Chemometrics and Intelligent Laboratory Systems最新文献_第2页

Non-invasive diagnosis of common glomerular diseases via Raman spectroscopy and machine learning: an integrated blood and urine analysis approach 通过拉曼光谱和机器学习无创诊断常见肾小球疾病：一种综合血液和尿液分析方法

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-21 DOI: 10.1016/j.chemolab.2026.105630

Mengyu Wu , Yuan Cao , Ruiyang Wang , Chongxuan Tian , Yang Li , Zunsong Wang

Background

Percutaneous renal biopsy faces three major challenges in clinical management: inherent procedural risks, inability to serially monitor disease activity, and sampling variability. These limitations underscore the demand for safer, repeatable diagnostic tools.

Objective

Our objective was to explore the potential of a liquid biopsy strategy utilizing paired blood and urine analysis via Raman spectroscopy and a 1D-CNN to facilitate the differentiation of common glomerular diseases from each other and from healthy individuals.

Methods

From January 2021 to January 2025, we collected serum and first-void morning urine from 170 biopsy-confirmed patients (81 membranous nephropathy, 36 IgA nephropathy, 33 diabetic nephropathy, 20 focal segmental glomerulosclerosis) and 21 healthy volunteers. Spectra were acquired on an Attenuated Total Reflection-8300 (ATR-8300) instrument (785 nm excitation) and preprocessed via third-order polynomial baseline correction and 13-point Savitzky–Golay smoothing. A 1D-CNN was trained on the combined spectral data; performance was assessed by accuracy, sensitivity, specificity, and Receiver Operating Characteristic - Area Under the Curve (ROC-AUC).

Results

The 1D-CNN model achieved 80.0 % accuracy, 76.2 % sensitivity, and 81.3 % specificity in five-class classification. ROC-AUCs ranged from 0.81 (FSGS) to 0.85 (IgA nephropathy), confirming robust discrimination across disease subtypes and controls. Characteristic Raman bands—e.g. phenylalanine (∼1003 cm⁻¹), Amide I (∼1655 cm⁻¹), and C–H stretching (2800–3000 cm⁻¹)—differed systematically among cohorts, reflecting underlying biochemical alterations.

Conclusions

Raman spectroscopy of paired blood and urine, coupled with deep learning, provides a rapid, label-free approach for minimally invasive classification of glomerular diseases. This integrated liquid biopsy strategy may enable early detection and precise stratification in nephrology, reducing reliance on invasive biopsy and informing personalized therapy.

背景：经皮肾活检在临床管理中面临三大挑战：固有的程序风险、无法连续监测疾病活动以及采样的可变性。这些限制强调了对更安全、可重复的诊断工具的需求。我们的目的是探索液体活检策略的潜力，利用拉曼光谱和1D-CNN对血液和尿液进行配对分析，以促进常见肾小球疾病彼此之间和健康个体之间的区分。方法从2021年1月至2025年1月，收集170例活检确诊患者（膜性肾病81例，IgA肾病36例，糖尿病肾病33例，局灶节段性肾小球硬化20例）和21名健康志愿者的血清和首次空晨尿。在衰减全反射-8300 （ATR-8300）仪器（785 nm激发）上获取光谱，并通过三阶多项式基线校正和13点Savitzky-Golay平滑进行预处理。在组合光谱数据上训练1D-CNN；通过准确性、灵敏度、特异性和受试者工作特征-曲线下面积（ROC-AUC）来评估疗效。结果1D-CNN模型在五类分类中准确率为80.0%，灵敏度为76.2%，特异度为81.3%。roc - auc范围从0.81 （FSGS）到0.85 （IgA肾病），证实了疾病亚型和对照之间的强大区别。特征拉曼波段：苯丙氨酸（~ 1003 cm−1）、酰胺I （~ 1655 cm−1）和C-H拉伸（2800-3000 cm−1）在队列中存在系统性差异，反映了潜在的生化改变。结论配对血液和尿液的拉曼光谱，结合深度学习，为肾小球疾病的微创分类提供了一种快速、无标记的方法。这种综合液体活检策略可以实现肾脏学的早期检测和精确分层，减少对侵入性活检的依赖，并为个性化治疗提供信息。

