Journal of Chemometrics最新文献

{"title":"Assessing Classification Models of Pharmaceuticals With Conformal Prediction","authors":"Karl S. Booksh,&nbsp;Caelin P. Celani,&nbsp;Nicole M. Ralbovsky,&nbsp;Joseph P. Smith","doi":"10.1002/cem.70017","DOIUrl":"https://doi.org/10.1002/cem.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>Conformal predictions transform a measurable, heuristic notion of uncertainty into statistically valid confidence intervals such that, for a future sample, the true class prediction will be included in the conformal prediction set at a predetermined confidence. In a Bayesian perspective, common estimates of uncertainty in multivariate classification, namely <i>p</i>-values, only provide the probability that the data fits the presumed class model, <i>P(D|M)</i>. Conformal predictions, on the other hand, address the more meaningful probability that a model fits the data, <i>P(M|D)</i>. Herein, two methods to perform inductive conformal predictions are investigated—the traditional Split Conformal Prediction that uses an external calibration set and a novel Bagged Conformal Prediction, closely related to Cross Conformal Predictions, that utilizes bagging to calibrate the heuristic notions of uncertainty. Methods for preprocessing the conformal prediction scores to improve performance are discussed and investigated. These conformal prediction strategies are applied to identifying four non-steroidal anti-inflammatory drugs (NSAIDs) from hyperspectral Raman imaging data. In addition to assigning meaningful confidence intervals on the model results, we herein demonstrate how conformal predictions can add additional diagnostics for model quality and method stability.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of ATR-FTIR Spectrum Combined With Ensemble Learning and Deep Learning for Identification of Amomum tsao-ko at Different Drying Temperatures","authors":"Gang He,&nbsp;Shao-bing Yang,&nbsp;Yuan-zhong Wang","doi":"10.1002/cem.70018","DOIUrl":"https://doi.org/10.1002/cem.70018","url":null,"abstract":"<div>\u0000 \u0000 <p><i>Amomum tsao-ko</i> Crevost et Lemaire (<i>A. tsao-ko</i>) is an important medicinal plant and flavoring spice. <i>A. tsao-ko</i> dried at different drying temperatures has different nutritional and medicinal values, leading to the phenomenon of substandard products in the market from time to time. In this study, attenuated total reflection–Fourier transform infrared spectroscopy (ATR-FTIR) data were pre-processed with SD, normalization, EWMA, SNV to compare their effects on the recognition ability of SVM, RF, XGBoost, and CatBoost models. Meanwhile, full-band and local-band 2DCOS profiles were obtained to characterize the differences in chemical features of <i>A. tsao-ko</i> dried by different drying temperatures and classified in conjunction with the ResNet model. The results show that although traditional machine learning can obtain better classification results, the classification efficiency is very unsatisfactory, and the correct classification rate is improved to 97% after derivative (SD) preprocessing. The 2DCOS atlas is able to visualize the feature information in the samples, which is further combined with the ResNet model to obtain 100% classification correctness with excellent generalization ability and convergence effect. The above study was able to provide new ideas for quality evaluation of <i>A. tsao-ko</i>.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidimensional Patterns of Gas Sensors for Assessing the Microbiological Indicators of Raw Milk","authors":"Anastasiia Shuba,&nbsp;Tatiana Kuchmenko,&nbsp;Ruslan Umarkhanov,&nbsp;Ekaterina Bogdanova,&nbsp;Ekaterina Anokhina,&nbsp;Inna Burakova","doi":"10.1002/cem.70007","DOIUrl":"https://doi.org/10.1002/cem.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>The paper discusses methods of using chemometrics methods for processing the output data of sensors with polycomposite coatings for analyzing the gas phase of raw milk and obtaining analytical information about its total microbiological contamination, the content of yeast and mold, and the presence of pathogenic microorganisms. To predict microbiological indicators of milk quality, the partial least squares regression and quadratic discriminant analysis were used. The initial data matrix included both an optimized set of sensor output data and calculated parameters at various data fusion levels. It is shown that multidimensional patterns of sensor output data differ depending on the task. A model for predicting the microbiological contamination of milk (QMAFAnM) with an error of 0.342 log CFU was obtained. It was shown that the sensitivity of classification of milk samples by the presence or absence of pathogenic microorganisms using discriminant analysis is 67%, and the specificity is 100% when using the calculated parameters of the sensor array. The proposed approaches can be applicable for processing data from various types of sensors when analyzing real objects with complex compositions.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Origin of the OECD Principles for QSAR Validation and Their Role in Changing the QSAR Paradigm Worldwide: An Historical Overview","authors":"Paola Gramatica","doi":"10.1002/cem.70014","DOIUrl":"https://doi.org/10.1002/cem.70014","url":null,"abstract":"<div>\u0000 \u0000 <p>The discussions in the QSAR community and the steps that led to the definition of the OECD Principles for the validation of QSAR models are illustrated here, framing the process in the general framework of QSAR modeling. The individual OECD Principles are presented, commenting on them in the light of significant publications that have appeared over the years, with particular attention to the aspects of statistical validation according to the chemometric approach. It will be highlighted how and to what extent the OECD Principles have influenced the subsequent work of all QSAR modelers and have led to a significant improvement in validated QSAR modeling applicable in the regulatory field and beyond.