Chemometrics and Intelligent Laboratory Systems最新文献_第7页

Time-resolved fluorescence spectroscopy and improved parallel factor framework-clustering analysis for oil spill type identification and concentration quantification 时间分辨荧光光谱和改进的平行因子框架聚类分析用于溢油类型识别和浓度定量

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-30 DOI: 10.1016/j.chemolab.2025.105564

Peiliang Wu , Zhiwei Wang , Yuhan Zhao , Deming Kong

Oil spills hidden below the sea surface and in a suspended state are known as submerged oil. Determining the source of an oil spill and evaluating the amount of oil spilled can provide a basis for the effective development of oil spill emergency response strategies and policies. Because of this, this paper proposes an oil spill species identification and concentration quantification analytical method based on the combination of time-resolved fluorescence spectroscopy (TRFS) and improved parallel factor framework-clustering analysis (IPFFCA). The IPFFCA model first decomposes the oil TRFS data to extract the loading matrix and reconstructs the landscape maps corresponding to each component based on the loading matrix. Subsequently, the non-negative least squares algorithm was employed to fit the component landscape maps to the unfolded actual spectra, thereby estimating the score matrix of the samples. Building upon this, the score matrix was used as input to develop oil species identification and concentration quantification models via particle swarm optimization support vector machine (PSO-SVM) and extreme gradient boosting (XGBoost), respectively. To verify the effectiveness of the proposed analytical method, six typical submerged oil samples were experimentally prepared, and their TRFS data were collected and analyzed. The experimental results show that the analytical method proposed in this paper achieves 92 % accuracy in the oil species identification task, the average coefficient of determination of the concentration prediction in the validation set of the six types of samples reaches 0.95, and the root mean square error is 0.08, indicating strong predictive performance.

隐藏在海面以下并处于悬浮状态的石油泄漏被称为水下石油。确定溢油源和评估溢油量可以为有效制定溢油应急战略和政策提供基础。为此，本文提出了一种基于时间分辨荧光光谱（TRFS）与改进的平行因子框架聚类分析（IPFFCA）相结合的溢油物种识别与浓度定量分析方法。IPFFCA模型首先对油品TRFS数据进行分解，提取加载矩阵，并根据加载矩阵重构各分量对应的景观图。随后，采用非负最小二乘算法将组分景观图拟合到展开的实际光谱中，从而估计样本的得分矩阵。在此基础上，以分数矩阵为输入，分别利用粒子群优化支持向量机（PSO-SVM）和极限梯度提升（XGBoost）技术建立油种识别和浓度量化模型。为了验证该分析方法的有效性，实验制备了6个典型的水下油样，并对其TRFS数据进行了采集和分析。实验结果表明，本文提出的分析方法在油类识别任务中准确率达到92%，6种样品验证集中浓度预测的平均决定系数达到0.95，均方根误差为0.08，具有较强的预测性能。

{"title":"Time-resolved fluorescence spectroscopy and improved parallel factor framework-clustering analysis for oil spill type identification and concentration quantification","authors":"Peiliang Wu , Zhiwei Wang , Yuhan Zhao , Deming Kong","doi":"10.1016/j.chemolab.2025.105564","DOIUrl":"10.1016/j.chemolab.2025.105564","url":null,"abstract":"<div><div>Oil spills hidden below the sea surface and in a suspended state are known as submerged oil. Determining the source of an oil spill and evaluating the amount of oil spilled can provide a basis for the effective development of oil spill emergency response strategies and policies. Because of this, this paper proposes an oil spill species identification and concentration quantification analytical method based on the combination of time-resolved fluorescence spectroscopy (TRFS) and improved parallel factor framework-clustering analysis (IPFFCA). The IPFFCA model first decomposes the oil TRFS data to extract the loading matrix and reconstructs the landscape maps corresponding to each component based on the loading matrix. Subsequently, the non-negative least squares algorithm was employed to fit the component landscape maps to the unfolded actual spectra, thereby estimating the score matrix of the samples. Building upon this, the score matrix was used as input to develop oil species identification and concentration quantification models via particle swarm optimization support vector machine (PSO-SVM) and extreme gradient boosting (XGBoost), respectively. To verify the effectiveness of the proposed analytical method, six typical submerged oil samples were experimentally prepared, and their TRFS data were collected and analyzed. The experimental results show that the analytical method proposed in this paper achieves 92 % accuracy in the oil species identification task, the average coefficient of determination of the concentration prediction in the validation set of the six types of samples reaches 0.95, and the root mean square error is 0.08, indicating strong predictive performance.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105564"},"PeriodicalIF":3.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464514","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}

