Chemometrics and Intelligent Laboratory Systems最新文献_第3页

Quantification of antibiotics in multicomponent drug formulations using UV–Vis spectrometer with PLS and MCR-ALS

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-18 DOI: 10.1016/j.chemolab.2025.105354

Hilthon A. Ramos , Igor Eduardo Silva Arruda , Lucas José de Alencar Danda , Rafaella F. Sales , Julia M. Fernandes , Monica Felts de La Roca Soares , Jose M. Amigo , M. Fernanda Pimentel , José Lamartine Soares Sobrinho

{"title":"Quantification of antibiotics in multicomponent drug formulations using UV–Vis spectrometer with PLS and MCR-ALS","authors":"Hilthon A. Ramos , Igor Eduardo Silva Arruda , Lucas José de Alencar Danda , Rafaella F. Sales , Julia M. Fernandes , Monica Felts de La Roca Soares , Jose M. Amigo , M. Fernanda Pimentel , José Lamartine Soares Sobrinho","doi":"10.1016/j.chemolab.2025.105354","DOIUrl":"10.1016/j.chemolab.2025.105354","url":null,"abstract":"<div><div>This study explores the potential of spectroscopic analysis combined with Partial Least Squares Regression (PLS) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the simultaneous quantification of antibiotics in multicomponent drug formulations, specifically clofazimine (CLZ) and dapsone (DAP). The analysis also evaluated the in vitro release profile of the drugs in a fixed-dose combination tablet. High-Performance Liquid Chromatography with Photodiode Array Detection (HPLC-PDA) was used as a reference analytical technique to validate and compare the chemometric models. Both PLS and MCR-ALS models demonstrated high accuracy, with MCR-ALS showing superior predictive capability for CLZ, while both models presented similar performance for DAP quantification. Notably, the results from both models were consistent with the dissolution profile, indicating no statistically significant differences between the spectroscopic and chromatographic quantification methods. Furthermore, the dissolution profile confirmed the immediate release of both active pharmaceutical ingredients (APIs), with no statistically significant differences between the spectroscopic and chromatographic quantification methods. This study highlights the efficiency and versatility of chemometric techniques as an alternative to conventional methods in the quality assessment of anti-leprosy medications.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"260 ","pages":"Article 105354"},"PeriodicalIF":3.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474506","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

Prediction of drying kinetics and energy consumption values of purple carrots dried in a temperature-controlled microwave dryer by decision tree, random forest and ada boost approaches 用决策树、随机森林和ada boost方法预测在温控微波干燥器中干燥的紫胡萝卜的干燥动力学和能耗值

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-15 DOI: 10.1016/j.chemolab.2025.105352

Mehmet Zahid Malaslı , Mehmet Cabir Akkoyunlu , Engin Pekel , Muhammed Taşova , Samet Kaya Dursun , Mustafa Tahir Akkoyunlu

{"title":"Prediction of drying kinetics and energy consumption values of purple carrots dried in a temperature-controlled microwave dryer by decision tree, random forest and ada boost approaches","authors":"Mehmet Zahid Malaslı , Mehmet Cabir Akkoyunlu , Engin Pekel , Muhammed Taşova , Samet Kaya Dursun , Mustafa Tahir Akkoyunlu","doi":"10.1016/j.chemolab.2025.105352","DOIUrl":"10.1016/j.chemolab.2025.105352","url":null,"abstract":"<div><div>In the literature have focused on modeling data obtained under drying conditions with different methods and comparing them with each other. However, any studies have been found on estimating the behavior of the same material under different drying conditions. Therefore, a study was conducted to predict the behavior of the same material under different drying conditions. In the study, primarily purple carrot slices were reduced from 6.13 ± 0.05 to 0.14 ± 0.018 g moisture/g dry matter value. Among the models, the drying rates were best estimated by the Midilli-Küçük (R<sup>2</sup>: 0.9993) model. The lowest energy consumption was determined as 0.285 kWh in the drying process at 70 °C. Estimation of intermediate values is very useful because experimental studies can be length and expensive. Sometimes, even if cost is not a concern, long-term experimental studies and the high number of experiment repetitions increase the importance of estimation methods for researchers. The decision tree, random forest and ada boost methods, which are fast operating methods, were used as estimation methods in this study. <em>MAPE</em> and <em>R</em><sup><em>2</em></sup> success values are expressed for all three methods. The Decision Tree method was found to be the most successful technique with the highest <em>R</em><sup><em>2</em></sup> value (0.96) and the lowest <em>MAPE</em> value (0.03).</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"260 ","pages":"Article 105352"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527522","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

