DISCOVERING THE CHEMICAL FACTORS BEHIND REGIONAL ROYAL JELLY DIFFERENCES VIA MACHINE LEARNING

Yıl 2023, , 49 - 60, 19.05.2023

Aslı Özkök , Merve Keskin , Aslı Elif Tanuğur , Elif Önder Yorulmaz Gökhan Silahtaroğlu

https://doi.org/10.31467/uluaricilik.1238027

Öz

This study aims to discover the characteristic chemical factors for determining the region of royal jelly using machine learning. 84 samples from 13 different regions of Turkey were used for the study, and the chemical parameters of moisture, pH, acidity, and 10-hydroxy-2-decanoic acid (10-HDA) were investigated. ANOVA test was conducted to determine whether there are differences between royal jelly from 13 locations concerning the four chemical values. In addition to the statistical tests, a machine learning model was used to find out what makes royal jelly different from each other. The descriptive statistics of the chemical analysis results of royal jelly showed the following values: moisture 63.05%±2.99, pH 3.67±0.08, acidity 45.32±3.55, and 10-HDA 2.40±0.24. Surprisingly, the machine learning model suggests that 10-HDA may be the most prominent parameter for determining the region of royal jelly. This information will help us identify royal jelly’s authenticity more easily.

Anahtar Kelimeler

Royal jelly, honeybee, machine learning, 10-HDA

Destekleyen Kurum

Bu çalışma SBS Bilimsel Bio Çözümler Inc. Bee&You Propolis R&D Center tarafından desteklenmiştir.

Proje Numarası

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Teşekkür

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Makine Öğrenimi Yoluyla Bölgesel Arı Sütü Farklarının Arkasındaki Kimyasal Faktörleri Keşfetmek

Öz

Bu çalışmanın amacı, makine öğrenmesi yoluyla arı sütünün bölgesini belirlemek için ayırt edici kimyasal faktörleri keşfetmektir. Çalışmada, Türkiye'nin 13 farklı bölgesinden 84 numune kullanılmış ve nem, pH, asitlik ve 10-hidroksi-2-dekanoik asit (10-HDA) kimyasal parametreleri incelenmiştir. 13 yerden toplanan arı sütleri arasında dört kimyasal değer açısından farklılık olup olmadığı ANOVA testi ile incelenmiştir. İstatistiksel testlere ek olarak, arı sütlerini birbirinden neyin ayırdığını keşfetmek için bir makine öğrenimi modeli kullanılmıştır. Arı sütü, kimyasal analiz sonuçlarının tanımlayıcı istatistikleri sırasıyla, nem %63,05±2,99, pH 3,67±0,08, asitlik 45,32±3,55 ve 10-HDA 2,40±0,24 olarak bulunmuştur. Şaşırtıcı bir şekilde, makine öğrenimi modeli, 10-HDA'nın arı sütünün bölgesini belirlemek için en belirgin parametre olabileceğini öne sürmektedir. Bu bilgi, arı sütünün doğruluğunun tespitini daha kolay öğrenmemize yardımcı olacaktır.

Anahtar Kelimeler

Arı sütü, bal arısı, makine öğrenimi, 10-HDA

Proje Numarası

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Kaynakça

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