Yoğurt, sütte bulunan laktozu laktik aside dönüştüren bakteriler kullanarak sütün fermantasyonu ile oluşan pıhtıdır. Laktik asit fermantasyonu sonucunda süt proteinleri koagüle olur ve yoğurda kendine özgü dokusunu ve asidik özelliğini kazandırır. Dünya genelinde yaygın olarak tüketilen yoğurt, insan sağlığının korunması ve sindirim sistemi sorunlarını giderme gibi çeşitli iyileştirici etkiler ile ilişkilendirilmiştir. Yoğurt üretiminde, sıcaklık ve diğer fiziksel faktörlerin etkisiyle protein ağındaki suyun ayrılması önemli bir sorun oluşturmaktadır. Bu sorunu çözmek için yoğurda mTGaz enzimi eklenebilmektedir. mTGaz, hidrofilik protein bağlarının etkileşimini teşvik ederek jelin su tutma kapasitesini artırır ve bu da yoğurt jelinin dayanıklılığını ve viskozitesini iyileştirmektedir. Transglutaminaz enzimi transferaz ailesinin bir üyesidir. Transglutaminaz, bir peptid bağlı glutamin kalıntısının γ-karboksiamid grubu ile lizin amino grubu dahil çeşitli primer aminler arasındaki bir açil transfer reaksiyonunu katalize eder. Bu da proteinler, peptitler ve primer aminler arasında kovalent çapraz bağ oluşturur. Mikrobiyal transglutaminaz, ABD, Kanada, Japonya, Brezilya ve Çin gibi birçok ülkede gıda üretiminde yasal olarak kullanılmaktadır. Genellikle güvenli kabul edilen (GRAS) transglutaminaz enzimi, Avrupa Birliği'nde gıda işleme yardımcı maddesi olarak onaylanmıştır ve süt ürünleri üretiminde içerik listesinde belirtilmeden kullanılabilmektedir. Başlangıçta hayvan dokularından elde edilen transglutaminaz, günümüzde genellikle mikrobiyal kaynaklardan elde edilmektedir. Son yıllarda yoğurt üretiminde mikrobiyal transglutaminaz enziminin kullanımı, ürünün kıvamı, yapısal dayanıklılığı, su tutma kapasitesi ve genel kalite özelliklerini geliştirmesinden dolayı artış göstermektedir. Mikrobiyal transglutaminaz, yoğurda iki yöntemle eklenir: Birinci yöntemde, enzim sütün içine eklenir ve ardından sütün sıcaklığı, enzimin etkinliğinin duracağı noktaya yükseltilerek inaktive edilir. Daha sonra starter kültürü eklenir. İkinci yöntemde ise mTGaz aktivitesi durdurulmadan, aynı anda starter kültürüyle birlikte sütün içine eklenir. Şu ana kadar süt ürünlerinde kullanılan transglutaminaz (mTGaz) enziminin varlığını veya miktarını güvenilir bir şekilde tespit edebilecek herhangi bir spesifik yöntem mevcut değildir. Bu çalışmanın amacı yoğurttaki transglutaminazın kullanımını, durumunu (aktif veya inaktif) ve konsantrasyon seviyelerini tespit edebilecek hızlı, uygun ve güvenilir bir modelin IR spektroskopisi kullanılarak geliştirilmesidir. Bu çalışma yoğurtta mikrobiyal transglutaminaz kullanımının tespitine yönelik literatürdeki ilk çalışma olacaktır. Bu çalışmada, yoğurt örnekleri orta ve yakın kızılötesi spektroskopisi ile analiz edilmiştir. Deneysel yoğurtlarda iki farklı mikrobiyal transglutaminaz enziminin konsantrasyon seviyeleri test edilmiştir. Bu konsantrasyon seviyeleri, 1 ünite ve 2 ünite olmak üzere belirlenmiş ve her iki konsantrasyon seviyelerinin, yoğurtlar üzerinde oluşturduğu farklı etkiler analiz edilmiştir. Yoğurtta bulunan mikrobiyal transglutaminaz enziminin durumunu aktif veya inaktif olarak tespit edebilmek amacıyla üretilen örneklerinin yarısında enzim ısıl işlem ile inaktive edilmiştir. Hem yakın kızılötesi (NIR) spektroskopisi hem de orta kızılötesi (MIR) spektroskopisi için, sınıf analojisinin yumuşak bağımsız modellemesi (SIMCA) ve kısmi en küçük kareler diskriminant analizi (PLS-DA) yaklaşımları, aktif mikrobiyal transglutaminaz enzimi içeren yoğurt örneklerinden kontrol örneğini ve enzimin aktif ve inaktiflik durumunu sınıflandırabilmiştir. Ancak SIMCA ve PLS-DA yaklaşımları hem NIR, hem de MIR spektroskopisi için enzim konsantrasyonunu ayırt edememiştir. Makine öğrenimi analizleri, yoğurt örneklerindeki mikrobiyal transglutaminaz enziminin varlığı, aktif veya inaktif durumunu ve konsantrasyon seviyelerini başarılı bir şekilde tespit etmiştir. Sonuç olarak, yoğurtta mikrobiyal transglutaminaz enziminin kullanımı ve aktif veya inaktif durumu, kullanılan tüm kemometrik yaklaşımlarla birlikte NIR spektroskopisi ve MIR spektroskopisi MIR kullanılarak başarılı bir şekilde tespit edilmiştir. Makine öğrenimi analizleri ise, sınıf analojisi SIMCA ve PLS-DA modelleriyle karşılaştırıldığında yoğurtlarda tespit edilen enzim konsantrasyon seviyelerinin ayırt edilmesi konusunda üstün bir performans göstermiştir. Makine öğrenimi modelinin üstün performansı, verilerdeki karmaşık ve doğrusal olmayan ilişkileri ele alma yeteneğine dayandırılabilir. Bu durum, modeli farklı konsantrasyon seviyelerine daha uyumlu hale getirerek, özellikle enzim seviyelerinin kontrolü ve tutarlı ürün kalitesinin sağlanması açısından yoğurt üretim süreçlerinin iyileştirilmesi için önemli bir fırsat sunmaktadır.
