Water content classification on cucurbitaceae family fruits from VIS/NIR spectroscopic data

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dc.authorid0000-0001-8570-2412
dc.contributor.authorÜrgen, Nurullah
dc.contributor.authorDemircan, Semiye
dc.date.accessioned2025-02-13T07:24:43Z
dc.date.available2025-02-13T07:24:43Z
dc.date.issued2024
dc.departmentBaşka Kurum
dc.description.abstractVisible and Near-Infrared (Vis-NIR) spectroscopy is a technique used to determine the chemical and physical properties of matter by analyzing electromagnetic radiation across a broad wavelength range, specifically from 400 to 2500 nm. In this application, the aim is to assess the quality attributes of six Cucurbitaceae family fruits, namely: zucchini, bitter melon, ridge gourd, melon, chayote, and cucumber, using a single classification model for all fruits rather than individual models. This classification model predicts whether it exceeds 90% according to fruits based on water content. Samples with water content above 90% are labeled as high-water content, while those below are categorized as low-water content. For preprocessing, Standard Normal Variate (SNV) and Neighborhood Components Analysis (NCA) methods were employed to optimize the feature space. The model was trained using a Support Vector Machine (SVM) classifier. Without feature extraction, the accuracy ranged from 90% to 92.5%; however, with feature extraction, the accuracy increased to 95%-97.5%. This classification model successfully predicts high water content, an essential indicator of product quality and productivity, across the dataset with high precision. By integrating comprehensive data processing and machine learning techniques, this study demonstrates a reliable method for assessing product quality, contributing significantly to the field of agricultural and food industry quality control.
dc.identifier.citationÜrgen, N., Demircan, S. (2024). Water content classification on cucurbitaceae family fruits from VIS/NIR spectroscopic data. Selcuk University Journal of Engineering Sciences, 23 (3), 106-111.
dc.identifier.endpage111
dc.identifier.issn2757-8828
dc.identifier.issue3
dc.identifier.startpage106
dc.identifier.urihttps://hdl.handle.net/20.500.12395/54403
dc.identifier.volume23
dc.language.isoen
dc.publisherSelçuk Üniversitesi
dc.relation.ispartofSelcuk University Journal of Engineering Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMachine Learning
dc.subjectWater Content Classification
dc.subjectVis/NIR Spectroscopy
dc.subjectCucurbitaceae
dc.subjectMakine Öğrenmesi
dc.subjectSu İçeriği Sınıflandırması
dc.subjectVis/NIR Spektroskopisi
dc.titleWater content classification on cucurbitaceae family fruits from VIS/NIR spectroscopic data
dc.typeArticle

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