A CHEAPLY NON-DESTRUCTIVE TECHNIQUE TO ESTIMATE HONEY QUALITY: THERMAL IMAGING AND MACHINE LEARNING

Mustafa Kibar

Makale detayları

A CHEAPLY NON-DESTRUCTIVE TECHNIQUE TO ESTIMATE HONEY QUALITY: THERMAL IMAGING AND MACHINE LEARNING

Yazarlar:

Mustafa Kibar

Yayıncı:

Bursa Uludağ University

Ana Başlıklar:

Arı ve İpek Böceği Yetiştiriciliği ve Islahı

Dergi:

Uludağ Arıcılık Dergisi Vol.24 Issue 1, pp.79-92

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001318061

pISSN:

2687-5594

Anahtar kelimeler:

Özet:

 The aim of this study was to estimate honey quality based on proline and Brix content using a thermal imaging and machine learning algorithm. The proline, Brix and color properties of twenty honey samples were determined. Proline and Brix values were classified and estimated using the classification and regression tree CART algorithm. The mean proline and Brix content in honey samples was 678.83192.16 mgkg and 83.20.79, respectively. CART analysis revealed that high proline honey samples had L values above 48.143 and b values below 35.416. In contrast, honey samples with low Brix values were characterized by L and a values below 55.860 and 53.660, respectively, and were identified as freshly harvested. The CART algorithm classified the proline and Brix values with an accuracy of 95 and 100, respectively p 0.001. As a result, whitish, bluish, blackish and greenish honeys are of higher quality due to high proline and low Brix content. However, to accurately assess honey quality based on its color traits, comprehensive studies with more honey samples and origin, are required.

 The aim of this study was to estimate honey quality based on proline and Brix content using a thermal imaging and machine learning algorithm. The proline, Brix and color properties of twenty honey samples were determined. Proline and Brix values were classified and estimated using the classification and regression tree CART algorithm. The mean proline and Brix content in honey samples was 678.83192.16 mgkg and 83.20.79, respectively. CART analysis revealed that high proline honey samples had L values above 48.143 and b values below 35.416. In contrast, honey samples with low Brix values were characterized by L and a values below 55.860 and 53.660, respectively, and were identified as freshly harvested. The CART algorithm classified the proline and Brix values with an accuracy of 95 and 100, respectively p 0.001. As a result, whitish, bluish, blackish and greenish honeys are of higher quality due to high proline and low Brix content. However, to accurately assess honey quality based on its color traits, comprehensive studies with more honey samples and origin, are required.

 The aim of this study was to estimate honey quality based on proline and Brix content using a thermal imaging and machine learning algorithm. The proline, Brix and color properties of twenty honey samples were determined. Proline and Brix values were classified and estimated using the classification and regression tree CART algorithm. The mean proline and Brix content in honey samples was 678.83192.16 mgkg and 83.20.79, respectively. CART analysis revealed that high proline honey samples had L values above 48.143 and b values below 35.416. In contrast, honey samples with low Brix values were characterized by L and a values below 55.860 and 53.660, respectively, and were identified as freshly harvested. The CART algorithm classified the proline and Brix values with an accuracy of 95 and 100, respectively p 0.001. As a result, whitish, bluish, blackish and greenish honeys are of higher quality due to high proline and low Brix content. However, to accurately assess honey quality based on its color traits, comprehensive studies with more honey samples and origin, are required.

 Bu çalışmanın amacı, termal görüntüleme ve makine öğrenmesi yaklaşımı kullanılarak baldaki prolin ve Brix içeriğine dayalı bal kalitesinin tahmin edilmesidir. 20 farklı bal örneğine ait prolin, Brix ve renk özellikleri belirlendi. Prolin ve Brix seviyeleri, sınıflandırma ve regresyon ağacı algoritması kullanılarak tahmin edildi ve sınıflandırıldı. Ballarda ortalama prolin ve Brix içeriği sırasıyla 678,83192,16 mgkg ve 83,20,79 olarak belirlendi. CART analizi ile yüksek prolinli balların L değerlerinin 48.143ün üzerinde ve b değerlerinin ise 35.416nın altında olduğu tespit edildi. Ancak, Brix değeri düşük olan balların ise sırasıyla 55.860 ve 53.660ın altında L ve a değerlerine sahip olduğu ve yeni hasat edildiği bulunmuştur. CART algoritması ile prolin ve Brix seviyeleri sırasıyla 95 ve 100 doğrulukla sınıflandırdı p 0.001. Sonuç olarak, beyazımsı, mavimsi, siyahımsı ve yeşilimsi balların yüksek prolin ve düşük Brix içeriği nedeniyle daha kaliteli olduğu belirlenmiştir. Ancak renk özelliklerine dayalı balın kalitesini doğru bir şekilde değerlendirmek için daha fazla ve farklı orijinli bal örnekleri ile kapsamlı çalışmalara ihtiyaç vardır.

Makale detayları

A CHEAPLY NON-DESTRUCTIVE TECHNIQUE TO ESTIMATE HONEY QUALITY: THERMAL IMAGING AND MACHINE LEARNING

Yazarlar:

Mustafa Kibar

Yayıncı:

Bursa Uludağ University

Ana Başlıklar:

Arı ve İpek Böceği Yetiştiriciliği ve Islahı

Dergi:

Uludağ Arıcılık Dergisi Vol.24 Issue 1, pp.79-92

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001318061

pISSN:

2687-5594

Adulteration

Proline

Brix

Honey quality

CART algorithmTağşiş

Prolin

Brix

Balın kalitesi

CART algoritması

Sayı

Makale detayları

Yazarlar: Mustafa Kibar

Yayıncı: Bursa Uludağ University

Ana Başlıklar: Arı ve İpek Böceği Yetiştiriciliği ve Islahı

Dergi: Uludağ Arıcılık Dergisi Vol.24 Issue 1, pp.79-92

Belge Tipi: Makale

Yayın Tarihi: 5/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001318061

pISSN: 2687-5594

Sayı

Yazarlar:

Mustafa Kibar

Yayıncı:

Bursa Uludağ University

Ana Başlıklar:

Arı ve İpek Böceği Yetiştiriciliği ve Islahı

Dergi:

Uludağ Arıcılık Dergisi Vol.24 Issue 1, pp.79-92

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001318061

pISSN:

2687-5594