Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Identifying 124 new anti-HIV drug candidates in a 37 billion-compound database: An integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulation","authors":"Alexandre de Fátima Cobre ,&nbsp;Anderson Ara ,&nbsp;Alexessander Couto Alves ,&nbsp;Moisés Maia Neto ,&nbsp;Mariana Millan Fachi ,&nbsp;Laize Sílvia dos Anjos Botas Beca ,&nbsp;Fernanda Stumpf Tonin ,&nbsp;Roberto Pontarolo","doi":"10.1016/j.chemolab.2024.105145","DOIUrl":"10.1016/j.chemolab.2024.105145","url":null,"abstract":"<div><p>Recent data from the World Health Organization reveals that in 2023, 38.8 million people were living with HIV. Within this population, there were 1.5 million new cases and 650 thousand deaths attributed to the disease<strong>.</strong> This study employs an integrated approach involving QSAR-based machine learning models, molecular docking, and molecular dynamics simulations to identify potential compounds for inhibiting the bioactivity of the CC chemokine receptor type 5 (CCR5) protein, a key entry point for the HIV virus. Using non-redundant experimental data from the CHEMBL database, 40 different machine learning algorithms were trained and the top four models (XGBoost, Histogram based gradient Boosting, Light Gradient Boosted Machine, and Extra Trees Regression) were utilized to predict <em>anti</em>-HIV bioactivity for 37 billion compounds in the ZINC-22 database. The screening resulted in the identification of 124 new <em>anti</em>-HIV drug candidates, confirmed through molecular docking and dynamics simulations. The study underscores the therapeutic potential of these compounds, paving the way for further in vitro and in vivo investigations. The convergence of machine learning and experimental findings presents a promising avenue for significant advancements in pharmaceutical research, particularly in the treatment of viral diseases such as HIV. To guarantee the reproducibility of our study, we have made the Python code (google colab) and the associated database available on GitHub. You can access them through the following link: GitHub Link: <span>https://github.com/AlexandreCOBRE/code</span><svg><path></path></svg>.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"250 ","pages":"Article 105145"},"PeriodicalIF":3.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MA-XRF datasets analysis based on convolutional neural network: A case study on religious panel paintings","authors":"Theofanis Gerodimos ,&nbsp;Ioannis Georvasilis ,&nbsp;Anastasios Asvestas ,&nbsp;Georgios P. Mastrotheodoros ,&nbsp;Aristidis Likas ,&nbsp;Dimitrios F. Anagnostopoulos","doi":"10.1016/j.chemolab.2024.105138","DOIUrl":"10.1016/j.chemolab.2024.105138","url":null,"abstract":"<div><p>Macroscopic X-ray fluorescence (MA-XRF) datasets are analyzed using Artificial Neural Networks. Specifically, Convolutional Neural Networks (CNNs) are trained by coupling the spectra acquired during the MA-XRF scan of two religious panel paintings (“icons”) with the associated Ground-Truth counts per characteristic transition line, as they are extracted by X-ray fluorescence fundamental parameters analysis. In total, twenty thousand XRF spectra were used for the CNN training. The trained neural networks were applied to analyze millions of MA-XRF spectra acquired during the scan of religious painting panels by computing the counts per pixel of X-ray characteristic transition lines and creating the elemental transition maps. Comparison of the CNN extracted results to the Ground-Truth (GT) shows remarkable agreement. The successful MA-XRF datasets analysis applying the CNN method paves an analytical path to the direction of the auto-identification of spectral lines, offering the means for the non-experienced XRF analyst to provide a state-of-the-art analysis and supporting the experienced user not to overlook hardly resolved transition lines.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"250 ","pages":"Article 105138"},"PeriodicalIF":3.