Yoğurt is a fermented dairy product formed by the action of bacteria that convert the lactose in milk into lactic acid. This fermentation process causes the milk proteins to coagulate, giving yogurt its distinctive texture and acidic properties. Yogurt is considered one of the most popular dairy products worldwide and has become an important part of the diet for individuals adopting a healthier lifestyle. It has been associated with various healing effects, such as supporting human health, strengthening immunity, promoting bone health, and alleviating digestive system issues. One of the most critical challenges in yogurt production is the separation of water entrapped within the protein network, which occurs as a result of temperature fluctuations or other physical factors. To address the problem of water separation trapped within the protein network, mTGase can be added to yogurt. This enzyme increases the water retention capacity of the gel by promoting the interaction of hydrophilic protein bonds. Transglutaminase enzyme is a member of the transferase family and is found in animals, plants, and microorganisms. Transglutaminase catalyzes an acyl transfer reaction between the γ-carboxyamide group of a peptide-linked glutamine residue (acyl donors) and various primary amines, including the amino group of lysine (acyl acceptors). This forms covalent cross-links between proteins, peptides, and primary amines. Microbial transglutaminase is widely approved for use in food production in various countries, such as the United States, Canada, Japan, Brazil, and China. It is classified as Generally Recognized as Safe (GRAS) and is authorized as a food processing aid in the European Union. When used as a processing aid, transglutaminase can be applied in dairy production without the need to be declared as an ingredient on the product label. Commercial production of transglutaminase enzyme was initially carried out by extraction from animal tissues, but today it is usually obtained from microbial sources. In recent years, the use of microbial transglutaminase enzyme in yogurt production has seen a notable increase, primarily due to their proven ability to enhance multiple aspects of the production process and the quality of the final product. Microbial transglutaminase enzyme is particularly effective in improving the consistency of the yogurt and strengthening the gel structure, which are critical factors in determining the overall texture. Additionally, they play a significant role in increasing the water-holding capacity of the product, thereby minimizing syneresis, which is the separation of liquid whey. Collectively, these improvements contribute to elevating the overall quality and consumer acceptance of the final yoğurt product. In yogurt production, microbial transglutaminase (mTGas) can be added through two main methods, each differing in the timing and handling of the enzyme and starter culture. In the first method, the enzyme is added to the milk, and the milk is subsequently heated to a specific temperature that ensures the enzyme becomes inactive, effectively stopping its activity before the fermentation process begins. Once the enzyme is inactivated, the starter culture is introduced to initiate fermentation. In the second method, the enzyme is not inactivated before the addition of the starter culture, allowing it to remain active throughout the fermentation process. During incubation, the starter culture lowers the pH level, and the enzyme continues its activity, contributing to the coagulation and texture development of the yogurt. To date, a specific and reliable method for detecting the presence or measuring the amount of transglutaminase enzyme in dairy products has not yet been established. This study aims to develop a model that is both rapid and practical, while also ensuring a high degree of reliability in identifying the utilization of transglutaminase