Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Enhancing IoT anomaly detection with the Dwarf Mongoose-Chaos optimized deep belief framework","authors":"Veena Potdar ,&nbsp;Mohan Govindasa Kabadi","doi":"10.1016/j.chemolab.2025.105558","DOIUrl":"10.1016/j.chemolab.2025.105558","url":null,"abstract":"<div><div>Anomaly detection is essential for identifying deviations from normal patterns in data, enabling the detection of security breaches or system faults, particularly in Internet of Things (IoT) networks. However, traditional machine learning (ML) and deep learning (DL) methods often struggle with the dynamic and complex nature of IoT environments, where attack patterns are non-linear, continuously evolving, and context-dependent. These models typically require large labeled datasets and retraining to adapt to new threats, which limits their responsiveness and scalability. Additionally, their high computational demands make real-time deployment on resource-constrained IoT devices challenging. Furthermore, many ML/DL models exhibit poor generalization, performing well in controlled scenarios but failing to maintain accuracy across diverse, real-world IoT settings with varying devices, protocols, and data distributions. To address these issues, this work proposes the Dwarf Mongoose-Chaos Optimized Deep Belief (DCODB) Framework, which combines advanced preprocessing, feature selection (FS), and classification techniques. Initial preprocessing involves Min-Max Normalization and One-Hot Encoding to scale numerical features and transform categorical data for effective model input. FS is optimized by the novel Dwarf Mongoose-Chaos Fusion Optimization (DMCFO), which is a swarm intelligence algorithm that leverages chaotic maps to improve the effectiveness of the Dwarf Mongoose Optimization Algorithm (DMO), reducing dimensionality and improving classification accuracy. The refined features are then classified using a Deep Belief Network (DBN), which processes hierarchical feature representations to differentiate between normal and anomalous behaviors in the NSL-KDD dataset. The proposed framework has been thoroughly assessed using diverse metrics, demonstrating its effectiveness in anomaly detection by achieving above 99 % Balanced Accuracy, along with exceptional Precision, Recall, F1 Score, Specificity, and the AUC-ROC curve. These high-performance metrics affirm the model's capability to deliver reliable and scalable anomaly detection in IoT environments, strengthening overall security.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105558"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High precision classification method for black tea: Deep learning combined with two-dimensional correlation spectroscopy","authors":"Long Liu ,&nbsp;Yifan Wang ,&nbsp;Bin Wang ,&nbsp;Xiaoxuan Xu ,&nbsp;Jing Xu","doi":"10.1016/j.chemolab.2025.105580","DOIUrl":"10.1016/j.chemolab.2025.105580","url":null,"abstract":"<div><div>Tea is a widely popular beverage across the globe. However, its medicinal content and value vary from one species to another. As a result, consumers need a quick and efficient method to distinguish between species. This paper introduces a method for species classification using two-dimensional correlation spectroscopy (2DCOS) images combined with deep learning (DL) models. Initially, 345 thin-section samples of five different black teas were prepared, and their near-infrared spectroscopy (NIRS) data were obtained. From this preprocessed one-dimensional NIRS data, 8280 2DCOS contour images and contour fill images were generated. MobileNet model with various bottleneck residual blocks was constructed, and trained using these 2DCOS images as samples, achieving a classification accuracy of 100 %. The model testing results indicated that the optimal NIRS data preprocessing method and 2DCOS image format are Standard Normal Variate transformation (SNV) and contour fill image. Furthermore, the classification results of one-dimensional NIRS data, 2DOCS matrix data, and 2DCOS image data were compared, showing that the 2DCOS images provide higher classification accuracy. Finally, comparative experiments were conducted between the MobileNet model and other deep learning models, demonstrating that the MobileNet model has the advantages of fewer parameters, lower computational load, high accuracy, and fast convergence speed. Therefore, combining 2DCOS images with the MobileNet model for black tea classification is effective. This paper offers a promising approach for the identification of black tea species, with extensive potential applications in species classification.