Journal of Chemometrics最新文献

{"title":"Nondestructive Identification of Wheat Seed Variety and Geographical Origin Using Near-Infrared Hyperspectral Imagery and Deep Learning","authors":"Apurva Sharma,&nbsp;Tarandeep Singh,&nbsp;Neerja Mittal Garg","doi":"10.1002/cem.3585","DOIUrl":"10.1002/cem.3585","url":null,"abstract":"<div>\u0000 \u0000 <p>Seed purity assurance is an important aspect of maintaining the quality standards of wheat seeds. It relies significantly on quality parameters, like varietal classification and geographical origin identification. Hyperspectral imaging (HSI) has emerged as an advanced nondestructive technique to determine various quality parameters. In recent years, several studies have utilized HSI for varietal classification, although a limited number of varieties were considered. Additionally, no attention has been paid to determining the geographical origin of wheat seeds. To address these gaps, two separate experiments were performed for varietal classification and geographical origin identification. The seeds from 96 varieties grown across 5 different agricultural regions in India were collected. Hyperspectral images of wheat seeds were acquired in the wavelength ranging 900–1700 nm. The spectral reflectance values were obtained from the region of interest (ROI) corresponding to each seed. Subsequently, the deep learning models (convolutional neural networks [CNNs]) were established and compared with two conventional algorithms, including support vector machines (SVMs) and K-nearest neighbors (KNNs). The experimental results indicated that the proposed CNN models outperformed the SVM and KNN models, achieving an overall accuracy of 94.88% and 99.02% for varietal classification and geographical origin identification, respectively. These results demonstrate that HSI combined with deep learning has the potential to accurately classify a large number of wheat varieties. Moreover, HSI can be used to precisely identify the geographical origins of wheat seeds. This study provides an accurate and nondestructive method that can assist in breeding, quality evaluation, and the development of high-quality wheat seeds.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738629","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":"Novelty and Similarity: Detection Using Data-Driven Soft Independent Modeling of Class Analogy","authors":"O. Y. Rodionova,&nbsp;N. I. Kurysheva,&nbsp;G. A. Sharova,&nbsp;A. L. Pomerantsev","doi":"10.1002/cem.3587","DOIUrl":"10.1002/cem.3587","url":null,"abstract":"<div>\u0000 \u0000 <p>Novelty and similarity are complex concepts that have numerous applications in various fields, including biology and medicine. Novelty detection is a technique used to determine whether a dataset is different from another dataset considered as a standard. Similarity detection is a technique used to determine whether two datasets belong to the same population. Novelty and similarity are closely related concepts; however, they are not complementary. Novelty is a much more popular one, and there are many publications about it. Similarity is, in fact, a new concept that has not yet been explored in depth. Classical statistics offers a large number of tools suitable for detection of similarity, mostly in the univariate case. At the same time, this topic has been insufficiently studied in the field of machine learning. This paper suggests several principles which are important for this research and also present a method for the detection of both novelty and similarity. The method uses a one-class classifier, known as Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA). Three examples illustrate our approach. The first one uses simulated data and demonstrates the performance of DD-SIMCA for the detection of novelty. The second example uses a real-world data and studies similarity of two groups of patients who participate in the evaluation of the effectiveness of the treatment of primary angle-closure glaucoma. The third example comes from medical diagnostics. This is a real-world publicly available data used for comparison of various classification algorithms.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738630","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":"Permutation Strategies for Inference in ANOVA-Based Models for Nonorthogonal Designs Including Continuous Covariates","authors":"Morten A. Rasmussen,&nbsp;Bekzod Khakimov,&nbsp;Jasper Engel,&nbsp;Jeroen Jansen","doi":"10.1002/cem.3580","DOIUrl":"10.1002/cem.3580","url":null,"abstract":"<p>Analysis of variance and linear models is undoubtedly one of the most useful statistical contributions to experimental and observational science. With the ability to characterize a system through multivariate responses, these methods have emerged to be general tools regardless of response dimensionality. Contemporary methods for establishing statistical inference, such as ANOVA simultaneous component analysis (ASCA), are based on Monte Carlo sampling; however, a flat uniform resampling scheme may violate the structure of the uncertainty for unbalanced designs as well as for observational data. In this work, we provide permutation strategies for inferential testing for unbalanced designs including interaction models and establish nonuniform randomization based on the concept of propensity score matching. Lastly, we provide a general method for modelling continuous covariates based on kernel smoothers. All methods are characterized on their ability to provide unbiased Type I error results.