引用次数: 0

Extraction of soil nutrient information from visible and near-infrared signals using deep learning models 利用深度学习模型从可见光和近红外信号中提取土壤养分信息

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-29 DOI: 10.1016/j.chemolab.2025.105561

Chunru Xiong , Jufang Hu , Ken Cai , Fangxiu Meng , Qinyong Lin , Huazhou Chen

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.

本研究旨在将深度学习算法与可见光和近红外（Vis-NIR）光谱技术相结合，构建土壤养分信息提取模型。提出了一种基于长短期记忆（LSTM）的深度学习框架，建立了全范围可见光-近红外光谱数据分析的最优校准模型。此外，设计了一个影响函数来选择信息丰富的波长变量，这是光谱学工程应用中降低模型维数和增强模型鲁棒性的重要目标。进行了土壤氮(N)、磷(P)、钾(K)含量预测试验。建模结果表明，该模型可以提高土壤养分信息提取的建模效率，并且在建模和预测过程中获得了比传统模型更高的精度。这将有效应对工程应用中的挑战，促进可见光-近红外光谱技术在土壤养分信息提取过程中的快速检测应用，并获得高精度的鲁棒模型。

{"title":"Extraction of soil nutrient information from visible and near-infrared signals using deep learning models","authors":"Chunru Xiong , Jufang Hu , Ken Cai , Fangxiu Meng , Qinyong Lin , 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":"2025-10-29","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}

引用次数: 0

Decoding brain tumor patterns in MRI images: Unleashing optimized insights with Progressive Wasserstein generative adversarial network 解码MRI图像中的脑肿瘤模式：利用渐进式Wasserstein生成对抗网络释放优化的见解

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-29 DOI: 10.1016/j.chemolab.2025.105563

G. Jenifa , B.R. Tapas Bapu , Vivekanandan M. , J. Senthil Murugan

Brain tumor (BT) detection and segmentation are of vital importance for accurate diagnosis, but are still difficult because of intricate brain anatomy, non-spherical tumor shapes, and low contrast of MRI images. Conventional manual methods are time-consuming and invasive with observer variability, whereas traditional machine learning (ML) approaches based on handcrafted features tend to miss subtle patterns of tumor areas. Even the deep learning (DL) models like CNNs, despite their effectiveness, have limitations such as high computation expenses, poor generalization to heterogeneous data, and complexity in delineating tumor boundaries accurately, which are subtle. These drawbacks are sought to be overcome by this manuscript, suggesting an innovative technique for automatic BT detection in MRI samples. Initially, the normalized gamma corrected contrast-limited adaptive histogram equalization (NG-CCLAHE) is introduced for enhancing the MRI image quality. Then, the Faster 2D-Otsu Thresholding technique is introduced for segmenting the tumor regions from the MRI samples. Followed by this, the synchroextracting Transform (SET) technique is employed to extract features, which are then optimized with an Improved Ladybug Beetle Optimization Algorithm (ILBOA). The improved features are fed into the PWGAN, allowing for more accurate and effective tumor detection. Experimental assessment using the Br35H Brain Tumor Detection 2020 dataset reflects high-level performance with 98.6 % accuracy, 92 % DSC, 95 % PDR, 23 % classification error, 37.8s computation time, and an F1-score of 98.59 %. These aspects identify the proposed approach's efficiency and competency in brain tumor patterns from MRI images.

