Food Engineering / Gıda Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/12

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Access type: Open accessAuthor: search.filters.author.Alamprese, Cristina

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  • Article
    Citation - WoS: 9
    Citation - Scopus: 8
    White Bean and Hazelnuts Flours: Application in Gluten-Free Bread
    (Academic Press, 2023) Tuna, Ayça; Cappa, Carola; Tokatlı, Figen; Alamprese, Cristina
    This study investigated the effects of white bean and hazelnut flour addition (15–30% alone or in combination) to a rice flour-corn starch mixture in gluten-free (GF) breads formulated according to a mixture design. The chemical composition of flours and pasting properties of their mixtures were investigated, as well as the spectroscopic characteristics and leavening performance of doughs. Physical properties of fresh and stored (up to 48 h) bread samples were analyzed. Bean and hazelnut flours had higher protein and fiber contents, and lower carbohydrates content than rice flour and corn starch. Although the reference bread made of rice flour-corn starch mixture (STD) resulted in the highest specific volume (7.0 mL/g) and the lowest hardness (0.43 N), the sample enriched with 15% hazelnut flour (H15) approached these characteristics the most (3.8 mL/g and 1.59 N, respectively). After 48 h of storage, H15 also showed lower hardness than STD. This study paves the way for new applications of white bean and hazelnut flours and showed as a simple reformulation can help to develop healthier bread: the European legal constraint for “fiber source” claim was achieved for breads with 15 or 30% hazelnut flour, and 30% bean-hazelnut mixture, with a fiber content of 3.34, 4.48, and 3.27 g/100g, respectively. © 2023 The Authors
  • Article
    Citation - WoS: 11
    Citation - Scopus: 10
    Ir Spectroscopy and Chemometrics for Physical Property Prediction of Structured Lipids Produced by Interesterification of Beef Tallow
    (Academic Press, 2019) Aktaş, Ayşe Burcu; Alamprese, Cristina; Fessas, Dimitrios; Özen, Banu
    The aim of this study was the application of infrared spectroscopy and chemometrics to predict slip melting point (SMP), melting points at different melted fat percentages (MP85, MP90, MP95), and consistency of structured lipids to provide fast and reliable methods for their characterization. Tallow was chemically or enzymatically interesterified with corn, canola, or safflower oils, at different ratios. Fourier-transform mid-infrared (FT-IR) and near-infrared (FT-NIR) spectra of melted and solid samples were collected. Partial-least-square regression models constructed after different spectra pre-treatments and variable selection were satisfactory. The best models were obtained with solid sample FT-NIR spectra: in cross-validation, determination coefficients and root mean square errors were, respectively, 0.85 and 1.7 degrees C for SMP, 0.85 and 2.8 degrees C for MP90, and 0.91 and 14 MPa for consistency. Infrared spectroscopy can be considered a promising tool to determine physical properties of interesterified fats.
  • Article
    Citation - WoS: 30
    Citation - Scopus: 30
    Discriminative Capacities of Infrared Spectroscopy and E-Nose on Turkish Olive Oils
    (Springer Verlag, 2017) Jolayemi, Olusola Samuel; Tokatlı, Figen; Buratti, Susanna; Alamprese, Cristina
    The potentials of Fourier transform (FT) near- (NIR) and mid-infrared (IR) spectroscopy, and electronic nose (e-nose) on varietal classification of Turkish olive oils were demonstrated. A total of 63 samples were analyzed, comprising Ayvalik, Memecik, and Erkence oils. Spectra were pretreated with standard normal variate and second derivative. Classification models were built with orthogonal partial least square-discriminant analysis (OPLS-DA), considering the single data sets and also the combined FT-NIR-IR spectra. OPLS-DA models were validated both by cross validation and external prediction. All the models gave good results, being the average correct classification percentages in prediction higher than 90% for spectroscopic data and equal to 82% for e-nose data. The combined FT-NIR-IR data set gave the best results in terms of coefficients of determination (0.95 and 0.67). Different e-nose sensors discriminated Ayvalik, Memecik, and Erkence oils, explaining their distinct aromatic profiles.