Rapid detection of the presence, activity and concentration of microbial transglutaminase in yogurt using infrared spectroscopy combined with chemometrics

Sıçramaz H., AYVAZ H., Menevseoglu A., Yaaqob M. A. H. A., Dogan M. A., Ozturk M.

Innovative Food Science and Emerging Technologies, cilt.105, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 105
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.ifset.2025.104225
  • Dergi Adı: Innovative Food Science and Emerging Technologies
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Biotechnology Research Abstracts, CAB Abstracts, Compendex, Food Science & Technology Abstracts, Veterinary Science Database
  • Anahtar Kelimeler: Acid gels, Cross-linking, Dairy, Machine learning, SIMCA
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

The goal of this study was to develop a rapid method by using near-infrared (NIR) diffuse reflectance and mid-infrared (MIR) spectroscopy to detect the use, status (active or inactive), and concentration of microbial transglutaminase (mTGase) in yogurt. Control samples were manufactured without mTGase. Two different levels of mTGase concentration were employed: 1 and 2 units. Half of the enzyme-added samples were inactivated after yogurt manufacture to detect the active/inactive status of mTGase. Both for NIR and MIR, analyzed via the soft independent modeling of class analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) approaches were able to classify the control sample from active mTGase-containing yogurts and enzyme status, but could not differentiate enzyme concentrations. Machine learning effectively determined mTGase presence, activity, and concentrations. In conclusion, NIR and MIR spectroscopy, combined with chemometric methods, successfully detected mTGase in yogurt, with machine learning outperforming SIMCA and PLS-DA in identifying enzyme levels.