Chemometrics and Intelligent Laboratory Systems最新文献

Statistics can be used in a variety of ways to present, compute, and critically analyze experimental data. To determine the significance and validity of the experimental data, a variety of statistical tests are used. Using a synthesized CoO/NiO/MnO₂ Nanocomposite, the present study used adsorption to remove the dye Bromophenol Blue (BPB) from a contaminated aqueous solution. In order to (a) determine the optimal pH of the solution, (b) confirm the experiment's success, and (c) investigate the effect of adsorbent dose on BPB dye removal from aqueous solutions. The experimental data were statistically analyzed through hypothesis testing using the t-test, paired t-test, and Chi-square test. The null hypothesis that the optimal pH value is 7 is accepted since t_observed (−1.979)<t_tabulated (−2.262). Since χ²_observed (1.052)< χ²_tabulated (3.841), null hypothesis that the higher adsorbent dose helps in higher % removal of dye is accepted. Both the obtained Freundlich adsorption isotherm and the Langmuir isotherm's R² values, which were both close to 1, indicate that the isotherms are favorable. Karl Pearson's relationship coefficient values for Langmuir and Freundlich adsorption isotherms found to be 0.9693 and 0.9994 respectively, which show a more significant level of connection between's the factors. The ANN model predicted adsorption percentage with regression value R is 0.996. ANN model result predict 99.60 % BPB dye adsorption using optimized parametric conditions. The ANN model produced values that were more precise, reliable, and reproducible, demonstrating its superiority.

The surge in data-driven soft sensors for industrial processes is evident. However, most of them suffer from the limitation of being black-box models and this will hamper their widespread use. In response to this challenge, this study proposes a physics-guided graph-learning soft sensor that integrates a physical understanding of industrial processes by incorporating graph-based concepts with process physics. The soft sensor first constructs physical information based on causal relationships between variables using the conditional Granger causality test. Subsequently, it autonomously learns the unique sample information of each observation while employing a regularization loss to ensure the sparsity of the learned information. The model employs a two-stream structure for spatiotemporal encoding of both the physical and sample information. The modeling and prediction results on a penicillin fermentation process indicate that, using the proposed method, the knowledge gained from the data aligns with existing prior knowledge. This approach shows promise in filling the gap between data-driven and physics-based modeling in chemical processes.

We introduce a heuristic to test the significance of fit of Self-Validated Ensemble Models (SVEM) against the null hypothesis of a constant response. A SVEM model averages predictions from nBoot fits of a model, applied to fractionally weighted bootstraps of the target dataset. It tunes each fit on a validation copy of the training data, utilizing anti-correlated weights for training and validation. The proposed test computes SVEM predictions centered by the response column mean and normalized by the ensemble variability at each of $nPoint$ points spaced throughout the factor space. A reference distribution is constructed by refitting the SVEM model to nPerm randomized permutations of the response column and recording the corresponding standardized predictions at the $nPoint$ points. A reduced-rank singular value decomposition applied to the centered and scaled $nPerm\times nPoint$ reference matrix is used to calculate the Mahalanobis distance for each of the nPerm permutation results as well as the jackknife (holdout) Mahalanobis distance of the original response column. The process is repeated independently for each response in the experiment, producing a joint graphical summary. We present a simulation driven power analysis and discuss limitations of the test relating to model flexibility and design adequacy. The test maintains the nominal Type I error rate even when the base SVEM model contains more parameters than observations.

Partial least squares (PLS) regression is a linear regression technique that performs well with high-dimensional regressors. Similar to many other supervised learning techniques, PLS is susceptible to the problem that the prediction and training data are drawn from different distributions, which deteriorates the PLS performance. To address this problem, an adaptive strategy via the minimum covariance determinant (MCD) estimator is proposed to improve the PLS model, which aims to find an appropriate training set for the adaptive construction of an accurate PLS model to fit the prediction data. In this study, an $h$-subset of the merged set of prediction and training data with the smallest covariance determinant is found via the MCD estimator, and the prediction and training data with Mahalanobis distances to the $h$-subset less than or equal to a cutoff that is the square root of a quantile of the chi-squared distribution are assumed to have the same distribution, then a PLS model is built on these training data. The proposed method is applied to three real-world datasets and compared with the results of classic PLS, the most significant improvement is obtained for the m5 prediction data in the corn dataset, where the root mean square error of prediction (RMSEP) is reduced from 0.149 to 0.023. For other datasets, our method can also perform better than PLS. The experimental results show the effectiveness of our method.

An important problem in compositional data analysis is variable selection in linear regression models with compositional covariates. In the context of microbiome data analysis, there is a demand for considering grouping information such as structures among taxa and multiple sampling sites, resulting in multiple compositional covariates. We develop and compare two different methods of variable selection and inference strategies, based on the debiased lasso and a resampling-based approach. Confidence intervals for individual regression coefficients, obtained from each of the two methods, are shown to be asymptotically valid even in a high-dimension, low-sample-size regime. However, microbiome data often have extremely small sample sizes, rendering asymptotic results unreliable. Through extensive numerical comparisons of the finite-sample performances of the two methods, we find that resampling-based approaches outperform the debiased compositional lasso in cases of extremely small sample sizes, showing higher positive predictive values. Conversely, for larger sample sizes, debiasing yields better results. We apply the proposed multiple compositional regression to steer microbiome data, identifying key bacterial taxa associated with important cattle quality measures.