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105580"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling-based computation of the sets of feasible solutions and feasible bands for noisy data","authors":"Mathias Sawall ,&nbsp;Tomass Andersons ,&nbsp;Chunhong Wei ,&nbsp;Christoph Kubis ,&nbsp;Klaus Neymeyr","doi":"10.1016/j.chemolab.2025.105565","DOIUrl":"10.1016/j.chemolab.2025.105565","url":null,"abstract":"<div><div>Multivariate curve resolution often suffers from solution ambiguity, with many nonnegative factorizations fitting the data equally well. Building on the algorithm of Laursen and Hobolth (2022), we present an efficient sampling algorithm that can handle noisy data even containing negative entries. The algorithm iteratively updates factor columns via affine combinations within a nested loop structure, effectively approximating the sets of feasible solutions, the feasible bands, as well as the dual profiles. We apply the algorithm to two <em>in situ</em> FTIR spectroscopic data sets tracking the decomposition and activation of rhodium carbonyl complexes for the hydroformylation process. A comparison against established algorithms for these data sets indicates the robustness and computational efficiency of the algorithm.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105565"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of NIR and Raman spectroscopy for determining used cooking oil properties using chemometric methods","authors":"Ivana Hradecká ,&nbsp;Kateřina Svobodová ,&nbsp;Aleš Vráblík ,&nbsp;Vladimír Hönig","doi":"10.1016/j.chemolab.2025.105552","DOIUrl":"10.1016/j.chemolab.2025.105552","url":null,"abstract":"<div><div>This study compares the performance of near-infrared (NIR) and Raman spectroscopy in the quantitative analysis of used cooking oil (UCO), focusing on critical parameters such as acid value, density, and kinematic viscosity. Monitoring these properties ensures that the feedstock meets the necessary specifications for optimal biofuel production, contributing to the sustainability and performance of the final product. NIR and Raman spectroscopy offers significant advantages by enabling rapid, real-time and non-destructive measurements of several properties at once.</div><div>Partial least squares (PLS) was employed, enabling the correlation between reference results and spectral information obtained by NIR and Raman spectroscopy. NIR spectroscopy demonstrated superior performance compared to Raman spectroscopy in analyzing UCO properties. Results revealed the better performance of NIR spectroscopy for the measurement of acid value (R²_P = 0.99, RMSEP = 0.087 mg KOH g⁻¹, RPD = 8.12), and kinematic viscosity at 40 °C (R²_P = 0.97, RMSEP = 0.325 mm²/s, and RPD = 5.20). Raman spectroscopy was pointed out as the most suitable for the determination of density at 15 °C (R²_P = 0.97, RMSEP = 0.167 kg m⁻³, RPD = 4.20). However, both techniques presented excellent results and are suitable for the accurate determination of UCO propreties.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105552"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marginal region-integrated regressive conditional variational autoencoder-generative adversarial network: A soft sensing enhancement method","authors":"Guo-yu Liu ,&nbsp;Qun-Xiong Zhu ,&nbsp;Yi Luo ,&nbsp;Wei Ke ,&nbsp;Yan-Lin He ,&nbsp;Yang Zhang ,&nbsp;Ming-Qing Zhang ,&nbsp;Yuan Xu","doi":"10.1016/j.chemolab.2025.105577","DOIUrl":"10.1016/j.chemolab.2025.105577","url":null,"abstract":"<div><div>In industrial processes, due to limitations of actual industrial production, many industrial data are difficult to obtain directly, which limits sample size and leads to uneven data distribution, ultimately affecting the fitting performance of soft sensing models. To address this challenge, we propose a marginal Isolation Mega Trend Diffusion with Regressor Conditional Variational Autoencoder-Generative Adversarial Network (IRCVGAN). designed to improve model accuracy by expanding the sample size. Specifically, the proposed method first applies the isolation forest algorithm to detect sparse marginal regions in the dataset, followed by Mega Trend Diffusion (MTD) to broaden the range of input data by generating virtual samples, thus increasing dataset diversity. Next, an improved regressive conditional Variational Autoencoder-Generative Adversarial Network (RCVAEGAN) is developed to perform fine-grained selection on the virtual samples generated by MTD. Furthermore, the mapping between input variables and production quality indicators is embedded in RCVAEGAN, enhancing the representativeness of the samples and improving the model’s fitting accuracy, the effectiveness of our proposed method is validated through function fitting tests and real-world industrial data from a purified terephthalic acid (PTA) solvent system.