脑肿瘤（BT）的检测和分割对于准确诊断至关重要，但由于脑解剖结构复杂，肿瘤形状非球形，MRI图像对比度低，因此检测和分割仍然很困难。传统的人工方法既耗时又具有侵入性，而且观测者的可变性很大，而传统的基于手工特征的机器学习（ML）方法往往会错过肿瘤区域的微妙模式。即使是cnn这样的深度学习（DL）模型，尽管它们很有效，但也存在计算费用高、对异构数据的泛化能力差、准确描绘肿瘤边界的复杂性等局限性，这些都是微妙的。这些缺点是寻求克服这篇手稿，建议在MRI样品中自动BT检测的创新技术。首先，引入归一化伽马校正对比度限制自适应直方图均衡化（NG-CCLAHE）来提高MRI图像质量。然后，引入更快的2D-Otsu阈值分割技术，从MRI样本中分割肿瘤区域。然后，采用同步提取变换（SET）技术提取特征，并用改进的瓢虫甲虫优化算法（ILBOA）对特征进行优化。改进的特征被输入到PWGAN中，允许更准确和有效的肿瘤检测。使用Br35H脑肿瘤检测2020数据集的实验评估反映出高水平的性能，准确率为98.6%，DSC为92%，PDR为95%，分类误差为23%，计算时间为37.8s， f1评分为98.59%。这些方面确定了所提出的方法在MRI图像中脑肿瘤模式的效率和能力。

{"title":"Decoding brain tumor patterns in MRI images: Unleashing optimized insights with Progressive Wasserstein generative adversarial network","authors":"G. Jenifa , B.R. Tapas Bapu , Vivekanandan M. , J. Senthil Murugan","doi":"10.1016/j.chemolab.2025.105563","DOIUrl":"10.1016/j.chemolab.2025.105563","url":null,"abstract":"<div><div>Brain tumor (BT) detection and segmentation are of vital importance for accurate diagnosis, but are still difficult because of intricate brain anatomy, non-spherical tumor shapes, and low contrast of MRI images. Conventional manual methods are time-consuming and invasive with observer variability, whereas traditional machine learning (ML) approaches based on handcrafted features tend to miss subtle patterns of tumor areas. Even the deep learning (DL) models like CNNs, despite their effectiveness, have limitations such as high computation expenses, poor generalization to heterogeneous data, and complexity in delineating tumor boundaries accurately, which are subtle. These drawbacks are sought to be overcome by this manuscript, suggesting an innovative technique for automatic BT detection in MRI samples. Initially, the normalized gamma corrected contrast-limited adaptive histogram equalization (NG-CCLAHE) is introduced for enhancing the MRI image quality. Then, the Faster 2D-Otsu Thresholding technique is introduced for segmenting the tumor regions from the MRI samples. Followed by this, the synchroextracting Transform (SET) technique is employed to extract features, which are then optimized with an Improved Ladybug Beetle Optimization Algorithm (ILBOA). The improved features are fed into the PWGAN, allowing for more accurate and effective tumor detection. Experimental assessment using the Br35H Brain Tumor Detection 2020 dataset reflects high-level performance with 98.6 % accuracy, 92 % DSC, 95 % PDR, 23 % classification error, 37.8s computation time, and an F1-score of 98.59 %. These aspects identify the proposed approach's efficiency and competency in brain tumor patterns from MRI images.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105563"},"PeriodicalIF":3.8,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569577","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}

引用次数: 0

When just-in-time learning meets deep learning: An industrial quality prediction practice on deep partial least squares model 当即时学习与深度学习相遇：深度偏最小二乘模型的工业质量预测实践

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-27 DOI: 10.1016/j.chemolab.2025.105555

Junhua Zheng , Zeyu Yang , Zhiqiang Ge

While deep learning has made great progresses in various application domains, the nature of computational expensive and reliance on large-scale data makes it inefficient or even impossible for small data modeling, particularly under the just-in-time learning framework. Effective combination of deep learning and just-in-time learning may explore great potentials for both two learning paradigms, thus should be attractive and beneficial to the research community. In this paper, an improved form of the lightweight deep partial least squares (PLS) model is developed under the framework of Just-in-time learning. Without complicated backpropagation and time-consuming parameter tuning algorithms, deep PLS provides a transparent model structure which also works well for small training data. As a result, fusion of those two learning strategies makes the new proposed method as a very promising predictive modeling tool in industrial soft sensor applications, the performance of which is evaluated and confirmed through a real industrial example.

