Water content classification on cucurbitaceae family fruits from VIS/NIR spectroscopic data
Yükleniyor...
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Selçuk Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Visible 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.
Açıklama
Anahtar Kelimeler
Machine Learning, Water Content Classification, Vis/NIR Spectroscopy, Cucurbitaceae, Makine Öğrenmesi, Su İçeriği Sınıflandırması, Vis/NIR Spektroskopisi
Kaynak
Selcuk University Journal of Engineering Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
23
Sayı
3
Künye
Ürgen, 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.