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引用次数: 0

Innovative approaches in anti-inflammatory research: QSAR-SVM-PCA, In vivo insights, and molecular docking of Laurus nobilis 抗炎研究的创新方法：QSAR-SVM-PCA，体内观察，以及月桂的分子对接

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-19 DOI: 10.1016/j.chemolab.2026.105634

Nassrine Ouafi , Asma Belkadi , Mohammed Kebir , Hichem Tahraoui , Walid Elfalleh , Fehmi Boufahja , Nasir A. Ibrahim , Nosiba S. Basher , Amin Mousavi Khaneghah , Noureddine Nesralah , Hamza Mousssa , Kahina Ighilahriz , Mustapha Mounir Bouhenna , Yacine Benguerba , Abdeltif Amrane

This study evaluates the anti-inflammatory potential of the ethanolic extract of Laurus nobilis leaves in chronic (acetic acid-induced ulcerative colitis) and acute (carrageenan-induced paw edema) inflammation models in vivo. Molecular docking studies were performed to investigate interactions between key phenolic compounds, including rutin, quercetin, and β-carotene, and inflammatory mediators such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and cyclooxygenase-2 (COX-2). The extract was obtained through cold maceration and analyzed via UPLC-MS/MS, identifying major compounds. In the chronic inflammation model, the Laurus nobilis extract significantly decreased the clinical disease index (weight loss, stool consistency, and rectal bleeding), reduced the colon weight-to-length ratio, and showed significant histological improvements compared to the control group. The acute model demonstrated a 68.84 % reduction in edema, comparable to ibuprofen's effect of 66.14 %. Molecular docking revealed strong binding affinities between β-carotene, quercetin, and rutin with COX-2, suggesting possible inhibition of inflammatory responses.

In contrast, quercetin and rutin exhibited complex interactions through hydrogen bonding with TNF-α and COX-2 active sites. A QSAR model, developed using Support Vector Machines (SVM) and Principal Component Analysis (PCA), effectively predicted the biological activity of the phenolic compounds, demonstrating high predictive capability and robustness. These findings support the traditional use of Laurus nobilis for managing inflammatory conditions and highlight its potential as an alternative therapeutic agent for inflammatory diseases, warranting further research.

本研究评估月桂叶乙醇提取物在慢性（醋酸诱导的溃疡性结肠炎）和急性（卡拉胶诱导的足跖水肿）炎症模型中的抗炎潜力。通过分子对接研究，研究了芦丁、槲皮素和β-胡萝卜素等关键酚类化合物与肿瘤坏死因子-α (TNF-α)、白细胞介素-1β (IL-1β)和环氧化酶-2 （COX-2）等炎症介质之间的相互作用。提取液经冷浸得到，通过UPLC-MS/MS进行分析，鉴定主要化合物。在慢性炎症模型中，月牙提取物显著降低了临床疾病指数（体重减轻、大便粘稠度、直肠出血），降低了结肠重长比，并与对照组相比有明显的组织学改善。急性模型显示水肿减少68.84%，与布洛芬66.14%的效果相当。分子对接发现β-胡萝卜素、槲皮素和芦丁与COX-2有很强的结合亲和力，提示可能具有抑制炎症反应的作用。相反，槲皮素和芦丁通过与TNF-α和COX-2活性位点的氢键表现出复杂的相互作用。利用支持向量机（SVM）和主成分分析（PCA）建立的QSAR模型能够有效预测酚类化合物的生物活性，具有较高的预测能力和鲁棒性。这些发现支持了月桂用于治疗炎症的传统用途，并突出了其作为炎症疾病替代治疗剂的潜力，值得进一步研究。

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引用次数: 0

Noise-robust contrastive ensemble learning for flotation process monitoring 面向浮选过程监测的噪声鲁棒对比集成学习

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-23 DOI: 10.1016/j.chemolab.2026.105649