虽然深度学习在各个应用领域取得了很大的进步，但计算成本高和依赖大规模数据的性质使得小数据建模效率低下甚至不可能，特别是在即时学习框架下。深度学习和即时学习的有效结合可以为这两种学习范式探索巨大的潜力，因此应该对研究界具有吸引力和有益的意义。本文在实时学习的框架下，提出了一种改进的轻量级深度偏最小二乘模型。没有复杂的反向传播和耗时的参数调整算法，深度PLS提供了一个透明的模型结构，也适用于小的训练数据。结果表明，这两种学习策略的融合使该方法在工业软测量应用中成为一种非常有前途的预测建模工具，并通过实际工业实例对其性能进行了评价和验证。

引用次数: 0

Advancing chemical manufacturing processes through data-driven approaches: A survey 通过数据驱动的方法推进化学制造过程：一项调查

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-22 DOI: 10.1016/j.chemolab.2025.105553

Yellam Naidu Kottavalasa, Lauro Snidaro

The chemical industry is the backbone of global manufacturing, driving innovation across multiple sectors. Since chemical processes are complex and dynamic in nature, it is still difficult to maintain efficiency, consistency in product, and optimize process parameters. Traditional approaches often fall short in handling these complexities, prompting manufacturers to adopt data-driven methodologies, including statistical models, machine learning techniques, and deep learning architectures. This survey discusses how these models help in fault detection, process optimization, and quality control. We examine the role of statistical models in capturing process variation, machine learning models in detecting patterns and anomalies, and neural networks in predictive maintenance and real-time monitoring. Additionally, we explore fusion-based architectures, including hybrid statistical, machine learning, and deep learning methods, that facilitate better fault detection and parameter estimation. The survey also highlights how data-driven approaches support sustainable chemical manufacturing by enabling real-time decisions, adaptive control, and effective process monitoring.

化工行业是全球制造业的支柱，推动着多个行业的创新。由于化学过程的复杂性和动态性，保持效率、产品一致性和优化工艺参数仍然是困难的。传统方法往往无法处理这些复杂性，这促使制造商采用数据驱动的方法，包括统计模型、机器学习技术和深度学习架构。本调查讨论了这些模型如何帮助故障检测、过程优化和质量控制。我们研究了统计模型在捕获过程变化中的作用，机器学习模型在检测模式和异常中的作用，以及神经网络在预测性维护和实时监控中的作用。此外，我们还探索了基于融合的架构，包括混合统计、机器学习和深度学习方法，以促进更好的故障检测和参数估计。该调查还强调了数据驱动方法如何通过实现实时决策、自适应控制和有效的过程监控来支持可持续化工制造。

{"title":"Advancing chemical manufacturing processes through data-driven approaches: A survey","authors":"Yellam Naidu Kottavalasa, Lauro Snidaro","doi":"10.1016/j.chemolab.2025.105553","DOIUrl":"10.1016/j.chemolab.2025.105553","url":null,"abstract":"<div><div>The chemical industry is the backbone of global manufacturing, driving innovation across multiple sectors. Since chemical processes are complex and dynamic in nature, it is still difficult to maintain efficiency, consistency in product, and optimize process parameters. Traditional approaches often fall short in handling these complexities, prompting manufacturers to adopt data-driven methodologies, including statistical models, machine learning techniques, and deep learning architectures. This survey discusses how these models help in fault detection, process optimization, and quality control. We examine the role of statistical models in capturing process variation, machine learning models in detecting patterns and anomalies, and neural networks in predictive maintenance and real-time monitoring. Additionally, we explore fusion-based architectures, including hybrid statistical, machine learning, and deep learning methods, that facilitate better fault detection and parameter estimation. The survey also highlights how data-driven approaches support sustainable chemical manufacturing by enabling real-time decisions, adaptive control, and effective process monitoring.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105553"},"PeriodicalIF":3.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412356","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}

