the quantitative analysis of spectral technology.

Peptide quantitative structure-activity relationship (pQSAR) is a specific extension of traditional QSARs from small-molecule drugs to bioactive peptides. Since peptides are linear biopolymers that are essentially different to small-molecule compounds in terms of their structural features such as ordering sequence, large size and intrinsic flexibility, the pQSAR methodology (including structural characterization and regression modelling) should be further exploited relative to traditional QSARs. Gaussian process (GP) serves as a pioneering Bayesian-based machine learning (ML) solution for tackling linear/nonlinear-hybrid regression issues in intricate domains. However, the applications of GP regression in QSAR and, particularly, the pQSAR still remain largely unexplored to date. In this work, we launched a comprehensive pQSAR study with GP regression modelling, aiming to the deep evaluation of GP performance based on different characterizations and also the systematic comparison of GP with other routine MLs. Here, we culled two distinct classes of peptide datasets, which separately comprise 12 panels of sophisticated benchmarks and 46 panels of extended samples, totally containing 8804 peptide samples and systematically resulting in 522 regression models. Our study indicated that the GP can generally provide an effective solution for many pQSAR problems with the potential to promote ML regression modelling in this area, which is comparable with or even better than those widely used methods on both the sophisticated benchmarks and extended samples. In addition, GP also has many advantages as compared to traditional MLs, such as hyperparameter self-consistency, overfitting resistance, interpretable output and estimable uncertainty.

In the development of data-driven soft sensors for product quality assessment in multi-unit manufacturing processes, the only information that is typically used as an input to the model is real-time measurements from field sensors. However, even if detailed knowledge of the mechanistic behavior of the process may not be available, information about the sequence of processing units, and their connectivity, is available, typically in graphical form through process flow diagrams. In this study, we investigate the use of sequential-orthogonalized partial least-squares (SO-PLS) regression as a way to capture connectivity information from a process flow diagram, and transfer it into a data-driven model to be used as a soft sensor in a multi-unit process. Connectivity between units is captured and translated into a block order that establishes a sequence for block regressions. Orthogonalization between two blocks is then carried out with the aim of eliminating overlapping data and retaining information that is unique to each block. Product quality is finally predicted by summing the contributions from each block, and the accuracy of prediction is enhanced due to the embedded dual feature-extraction procedure, which combines orthogonalization and latent-variable extraction. The effectiveness of the proposed approach is illustrated by comparing the quality prediction performance of two soft sensors for a simulated multi-unit continuous process: one using standard PLS and one using SO-PLS. Superior performance of the SO-PLS soft sensor is achieved, even more markedly so when fewer field measurements are available to build the soft sensor.

In this study, multidimensional characterization data such as chromaticity value, texture and compositional content of Isatidis Radix from different regions (Anhui; Hubei; Shaanxi; Xinjiang) were collected. By multivariate statistical analysis, 44 characterization factors (VIP >1, P < 0.05) were selected to distinguish the origin of Isatidis Radix. In addition, a unique artificial intelligence algorithm was created and optimized by merging 44 characterization factors with the deep belief network (DBN) classification algorithm. Compared with the traditional discriminant analysis method, the accuracy of this new method was significantly improved, and the discrimination rate of Isatidis Radix origin reached 100 %, and the traceability accuracy of Isatidis Radix also reached 100 %. This study supports the development of intelligent algorithms based on data fusion to track the origin of more agricultural products.

In modern complex industrial operations, timely fault detection is imperative. While statistical process monitoring is widely used in practice, conventional approaches are usually insensitive to incipient faults (IFs) whose magnitudes are not obvious. To this end, an innovative approach is presented for IF detection in dynamic processes. To begin with, canonical variate residuals (CVRs) are generated by using the canonical variate dissimilarity analysis (CVDA) algorithm. The next step involves calculating statistics for the CVRs and arranging a corresponding statistic matrix. Afterward, the Mahalanobis distance index is constructed for fault detection purpose. The main reasons that this developed approach possesses high sensitivity to IFs in dynamic processes lie in the utilization of CVDA and the idea of monitoring extracted statistics rather than original residuals. Finally, its effectiveness and merits are demonstrated via a numerical example and a benchmark process.

