[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125639-en":3,"doc-seo-125639-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125639,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools","Rice quality evaluation requires fast, reliable methods because rice (Oryza sativa) strongly influences food products. Near-infrared (NIR) spectroscopy combined with machine learning algorithms—interval partial least squares (iPLS), synergy interval PLS (siPLS), and artificial neural networks (ANNs)—enabled prediction of pasting parameters including breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR) from 166 samples. iPLS and siPLS models achieved strong regression performance, and ANN further improved calibration and testing results, supported by specific influential spectral regions.","applied sciences  \nArticle  \nDevelopment of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools  \nPedro Sousa Sampaio 1,2,3, *, Bruna Carbas 1,4 and Carla Brites 1,2  \nCitation: Sampaio, P.S.; Carbas, B.; Brites, C. Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools. Appl. Sci. 2023, 13, 9081. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app13169081](10.3390/app13169081)  \nAcademic Editors: Saulius Juodkazis, Xihui Bian, Jin Yu and Qunbo Lv  \nReceived: 12 March 2023  \nRevised: 11 June 2023  \nAccepted: 14 June 2023  \nPublished: 9 August 2023  \nCopyright: © 2023 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Instituto Nacional de Investigaç¢o Agr¡ria e Veterin¡ria (INIAV), Av. da Repóblica, Quinta do Marqu¶s, 2780-157 Oeiras, Portugal  \n2 GREEN-IT BioResources for Sustainability Unit, Institute of Chemical and Biological Technology Antânio Xavier, ITQB NOVA, Av. da Repóblica, 2780-157 Oeiras, Portugal  \n3 Computaç¢o e Cogniç¢o Centrada nas Pessoas, BioRG—Biomedical Research Group, Lusâfona University, Campo Grande, 376, 1749-019 Lisbon, Portugal  \n4 Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr¡s-os-Montes and Alto Douro (CITAB-UTAD), 5000-801 Vila Real, Portugal  \n* [Correspondence: pedro.sampaio@ulusofona.pt](Correspondence: pedro.sampaio@ulusofona.pt); Tel.: +351-217515500 (ext. 654)  \nAbstract: Due to the importance of rice (Oryza sativa) in food products, developing strategies to evaluate its quality based on a fast and reliable methodology is fundamental. Herein, near-infrared (NIR) spectroscopy combined with machine learning algorithms, such as interval partial least squares (iPLS), synergy interval PLS (siPLS), and artificial neural networks (ANNs), allowed for the development of prediction models of pasting parameters, such as the breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), from 166 rice samples. The models developed using iPLS and siPLS were characterized, respectively, by the following regression values: BD (R = 0.84; R = 0.88); FV (R = 0.57; R = 0.64); PV (R = 0.85; R = 0.90); ST (R = 0.85; R = 0.88); and TR (R = 0.85; R = 0.84) . Meanwhile, ANN was also tested and allowed for a significant improvement in the models, characterized by the following values corresponding to the calibration and testing procedures: BD (Rcal = 0.99; R test = 0.70), FV (Rcal = 0.99; R test = 0.85), PV (Rcal = 0.99; R test = 0.80), ST (Rcal = 0.99; R test = 0.76), and TR (Rcal = 0.99; R test = 0.72) . Each model was characterized by a specific spectral region that presented significative influence in terms of the pasting parameters. The machine learning models developed for these pasting parameters represent a significant tool for rice quality evaluation and will have an important influence on the rice value chain, since breeding programs focus on the evaluation of rice quality.  \nKeywords: artificial neural network; NIR spectroscopy; pasting parameters; rice  \n1. Introduction  \nThe assessment of quality traits in rice (Oryza sativa L.) can be considered a very important issue, as these parameters play an important role for both consumers and industry. The assessment of these traits can be performed by the measurement of the physical parameters of the grain, its biochemical composition, its cooking properties, and its milling performance. The most interesting quality parameters are related to physical properties (weight, grain volume), appearance (color, size, shape, smoothness, and hardness), flow properties, biochemic","cbCaidv0DZnaw19A","https://ap.wps.com/l/cbCaidv0DZnaw19A","pdf",2603365,1,14,"English","en",105,"# Introduction\n## Rice quality assessment and pasting properties\n## RVA and pasting parameter definitions\n## Relevance for food processing and breeding","[{\"question\":\"What data and techniques were used to build the rice prediction models?\",\"answer\":\"The models used near-infrared (NIR) spectroscopy data combined with machine learning methods, including iPLS, siPLS, and artificial neural networks (ANNs). Predictions were made from 166 rice samples.\"},{\"question\":\"Which pasting parameters were predicted in the study?\",\"answer\":\"The study predicted breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), derived from rice pasting profiles.\"},{\"question\":\"How did ANN compare with iPLS and siPLS in model performance?\",\"answer\":\"ANN was tested alongside iPLS and siPLS and provided a significant improvement, with calibration and testing correlations reported for each pasting parameter (e.g., BD, FV, PV, ST, TR).\"}]","Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools | PDF",1785900361,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-of-prediction-models-for-the-pasting-parameters-of-rice-based-on-near-infrared-and-machine-learning-tools","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-of-prediction-models-for-the-pasting-parameters-of-rice-based-on-near-infrared-and-machine-learning-tools/125639/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and techniques were used to build the rice prediction models?","Question",{"text":75,"@type":76},"The models used near-infrared (NIR) spectroscopy data combined with machine learning methods, including iPLS, siPLS, and artificial neural networks (ANNs). Predictions were made from 166 rice samples.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which pasting parameters were predicted in the study?",{"text":80,"@type":76},"The study predicted breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), derived from rice pasting profiles.",{"name":82,"@type":73,"acceptedAnswer":83},"How did ANN compare with iPLS and siPLS in model performance?",{"text":84,"@type":76},"ANN was tested alongside iPLS and siPLS and provided a significant improvement, with calibration and testing correlations reported for each pasting parameter (e.g., BD, FV, PV, ST, TR).","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]