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Sexalinear Decomposition Algorithm for Analyzing the Chemical Sexalinear Data Array","authors":"Yue-Yue Chang,&nbsp;Qiu-Na Shi,&nbsp;Tong Wang,&nbsp;Hai-Long Wu,&nbsp;Ru-Qin Yu","doi":"10.1002/cem.70013","DOIUrl":"https://doi.org/10.1002/cem.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>With the development of analytical instrument towards more and more high-way and complex, it is very important and meaningful work to obtain ultra-high-way chemical data and explore its analytical methods. In this paper, a novel and excellent six-way algorithm combination method (six-way ACM) was proposed. In addition, a real chemically meaningful ultra-high-way sexalinear data array was obtained and constructed for the first time. The proposed six-way data array has highly collinearity, which puts forward higher requirements for parsing this data array to a certain extent. To verify the feasibility of the proposed algorithm, it was used to analyze the above real sexalinear six-way data array and a series of simulated six-way data arrays with different noise levels. The results of real data and simulated data demonstrate that the proposed method can be well used in the analysis of six-way data arrays and shows fascinating performance, including insensitive to excessive number of components, fast convergence speed, and suitable for high collinearity and high noise data. Compared with three-way, four-way, and five-way calibration methods, the six-way ACM provides higher sensitivity, a lower limit of detection, a lower limit of quantification, and more stable and accurate results, showing an outstanding “higher-order advantages” and better ability to handle collinearity problems. This work provides not only data analysis method for high-order instruments that may emerge in the future but also real data support and methodological reference for theoretical research on high-order tensor algebra.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Wavelength Selection for Limited Near-Infrared Spectral Data via Genetic Algorithm and Hybrid Regression","authors":"Esra Pamukçu","doi":"10.1002/cem.70015","DOIUrl":"https://doi.org/10.1002/cem.70015","url":null,"abstract":"<p>Spectral data often contains a large number of variables that are highly correlated. Although Partial Least Squares (PLS) regression is specifically designed to handle issues arising from limited sample sizes, its effectiveness may still diminish in e<i>x</i>tremely small datasets, making it challenging to construct a calibration model with high predictive performance. This study introduces a new framework, the Genetic Algorithm and Hybrid Regression Model (GAHRM), designed specifically for variable selection and regression in high-dimensional, low-sample-size spectral datasets. GAHRM integrates Hybrid Regression, which constructs regression models using a covariance structure that is first stabilized through Thomaz Stabilization and then regularized, with Genetic Algorithm (GA), an efficient optimization technique for selecting the best subset of variables among a vast model space. Unlike traditional approaches that rely on exhaustive search for model selection criteria, GAHRM leverages GA to navigate the exponentially large search space, enabling computationally feasible and robust model construction. The effectiveness of GAHRM was validated on the benchmark “Gasoline” dataset, where it demonstrated superior performance compared to PLS in terms of prediction accuracy and model selection efficiency. These results highlight GAHRM as a powerful alternative for wavelength selection and calibration modeling in challenging data scenarios.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principal Components Analysis: Row Scaling and Compositional Data","authors":"Richard G. Brereton","doi":"10.1002/cem.3606","DOIUrl":"https://doi.org/10.1002/cem.3606","url":null,"abstract":"<p>Row scaling is sometimes called normalisation, but this term is also sometimes used for column standardisation, so we will avoid the latter term in this article, to prevent confusion.</p><p>Of course, whether this improvement is observed does depend on the structure of the data, but if the difference between samples is primarily due to the relative concentrations or proportions and the amount of sample is not easy to control, row scaling to constant total often results in an improvement. It can be combined with other approaches for column transformation such as standardisation as discussed in the previous article.</p><p>If there are only two variables, the simplex is a line. In Figure 4, we illustrate the scores first 2 PCs of the dataset formed by the first two variables from Table 1. We see that after row scaling there is only one non-zero PC. In this case, the position along the line relates to the class membership of each object, although this is not always so and depends on an appropriate choice of variables.