引用次数: 0

Adversarial Domain Adaptation Guided by Farthest Distance for open set electronic nose drift compensation 基于最远距离制导的开集电子鼻漂移补偿对抗域自适应

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-22 DOI: 10.1016/j.chemolab.2025.105554

Yong Pan , Chuandong Li , Jiang Xiong , Ziye Hou , Youbin Yao

With advancements in modern science and technology, electronic noses (ENs) have gained significant attention for their applications in environmental monitoring, food quality inspection, and medical equipment. ENs mimic biological olfactory systems to classify gases using arrays of sensors and pattern recognition models. However, gas sensor drift poses a major challenge, leading to performance degradation in EN systems. To address this, Domain Adaptation (DA) methods align source domain data with target domain drift data. While traditional DA methods assume identical class compositions in both domains, this is often unrealistic in practice, leading to suboptimal results. Open Set Domain Adaptation (OSDA) methods address unknown classes in the target domain, but they often focus too much on distinguishing unknown classes, neglecting accurate recognition of known classes. To overcome these limitations, we propose the Adversarial Domain Adaptation Guided by Farthest Distance (ADA-FDG), comprising two complementary modules: Farthest Distance Guide (FDG) and Confidence Normalized Adaptive Factor (CNAF). FDG adaptively builds a guide set that lies farthest from the source distribution in feature space, ensuring adversarial alignment learns to the edge region distribution. CNAF assigns a weight to each batch proportional to its classification confidence, preventing unknown-class samples from contaminating the ADA process. By integrating FDG and CNAF in an adversarial training framework, ADA-FDG achieves more precise alignment of source and target distributions while preserving clear separation between known and unknown classes. Extensive experiments on two benchmark datasets demonstrate that ADA-FDG consistently outperforms state-of-the-art closed and open set DA methods, delivering significant improvements in overall, known-class, and unknown-class accuracy.

随着现代科学技术的进步，电子鼻在环境监测、食品质量检测、医疗设备等方面的应用越来越受到人们的重视。ENs模拟生物嗅觉系统，利用传感器阵列和模式识别模型对气体进行分类。然而，气体传感器漂移带来了重大挑战，导致EN系统的性能下降。为了解决这个问题，域适应（DA）方法将源域数据与目标域漂移数据对齐。虽然传统的数据处理方法在两个领域中假设相同的类组成，但这在实践中往往是不现实的，从而导致次优结果。开放集域自适应（Open Set Domain Adaptation， OSDA）方法主要针对目标域中的未知类，但往往过于关注识别未知类，而忽略了对已知类的准确识别。为了克服这些限制，我们提出了由最远距离引导的对抗域自适应（ADA-FDG），它由最远距离引导（FDG）和置信归一化自适应因子（CNAF）两个互补模块组成。FDG自适应地构建距离源分布在特征空间中最远的引导集，保证对抗性对齐学习到边缘区域分布。CNAF为每个批次分配与其分类置信度成比例的权重，防止未知类别的样品污染ADA过程。通过在对抗性训练框架中集成FDG和CNAF， ADA-FDG实现了更精确的源和目标分布对齐，同时保留了已知和未知类别之间的明确分离。在两个基准数据集上进行的大量实验表明，ADA-FDG始终优于最先进的封闭集和开放集数据分析方法，在总体、已知类和未知类精度方面都有显著提高。

