Fruit juice–alcohol mixture analysis using machine learning and electronic nose
電子鼻運用智能識別算法分析果汁中酒精混合物
Emre Ordukaya Bekir Karlik
Abstract
The aim of this study is to analyze the raw data collected from a fruit juice–alcohol mixture (a fruit juice–alcohol mixture and a fruit juice–multiple alcohol mixture) and the Halal authentication of a fruit juice–alcohol mixture with electronic nose. Machine learning techniques such as naïve Bayesian classifier, K‐nearest neiors (K‐NN), linear discriminant analysis (LDA), decision tree, artificial neural network (ANN), and support vector machine (SVM) were used to classify the feature of these collected raw data. There are three types of classification: the first one is a fruit juice and an alcohol mixture type; the second is a fruit juice and multiple alcohol mixture types, and the third is a Halal authentication of a fruit juice and alcohol mixture. We aimed at making cocktails with more successful results on the first two types of classification in the work. Also, we focused on Halal authentication of fruit juice–alcohol mixture in the third classification
本研究的目的是分析從果汁-酒精混合物(果汁-酒精混合物和果汁-多元酒精混合物)中收集的原始數據，以及帶有電子鼻的果汁-酒精混合物的清真認證。采用納維貝葉斯分類器、k近鄰(K-NN)、線性判別分析(LDA)、決策樹、人工神經網絡(ANN)和支持向量機(SVM)等機器學習技術對采集到的原始數據進行特征分類。有三種分類：種是果汁和酒精混合物類型；第二種是果汁和多種酒精混合物類型；第三種是果汁和酒精混合物的清真認證。我們的目標是使雞尾酒在前兩類分類上取得更為成功的結果。此外，我們重點研究了第三類果汁-酒精混合物的清真認證。