Mingxi Ai , Jin Zhang , Zhaohui Tang , Yongfang Xie

Froth flotation is a widely used mineral beneficiation technique, where effective process monitoring is essential for optimizing mineral separation. However, in practical industry, manual labeling suffers from noises, leading to a significant portion of incorrectly labeled data. Though deep learning monitoring models are powerful in capturing complex visual patterns, their high capacity makes them vulnerable to overfitting noisy labels, hindering robust model development. To address this challenge, this study proposes a noise-robust contrastive ensemble learning method for practical industrial process monitoring. The method first constructs multiple diverse monitoring models in distinct representation spaces using a novel disparity contrastive learning strategy. Then, clean and mislabeled data for each sub-model are distinguished by measuring the inter-model consensus and intra-model uncertainty of its peer models. Finally, a structure-consistency-based semi-supervised learning strategy is proposed to refine these sub-models by treating mislabeled data as unlabeled, encouraging representation-aligned predictions through mutual information maximization. Through iterative noisy-label identification and semi-supervised refinement, robust monitoring model are obtained even with heavily corrupted training data. Extensive experiments on industrial froth flotation data demonstrate the effectiveness and advantages of the proposed method compared to existing state-of-the-art noise-robust learning techniques.

泡沫浮选是一种应用广泛的选矿技术，有效的过程监控是优化选矿的关键。然而，在实际工业中，人工标注受到噪声的影响，导致很大一部分标注错误的数据。虽然深度学习监测模型在捕获复杂的视觉模式方面很强大，但它们的高容量使它们容易受到过拟合噪声标签的影响，从而阻碍了鲁棒模型的开发。为了解决这一挑战，本研究提出了一种用于实际工业过程监测的噪声鲁棒对比集成学习方法。该方法首先使用一种新的视差对比学习策略在不同的表示空间中构建多个不同的监测模型。然后，通过测量同级模型的模型间一致性和模型内不确定性来区分每个子模型的干净和错误标记数据。最后，提出了一种基于结构一致性的半监督学习策略，通过将错误标记的数据视为未标记的数据来改进这些子模型，并通过相互信息最大化来鼓励表征一致的预测。通过迭代噪声标签识别和半监督改进，即使在训练数据严重损坏的情况下也能获得鲁棒监测模型。工业泡沫浮选数据的大量实验表明，与现有的最先进的噪声鲁棒学习技术相比，所提出的方法具有有效性和优越性。

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引用次数: 0

Evaluating calibration models in isotope geochemistry: Lessons from carbonates and sulfides 评估同位素地球化学中的校准模型：来自碳酸盐和硫化物的教训

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-23 DOI: 10.1016/j.chemolab.2026.105640

Alban Petitjean , Olivier Musset , Ludovic Duponchel , Christophe Thomazo

Geology routinely employs isotopic geochemistry with the main objective of measuring radiogenic or stable isotopic compositions to reconstruct the history of the Earth. A critical aspect of this analytical process lies in verifying the accuracy and reliability of the measurements performed. To this end, standards or reference materials are repeatedly analyzed enabling calibration or adjustment of experimental instruments. In order to ensure a strong correlation between the reference values and the averaged measurements, a linear regression is the most widely adopted. Among the available methodologies, this work advocates for the use of models compliant with the ISO 28037:2010 standard, which is specifically designed to perform linear regression in a statistically robust manner. The guidelines established by this standard are, regrettably, not always implemented correctly, and the statistical nature of the measurements is frequently overlooked. This study provides a detailed examination of the methodologies advocated by the standard, with the objective of facilitating their application to geochemical problems specifically, issues related to isotopic measurement by revisiting the underlying theoretical principles, assumptions, and the respective advantages and limitations inherent to each approach. To facilitate implementation and respect recommendations, we propose a software application developed in Python 3.14. This computational tool has been tested and validated using experimental datasets obtained from isotopic analyses of carbon, oxygen, and sulfur elements of fundamental interest in geological studies. The objective of this study is therefore to clearly and practically illustrate the challenges involved in geochemical calibration and adjustment.

