Makine Öğrenimi Yoluyla Bölgesel Arı Sütü Farklarının Arkasındaki Kimyasal Faktörleri Keşfetmek

Author:

ÖZKÖK Aslı1ORCID,KESKİN Merve2ORCID,TANUĞUR Aslı ElifORCID,ÖNDER YORULMAZ ElifORCID,SİLAHTAROĞLU Gökhan3ORCID

Affiliation:

1. Hacettepe University

2. BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ

3. İSTANBUL MEDİPOL ÜNİVERSİTESİ

Abstract

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.

Funder

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

Publisher

Uludag Aricilik Dergisi

Subject

Insect Science,Ecology, Evolution, Behavior and Systematics,Food Science

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