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141045310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic non-destructive estimation of polyphenol oxidase and peroxidase enzyme activity levels in three bell pepper varieties by Vis/NIR spectroscopy imaging data based on machine learning methods","authors":"Meysam Latifi Amoghin ,&nbsp;Yousef Abbaspour-Gilandeh ,&nbsp;Mohammad Tahmasebi ,&nbsp;Juan Ignacio Arribas","doi":"10.1016/j.chemolab.2024.105137","DOIUrl":"10.1016/j.chemolab.2024.105137","url":null,"abstract":"<div><p>The browning process of food products if often formed upon cutting and damage during their processing, transport, and storage, amongst other potential sources and reasons. Enzymic browning can be mainly due to polyphenol oxidase (PPO) and peroxidase (POD) enzymes. Visible/near-infrared (Vis/NIR) imaging spectroscopy in the range of 350–1150 nm was used in this study for automatic and non-destructive evaluation of PPO and POD activity levels in three bell pepper varieties (red, yellow, orange; N = 30), with a total of 30 inputs samples in each variety. The spectral data were then modeled by the partial least squares regression (PLSR) throughout the whole spectral range, without using any subset of the most effective wavelength (EW) values. Regression determination coefficient (R²) values for the estimation (prediction) of POD enzyme activity levels were 0.794, 0.772, and 0.726 for red, yellow, and orange bell peppers, respectively, all over the validation set. At the same time, the activity levels of PPO enzyme over bell peppers showed R² values of 0.901, 0.810, and 0.859, for red, yellow, and orange bell peppers, respectively, all over the validation set. In addition, a combination of support vector machine (SVM) with either genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), or imperialistic competitive algorithms (ICA) hybrid machine learning (ML) techniques were used to select the optimal (discriminant) spectral EW wavelength values, and regression performance was consistently improved, to judge from higher regression fit R² values. Either 14 or 15 EWs were computed and selected in order of their discriminative power using previously mentioned ML techniques. The hybrid SVM-PSO method resulted the best one in the process of selecting the most effective wavelength values (nm). On the other hand, three regression methods comprising PLSR, multiple least regression (MLR), and neural network (NN), were employed to model the SVM-PSO selected EWs. The ratio of performance to deviation (RPD), the R² and the root mean square error (RMSE), over the test set, for the non-linear NN regression method exhibited better results as compared to the other two regression methods, being closely followed by PLSR, and therefore NN regression method was selected as the best approach for modeling the most effective spectral wavelength values in this study.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"250 ","pages":"Article 105137"},"PeriodicalIF":3.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924000777/pdfft?md5=1c66f4c9e2d7fdb5e8fd71595aa511f4&pid=1-s2.0-S0169743924000777-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026864","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}

{"title":"A new sub-class linear discriminant for miniature spectrometer based food analysis","authors":"Omar Nibouche ,&nbsp;Fayas Asharindavida ,&nbsp;Hui Wang ,&nbsp;Jordan Vincent ,&nbsp;Jun Liu ,&nbsp;Saskia van Ruth ,&nbsp;Paul Maguire ,&nbsp;Enayet Rahman","doi":"10.1016/j.chemolab.2024.105136","DOIUrl":"10.1016/j.chemolab.2024.105136","url":null,"abstract":"<div><p>The well-known and extensively studied Linear Discriminant Analysis (LDA) can have its performance lowered in scenarios where data is not homoscedastic or not Gaussian. That is, the classical assumptions when LDA models are built are not applicable, and consequently LDA projections would not be able to extract the needed features to explain the intrinsic structure of data and for classes to be separated. As with many real word data sets, data obtained using miniature spectrometers can suffer from such drawbacks which would limit the deployment of such technology needed for food analysis. The solution presented in the paper is to divide classes into subclasses and to use means of sub classes, classes, and data in the suggested between classes scatter metric. Further, samples belonging to the same subclass are used to build a measure of within subclass scatterness. Such a solution solves the shortcoming of the classical LDA. The obtained results when using the proposed solution on food data and on general machine learning datasets show that the work in this paper compares well to and is very competitive with similar sub-class LDA algorithms in the literature. An extension to a Hilbert space is also presented; and the kernel version of the presented solution can be fused with its linear counter parts to yield improved classification rates.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"250 ","pages":"Article 105136"},"PeriodicalIF":3.9,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924000765/pdfft?md5=79caa0e3ce066c5537d9c639d217ec83&pid=1-s2.0-S0169743924000765-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055788","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}