地质学通常使用同位素地球化学，其主要目的是测量放射性成因或稳定同位素组成，以重建地球的历史。该分析过程的一个关键方面在于验证所进行测量的准确性和可靠性。为此，反复分析标准或参考物质，以便校准或调整实验仪器。为了保证参考值和平均测量值之间有很强的相关性，最广泛采用的是线性回归。在可用的方法中，本工作提倡使用符合ISO 28037:2010标准的模型，该模型专门用于以统计稳健的方式执行线性回归。遗憾的是，这个标准所建立的指导方针并不总是得到正确的执行，而且测量的统计性质经常被忽视。本研究对该标准所倡导的方法进行了详细的审查，目的是通过重新审视每种方法的基本理论原理、假设以及各自固有的优点和局限性，促进它们在地球化学问题上的具体应用，特别是与同位素测量有关的问题。为了方便实现和尊重建议，我们提出了一个用Python 3.14开发的软件应用程序。这个计算工具已经使用从碳、氧和硫元素的同位素分析中获得的实验数据集进行了测试和验证，这些元素是地质研究中最基本的兴趣。因此，本研究的目的是清楚而实际地说明地球化学定标与平差所涉及的挑战。

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引用次数: 0

Automated spectral peak detection with machine learning: Parameter optimization and effective parameter space analysis with SciPy’s find_peaks 基于机器学习的自动光谱峰检测：使用SciPy的find_peaks进行参数优化和有效参数空间分析

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-27 DOI: 10.1016/j.chemolab.2026.105651

Thanakrit Yoongsomporn, Sivakorn Kanharattanachai, Pongsapak Lueangratana, Tsubasa Yamashita, Chia Hsiu Chen, Mitsuru Irie, Chaiyanut Jirayupat

We propose a machine learning approach for automating parameter selection in the “find_peaks” function from Python’s SciPy module, a widely-used threshold-based tool for peak detection. Our method determines optimal detection parameters by analyzing the unique characteristics of each spectrum, eliminating the need for manual parameter tuning. The model, trained and cross-validated on 2000 generated spectra with diverse characteristics, achieved an average F1-score of 0.940 for peak identification. Moreover, the peak detection performance of our proposed method was validated using experimental spectra, delivering F1-scores of 0.952, 0.946, and 0.893 for XRD, GC–MS, and Raman spectra respectively, significantly outperforming both default parameter configurations and CNN-based detection approaches. Our analysis revealed that optimal parameters exist within ranges called “effective parameter spaces” that vary based on each spectrum’s characteristics. This finding confirms that our model effectively captures the relationship between spectral properties and their corresponding effective parameter spaces, resulting in consistently high performance across diverse spectral data types.

我们提出了一种机器学习方法，用于在Python的SciPy模块中的“find_peaks”函数中自动选择参数，SciPy是一种广泛使用的基于阈值的峰值检测工具。我们的方法通过分析每个光谱的独特特征来确定最佳检测参数，从而消除了手动调整参数的需要。该模型对2000个具有不同特征的光谱进行训练和交叉验证，峰识别的平均f1得分为0.940。此外，利用实验光谱验证了我们提出的方法的峰检测性能，XRD， GC-MS和Raman光谱的f1得分分别为0.952,0.946和0.893，显著优于默认参数配置和基于cnn的检测方法。我们的分析表明，最佳参数存在于“有效参数空间”的范围内，该范围根据每个光谱的特征而变化。这一发现证实了我们的模型有效地捕获了光谱属性与其相应的有效参数空间之间的关系，从而在不同的光谱数据类型中保持一致的高性能。

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引用次数: 0

Predictive modeling of Oil–Water interfacial tension using biosurfactant parameters and machine learning approaches 基于生物表面活性剂参数和机器学习方法的油水界面张力预测建模

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-08 DOI: 10.1016/j.chemolab.2026.105632

Mustafa Abdullah , Raed Alfilh , Tariq Abdulkader Alrihaim , Raghavendra Rao P S , Abinash Mahapatro , Karthikeyan A , Harjot Singh Gill , Yashwant Singh Bisht , Siya Singla , Tabib Shahzada