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105577"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of caper bud drying using the DT_LSBOOST model: A predictive approach to improve quality and efficiency","authors":"Chafika Lakhdari ,&nbsp;Hocine Remini ,&nbsp;Samia Djellal ,&nbsp;Meriem Adouane ,&nbsp;Hichem Tahraoui ,&nbsp;Abdeltif Amrane ,&nbsp;Farid Dahmoune ,&nbsp;Merve Yavuz-Düzgün ,&nbsp;Elif Feyza Aydar ,&nbsp;Evren Demircan ,&nbsp;Zehra Mertdinç ,&nbsp;Beraat Ozçelik ,&nbsp;Nabil Kadri","doi":"10.1016/j.chemolab.2025.105585","DOIUrl":"10.1016/j.chemolab.2025.105585","url":null,"abstract":"<div><div><em>Capparis spinosa</em> L. buds undergo salting and drying to enhance their shelf life and organoleptic properties. This study evaluates the impact of four drying methods: oven drying (OD), vacuum drying (VD), freeze-drying (FD), and microwave drying (MD) on the physicochemical, antioxidant, and microbiological properties of dried caper buds. Salting reduced the initial moisture content from 508.50 % to 168.59 % (db), while drying further decreased it to approximately 9 %. Drying time varied significantly, with MD achieving the shortest duration (0.19–0.75h) and OD requiring the longest (reaching 49.66h). FD exhibited the highest energy consumption (60.77 kWh/kg), followed by VD, while OD and MD were the least energy-intensive (0.54–3.10 kWh/kg and 1.34–2.18 kWh/kg, respectively). FD preserved the most chlorophyll (193.63 μg/g DW) and total phenolic content (28.98 mgGAE/g DW), whereas MD at 200 W resulted in the lowest TPC (9.88 mgGAE/g DW). FD samples also showed superior antioxidant activities in both ABTS and FRAP assays. In contrast, OD and MD increased browning and degraded quality attributes. Multivariate analyses (PCA and clustering) highlighted FD as optimal for preserving quality, while MD was the most detrimental. Microbiological analysis confirmed that dried capers met food safety standards. A predictive model using Decision Tree coupled with Least Squares Boosting (DT_LSBOOST) achieved exceptional accuracy (R = 0.9999, RMSE = 0.0564, ESP = 0.2028, MAE = 0.0305), providing a reliable tool for optimizing drying parameters. Overall, freeze-drying emerged as the best method to retain nutritional and bioactive properties of capers, and the developed predictive model offers an innovative approach to enhancing caper processing efficiency.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105585"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and statistical analysis of cancer drugs using M-polynomial indices for their characteristics","authors":"Qasem M. Tawhari ,&nbsp;Muhammad Naeem ,&nbsp;Saba Maqbool ,&nbsp;Syed Muhammad Kashif Raza ,&nbsp;Adnan Aslam","doi":"10.1016/j.chemolab.2025.105590","DOIUrl":"10.1016/j.chemolab.2025.105590","url":null,"abstract":"<div><div>This study computes M-polynomial indices for Doxorubicin and Mitoxantrone, two widely used anthracycline and anthracenedione anticancer drugs, respectively. Doxorubicin, a potent topoisomerase II inhibitor, is commonly employed in treating various cancers, including breast, ovarian, and leukemia. Mitoxantrone, with its unique DNA-intercalating properties, is effective against acute myeloid leukemia, breast cancer, and non-Hodgkin’s lymphoma. We produced M-polynomial indices by partitioning graph edges depending on degree and adjacency matrix. A Python algorithm is written using an adjacency matrix to efficiently compute the indices, reducing calculation time from days to minutes and eliminating human error. Simple linear regression models in SPSS software are used to create QSPR and predict the physical attributes of cancer medicines. Our findings show that M-polynomial indices accurately predict physical attributes, providing important insights into the structural requirements for maximum anticancer action. In addition, we proposed models for each physical attribute. This study aids in the development of new cancer therapies and the prediction of physical features for uncharacterized medications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105590"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extraction of soil nutrient information from visible and near-infrared signals using deep learning models","authors":"Chunru Xiong ,&nbsp;Jufang Hu ,&nbsp;Ken Cai ,&nbsp;Fangxiu Meng ,&nbsp;Qinyong Lin ,&nbsp;Huazhou Chen","doi":"10.1016/j.chemolab.2025.105561","DOIUrl":"10.1016/j.chemolab.2025.105561","url":null,"abstract":"<div><div>This study aims to combine the deep learning algorithm and the visible and near-infrared (Vis-NIR) spectroscopy technology to build a soil nutrient information extraction model. A deep learning framework based on Long Short-Term Memory (LSTM) is proposed to establish optimal calibration model for the analysis of the full range of Vis-NIR spectral data. Moreover, an influence function is designed to select the informative wavelength variables, which is an important goal in engineering application of spectroscopy for reducing the model dimensionality and enhancing model robustness. Experiment was performed for the prediction of nitrogen (N), phosphorus (P) and potassium (K) contents of soil. The modeling results showed that the proposed model could improve the modeling efficiency of soil nutrient information extraction, and also obtained higher accuracy in the modeling and predictive procedures than the conventional model. This will provide effective response to the challenges in engineering applications, to promote the Vis-NIR spectroscopy technology be applied for fast detection, and to obtain robust models with high precisions in soil nutrient information extraction process.