Developing a novel and intelligent chemometrics-assisted molecularly imprinted electrochemical sensor: Application to the improvement of the efficiency of the treatment of Parkinson's disease

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-15 DOI: 10.1016/j.chemolab.2025.105351

Faramarz Jalili , Ali R. Jalalvand

{"title":"Developing a novel and intelligent chemometrics-assisted molecularly imprinted electrochemical sensor: Application to the improvement of the efficiency of the treatment of Parkinson's disease","authors":"Faramarz Jalili , Ali R. Jalalvand","doi":"10.1016/j.chemolab.2025.105351","DOIUrl":"10.1016/j.chemolab.2025.105351","url":null,"abstract":"<div><div>In this work, a novel chemometrics-assisted electrochemical approach has been developed based on fabrication of a novel electrochemical sensor under computerized methods for simultaneous determination of levodopa (LD), carbidopa (CD) and benserazide (BA) in the presence of indigo carmine (IC) as uncalibrated interference. A glassy carbon electrode (GCE) was modified with multiwalled carbon nanotubes-1-butyl-3-methylimidazolium chloride, [bmim]Cl (MWCNTs-IL), and triple templates molecularly imprinted polymers (TTMIPs) were electrochemically synthesized onto its surface. The effects of experimental parameters on response of the sensor were screened and optimized by Min Run screening and central composite design, respectively. Under optimized conditions, the third-order hydrodynamic differential pulse voltammetric data were recorded and modeled by MCR-ALS, PARAFAC2, U-PLS/RTL, N-PLS-RTL, U-PCA/RTL, and APARAFAC to select the best algorithm for assisting the sensor with the aim of simultaneous determination of LD, CD and BA in the presence of IC as uncalibrated interference. Our results confirmed MCR-ALS showed the best performance to assist the sensor for the analysis of synthetic samples. The TTMIPs/MWCNTs-IL/GCE assisted by MCR-ALS was also successful in analysis of pharmaceuticals used as medications to the treatment of Parkinson's disease, and its performance was comparable with HPLC-UV as the refence method.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"259 ","pages":"Article 105351"},"PeriodicalIF":3.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429894","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

Identification of therapeutic allergen products using their Raman spectral fingerprint

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-14 DOI: 10.1016/j.chemolab.2025.105340

Christian Ickes , Pirya Rani , Kristiyana Tsenova , Johanna Echternach , Frank Führer , Detlef Bartel , Christel Kamp

{"title":"Identification of therapeutic allergen products using their Raman spectral fingerprint","authors":"Christian Ickes , Pirya Rani , Kristiyana Tsenova , Johanna Echternach , Frank Führer , Detlef Bartel , Christel Kamp","doi":"10.1016/j.chemolab.2025.105340","DOIUrl":"10.1016/j.chemolab.2025.105340","url":null,"abstract":"<div><div>Raman spectroscopy is a widely used technique for the identification of chemical substances and in the quality control of pharmaceutical products. Inelastic scattering of laser light generates unique fingerprints of chemical substances which allows for identification of products and quantification of active components. Using this non-destructive technique for biomedicines like vaccines or therapeutic allergen products introduces new challenges in terms of experimental setup, spectral processing, and their standardization. We explore experimental setups and use machine learning techniques to evaluate the potential of Raman spectroscopy to distinguish between therapeutic allergen products from different manufacturers with closely related bee and wasp venoms as Active Pharmaceutical Ingredients (APIs). A comparison of various models shows that a differentiation of products is possible based on their Raman spectra at accuracies above 95%. A deeper analysis allows to identify key regions in the spectra for differentiation. These can guide further research towards the identification and quantification of biochemical compounds of interest. In conclusion, this proof-of-concept study shows the applicability of Raman spectroscopy in the quality assurance of biomedicines and suggests directions for further in-depth analyses.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"260 ","pages":"Article 105340"},"PeriodicalIF":3.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GAINET: Enhancing drug–drug interaction predictions through graph neural networks and attention mechanisms

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-13 DOI: 10.1016/j.chemolab.2025.105337

Bihter Das, Huseyin Alperen Dagdogen, Muhammed Onur Kaya, Ozkan Tuncel, Muhammed Samet Akgul, Resul Das