{"title":"Adversarial Domain Adaptation Guided by Farthest Distance for open set electronic nose drift compensation","authors":"Yong Pan , Chuandong Li , Jiang Xiong , Ziye Hou , Youbin Yao","doi":"10.1016/j.chemolab.2025.105554","DOIUrl":"10.1016/j.chemolab.2025.105554","url":null,"abstract":"<div><div>With advancements in modern science and technology, electronic noses (ENs) have gained significant attention for their applications in environmental monitoring, food quality inspection, and medical equipment. ENs mimic biological olfactory systems to classify gases using arrays of sensors and pattern recognition models. However, gas sensor drift poses a major challenge, leading to performance degradation in EN systems. To address this, Domain Adaptation (DA) methods align source domain data with target domain drift data. While traditional DA methods assume identical class compositions in both domains, this is often unrealistic in practice, leading to suboptimal results. Open Set Domain Adaptation (OSDA) methods address unknown classes in the target domain, but they often focus too much on distinguishing unknown classes, neglecting accurate recognition of known classes. To overcome these limitations, we propose the Adversarial Domain Adaptation Guided by Farthest Distance (ADA-FDG), comprising two complementary modules: Farthest Distance Guide (FDG) and Confidence Normalized Adaptive Factor (CNAF). FDG adaptively builds a guide set that lies farthest from the source distribution in feature space, ensuring adversarial alignment learns to the edge region distribution. CNAF assigns a weight to each batch proportional to its classification confidence, preventing unknown-class samples from contaminating the ADA process. By integrating FDG and CNAF in an adversarial training framework, ADA-FDG achieves more precise alignment of source and target distributions while preserving clear separation between known and unknown classes. Extensive experiments on two benchmark datasets demonstrate that ADA-FDG consistently outperforms state-of-the-art closed and open set DA methods, delivering significant improvements in overall, known-class, and unknown-class accuracy.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105554"},"PeriodicalIF":3.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145358703","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}

引用次数: 0

Comparison of NIR and Raman spectroscopy for determining used cooking oil properties using chemometric methods 化学计量法测定用过食用油性质的近红外光谱与拉曼光谱的比较

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-21 DOI: 10.1016/j.chemolab.2025.105552

Ivana Hradecká , Kateřina Svobodová , Aleš Vráblík , Vladimír Hönig

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.

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.

本研究比较了近红外（NIR）和拉曼光谱在废油（UCO）定量分析中的性能，重点关注酸值、密度和运动粘度等关键参数。监测这些特性可确保原料符合最佳生物燃料生产的必要规格，有助于最终产品的可持续性和性能。近红外和拉曼光谱具有显著的优势，可以一次对几种特性进行快速、实时和非破坏性的测量。采用偏最小二乘法（PLS），使参考结果与近红外光谱和拉曼光谱获得的光谱信息相互关联。与拉曼光谱相比，近红外光谱在分析UCO性质方面表现出优越的性能。结果表明，在40°C时，近红外光谱法能较好地测定酸值（R2P = 0.99, RMSEP = 0.087 mg KOH g⁻¹，RPD = 8.12）和运动粘度（R2P = 0.97, RMSEP = 0.325 mm²/s, RPD = 5.20）。指出拉曼光谱法最适用于15°C时的密度测定（R2P = 0.97, RMSEP = 0.167 kg m⁻³，RPD = 4.20）。然而，两种方法都取得了很好的结果，适合于精确测定UCO的性质。

{"title":"Comparison of NIR and Raman spectroscopy for determining used cooking oil properties using chemometric methods","authors":"Ivana Hradecká , Kateřina Svobodová , Aleš Vráblík , 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 (R<sup>2</sup><sub>P</sub> = 0.99, RMSEP = 0.087 mg KOH g⁻¹, RPD = 8.12), and kinematic viscosity at 40 °C (R<sup>2</sup><sub>P</sub> = 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 (R<sup>2</sup><sub>P</sub> = 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":"2025-10-21","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}

引用次数: 0

Chemometric modelling of anticancer drugs using CatBoost regression and graphical derivatives 使用CatBoost回归和图形衍生物的抗癌药物化学计量学建模

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-17 DOI: 10.1016/j.chemolab.2025.105551

