Aykas, Didem PerenSinir, Gülşah ÖzcanBorba, Karla Rodrigues2024-10-142024-10-142023-07-030308-8146https://doi.org/10.1016/j.foodchem.2023.136727https://www.sciencedirect.com/science/article/pii/S0308814623013456https://hdl.handle.net/11452/46353We aimed to develop portable Fourier transform infrared (FT-IR) spectroscopy-based prediction algorithms for the key quality characteristics (soluble solids, water activity, pH, sucrose, glucose, fructose, fructose/glucose, hydroxymethylfurfural) of various types of molasses, establish their legitimacy, and create a model to separate them based on their botanical origin. Samples labeled as carob (n = 27), grape (n = 24), Juniper (n = 13), and mulberry (n = 12) were purchased from different local markets in Turkey. Labeling issues were revealed in five carob and seven grape molasses, and those samples classified as non-authentic by the FT-IR algorithms were corroborated by reference analysis. Partial least squares regression models generated to predict the key quality traits of Turkish molasses demonstrated excellent correlation with reference analysis (R-Val(2) = 0.96) and low standard error of prediction (SEP <= 2.88). The FT-IR sensor provided a feasible approach for molasses testing to assess its quality through manufacturing and storage, also provided a powerful tool to -ensure proper product labeling.eninfo:eu-repo/semantics/closedAccessTransform-infrared-spectroscopySugarSyrupsMirRegressionHoneysMolassesPekmezFt-ir spectroscopyQuality traitsAuthenticationScience & technologyPhysical sciencesLife sciences & biomedicineChemistry, appliedFood science & technologyNutrition & dieteticsChemistryArticle00102954490000142710.1016/j.foodchem.2023.1367271873-7072