Oil–water interfacial tension (IFT) governs many industrial phenomena in petroleum recovery and environmental remediation, yet its experimental determination under complex biosurfactant–crude oil–water conditions is laborious and resource-intensive. This study aimed to develop a robust machine learning framework capable of accurately predicting IFT from physicochemical descriptors of biosurfactant and crude oil systems, thereby reducing empirical dependency through data-driven modeling. A dataset containing 1480 laboratory measurements was compiled from peer-reviewed sources and characterized by eight explanatory variables, Head charge, Molecular Weight, hydroxyl and carboxyl group counts (OH, COOH), biosurfactant concentration, oil API gravity, oil–air interfacial tension, and acid number. Models including Decision Tree, Random Forest, AdaBoost, Ensemble Learning, Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP-ANN) were optimized and evaluated using five-fold cross-validation with R², MSE, and AARE % metrics. Results evidenced superior performance for ensemble-based algorithms, particularly Random Forest (R² = 0.957, MSE = 0.46) and AdaBoost (R² = 0.978, MSE = 0.24), exhibiting high stability and minimal prediction error. SHAP (SHapley Additive exPlanations) analysis identified COOH and oil compositional variables (API and acid number) as the most influential predictors, aligning with theoretical expectations regarding polarity and molecular orientation at the oil–water interface. Overall findings demonstrate that properly tuned ensemble learning provides a physically interpretable, highly accurate surrogate to laboratory IFT measurement, revealing clear structural–functional dependencies across biosurfactant systems and supporting its broader integration into predictive material design and green petroleum applications.

油水界面张力（IFT）控制着石油开采和环境修复中的许多工业现象，但在复杂的生物表面活性剂-原油-水条件下进行油水界面张力的实验测定是费力且耗费资源的。本研究旨在开发一个强大的机器学习框架，能够从生物表面活性剂和原油系统的理化描述符中准确预测IFT，从而通过数据驱动的建模减少对经验的依赖。数据集包含1480个实验室测量数据，由8个解释变量组成：头电荷、分子量、羟基和羧基计数（OH、COOH）、生物表面活性剂浓度、石油API重力、油气界面张力和酸值。包括决策树、随机森林、AdaBoost、集成学习、支持向量回归（SVR）、卷积神经网络（CNN）和多层感知器（MLP-ANN）在内的模型进行了优化，并使用R2、MSE和AARE %指标进行了五重交叉验证。结果表明，基于集合的算法具有较好的预测性能，特别是Random Forest （R2 = 0.957, MSE = 0.46）和AdaBoost (R2 = 0.978, MSE = 0.24)，具有较高的稳定性和最小的预测误差。SHAP （SHapley Additive exPlanations）分析确定COOH和油成分变量（API和酸值）是最具影响力的预测因素，与油水界面极性和分子取向的理论预期一致。总体研究结果表明，适当调整的集成学习为实验室IFT测量提供了物理上可解释的、高度准确的替代方法，揭示了生物表面活性剂系统之间清晰的结构-功能依赖关系，并支持其更广泛地集成到预测材料设计和绿色石油应用中。

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引用次数: 0

MGD-CNN: An end-to-end convolutional neural network model for collaborative preprocessing of Raman spectra MGD-CNN：一种用于拉曼光谱协同预处理的端到端卷积神经网络模型

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-30 DOI: 10.1016/j.chemolab.2026.105655

Yuyan Liao , Zilong Wang , Yunfeng Li , Yuting Li , Pei Liang

This paper introduces an end-to-end convolutional neural network—Morphology-Gaussian Guided Dual-Branch Convolutional Neural Network (MGD-CNN)—for Raman spectral preprocessing, designed to integrate baseline estimation and signal denoising through a collaborative training mechanism. This approach enhances signal quality while preserving key spectral features. The method employs a dual-module co-training scheme that unifies baseline estimation and denoising into a single convolutional network, utilizing a customized deep convolutional architecture to automatically learn spectral characteristics, enabling fully automated signal processing. In the comparative evaluation of preprocessing performance, the proposed model achieves a spectral signal-to-noise ratio (SSNR) of 562.93, a coefficient of determination (R²) of 0.9572, and a root mean square error (RMSE) of 0.0242, significantly outperforming conventional methods and setting a new benchmark for these core metrics. Furthermore, in downstream classification tasks, the preprocessed spectra improve the classification accuracy to 99.15 %, underscoring the method's exceptional ability to preserve discriminative spectral information. This method provides a high-precision, efficient, and adaptive preprocessing solution for Raman spectral analysis.

