Analysis of plant-based commercial milk substitutes using ATR-FTIR spectroscopy

Brito A. L. B., Duarte C., FAUSTO R.

Journal of Food Composition and Analysis, cilt.145, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 145
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.jfca.2025.107786
  • Dergi Adı: Journal of Food Composition and Analysis
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Analytical Abstracts, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database
  • Anahtar Kelimeler: Chemometrics, Infrared spectroscopy, PCA, Plant-based milk substitutes
  • İstanbul Kültür Üniversitesi Adresli: Evet

Özet

Plant-based beverages have emerged as substitutes for cow's milk, and their consumption has been steadily increasing in recent years, largely due to health-related reasons, such as milk protein allergies or lactose intolerance. This raises the need for development of analytical approaches for these products that can capable of accurately identifying and classifying these products. This study focused on almond, rice, oat, and soy plant-based milk substitute drinks, which are the most popular among this type of beverages. Infrared spectroscopy data have been used to develop a fast, cost-effective and easy to implement in different settings chemometrics model for these beverages, which allows their classification according to their nature and compositional variability. It was found that the use of the spectral region of the characteristic Amide I and II bands of the proteins led to an optimal description of the data by the first two principal components in the developed Principal Component Analysis (PCA) model. For oat, rice, and soy beverages, distinct characteristic spectroscopic features allowed their successful clustering using the chemometric approach, while the results obtained for the studied almond beverages evidenced their significant compositional variability, resulting in a less defined clustering. These results are consistent with the known nutritional information for the different types of beverages.