Yingxuan Huang , Muhammad Farhan Hanif , Eiman Maqsood , Mudassar Rehman

In this work, a chemometric methodology based on graph topology descriptors and CatBoost regression is proposed for predicting the physicochemical properties of anticancer drugs. Molecular structures were encoded as graphs, and degree-based topological descriptors were derived to capture their complexity. These descriptors were used in the construction of regression models predicting boiling point, molar refractivity, and polarizability. The first statistical analysis with linear and cubic regression demonstrated that models of order higher than unity were able to take into account the non-linear dependence of descriptors vs. molecular properties. CatBoost regression model was used for improved predictability and better interpretability. This model exhibits a coefficient of determination

(R^{2})

of 0.997 for the prediction of boiling point and superior performance across all the other two properties, with average absolute errors lower than 2%. Of importance, we identified several graph descriptors as important predictors, which confirmed their chemometric relevance. The method may contribute with useful information as a complementary method to current machine learning-based models used for prediction of drug properties in chemoinformatics or pharmaceutical drug development, it integrates chemical graph theory with intelligent reasoning and modeling for a more fault tolerant and generalized 1 solution to drug property prediction.

在这项工作中，提出了一种基于图拓扑描述符和CatBoost回归的化学计量学方法来预测抗癌药物的物理化学性质。将分子结构编码为图形，并推导出基于度的拓扑描述符来捕获其复杂性。这些描述符被用于构建预测沸点、摩尔折射率和极化率的回归模型。线性和三次回归的第一次统计分析表明，高于单位阶的模型能够考虑到描述符与分子性质的非线性依赖。CatBoost回归模型用于提高可预测性和更好的可解释性。该模型在沸点预测上的决定系数（R2）为0.997，在其他两项性能上均表现优异，平均绝对误差小于2%。重要的是，我们确定了几个图形描述符作为重要的预测因子，这证实了它们的化学计量学相关性。该方法可以为化学信息学或药物开发中用于预测药物性质的当前基于机器学习的模型提供有用的信息，它将化学图论与智能推理和建模相结合，为药物性质预测提供了更容错和更广义的解决方案。

{"title":"Chemometric modelling of anticancer drugs using CatBoost regression and graphical derivatives","authors":"Yingxuan Huang , Muhammad Farhan Hanif , Eiman Maqsood , Mudassar Rehman","doi":"10.1016/j.chemolab.2025.105551","DOIUrl":"10.1016/j.chemolab.2025.105551","url":null,"abstract":"<div><div>In this work, a chemometric methodology based on graph topology descriptors and CatBoost regression is proposed for predicting the physicochemical properties of anticancer drugs. Molecular structures were encoded as graphs, and degree-based topological descriptors were derived to capture their complexity. These descriptors were used in the construction of regression models predicting boiling point, molar refractivity, and polarizability. The first statistical analysis with linear and cubic regression demonstrated that models of order higher than unity were able to take into account the non-linear dependence of descriptors vs. molecular properties. CatBoost regression model was used for improved predictability and better interpretability. This model exhibits a coefficient of determination <span><math><mrow><mo>(</mo><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></math></span> of 0.997 for the prediction of boiling point and superior performance across all the other two properties, with average absolute errors lower than 2%. Of importance, we identified several graph descriptors as important predictors, which confirmed their chemometric relevance. The method may contribute with useful information as a complementary method to current machine learning-based models used for prediction of drug properties in chemoinformatics or pharmaceutical drug development, it integrates chemical graph theory with intelligent reasoning and modeling for a more fault tolerant and generalized 1 solution to drug property prediction.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105551"},"PeriodicalIF":3.8,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145358697","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}

引用次数: 0

Robust soft sensor development based on Dirichlet process mixture of regression model for multimode processes 基于Dirichlet过程混合回归模型的多模过程鲁棒软传感器开发

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-11 DOI: 10.1016/j.chemolab.2025.105550

Changrui Xie, Xi Chen

Industrial processes often exhibit multimode characteristics due to factors like load variations, equipment changes, and feedstock fluctuations. This paper introduces a Dirichlet Process-based Twofold-Robust Mixture Regression Model (DPR²MRM) for multimode processes. As a Bayesian nonparametric model, it automatically determines the number of mixture components from observed data using Dirichlet process mixture techniques, avoiding underfitting and overfitting. The model employs a Student's-t mixture model for input space learning, leveraging its long-tail properties for robust mode identification. For each mode, a regression model is built to capture the relationship between inputs and outputs, incorporating Student's-t noise to ensure robustness against output space outliers. The optimal posteriors of the model parameters are inferenced within a full Bayesian framework, and an analytical posterior predictive distribution is derived. The effectiveness of the DPR²MRM is demonstrated through a numerical example and two industrial applications.