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105561"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145398088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality-driven divisive K-Means: A new clustering strategy for MALDI imaging data for a more precise and less biased characterization of complex biological tissues","authors":"Ruggero Guerrini ,&nbsp;Nina Ogrinc ,&nbsp;Emilien Colin ,&nbsp;Riad Tebbakha ,&nbsp;Christophe Attencourt ,&nbsp;Ahmed Boudahi ,&nbsp;Sylvie Testelin ,&nbsp;Stéphanie Dakpé ,&nbsp;Isabelle Fournier ,&nbsp;Ludovic Duponchel","doi":"10.1016/j.chemolab.2025.105578","DOIUrl":"10.1016/j.chemolab.2025.105578","url":null,"abstract":"<div><div>Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) has become the foremost technique for molecular characterization of complex biological tissues, owing to its unparalleled sensitivity, broad molecular coverage, and high spatial resolution. While targeted analysis is traditionally dominated MALDI imaging workflows, the inherent limitations of hypothesis-driven approaches have fueled interest in untargeted strategies. Clustering, particularly k-means-based methods, has emerged as a powerful tool for exploring spectral datasets without predefined assumptions. However, conventional k-means struggles with heterogeneous spectral distributions, prompting the adoption of bisecting k-means in MALDI imaging. Despite its hierarchical structure, bisecting k-means introduces biases by arbitrarily merging or fragmenting clusters, potentially distorting biological interpretations. This study introduces Quality-Driven Divisive k-means, a novel clustering approach that retains the hierarchical nature of bisecting k-means while dynamically optimizing the number of clusters at each partitioning level. Using MALDI imaging of squamous cell carcinoma tissues from the tongue, we illustrate the potential of Quality-Driven Divisive k-means to provide a more faithful representation of molecular architectures, mitigating the distortions inherent to fixed binary partitioning. Our findings suggest that adaptive clustering methodologies could enhance spectroscopic imaging, paving the way for more accurate tissue characterization in biomedical and clinical research.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105578"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature extraction using differential amplification singular value decomposition in Vis–NIR spectroscopy: Application to cigarette brand identification","authors":"Biao Tang ,&nbsp;Chengbo Yang ,&nbsp;Jianchun Li ,&nbsp;Jingjun Wu","doi":"10.1016/j.chemolab.2025.105579","DOIUrl":"10.1016/j.chemolab.2025.105579","url":null,"abstract":"<div><div>Rapid and accurate identification of cigarette brands is crucial for combating counterfeiting and protecting tax revenue. Vis–NIR spectroscopy combined with machine learning is a promising identification method. Nevertheless, redundant information abounds in high-dimensional spectral data, which affects classification accuracy. To address this challenge, this study proposes a novel feature extraction method ― Differential amplification singular value decomposition (DA-SVD). This method optimizes the feature projection direction by amplifying both the individual differences among samples and the overall differences between classes, thereby achieving effective dimensionality reduction of spectral data. By applying DA-SVD, the classification accuracy of KNN, SVM, and RF models on the test set significantly increased from 36 %, 34 %, and 30 % (based on the original data) to 98 % for all models, with precision, sensitivity, and F1 score reaching 97.86 %, 98.14 %, and 97.86 %, respectively, and all outperforming conventional feature extraction methods such as LDA, SVD, and PCA. The experimental results further demonstrated that DA-SVD could achieve satisfactory classification performance without additional preprocessing steps (outlier detection and spectral denoising). In addition, the 10-fold cross-validation results confirmed the stability of the DA-SVD method, and validation on public datasets further demonstrated its generalization ability and applicability. Overall, DA-SVD provides an efficient and robust feature extraction strategy that, when combined with machine learning, enables reliable cigarette brand identification and has broad potential for other spectroscopic applications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105579"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}