A Novel Chemometric Learning Of Virgin And Deep Frying Olive-Oil By Fourier Transform Infrared Spectroscopy (FT-IR)

Kerim Karadağ; Gizem Yucegonul; Eyyüp Karaoğul

Makale detayları

A Novel Chemometric Learning Of Virgin And Deep Frying Olive-Oil By Fourier Transform Infrared Spectroscopy (FT-IR)

Yazarlar:

Kerim Karadağ | Gizem Yucegonul | Eyyüp Karaoğul

Yayıncı:

Dicle University

Ana Başlıklar:

Elektronikte Ölçme Tekniği

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 2, pp.293-300

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320903

pISSN:

1309-8640

Anahtar kelimeler:

Özet:

 The aim of this study is to examine the machine learning of chemometrically fried oils in virgin olive oil and eight times used olive oil compared using Fourier Transform Infrared spectroscopy FT-IR. Deep-Frying Oils DFO was carried out 8 times for 20 minutes. Because of chemical quality of oils, Cis, Trans, Ester, Methyl, Carbonyl, peroxide, unsatrated peroxide and ether groups were used in These results were evaluated by classification and regression using machine learning methods. For these evaluations, firstly classification and regression were made using all properties of these index. In classification models, Support Vector Machines SVM, K Closest Neighborhood KNN, Decision Tree DT were used. The evaluation was carried out in two stages. In the first stage, half of the dataset was used for training and the other half for testing. In the second stage, all data data was used for training and testing using cross validation CV method. The success results obtained using the all data set were 94 with Support Vector Machines and K Nearest Neighborhood methods. According to chemometric strategy, differences between virgin olive oils and DFO were found by high accurancy in this study. This phenomenon also could be possible for other oil type and degree of purity. Results illustrated that the method is very suitable and exact for detection deteriotion of olive oil.

 The aim of this study is to examine the machine learning of chemometrically fried oils in virgin olive oil and eight times used olive oil compared using Fourier Transform Infrared spectroscopy FT-IR. Deep-Frying Oils DFO was carried out 8 times for 20 minutes. Because of chemical quality of oils, Cis, Trans, Ester, Methyl, Carbonyl, peroxide, unsatrated peroxide and ether groups were used in These results were evaluated by classification and regression using machine learning methods. For these evaluations, firstly classification and regression were made using all properties of these index. In classification models, Support Vector Machines SVM, K Closest Neighborhood KNN, Decision Tree DT were used. The evaluation was carried out in two stages. In the first stage, half of the dataset was used for training and the other half for testing. In the second stage, all data data was used for training and testing using cross validation CV method. The success results obtained using the all data set were 94 with Support Vector Machines and K Nearest Neighborhood methods. According to chemometric strategy, differences between virgin olive oils and DFO were found by high accurancy in this study. This phenomenon also could be possible for other oil type and degree of purity. Results illustrated that the method is very suitable and exact for detection deteriotion of olive oil.

 The aim of this study is to examine the machine learning of chemometrically fried oils in virgin olive oil and eight times used olive oil compared using Fourier Transform Infrared spectroscopy FT-IR. Deep-Frying Oils DFO was carried out 8 times for 20 minutes. Because of chemical quality of oils, Cis, Trans, Ester, Methyl, Carbonyl, peroxide, unsatrated peroxide and ether groups were used in These results were evaluated by classification and regression using machine learning methods. For these evaluations, firstly classification and regression were made using all properties of these index. In classification models, Support Vector Machines SVM, K Closest Neighborhood KNN, Decision Tree DT were used. The evaluation was carried out in two stages. In the first stage, half of the dataset was used for training and the other half for testing. In the second stage, all data data was used for training and testing using cross validation CV method. The success results obtained using the all data set were 94 with Support Vector Machines and K Nearest Neighborhood methods. According to chemometric strategy, differences between virgin olive oils and DFO were found by high accurancy in this study. This phenomenon also could be possible for other oil type and degree of purity. Results illustrated that the method is very suitable and exact for detection deteriotion of olive oil.

 Bu çalışmanın amacı, Fourier dönüşümlü kızılötesi spektroskopisi FT-IR kullanılarak karşılaştırılan saf zeytinyağı ve sekiz kez kullanılmış zeytinyağında kemometrik yöntemde kızartılmış yağların makine öğrenmesini incelemektir. Deep frying oils DFO 20 dakika boyunca 8 kez kullanılmıştır. Yağların kimyasal kalitesi için Cis, Trans, Ester, Metil, Karbonil, Peroksit, doymamış Peroksit ve Eter grupları incelenmiştir. Bu sonuçlar makine öğrenmesi yöntemleri kullanılarak sınıflandırma ve regresyon yapılarak değerlendirilmiştir. Bu değerlendirmeler için öncelikle bu indekslerin tüm özellikleri kullanılarak sınıflandırma ve regresyon yapılmıştır. Sınıflandırma modellerinde Destek Vektör Makineleri SVM, K En Yakın Komşuluk KNN, Karar Ağacı DT kullanılmıştır. Değerlendirme iki aşamada gerçekleştirilmiştir. İlk aşamada veri setinin yarısı eğitim, diğer yarısı da test için kullanılmıştır. İkinci aşamada, tüm veriler çapraz doğrulama CV yöntemi kullanılarak eğitim ve test için kullanılmıştır. Tüm veri seti kullanılarak başarı sonuçları SVM ve KNN yöntemleri ile 94 elde edilmiştir. Bu çalışmada kemometrik stratejiye göre saf zeytinyağı ile DFO arasındaki farklılıklar yüksek doğrulukla bulunmuştur. Bu olay ayrıca diğer yağ türleri ve saflık dereceleri için de mümkün olabilir. Sonuç olarak, bu yöntemin zeytinyağının bozulmasını tespit etmek için çok uygun ve doğru olduğunu göstermiştir.

Makale detayları

A Novel Chemometric Learning Of Virgin And Deep Frying Olive-Oil By Fourier Transform Infrared Spectroscopy (FT-IR)

Yazarlar:

Kerim Karadağ | Gizem Yucegonul | Eyyüp Karaoğul

Yayıncı:

Dicle University

Ana Başlıklar:

Elektronikte Ölçme Tekniği

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 2, pp.293-300

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320903

pISSN:

1309-8640

FT-IR

CHEMOMETRIC METHOD

OLIVE OIL

DEEP FRYING OILFT-IR

KEMOMETRİK ÖĞRENME

ZEYTİNYAĞI

KIZARTMA YAĞI

Sayı

Makale detayları

Yazarlar: Kerim Karadağ | Gizem Yucegonul | Eyyüp Karaoğul

Yayıncı: Dicle University

Ana Başlıklar: Elektronikte Ölçme Tekniği

Dergi: Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 2, pp.293-300

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001320903

pISSN: 1309-8640

Sayı

Yazarlar:

Kerim Karadağ | Gizem Yucegonul | Eyyüp Karaoğul

Yayıncı:

Dicle University

Ana Başlıklar:

Elektronikte Ölçme Tekniği

Dergi:

Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi Vol.15 Issue 2, pp.293-300

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320903

pISSN:

1309-8640