enzyme in yogurt. The objective of this study is to develop a model that is not only rapid and practical but also highly reliable in detecting the use of transglutaminase enzyme in yogurt. This model aims to determine its utilization, assess its status (whether it is active or inactive), and measure its concentration levels. By utilizing infrared spectroscopy, this research will fill a significant gap in the field and, furthermore, will be the first study to reveal the application of microbial transglutaminase in yogurt within the existing literature. In this study, yogurt samples were analyzed using mid- and near-infrared spectroscopy, which provides a faster and more reliable way to analyze food samples compared to traditional methods. Infrared spectroscopy is a well-known analytical technique that efficiently and accurately identifies the chemical composition and molecular structure of a sample. This makes it especially useful in food analysis, as it offers a non-destructive and quick alternative to conventional testing methods, improving both the speed and accuracy of the analysis. In the experimental yogurt, two different concentration levels of the microbial transglutaminase enzymes were tested in order to evaluate their effects. These concentration levels were determined to be unit 1 and unit 2 to ensure clarity during analysis. This allowed for a clearer understanding of how each concentration level influenced the yogurt properties. In half of the samples produced, the activity of microbial transglutaminase enzyme was inactivated. This was done to evaluate the status of the enzyme, specifically to determine whether it was in an active or inactive state. For near-infrared (NIR) and mid-infrared (MIR) spectroscopy, both the class-independent analogy modeling approach (SIMCA) and partial least squares discriminant analysis (PLS-DA) were able to classify the control sample from active mTGase - containing yogurt samples. Both the class-independent analogy modeling approach (SIMCA) and partial least squares discriminant analysis (PLS-DA) were successful in classifying the samples based on their microbial transglutaminase enzyme status, effectively differentiate between the active and inactive states of the mTGase. However, the class-independent analogy modeling approach (SIMCA) and partial least squares discriminant analysis (PLS-DA) were unable to differentiate between enzyme concentration levels using mid-infrared )MIR) and near-infrared spectroscopy (NIR) techniques. On the other hand, machine learning analysis technique successfully identified not only the utilization of the microbial transglutaminase enzyme but also its active or inactive state, as well as the concentration level in the yogurt samples. These advanced analytical techniques provided a more accurate and reliable means of assessing the enzyme's characteristics in the yogurt. In conclusion, the utilization, as well as the active or inactive state of mTGase enzyme in yogurt, was successfully detected through the application of near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques. These methods, along with the various chemometric approaches applied in this study, provided an effective means of analyzing the enzyme's status in the yogurt samples. The use of NIR and MIR spectroscopy enabled a precise identification of the enzyme's activity, allowing for a clear differentiation between its active and inactive state. Furthermore, the integration of chemometric techniques contributed significantly to the analysis, enhancing the reliability and accuracy of the results obtained. The machine learning analysis showed better performance in differentiate enzyme concentration levels in yogurt compared to SIMCA and PLS-DA models. The machine learning model achieved high accuracy in identifying the different enzyme concentration levels, allowing for better and more precise classification. The improved performance of the machine learning model can be attributed to its ability to handle complex, non-linear relationships in the data. This makes it more adaptable to different concentration levels. This advancement provides a valuable opportunity to improve yogurt production, especially in terms of controlling enzyme levels and ensuring consistent product quality.
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