本文介绍了一种用于拉曼光谱预处理的端到端卷积神经网络形态学-高斯引导双分支卷积神经网络（MGD-CNN），通过协同训练机制将基线估计和信号去噪结合起来。这种方法在保留关键频谱特征的同时提高了信号质量。该方法采用双模块协同训练方案，将基线估计和去噪统一到一个卷积网络中，利用定制的深度卷积架构自动学习频谱特征，实现全自动化信号处理。在预处理性能的对比评价中，该模型的频谱信噪比（SSNR）为562.93，决定系数（R2）为0.9572，均方根误差（RMSE）为0.0242，显著优于传统方法，为这些核心指标设定了新的基准。此外，在下游分类任务中，预处理后的光谱分类精度提高到99.15%，表明该方法具有出色的保留鉴别光谱信息的能力。该方法为拉曼光谱分析提供了一种高精度、高效、自适应的预处理方案。

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引用次数: 0

Time-resolved simulation of hybrid nano-milk flow in an electromagnetic vibration channel with parabolic thermal ramping: A Python AI approach 具有抛物型热斜坡的电磁振动通道中混合纳米奶流动的时间分辨模拟：Python AI方法

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-26 DOI: 10.1016/j.chemolab.2026.105647

Sanatan Das , Poly Karmakar

This research paper explores the innovative application of artificial intelligence (AI) in understanding the behaviors of silver and magnesium oxide nanoparticles within milk flow. This study utilizes a specially designed vibrating electromagnetic channel to observe the effects under controlled parabolic thermal ramping and oscillatory pressure variations. This framework couples essential physical mechanisms-radiative emission, thermal sinks, and porous matrix interactions-where Darcy's law quantifies the permeability-driven viscous drag. The mechanics of milk flow through an electromagnetically activated channel are meticulously formulated and solved using mathematical and computational methods, with the Laplace transform (LT) technique facilitating a streamlined solution to the equations. The analysis concentrates on flow metrics, presenting results through detailed graphical representations. Significant findings comprise the enhancement of thermal conductivity and flow viscosity due to the nanoparticles, which improve heat transport efficiency and modify flow patterns. The operational control of milk flow dynamics shows dual dependencies-momentum amplification via electromagnetic intensity (Hartmann number) versus suppression through electrode spacing, while thermal management reveals frequency-dependent shear stress (SS) augmentation and rate of heat transfer (RHT) enhancement through optimized heat uptake parameter. An artificial neural network (ANN) is calibrated to emulate the LT solver's outputs for wall SS and RHT. The ANN achieves high fidelity

(R^{2} > 0.99)

in predicting these metrics across the parameter space explored in the LT simulations, but its generalization to experimental or real dairy systems remains unvalidated and is a focus of future work. The key findings demonstrate the potential of integrating advanced materials and AI technologies to improve product characteristics and processing efficiency.

本研究探讨了人工智能（AI）在理解银和氧化镁纳米颗粒在牛奶流动中的行为方面的创新应用。本研究利用特别设计的振动电磁通道，观察受控抛物线式热斜坡和振荡压力变化下的效应。该框架结合了基本的物理机制——辐射发射、热汇和多孔基质相互作用——其中达西定律量化了渗透率驱动的粘性阻力。牛奶通过电磁激活通道流动的力学是精心制定的，并使用数学和计算方法解决，与拉普拉斯变换（LT）技术促进方程的流线型解决方案。分析集中在流量指标上，通过详细的图形表示来呈现结果。重要的发现包括由于纳米颗粒提高了导热性和流动粘度，从而提高了热传导效率并改变了流动模式。乳流动力学的操作控制显示出双重依赖关系——电磁强度（哈特曼数）对动量的放大和电极间距的抑制，而热管理显示出频率相关的剪切应力（SS）增加和热传递率（RHT）增强，通过优化热吸收参数。校准了人工神经网络（ANN）来模拟LT解算器对wall SS和RHT的输出。人工神经网络在预测LT模拟中探索的参数空间中的这些指标方面实现了高保真度（R2>0.99），但其在实验或真实乳制品系统中的推广仍然未经验证，这是未来工作的重点。这些关键发现表明，将先进材料和人工智能技术相结合，可以改善产品特性和加工效率。