Sparse-based models are a powerful tools for data compression, variable reduction, and model complexity reduction. Nevertheless, their major issue is the high computational time needed in large matrices. This manuscript proposes, for the first time, to couple randomised decomposition as a first step before sparsity calculations, followed by a projection of the full data onto a reduced-sparse set of loadings that will drastically reduce the time needed for calculations and built models that are equally reliable as their sparse-based homologous. While this new approach might be valid for several scenarios (exploration, regression and classification), we will focus on exploration methods (like Principal Component Analysis – PCA) applied to large datasets of hyperspectral images. Two datasets of different complexity have been tested, and the benefits of the coupled randomisation and sparse PCA (rsPCA) are extensively studied.

Raman spectroscopy represents an advanced process analytical technology to monitor and control chemical and biochemical processes. This study presents an autoencoder-based methodology that simulates Raman spectra from process variables and predicts the concentrations of different chemicals. The methodology accurately predicts concentrations from the spectra, even considering the temperature influences, and can work as an anomaly detector in process monitoring. The proposed methodology has significant implications for the optimization of industrial processes, improving process efficiency, reducing waste, and minimizing costs. It can also be extended to other industrial processes and imaging spectroscopy techniques, making it a valuable tool for process monitoring. This study highlights the effectiveness of autoencoders in simulating spectra and quantitative analysis, contributing significantly to the field of process monitoring. It has the potential to revolutionize industrial process monitoring and optimization, leading to substantial improvements in productivity and sustainability.

Clustering is an unsupervised machine learning methodology widely used in several sciences to find groups of similar patterns in complex data. The results generated by clustering algorithms generally depend on user-defined input parameters such as the number of expected clusters, which can have a great impact on the homogeneity of the identified clusters.

Clustering validity indices (CVIs) are an effective method for determining the optimal number of clusters that best fit the natural partition of a dataset. They do not require any underlying assumption nor a priori knowledge about the true dataset structure. Since 1965, many cluster validity indices have been proposed in the literature and used in several different applications.

In this paper, the performance of 68 cluster validity indices was evaluated on 21 real-life research and simulated datasets. CVIs were compared on the same partition for each dataset, which was searched for by the k-means clustering algorithm. Multivariate chemometric methods were applied to disclose mutual relationships among the indices and to select those that are more effective in terms of accuracy and reliability.

Generating chemical element maps of paintings from X-ray fluorescence (XRF) data is a very valuable tool for the scientific community of conservators and art historians. Hand-held XRF scanners are cheap and easily portable but their use provides scans with a few data, so additional analytical tools are needed to obtain reliable chemical element maps from them. Recently, the software tool SmART_Scan was released, which uses an algorithm based on the minimum hypercube distance (MHD) to compute this kind of maps. In this paper, we propose a new methodology to address this problem by using machine learning algorithms for regression as alternative and more accurate techniques than MHD. We tested MHD versus eight machine learning regression algorithms on two paintings with different features. Our results showed that machine learning algorithms Random Forest and kNN significantly outperformed MHD in Mean Squared Error (MSE) and coefficient of determination (R²) for all the experiments. When using experts’ data and a hold-out validation, kNN was the best-ranked algorithm. Random Forest was the best-ranked algorithm when cross-validation was used. We did not find significant differences in average MSE nor in R² between kNN and Random Forest, so we can conclude that Random Forest is the best-suited algorithm for computing chemical element maps of paintings from XRF data.

Transmission Raman Spectroscopy (TRS) was implemented as an ‘Extended’ Content Uniformity (ECU) method for un-coated tablets for a commercial pharmaceutical product. By sampling un-coated tablets throughout the duration of the tablet compression stage, it can be demonstrated that the material from the preceding blend step was of uniform composition, and therefore the blend and compression unit-operations were in a state of control. TRS was selected as a rapid, non-destructive measurement that can be automated through the use of a sample tray that can hold many tablets. In this work, the performance of a multivariate calibration model (PLS) deployed to two Transmission Raman Spectrometers co-located within the same QC laboratory was studied using data obtained over a 3-year period. The aim of the investigation was to assess the impact of various annual instrument maintenance events, and to evaluate several chemometric methods for reducing or eliminating the spectral effects that led to deterioration of a models performance. Linear orthogonal projection approaches such as Transfer by Orthogonal Projection (TOP), Dynamic Orthogonal Projection (DOP) and Unsupervised Dynamic Orthogonal Projection (uDOP) were applied, along with a more recent, non-linear method called Transfer Component Analysis-Orthogonal Projection (TCA-OP). This works shows that each method has merits, depending on the nature of the spectral/model correction required. In most cases, the model performance could be fully restored, or significantly improved. This work also highlights how these various methods can be useful tools to better understand the root-cause for a deterioration in model performance.