由于负荷变化、设备变化和原料波动等因素，工业过程经常表现出多模式特性。介绍了一种基于Dirichlet过程的多模过程双鲁棒混合回归模型（DPR2MRM）。该模型是一种贝叶斯非参数模型，利用Dirichlet过程混合技术，从观测数据中自动确定混合分量的个数，避免了欠拟合和过拟合。该模型采用Student -t混合模型进行输入空间学习，利用其长尾特性进行鲁棒模式识别。对于每种模式，都建立了一个回归模型来捕捉输入和输出之间的关系，并结合Student's-t噪声来确保对输出空间异常值的鲁棒性。在全贝叶斯框架内推导出模型参数的最优后验，并推导出分析后验预测分布。通过一个数值算例和两个工业应用验证了DPR2MRM的有效性。

{"title":"Robust soft sensor development based on Dirichlet process mixture of regression model for multimode processes","authors":"Changrui Xie, Xi Chen","doi":"10.1016/j.chemolab.2025.105550","DOIUrl":"10.1016/j.chemolab.2025.105550","url":null,"abstract":"<div><div>Industrial processes often exhibit multimode characteristics due to factors like load variations, equipment changes, and feedstock fluctuations. This paper introduces a Dirichlet Process-based Twofold-Robust Mixture Regression Model (DPR<sup>2</sup>MRM) for multimode processes. As a Bayesian nonparametric model, it automatically determines the number of mixture components from observed data using Dirichlet process mixture techniques, avoiding underfitting and overfitting. The model employs a Student's-<em>t</em> mixture model for input space learning, leveraging its long-tail properties for robust mode identification. For each mode, a regression model is built to capture the relationship between inputs and outputs, incorporating Student's-<em>t</em> noise to ensure robustness against output space outliers. The optimal posteriors of the model parameters are inferenced within a full Bayesian framework, and an analytical posterior predictive distribution is derived. The effectiveness of the DPR<sup>2</sup>MRM is demonstrated through a numerical example and two industrial applications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"267 ","pages":"Article 105550"},"PeriodicalIF":3.8,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325250","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}

引用次数: 0

Estimation of penalized single index models with exact shape constraints 具有精确形状约束的惩罚单指标模型的估计

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-10 DOI: 10.1016/j.chemolab.2025.105547

Qing Wei , Vincent Chan , Kexin Xie , Kam-Wah Tsui , Xinwei Deng

For statistical analysis for various real-data applications, the relationship between the response and predictor variables is often complicated with certain constraints from the domain knowledge. While some predictor variables can be more important than others, it is important to enable model estimation and variable selection simultaneously for quantifying nonlinear patterns in the data with domain-knowledge constraints. In this work, we propose a penalized single index model that allows incorporation of prior shape information into the nonlinear function and performs shrinkage and variable selection simultaneously. The proposed methods also incorporate the need for robust model estimation and an efficient computational algorithm. The performance of the proposed method is evaluated by both simulation study and real-data studies of fuel consumption and DNA methylation.

在各种实际数据应用的统计分析中，响应变量和预测变量之间的关系往往很复杂，并且受到领域知识的一定约束。虽然一些预测变量可能比其他预测变量更重要，但同时启用模型估计和变量选择对于量化具有领域知识约束的数据中的非线性模式非常重要。在这项工作中，我们提出了一个惩罚性的单指数模型，允许将先验形状信息合并到非线性函数中，并同时执行收缩和变量选择。所提出的方法还结合了对鲁棒模型估计和高效计算算法的需求。该方法的性能通过模拟研究和燃料消耗和DNA甲基化的实际数据研究进行了评估。

引用次数: 0