In this work, a novel electrochemical sensor was fabricated for simultaneous determination of patulin (PT) and citrinin (CT) in apple and pear fruit samples. A glassy carbon electrode (GCE) was modified with graphene-multiwalled carbon nanotubes-ionic liquid (Gr-MWCNTs-IL) which was used as a platform to electrochemical synthesis of molecularly imprinted polymers (MIPs) by using PT and CT as templates, maleic acid as a functional monomer, and ethylene glycol dimethacrylate as a cross linker with the aim of preconcentration and simultaneous determination of the PT and CT. Experimental variables affecting fabrication of the structure of the sensor and hydrodynamic differential pulse voltammetric (HDPV) response of the sensor were optimized by a small central composite design and desirability function. After optimization, the HDPV responses of the sensor were calibrated by multivariate calibration methods in the ranges of 0.5–13 fM and 1.5–18 fM for PT and CT, respectively, with the help of PLS-1, RBF-PLS, rPLS, LS-SVM, and RBF-ANN with the aim of selecting the best algorithm to assist the sensor. Our results confirmed the best performance was observed from RBF-ANN which was used for the analysis of apple and pear fruit samples. Limit of detections of the sensor assisted by RBF-ANN for determination of PT and CT were 0.08 and 0.61 fM, respectively. Several commercial brands were analyzed by the use of sensor assisted by RBF-ANN and HPLC-UV, and the results confirmed performance of the sensor was admirable and comparable with the reference method with lower cost, faster response, and easier procedure which made it to be a reliable alternative method for simultaneous determination of PT and CT in real matrices.

Extended Multiplicative Signal Correction (EMSC) is a multivariate linear modelling technique for multi-channel measurements that can identify and correct for different types of systematic variation patterns, known or unknown. It is typically used for pre-processing to separate light absorbance spectra, obtained by diffuse reflectance of intact samples, into three main sources of variation: additive variations due to chemical composition (≈Beer's law), mixed multiplicative and additive variations due to physical light scattering (≈Lambert's law) and more or less random measurement noise. The present work evaluates the use of EMSC to pre-process near infrared spectra obtained by hyperspectral imaging of Scots pine sapwood, inoculated with two different basidiomycete fungi and at various degradation stages. The spectral changes due to fungal decay and resulting mass loss are assessed by interpretation of the EMSC parameters and the partial least squares regression (PLSR) results. Including a cellulose (analyte) or bound water (interferent) spectral profile in the EMSC pre-processing model generally improves the predictive performance of the PLS modelling, but it can also make it worse. The inclusion of the additional polynomial baselines does not necessarily lead to a better separation of the physical and chemical effects present in the spectra. The estimated EMSC parameters provide insight into the differences in decay mechanisms. A detailed analysis of the EMSC results highlights advantages and disadvantages of using a complex pre-processing model.

A disease caused by cellular alterations that is unrestrained cell growth and division is cancer. Many anticancer medications, including those used to treat blood, breast, and skin cancer, may have their physical, chemical, and biological features predicted. This paper presents novel distance-based topological indices (TIs) computed using the suggested KP-polynomial with blood cancer drugs. The objective of the QSPR investigation is to determine the mathematical correlation between the analyzed properties (such as Molar Volume, Refractive Index, etc.) and different descriptors associated with the molecular structure of the medications. A polynomial regression model is employed to assess the predictive capability of TIs. The results are represented using a correlation coefficient to establish the connection between the predicted and observed values of blood cancer drugs. This theoretical method could potentially enable chemists and health care professionals to anticipate the characteristics of blood cancer drugs without the need for actual experimental tests. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient multicriteria decision making technique TOPSIS for ranking of said disease treatment drugs and physicochemical characteristics.