</p><p>In the case of the data in Table 1, row scaling improves visualisation of the class differences and structure in the data in this case. However, row scaling is not always appropriate. If the absolute values of each variable are known accurately (e.g., the amount of sample extracted can be kept constant or calibrated to a known standard), compositional data lose information. In addition, sometimes there may be one or two very intense variables that are of subsidiary interest; for example, a primary metabolite that is very intense but has little or no relationship to the factors of interest; the proportions will be dominated by this uninteresting factor.</p><p>However, row scaling is a common procedure in many areas of chemometrics. There is a significant statistical literature about multivariate compositional data. If the main aim of an analysis is qualitative, for example, to separate groups or find outliers, often some of the more elaborate statistical considerations are of secondary importance. If, however, the data are to be used for statistical inference, such as hypothesis tests or <i>p</i> values or estimation, it is a good idea to look closely at the classical literature in order to best interpret and process compositional data.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Lead Chrome Green in Tea Based on Near-Infrared Reflectance Spectroscopy","authors":"Xiaogang Jiang,&nbsp;Penghui Cheng,&nbsp;Kang Ge,&nbsp;Siwei Lv,&nbsp;Yande Liu","doi":"10.1002/cem.70011","DOIUrl":"https://doi.org/10.1002/cem.70011","url":null,"abstract":"<div>\u0000 \u0000 <p>Tea color is a part of tea quality, and illegal addition of lead chrome green (LCG) to improve tea quality cannot be identified by human eyes. This paper is based on near-infrared (NIR) reflectance spectroscopy to detect LCG stained tea and to investigate the feasibility of qualitative and quantitative methods. Firstly, the LCG in tea was qualitatively analyzed by partial least squares discriminant analysis (PLS-DA), random forest (RF), and least squares support vector machine (LSSVM) classification models, and the results showed that the classification accuracy of LSSVM reached 100%. For quantitative analysis, Savitzky–Golay convolutional smoothing (S-G) preprocessing combined with three feature extraction algorithms, namely, joint competitive adaptive weighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were used to build partial least squares (PLS), RF, and LSSVM regression models sequentially on the preprocessed data. The S-G-UVE-LSSVM showed the best regression prediction ability in detecting LCG in tea, with a tested <i>R</i>² of 0.96. These results show the feasibility of NIR spectroscopy for the detection of added LCG in tea.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of Halitosis by Exhaled Breath Analysis Using Semiconductor Metal Oxide Sensors and Chemometric Methods","authors":"Mikhail Saveliev,&nbsp;Andrey Volchek,&nbsp;Galina Lavrenova,&nbsp;Ol'ga Malay,&nbsp;Mikhail Grevtsev,&nbsp;Igor Jahatspanian","doi":"10.1002/cem.70012","DOIUrl":"https://doi.org/10.1002/cem.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>Halitosis is a condition associated with bad breath. Although halitosis is a disease in its own right, it is often a symptom of more serious diseases (diabetes mellitus, renal failure, azotemia, etc.). The currently used method for diagnosing halitosis is the organoleptic method, which relies on a trained specialist evaluating the patient's breath odor. This approach to diagnosing halitosis is subjective, uncomfortable for both patient and doctor, and necessitates the involvement of a specially trained professional. As an alternative, instrumental diagnostics employing metal oxide semiconductor (MOS) sensor arrays offer a promising avenue by enabling patient classification through predeveloped models. This paper considers the application of seven MOS sensors of different compositions at three different temperatures. Different methods of chemometric data analysis were applied: <i>k</i>-nearest neighbors (kNN), decision trees (DT), support vector machine (SVM), logistic regression (LR), and projection on latent structures discrimination analysis (PLSDA). All applied methods demonstrated their effectiveness and achieved selectivity, sensitivity, and accuracy values exceeding 85%. Additionally, a combined classifier leveraging responses from all previously studied classifiers was explored, achieving near-perfect classification accuracy.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiple Linear Regression–Based Algorithm to Correct for Cosmic Rays in Raman Images","authors":"Hery Mitsutake,&nbsp;Eneida de Paula,&nbsp;Heloisa N. Bordallo,&nbsp;Douglas N. Rutledge","doi":"10.1002/cem.70000","DOIUrl":"https://doi.org/10.1002/cem.70000","url":null,"abstract":"<p>Raman imaging is a powerful technique for simultaneously obtaining chemical and spatial information on diverse materials. One of the most common detectors used on Raman equipment is the charge coupled detector (CCD) due its high sensitivity. However, CCDs are also sensitive to cosmic rays, that generate very narrow and intense signals: cosmic ray spikes. Since these peaks can be very intense and numerous, it is important to eliminate them before any data analysis. Some methods to do this use comparison of neighboring pixels to identify spikes, but when using the line-scanning acquisition mode, it is common that these spikes appear in two or more pixels close together. Thus, in this work, a new algorithm has been developed to correct for cosmic ray spikes in Raman images, based on multiple linear regression (MLR). This algorithm takes less than 1 min in images with more than 70,000 spectra and removes all spikes, even those at low intensity.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"39 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}