{"title":"GAINET: Enhancing drug–drug interaction predictions through graph neural networks and attention mechanisms","authors":"Bihter Das, Huseyin Alperen Dagdogen, Muhammed Onur Kaya, Ozkan Tuncel, Muhammed Samet Akgul, Resul Das","doi":"10.1016/j.chemolab.2025.105337","DOIUrl":"10.1016/j.chemolab.2025.105337","url":null,"abstract":"<div><div>Drug–drug interactions (DDIs) are a significant challenge in modern healthcare, especially in polypharmacy, where patients are given more than one drug at the same time. Accurate prediction of DDIs plays an important role in reducing adverse effects and improving recovery in patients. In this study, we propose GAINET, a derivative of the graph-based neural network model enhanced with attention mechanisms, to accurately improve the prediction of drug–drug interactions. The model effectively learns interaction models by focusing on critical features in drug structures and their interactions with each other through molecular graph representations. For the performance evaluation of GAINET, which is trained on the DrugBank dataset containing 191,870 DDI examples, basic metrics such as AUC-ROC, F1 score, precision and recall are used. The obtained accuracy of 0.9050, F1 score of 0.9096 and AUC-ROC of 0.9505 indicate that GAINET outperforms many state-of-the-art models and has good generalization ability even on previously untested data. Moreover, the molecular attention mechanism enables interpretable predictions by highlighting the interaction-specific molecular substructures. All these findings indicate that GAINET, our proposed model for DDI prediction, can serve as a valuable and useful tool and advance the development of reliable pharmacological treatments.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"259 ","pages":"Article 105337"},"PeriodicalIF":3.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421365","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

DeepSMOTE with Laplacian matrix decomposition for imbalance instance fault diagnosis

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-12 DOI: 10.1016/j.chemolab.2025.105338

Yuan Xu, Rui-Ze Fan, Yan-Lin He, Qun-Xiong Zhu, Yang Zhang, Ming-Qing Zhang

{"title":"DeepSMOTE with Laplacian matrix decomposition for imbalance instance fault diagnosis","authors":"Yuan Xu, Rui-Ze Fan, Yan-Lin He, Qun-Xiong Zhu, Yang Zhang, Ming-Qing Zhang","doi":"10.1016/j.chemolab.2025.105338","DOIUrl":"10.1016/j.chemolab.2025.105338","url":null,"abstract":"<div><div>In industrial environments, the unpredictability and irreproducibility of faults often result in insufficient sample sizes and atypical data features, significantly increasing the challenges faced by traditional fault diagnosis methods. To address these issues, this paper proposes a novel fault diagnosis approach that integrates the Borderline embedded deep synthetic minority oversampling technique (BE-DeepSMOTE) with Laplacian matrix decomposition, with the aim of tackling fault identification problems in imbalanced data scenarios. BE-DeepSMOTE employs a deep encoder–decoder framework to enable end-to-end learning and reconstruction of multi-dimensional features. It further incorporates the Borderline SMOTE technique to oversample minority class instances in the feature space, thereby enhancing their representation while ensuring statistical consistency with the original dataset to mitigate data imbalance. Furthermore, we introduce an ensemble classifier that combines Adaboost with Laplacian matrix decomposition. This ensemble classifier leverages the synergy of multiple weak classifiers to extract geometric properties and graph structure similarities from the data, while employing an adaptive weighting mechanism to improve the diagnostic accuracy. Experimental results from two industrial processes demonstrate that the proposed approach significantly enhances the diagnostic accuracy and stability in imbalanced instance environments.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"259 ","pages":"Article 105338"},"PeriodicalIF":3.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403647","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

Improved salp swarm optimization algorithm based on a robust search strategy and a novel local search algorithm for feature selection problems

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-07 DOI: 10.1016/j.chemolab.2025.105343

Mahdieh Khorashadizade, Elham Abbasi, Seyed Abolfazl Shahzadeh Fazeli

{"title":"Improved salp swarm optimization algorithm based on a robust search strategy and a novel local search algorithm for feature selection problems","authors":"Mahdieh Khorashadizade, Elham Abbasi, Seyed Abolfazl Shahzadeh Fazeli","doi":"10.1016/j.chemolab.2025.105343","DOIUrl":"10.1016/j.chemolab.2025.105343","url":null,"abstract":"<div><div>The enormous challenge in data science and data mining for knowledge extraction is confronting an expansive high number of data dimensions. Because the process of extracting knowledge from data can become more complex and memory-consuming. Not only the presence of all features doesn't help the learning process, but also it can sometimes decrease the model's efficiency. To enhance the model's efficiency and reduce the problem's complexity, various feature selection algorithms are designed and implemented. In this paper, a novel and highly effective algorithm based on the salp swarm optimization algorithm for solving complex problems is proposed for feature selection. In the proposed method, an unexpected event that causes the chain to break apart (such as hitting an obstacle or the death of the chain leader, etc.) is modeled which is not taken into account in the salp swarm optimization algorithm. Also, the exploration capability is improved by modifying the updating the position of the chain leader. Additionally, an innovative local search algorithm has been embedded into the proposed algorithm to enhance its exploitation. The proposed approach is implemented on 14 datasets, and the results are compared by two terms, classification accuracy and number of selected features. Additionally, the effectiveness of the proposed method is tested on 2 widely used chemical datasets. The modifications that are applied to the standard salp swarm algorithm reduce the probability of getting stuck in the local optimum and simultaneously, increase the diversity of solutions. The results show that the proposed algorithm has performed significantly better than other algorithms in solving the feature selection problem.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105343"},"PeriodicalIF":3.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377665","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

Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-07 DOI: 10.1016/j.chemolab.2025.105341

Andreas Kartakoullis , Nicola Caporaso , Martin B. Whitworth , Ian D. Fisk

{"title":"Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images","authors":"Andreas Kartakoullis , Nicola Caporaso , Martin B. Whitworth , Ian D. Fisk","doi":"10.1016/j.chemolab.2025.105341","DOIUrl":"10.1016/j.chemolab.2025.105341","url":null,"abstract":"<div><div>In this study, an ad-hoc image processing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"259 ","pages":"Article 105341"},"PeriodicalIF":3.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of aluminum alloy using laser-induced breakdown spectroscopy combined with discriminative restricted Boltzmann machine

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-04 DOI: 10.1016/j.chemolab.2025.105342

Yujia Dai , Qing Ma , Tingsong Zhang , Shangyong Zhao , Lu Zhou , Xun Gao , Ziyuan Liu

{"title":"Classification of aluminum alloy using laser-induced breakdown spectroscopy combined with discriminative restricted Boltzmann machine","authors":"Yujia Dai , Qing Ma , Tingsong Zhang , Shangyong Zhao , Lu Zhou , Xun Gao , Ziyuan Liu","doi":"10.1016/j.chemolab.2025.105342","DOIUrl":"10.1016/j.chemolab.2025.105342","url":null,"abstract":"<div><div>Laser-Induced Breakdown Spectroscopy (LIBS), combined with modern machine learning tools, has emerged as a powerful technique for metal material identification, leveraging its high sensitivity and rapid response. However, the current spectral data analysis methods typically involve a two-step process of dimensionality reduction and model learning, lacking seamless integration. In this study, we address this issue by investigating a discriminative learning approach based on LIBS, utilizing the Discriminative Restricted Boltzmann Machine (DRBM). We apply LIBS technology in conjunction with DRBM for spectral feature selection and classification of five distinct small-sample aluminum alloy samples. The learned spectral latent distribution from the generative model component of DRBM effectively regularizes the discriminative process, thereby overcoming the problem of training overfitting arising from the high-dimensional small-sample limitation. This results in a stable and generalizable qualitative analysis model independent of empirical knowledge. The approach presented in this study achieves a 100 % accuracy, surpassing the best-performing traditional machine learning method (PCA-RF) by 13.33 % in accuracy and demonstrating a similar improvement compared to a Backpropagation Neural Network (BPNN) with the same structure.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105342"},"PeriodicalIF":3.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349115","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

A latent space-based multivariate capability index: A new paradigm for raw material supplier selection in industry 4.0

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-02-04 DOI: 10.1016/j.chemolab.2025.105339

Joan Borràs-Ferrís , Carl Duchesne , Alberto Ferrer

{"title":"A latent space-based multivariate capability index: A new paradigm for raw material supplier selection in industry 4.0","authors":"Joan Borràs-Ferrís , Carl Duchesne , Alberto Ferrer","doi":"10.1016/j.chemolab.2025.105339","DOIUrl":"10.1016/j.chemolab.2025.105339","url":null,"abstract":"<div><div>We present a novel Latent Space-based Multivariate Capability Index (<em>LSb-MC</em><sub><em>pk</em></sub>) aligned with the Quality by Design initiative and used as a criterion for ranking and selecting suppliers for a particular raw material used in a manufacturing process. The novelty of this new index is that, contrary to other multivariate capability indexes that are defined either in the raw material space or in the Critical Quality Attributes (CQAs) space of the product manufactured, this new <em>LSb-MC</em><sub><em>pk</em></sub> is defined in the latent space connecting both spaces. This endows the new index with a clear advantage over classical ones as it quantifies the capacity of each raw material supplier of providing assurance of quality with a certain confidence level for the CQAs of the manufactured product before manufacturing a single unit of the product. All we need is a rich database with historical information of several raw material properties along with the CQAs. Besides, we present a novel methodology to carry out the diagnosis for assignable causes when a supplier does not score a good capability index. The proposed <em>LSb-MC</em><sub><em>pk</em></sub> is based on Partial Least Squares (PLS) regression, and it is illustrated using data from both an industrial and a simulation study.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105339"},"PeriodicalIF":3.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386824","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