Gıda Bilimi ve Teknolojisi Alanında Yapay Zekâ Uygulamaları

Bilgi teknolojileri kullanma isteğiyle, araştırma ve uygulamalarda elde edilen bilginin toplanması, depolanması, sınıflanması, yönetimi ve kullanımını etkinleştirmek, kolaylaştırmak ve yaymak için araçlar, yöntemler geliştirmek ve kullanımını öğretmek amacında olan yapay zekâ (YZ) modelleme programları kullanılır hale gelmiştir. Bu programlar sayısal işaret işleme, kontrol tasarımı, haberleşmeden; GC, HPLC, kütle spektrofotometre datalarının analizi, RNA ve DNA tiplemesi, proteinlerin yapılarının tanımlanması, mikroskobik görüntülerin tanımlanması, biyokütle ve mikrobiyal gelişim tahminleri, gıdalarda raf ömrünün belirlenmesi, mikroorganizmaların tanımından molekül yapılarının belirlenmesine kadar birçok alanda kullanılabilmektedir. Gıda mühendisliğinde ise özellikle yapay sinir ağları (neural network), bulanık mantık (fuzzy logic) ve genetik algoritma (genetic algorithm) kullanılmaktadır. Gıdalarda ürün derecelendirme, sınıflandırma, proses modelleme ve optimizasyonu, kalite kontrolünün izlenmesi, görüntünün sayısal verilere dönüştürülmesi, ürün tasarımı, depolama sistemlerinin kontrolü, ürün rekoltesinin tahmini gibi alanlarda; endüstriyel ekmek mayası fermantasyonunda biokütle kestirimi, hamurun rheolojik özelliklerinin belirlenmesi, gıdalarda ısı prosesi değerlendirmesinde, görünür gözeneklilik, sıcaklık ve nem içeriğine göre ısı geçirgenliği tahmininde, antosiyonin içeriklerinin belirlenerek şarapların sınıflandırılması, meyve, sebze ve kuruyemişlerin morfolojik özelliklerine göre sınıflandırılması vb. modelleme uygulamaları yapılmıştır.

Anahtar Kelimeler:

Yapay zekâ, yapay sinir ağları, bulanık mantık, genetik algoritma

Applications of Artificial Intelligence in Food Science and Technology Area (Turkish with English Abstract)

Artificial intelligence (AI) is relatively new computational tools that have found extensive utilization in solving many complex real-world problems. AI has been utilized in a variety of applications ranging from modeling, classification, pattern recognition, and multivariate data analysis. Sample applications include numerical sign processing, control design, communication technologies; interpreting pyrolysis mass spectrometry, GC, and HPLC data, pattern recognition of DNA, RNA, protein structure, and microscopic images, prediction of microbial growth, biomass, and shelf life of food products, and identification of microorganisms and molecules. In food processing and engineering especially artificial neural networks, fuzzy logic and genetic algorithms techniques have been used to improve performance. Artificial intelligence techniques have been recently introduced as a tool for data analysis in food science and industry. AI has been used in food science and technology for classification, process modelling and optimization, quality control of foods prediction of dough rheological properties, classification of wine depending on anthocyanins content, prediction of the maximum or minimum temperature reached in the sample after pressurization and the time needed for thermal re-equilibration in the high-pressure food processing system, classification of fruits and vegetables according to their morphologic properties.

Keywords:

Artificial neural network, neural networks, fuzzy logic, genetic algorithm. ,

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