[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128128-en":3,"doc-seo-128128-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128128,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning-Based Spectral Analyses for Camellia japonica Cultivar Identification","Camellia japonica is an ornamental species with important biological properties, and its thousands of cultivars require accurate identification. Infrared spectroscopy enables fast, reliable plant identification, but performance depends strongly on data analysis choices such as spectra pre-processing, feature selection, and chemometric models. This study compares two machine learning approaches using near-infrared (NIR) and Fourier transform infrared (FTIR) spectra from 15 cultivars (38 plants), evaluating prediction accuracy for cultivar discrimination.","Article  \nMachine Learning-Based Spectral Analyses for Camellia japonica Cultivar Identification  \nPedro Miguel Rodrigues   \nAcademic Editors: Eun Kyoung Seo and Félix Tomi  \nReceived: 14 November 2024  \nRevised: 22 January 2025  \nAccepted: 23 January 2025  \nPublished: 25 January 2025  \nCitation: Rodrigues, P.M.; Sousa, C. Machine Learning-Based Spectral Analyses for Camellia japonica Cultivar Identification. Molecules 2025, 30, 546 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)molecules30030546  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nand Clara Sousa *  \nCBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal; [pmrodrigues@ucp.pt](pmrodrigues@ucp.pt)  \n* Correspondence: [cssousa@ucp.pt](cssousa@ucp.pt)  \nAbstract: Camellia japonica is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, C. japonica has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mandatory. Infrared spectroscopy is currently recognized as an accurate and rapid technique for species and/or subspecies identifications, including in plants. However, selecting proper analysis tools (spectra pre-processing, feature selection, and chemometric models) highly impacts the accuracy of such identifications. This study tests the impact of two distinct machine learning-based approaches for discriminating C. japonica cultivars using near-infrared (NIR) and Fourier transform infrared (FTIR) spectroscopies. Leaves infrared spectra (NIR—obtained in a previous study; FTIR—obtained herein) of 15 different C. japonica cultivars (38 plants) were modeled and analyzed via different machine learning-based approaches (Approach 1 and Approach 2), each combining a featureselection method plus a classifier application. Regarding Approach 1, NIR spectroscopy emerged as the most effective technique for predicting C. japonica cultivars, achieving 81.3% correct cultivar assignments. However, Approach 2 obtained the best results with FTIR spectroscopy data, achieving a perfect 100.0% accuracy in cultivar assignments. When comparing both approaches, Approach 2 also improved the results for NIR data, increasing the correct cultivar predictions by nearly 13% . The results obtained in this study highlight the importance of chemometric tools in analyzing infrared data. The choice of a specific data analysis approach significantly affects the accuracy of the technique. Moreover, the same approach can have varying impacts on different techniques. Therefore, it is not feasible to establish a universal data analysis approach, even for very similar datasets from comparable analytical techniques.  \nKeywords: chemometrics; feature selection; machine learning; infrared spectroscopy; plant typing  \n1. Introduction  \nCamellia japonica is an evergreen shrub renowned for its vibrant blooms, holding significant cultural relevance. In many Asian cultures, particularly Japan and China, the camellia flower symbolizes longevity, prosperity, and good fortune. Its association with beauty and refinement has made it a popular choice for gardens, art, and literature throughout history. Beyond its aesthetic appeal, C. japonica is also recognized for its biological properties and economic relevance [1] . Its oil, extracted from the seeds, is used in various cosmetic and skincare products due to its moisturizing and antioxidant properties. Also, the plant’s wood is highly appreciated for its durability and is used in woodworki","cbCailqOjocXrxyg","https://ap.wps.com/l/cbCailqOjocXrxyg","pdf",1234583,6,1,13,"English","en",105,"# Introduction\n## Camellia japonica importance and cultivar discrimination\n## Infrared spectroscopy as a rapid identification method\n## Role of data analysis and machine learning in spectral interpretation","[{\"question\":\"What problem does the study address for Camellia japonica?\",\"answer\":\"The study addresses the need for accurate discrimination among thousands of Camellia japonica cultivars, which show similar appearances and are often difficult to label correctly.\"},{\"question\":\"Which spectral techniques and data were used?\",\"answer\":\"The analysis uses near-infrared (NIR) and Fourier transform infrared (FTIR) leaf spectra. NIR data came from a previous study, while FTIR data were obtained in this work.\"},{\"question\":\"How do the two machine learning approaches compare in accuracy?\",\"answer\":\"Approach 1 achieved 81.3% correct cultivar assignments using NIR data, while Approach 2 achieved 100.0% accuracy using FTIR data and improved NIR predictions by nearly 13%.\"}]","Machine Learning-Based Spectral Analyses for Camellia japonica Cultivar Identification | PDF",1785944978,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-spectral-analyses-for-camellia-japonica-cultivar-identification","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-spectral-analyses-for-camellia-japonica-cultivar-identification/128128/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the study address for Camellia japonica?","Question",{"text":77,"@type":78},"The study addresses the need for accurate discrimination among thousands of Camellia japonica cultivars, which show similar appearances and are often difficult to label correctly.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which spectral techniques and data were used?",{"text":82,"@type":78},"The analysis uses near-infrared (NIR) and Fourier transform infrared (FTIR) leaf spectra. NIR data came from a previous study, while FTIR data were obtained in this work.",{"name":84,"@type":75,"acceptedAnswer":85},"How do the two machine learning approaches compare in accuracy?",{"text":86,"@type":78},"Approach 1 achieved 81.3% correct cultivar assignments using NIR data, while Approach 2 achieved 100.0% accuracy using FTIR data and improved NIR predictions by nearly 13%.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]