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引用次数: 0

Integrative chemometric and intelligent modeling approaches for pollutant adsorption: Synergistic insights from experimental design, artificial intelligence (AI), and DFT applied to bisphenol A, β-naphthol, and eriochrome black 污染物吸附的综合化学计量学和智能建模方法：实验设计，人工智能（AI）和DFT应用于双酚A， β-萘酚和铬黑的协同见解

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-01-16 DOI: 10.1016/j.chemolab.2026.105639

Taoufiq Bouzid, Abdelali Grich, Aicha Naboulsi, Hicham Yazid, Ali Elbasraoui, Abdelmajid Regti, Mamoune El Himri, Mohammadine El Haddad

Water pollution has emerged as one of the most pressing environmental challenges in recent years. Various solutions have been investigated to mitigate this issue, and among them, adsorption has proven to be an innovative and efficient technique for removing pollutants from water. In this review, we highlight the combined use of Artificial Intelligence (AI), experimental design, and Density Functional Theory (DFT) calculations three powerful tools whose integration in adsorption studies has not yet been reported in the literature.

Within the scope of AI, we explore the selection and evaluation of different models applied to adsorption processes. Specifically, we discuss Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Random Forests (RF), and Linear Regression. Overall, ANN emerged as the most effective approach, as it can account for multiple factors influencing adsorption simultaneously. In this section, we also define AI concepts, describe the models, explain data collection strategies, and outline validation methods to ensure accurate prediction of adsorption performance.

Regarding experimental design, we compare two approaches: the full factorial design and the central composite design. We demonstrate the advantages of the central composite design in optimizing adsorption conditions. This part also details how experimental plans are structured, validated, and how outputs such as 3D surface plots and other response analyses can be leveraged to extract valuable insights.

Finally, we examine the role of DFT calculations, emphasizing their ability to provide a deeper understanding of adsorption mechanisms at the molecular level.

To illustrate the practical application of these integrated methodologies, we present case studies on three representative pollutants: Bisphenol A, β-naphthol, and Eriochrome Black T, which exemplify the diversity of contaminants that can be addressed through this approach.

近年来，水污染已成为最紧迫的环境挑战之一。人们已经研究了各种解决方案来缓解这一问题，其中，吸附已被证明是一种从水中去除污染物的创新和有效技术。在这篇综述中，我们强调了人工智能（AI）、实验设计和密度泛函理论（DFT）计算这三种强大工具在吸附研究中的结合使用，这些工具在文献中尚未报道。在人工智能的范围内，我们探索了应用于吸附过程的不同模型的选择和评估。具体来说，我们讨论了人工神经网络（ANN）、k近邻（KNN）、随机森林（RF）和线性回归。总的来说，人工神经网络是最有效的方法，因为它可以同时考虑影响吸附的多种因素。在本节中，我们还定义了AI概念，描述了模型，解释了数据收集策略，并概述了验证方法，以确保准确预测吸附性能。在实验设计方面，我们比较了两种方法：全因子设计和中心复合设计。我们证明了中心复合设计在优化吸附条件方面的优势。本部分还详细介绍了实验计划的结构，验证以及如何利用诸如3D表面图和其他响应分析等输出来提取有价值的见解。最后，我们研究了DFT计算的作用，强调了它们在分子水平上提供对吸附机制更深层次理解的能力。为了说明这些综合方法的实际应用，我们介绍了三种代表性污染物的案例研究：双酚A、β-萘酚和铬黑T，这说明了通过这种方法可以解决的污染物的多样性。

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引用次数: 0

Corrigendum to “An enhanced stacking ensemble learning strategy for product quality estimation in complex industrial processes considering multi-timescale data” [Chemometr. Intellig. Lab. Syst. 269 (2026) 105635] “考虑多时间尺度数据的复杂工业过程中产品质量估计的增强堆叠集成学习策略”[chemometer]的更正。Intellig。实验室。系统269 (2026)105635]

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2026-03-15 Epub Date: 2026-02-06 DOI: 10.1016/j.chemolab.2026.105657

Weihang Sun, Xin Jin, Sen Xie, Rui Wang

引用次数: 0