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738631","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":"A comprehensive tutorial on data-driven SIMCA: Theory and implementation in web","authors":"Sergey Kucheryavskiy,&nbsp;Oxana Rodionova,&nbsp;Alexey Pomerantsev","doi":"10.1002/cem.3560","DOIUrl":"https://doi.org/10.1002/cem.3560","url":null,"abstract":"","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 7","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597073","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":"ATR-FTIR Spectroscopy Preprocessing Technique Selection for Identification of Geographical Origins of Gastrodia elata Blume","authors":"Hong Liu,&nbsp;Honggao Liu,&nbsp;Jieqing Li,&nbsp;Yuanzhong Wang","doi":"10.1002/cem.3579","DOIUrl":"10.1002/cem.3579","url":null,"abstract":"<div>\u0000 \u0000 <p><i>Gastrodia elata</i> Blume from different regions varies in growth conditions, soil types, and climate, which directly affects the content and quality of its medicinal components. Accurately identifying the origin can effectively ensure the medicinal value of <i>G. elata</i> Bl., prevent the circulation of counterfeit products, and thus protect the interests and health of consumers. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is a rapid and effective method for verifying the authenticity of traditional Chinese medicines. However, the presence of scattering effects in the spectra poses challenges in establishing reliable discrimination models. Therefore, employing appropriate scattering correction techniques is crucial for improving the quality of spectral data and the accuracy of discrimination models. This study uses two ensemble preprocessing approaches; the first type is series fusion of scatter correction technologies (SCSF), and another method is sequential preprocessing through orthogonalization (SPORT). Four discriminant models were established using a single scattering correction technique and two ensemble preprocessing approaches. The results show that the data-driven version of the soft independent modeling of class analogy (DD-SIMCA) model built based on multiplicative scatter correction (MSC) preprocessing has a sensitivity of 0.98 and a specificity of 0.91, able to effectively distinguish whether a sample of <i>G. elata</i> Bl. originates from Zhaotong. In addition, three discriminant models including support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), and three gradient boosting machine (GBM) algorithms built using the ensemble preprocessing approach have good classification and generalization capabilities. Among them, the SCSF-PLS-DA model has the best performance with 99.68% and 98.08% accuracy for the training and test sets, respectively, and F1 of 0.97; the SPORT-SVM model achieved the second-best classification ability. The results show that the ensemble preprocessing approach used can improve the success rate of <i>G. elata</i> Bl. geographical origin classification.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 10","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551403","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":"Firefly Interval Selection Combined With Extreme Learning Machine for Spectral Quantification of Complex Samples","authors":"Shuyu Wang,&nbsp;Xudong Zhang,&nbsp;Prisca Mpango,&nbsp;Hao Sun,&nbsp;Xihui Bian","doi":"10.1002/cem.3578","DOIUrl":"10.1002/cem.3578","url":null,"abstract":"<div>\u0000 \u0000 <p>Firefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA-ELM is compared with full-spectrum PLS, ELM, genetic algorithm-ELM (GA-ELM), and particle swarm optimization-ELM (PSO-ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near-infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA-ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511200","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":"Minimum Spanning Tree-Based Clustering for Chemical Evaluation of Commercial Nail Polish Samples Using Spectroanalytical Data","authors":"Heloisa Froehlick Castello,&nbsp;Felipe Lopes Rodrigues Silva,&nbsp;Dennis Silva Ferreira,&nbsp;Alexandre Luis Magalhães Levada,&nbsp;Edenir Rodrigues Pereira-Filho,&nbsp;Fabiola Manhas Verbi Pereira","doi":"10.1002/cem.3575","DOIUrl":"10.1002/cem.3575","url":null,"abstract":"<div>\u0000 \u0000 <p>This study discusses potential toxic elements detection in conventional nail polish, including Cr and Pb. The noteworthy results highlight well-established potential risks of elevated Cr and Pb concentrations. These elements are not allowed in the European Union. Implementing the minimum spanning tree (MST) approach and the isolation forest algorithm effectively clustered samples. Forty-five samples were analyzed, and four clusters were identified. Two presented six samples with high concentrations of Fe (Cluster 1 with four samples) and Cr and Pb (Cluster 2 with two samples). The other 39 samples presented low concentrations of the determined elements (Co, Cr, Cu, Fe, Ni, and Pb). Cadmium, Zn, and Mn were not detected in any of the analyzed samples. Furthermore, integrating energy-dispersive x-ray fluorescence (ED-XRF) and laser-induced breakdown spectroscopy (LIBS) enabled fast direct analysis of nail polish samples, streamlining a swift and reliable data acquisition process. This research underscores the importance of ongoing vigilance and monitoring of potential health hazards associated with nail polish formulations, especially in regions with regulatory restrictions on certain elements.