{"title":"Experimental-based groundwater salinization from the carbonate aquifer of eastern Saudi Arabia: Insight into machine learning coupled with meta-heuristic algorithms","authors":"Mohammed Benaafi ,&nbsp;Sani I. Abba ,&nbsp;Mojeed Opeyemi Oyedeji ,&nbsp;Auwalu Saleh Mubarak ,&nbsp;Jamilu Usman ,&nbsp;Isam H. Aljundi","doi":"10.1016/j.chemolab.2024.105135","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105135","url":null,"abstract":"<div><p>Groundwater (GW) salinization of coastal aquifers has become a serious problem for attaining sustainable water resource management in Saudi Arabia and other parts of the world. Therefore, it is crucial to assess the extent of this salinization to protect and manage our water resources effectively. This research proposed real fieldwork GW samples at several locations supported with experimental based on chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS) to analyze several GW physical, chemical, and hydro-geochemical elements. In this study, we model GW salinization with machine learning algorithms such as support vector regression, gaussian process regression, artificial neural networks, and least squares ensemble boosting regression tree. The performance of the standalone models was optimized with metaheuristic optimization-based algorithms such as fuzzy hybridized genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO). The outcomes based on three variable input combinations were validated using several performance indicators and graphical methods. The quantitative analysis indicated that GPR-Combo1(MAE = 0.006 mg/L), Ensm- Combo2 (MAE = 0.025 mg/L), and GPR- Combo3 (MAE = 0.078 mg/L) proved merit among the standalone combinations. Where combo 1, 2, and 3 stand for model combinations derived from feature selection. The cumulative probability function (CPF) demonstrated that heuristic optimization ANFIS-GA (MAE = 0.0025 mg/L, MAPE = 0.19183) and ANFIS-PSO (MAE = 0.0018 mg/L, MAPE = 0.0723) outperformed the standalone error accuracy and served reliable approach. Both the standalone models and heuristic algorithms used for GW salinization modeling have demonstrated promising results in accurately predicting salinity. This approach could aid in effectively managing the GW resources for sustainable development.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"249 ","pages":"Article 105135"},"PeriodicalIF":3.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the factor ambiguity of MCR problems for blockwise incomplete data sets","authors":"Martina Beese ,&nbsp;Tomass Andersons ,&nbsp;Mathias Sawall ,&nbsp;Cyril Ruckebusch ,&nbsp;Adrián Gómez-Sánchez ,&nbsp;Robert Francke ,&nbsp;Adrian Prudlik ,&nbsp;Robert Franke ,&nbsp;Klaus Neymeyr","doi":"10.1016/j.chemolab.2024.105134","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105134","url":null,"abstract":"<div><p>Multivariate curve resolution (MCR) methods are sometimes faced with missing or erroneous data, e.g., due to sensor saturation. In some cases, an estimation of the missing data is possible, but often MCR works with the largest submatrix without missing entries. This ignores all rows and columns of the data matrix that contain missing values. A successful approach to deal with incomplete data multisets has been proposed by Alier and Tauler (2013), but it does not include a factor ambiguity analysis. Here, the missing data problem is addressed in combination with a factor ambiguity analysis. An approach is presented that minimizes the factor ambiguity by extracting a maximum of spectral information even from incomplete rows and columns of the spectral data matrix. The method requires a high signal-to-noise ratio. Applications are presented for UV/Vis and HSI data.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"249 ","pages":"Article 105134"},"PeriodicalIF":3.9,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924000741/pdfft?md5=bb7d17fc695f88d0275f3839df0eb621&pid=1-s2.0-S0169743924000741-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140815811","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}

