Journal of Chemometrics最新文献

The quantitative structure–activity relationship (QSAR) as an effective and promising model to better understands the relationship between chemical activity and chemical compounds is usually used in modeling chemical datasets. Kernel ridge regression (KRR) has attracted the interest of scholars recently because of its non-iterative methodology for problem solving. KRR is a highly regarded and practical machine learning approach that has successfully tackled classification and regression issues. So is a regression method that uses a nonlinear kernel function to define an inner product in a higher-dimensional transformed space. This allows for generalization performance based on regularization least squares solution. However, the performance of KRR is affected by the choices of the values of the hyper-parameters that define the type of kernel. This has a major processing cost, uses memory, and is also accompanied by poor accuracy performance when studying the prior methods of determining these hyper-parameter values. Thus, the main highlighted enhancement in this paper is the enhancement of the coati optimization algorithm by applying elite opposite-based learning to increase the density of population around the search space to optima for the proper selection of the best hyperparameters. Thus, it is necessary to verify and compare its work with the proposed improvement of KRR in increasing its performance, seven public chemical datasets were used. Based on several assessment criteria, the results show that the proposed improvement is superior to all the baseline methods regarding the classification performance.

In critical domains including medicinal chemistry, biomedicine, metabolomics, and computational toxicology, class imbalance in datasets and poor recognition accuracy for minority classes remain persistent challenges. While previous studies have employed resampling and feature selection techniques to address data imbalance and enhance classification performance, most approaches have focused on single-algorithm solutions rather than hybrid methodologies. Hybrid algorithms offer distinct advantages by integrating the strengths of multiple techniques, thereby providing more comprehensive and efficient solutions for handling imbalanced data. This study proposes HiBBKA, a novel hybrid algorithm combining radial-based under-sampling with SMOTE (RBU-SMOTE) and an improved binary black-winged kite algorithm (iBBKA) for feature selection. The proposed framework operates through two key phases: First, the RBU-SMOTE resampling method synergistically integrates radial-based under-sampling (RBU) with the synthetic minority oversampling technique (SMOTE), effectively addressing class-imbalance distribution while enhancing the quality of synthesized samples. Second, the enhanced iBBKA feature selection algorithm systematically identifies the most discriminative features critical for classification tasks. We comprehensively evaluate RBU-SMOTE and HiBBKA using multiple classifiers across 16 imbalanced datasets, including real-world medical datasets, with particular emphasis on the minority class performance. Experimental results demonstrate that RBU-SMOTE achieves competitive performance compared to existing resampling methods, while the complete HiBBKA framework significantly outperforms state-of-the-art algorithms in overall classification metrics, particularly in the minority class recognition.

C. torulosa, known as the Himalayan or Bhutan cypress, is a significant evergreen conifer that typically reaches heights between 20 and 45 m. This species is primarily found in the Himalayan regions of Bhutan, northern India, Nepal, and Tibet. In this study, we utilized ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in positive ion mode, along with chemometric analysis, to investigate the metabolomic profiles of C. torulosa needles collected from 14 geographically distinct areas in Uttarakhand and Himachal Pradesh. Various statistical techniques, including ANOVA, Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), violin plots, scatter plots, box-and-whisker plots, and heatmaps, were employed to illustrate the relative quantitative differences among compounds based on their peak intensities across these regions. Our investigation revealed 34 marker compounds consistently detected across all samples (locations). These compounds were screened using rigorous filtering criteria, incorporating a moderated t-test and multiple testing adjustments using the Benjamini–Hochberg false discovery rate (FDR) approach. Furthermore, we pioneered the identification of the phenylpropanoid and flavonoid biosynthesis pathways in C. torulosa, providing new insights into its metabolic profile. This work establishes a foundational reference for future research into the species metabolome, helping guide studies in areas like genetic diversity, ecological adaptations, and climate resilience in C. torulosa. Mapping these pathways deepens scientific knowledge of C. torulosa's metabolic processes, contributing to a clearer understanding of its unique biochemical makeup.

The process of selecting representative samples is crucial for establishing an accurate calibration model. To enhance the representativeness of the samples, a method for sample selection, utilizing the degree of anomaly as the evaluation criterion, is proposed. Initially, anomaly scores corresponding to various detection methods are obtained to ensure a comprehensive evaluation. These scores are then normalized by the confidence lower limit to establish a consistent scoring criterion. Subsequently, the weights of different detection methods are determined through eigenvector centrality analysis of a graph, where the methods serve as nodes and the similarity acts as weighted edges. Finally, the comprehensive anomaly scores are computed as the sum of weighted scores and are subsequently sorted. Representative samples are selected using a uniformly spaced sampling approach, with the spacing determined by a predefined and provided sample number. The efficacy of the method is validated across different sample sets.

The diffusion map–based k-nearest neighbor (DM-kNN) rule faces two challenges in multimode batch process monitoring. Firstly, the DM method encounters difficulties in projecting new samples. The training samples are repeatedly feature extracted, resulting in a time-consuming process. Faulty samples may be merged into normal samples and modeled together, which does not meet the requirements for fault detection. Secondly, DM-kNN has poor monitoring performance for multimode processes with significant variance differences. This paper proposes a technique called the expandable DM–based weighted k-nearest neighbor (EDM-WkNN) to solve these two issues. The expandable DM constructs a local projection matrix to attain the projecting of new samples. The effect of mode variance differences is eliminated by introducing weighted distances in statistic to overcome the difficulties caused by variance differences. We compare EDM-WkNN with classical fault detection methods through numerical examples and the fed-batch fermentation penicillin (FBFP) process. Our experiments confirm that the EDM-WkNN method effectively monitors faults in multimode batch processes.

This study designs a statistical process control tool that effectively detects small and moderate shifts in process parameters, to address challenges in quality monitoring. The proposed control chart employs advanced statistical detection techniques to enhance sensitivity while reducing false alarms, thus improving detection performance in various applications. This methodology is applied in a real-life context within an aquatic ecotoxicology laboratory, where daily monitoring of water pH levels is essential for safeguarding the health of sensitive aquatic organisms, such as mysids. The laboratory environment is meticulously controlled to simulate natural conditions, and our application of the proposed control chart ensures that any deviations from the optimal pH level are detected promptly, thereby maintaining water quality and supporting the reliability of experimental outcomes. The paper comprehensively evaluates the performance of the proposed control chart in both zero-state and steady-state conditions, offering valuable insights for practitioners in the field. We present empirical evidence demonstrating that the proposed control chart significantly outperforms traditional control charts, including Shewhart, CUSUM, and EWMA, particularly in detecting small to moderate shifts in water pH levels. Furthermore, we provide optimal parameter settings tailored for specific monitoring scenarios, enhancing the applicability of proposed control chart for quality control in laboratory environments.