</p>\u0000 </div>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511201","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":"Detection of moisture content of edamame based on the fusion of reflectance and transmittance spectra of hyperspectral imaging","authors":"Bin Li,&nbsp;Cheng-tao Su,&nbsp;Hai Yin,&nbsp;Ji-ping Zou,&nbsp;Yan-de Liu","doi":"10.1002/cem.3574","DOIUrl":"10.1002/cem.3574","url":null,"abstract":"<p>Edamame is a nutritious and economically valuable soybean. The moisture content is an important indicator of the quality of the edamame. The traditional methods in the detection of moisture content of edamame have the disadvantage of large detection errors. In this research, the fusion of transmittance and reflectance spectra of hyperspectral imaging combined with chemometrics was proposed to predict the moisture content of edamame. Also, the effect of different preprocessing of the spectra on the predictive performance was analyzed. Single spectra, primary fusion spectra, and intermediate fusion spectra were established as the prediction models for partial least squares regression (PLSR) and partial least squares support vector regression (LSSVR), respectively. The results of the prediction models showed that the spectral transform absorption (STA) combined with PLSR has the best prediction performance for a single spectrum with predictive correlation (R_P) of 0.7749 and ratio of prediction to deviation (RPD) of 1.7. Standard normal variate (SNV) combined with LSSVR has the best prediction performance for primary fusion spectra with R_P of 0.8821 and RPD of 1.9. SNV combined with LSSVR has the best prediction performance for intermediate fusion spectra with R_P of 0.9149 and RPD of 2.4. The R_p and RPD of prediction models of the moisture content of edamame based on fusion spectra were significantly improved compared with single spectra. Compared with primary fusion, intermediate fusion is a more suitable fusion strategy. This research provides experimental basis for the prediction of moisture content of edamame using spectral fusion combined with chemometrics.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350433","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":"Characterisation of Position-Dependant Ripening Dynamics of Nectarines Using Near-Infrared Spectroscopy and ASCA","authors":"Jokin Ezenarro,&nbsp;Daniel Schorn-García,&nbsp;Anna Palou,&nbsp;Montserrat Mestres,&nbsp;Laura Aceña,&nbsp;Maribel Abadias,&nbsp;Ingrid Aguiló-Aguayo,&nbsp;Olga Busto,&nbsp;Ricard Boqué","doi":"10.1002/cem.3576","DOIUrl":"10.1002/cem.3576","url":null,"abstract":"<p>Nectarines, a popular pit fruit closely related to peaches, are renowned for their nutritional value and associated health benefits. However, challenges arise in maintaining optimal organoleptic properties during harvest and handling, eventually leading to production waste and heterogeneous quality in the fruit that arrives to the consumer. This study investigates the impact of nectarine position on trees during the whole ripening process using non-destructive near-infrared (NIR) spectroscopy. Nectarines exposed to more sunlight mature faster and this influences sugar content and acidity, emphasising the significance of considering height, prominence and orientation in ripening dynamics of the fruit. Different data unfolding strategies were compared, using ANOVA-Simultaneous Component Analysis (ASCA) to reveal the significance of in-tree position factors at different ripening stages, and observing high significance at harvest. This underscores the necessity for growers and handlers to consider these factors for reducing waste. NIR spectroscopy, with adequate data analysis, is a valuable tool for the holistic analysis of fruit ripening, providing crucial insights for maintaining optimal fruit organoleptic properties from harvest to consumer.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cem.3576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367593","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":"Generating realistic infrared spectra using artificial neural networks","authors":"László Győry,&nbsp;Szilveszter Gergely,&nbsp;Pál Péter Hanzelik","doi":"10.1002/cem.3573","DOIUrl":"10.1002/cem.3573","url":null,"abstract":"<p>Artificial spectra were generated to match the different acid solubility properties of the rocks. The purpose of generating artificial spectra was to increase the number of samples available for future data processing with a convolutional neural network. The samples were collected from different geological matrices during targeted rock tests to support industrial applications. The inherent characteristics of the samples are their uneven distribution in the parameter space of the features and their limited availability for data-intensive studies. Both data set characteristics constrain the prediction performance of the machine learning methods to estimate the unknown solubility of samples in the chosen acids. If the sample multiplication techniques are performed without considering the relationship between solubility of samples and their infrared spectra, the synthetic samples adversely impact the efficacy of the prediction method. By utilizing a dimensionality reduction technique (principal component analysis) and a neural network, we established a relationship between the solubility of the samples and their infrared spectra. Infrared spectra of the samples used for learning the model could be efficiently reproduced and infrared spectra of created samples could be generated. The reliability of the applied method has been shown by the comparison of the original and artificial spectra through a mean Pearson correlation coefficient and by comparing the closest neighbors to each other. This method can be used to create new samples and their infrared spectra, where different constraints must be met and the samples must be connected to the infrared spectrum.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":"38 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141196169","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}