{"title":"Addressing adulteration challenges of dried oregano leaves by NIR HyperSpectral Imaging","authors":"Veronica Ferrari ,&nbsp;Rosalba Calvini ,&nbsp;Camilla Menozzi ,&nbsp;Alessandro Ulrici ,&nbsp;Marco Bragolusi ,&nbsp;Roberto Piro ,&nbsp;Alessandra Tata ,&nbsp;Michele Suman ,&nbsp;Giorgia Foca","doi":"10.1016/j.chemolab.2024.105133","DOIUrl":"https://doi.org/10.1016/j.chemolab.2024.105133","url":null,"abstract":"<div><p>Dried oregano leaves are particularly prone to adulteration because of their widespread distribution and their easy mixing with leaves of other plants of lower commercial value, such as olive, myrtle, strawberry tree, or sumac. To reveal the presence of adulteration, in this study we considered an untargeted analytical approach, which instead of involving the <em>a priori</em> selection of specific compounds of interest is focused on defining the characteristic spectral signature of authentic oregano with respect to its most frequent adulterants. NIR HyperSpectral Imaging (NIR-HSI) represents a state-of-the-art, rapid and non-destructive technique, allowing for the collection of both spectral and spatial information from the sample, making it particularly suitable for characterizing visually heterogeneous samples.</p><p>Authentication issues are typically assessed through class modelling techniques and Soft Independent Modelling of class Analogy (SIMCA) is one of the most used algorithms in this scenario. However, the high variability and heterogeneity within the authentic oregano class resulted in poor outcomes when SIMCA was applied. As an alternative, Soft Partial Least Squares Discriminant Analysis (Soft PLS-DA) algorithm was applied to differentiate authentic oregano samples from pure adulterants. Soft PLS-DA represents a hybrid approach that combines the advantages of both discriminant and class modelling techniques. The resultant classification model has indeed led to promising results, achieving a prediction efficiency of 92.9 %. Finally, based on the percentage of pixels predicted as oregano in the Soft-PLSDA prediction images, a threshold value of 10 % was established, serving as a detection limit of NIR-HSI to distinguish authentic oregano samples from adulterated ones.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"249 ","pages":"Article 105133"},"PeriodicalIF":3.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016974392400073X/pdfft?md5=9ca1205b6902ee41304da3031bdead5a&pid=1-s2.0-S016974392400073X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140640785","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}

{"title":"Application of ANN, hypothesis testing and statistics to the adsorptive removal of toxic dye by nanocomposite","authors":"Thamraa Alshahrani ,&nbsp;Ganesh Jethave ,&nbsp;Anil Nemade ,&nbsp;Yogesh Khairnar ,&nbsp;Umesh Fegade ,&nbsp;Monali Khachane ,&nbsp;Amir Al-Ahmed ,&nbsp;Firoz Khan","doi":"10.1016/j.chemolab.2024.105132","DOIUrl":"10.1016/j.chemolab.2024.105132","url":null,"abstract":"<div><p>Statistics can be used in a variety of ways to present, compute, and critically analyze experimental data. To determine the significance and validity of the experimental data, a variety of statistical tests are used. Using a synthesized CoO/NiO/MnO₂ Nanocomposite, the present study used adsorption to remove the dye Bromophenol Blue (BPB) from a contaminated aqueous solution. In order to (a) determine the optimal pH of the solution, (b) confirm the experiment's success, and (c) investigate the effect of adsorbent dose on BPB dye removal from aqueous solutions. The experimental data were statistically analyzed through hypothesis testing using the <em>t</em>-test, paired <em>t</em>-test, and Chi-square test. The null hypothesis that the optimal pH value is 7 is accepted since t_observed (−1.979)&lt;t_tabulated (−2.262). Since χ²_observed (1.052)&lt; χ²_tabulated (3.841), null hypothesis that the higher adsorbent dose helps in higher % removal of dye is accepted. Both the obtained Freundlich adsorption isotherm and the Langmuir isotherm's R² values, which were both close to 1, indicate that the isotherms are favorable. Karl Pearson's relationship coefficient values for Langmuir and Freundlich adsorption isotherms found to be 0.9693 and 0.9994 respectively, which show a more significant level of connection between's the factors. The ANN model predicted adsorption percentage with regression value R is 0.996. ANN model result predict 99.60 % BPB dye adsorption using optimized parametric conditions. The ANN model produced values that were more precise, reliable, and reproducible, demonstrating its superiority.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"249 ","pages":"Article 105132"},"PeriodicalIF":3.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140767102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-guided graph learning soft sensor for chemical processes","authors":"Yi Liu ,&nbsp;Mingwei Jia ,&nbsp;Danya Xu ,&nbsp;Tao Yang ,&nbsp;Yuan Yao","doi":"10.1016/j.chemolab.2024.105131","DOIUrl":"10.1016/j.chemolab.2024.105131","url":null,"abstract":"<div><p>The surge in data-driven soft sensors for industrial processes is evident. However, most of them suffer from the limitation of being black-box models and this will hamper their widespread use. In response to this challenge, this study proposes a physics-guided graph-learning soft sensor that integrates a physical understanding of industrial processes by incorporating graph-based concepts with process physics. The soft sensor first constructs physical information based on causal relationships between variables using the conditional Granger causality test. Subsequently, it autonomously learns the unique sample information of each observation while employing a regularization loss to ensure the sparsity of the learned information. The model employs a two-stream structure for spatiotemporal encoding of both the physical and sample information. The modeling and prediction results on a penicillin fermentation process indicate that, using the proposed method, the knowledge gained from the data aligns with existing prior knowledge. This approach shows promise in filling the gap between data-driven and physics-based modeling in chemical processes.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"249 ","pages":"Article 105131"},"PeriodicalIF":3.9,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140635693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A randomized permutation whole-model test heuristic for Self-Validated Ensemble Models (SVEM)","authors":"Andrew T. Karl","doi":"10.1016/j.chemolab.2024.105122","DOIUrl":"10.1016/j.chemolab.2024.105122","url":null,"abstract":"<div><p>We introduce a heuristic to test the significance of fit of Self-Validated Ensemble Models (SVEM) against the null hypothesis of a constant response. A SVEM model averages predictions from <em>nBoot</em> fits of a model, applied to fractionally weighted bootstraps of the target dataset. It tunes each fit on a validation copy of the training data, utilizing anti-correlated weights for training and validation. The proposed test computes SVEM predictions centered by the response column mean and normalized by the ensemble variability at each of <span><math><mrow><mi>n</mi><mi>P</mi><mi>o</mi><mi>i</mi><mi>n</mi><mi>t</mi></mrow></math></span> points spaced throughout the factor space. A reference distribution is constructed by refitting the SVEM model to <em>nPerm</em> randomized permutations of the response column and recording the corresponding standardized predictions at the <span><math><mrow><mi>n</mi><mi>P</mi><mi>o</mi><mi>i</mi><mi>n</mi><mi>t</mi></mrow></math></span> points. A reduced-rank singular value decomposition applied to the centered and scaled <span><math><mrow><mi>n</mi><mi>P</mi><mi>e</mi><mi>r</mi><mi>m</mi><mo>×</mo><mi>n</mi><mi>P</mi><mi>o</mi><mi>i</mi><mi>n</mi><mi>t</mi></mrow></math></span> reference matrix is used to calculate the Mahalanobis distance for each of the <em>nPerm</em> permutation results as well as the jackknife (holdout) Mahalanobis distance of the original response column. The process is repeated independently for each response in the experiment, producing a joint graphical summary. We present a simulation driven power analysis and discuss limitations of the test relating to model flexibility and design adequacy. The test maintains the nominal Type I error rate even when the base SVEM model contains more parameters than observations.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"249 ","pages":"Article 105122"},"PeriodicalIF":3.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794829","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}