Browsing by Author "Perez Correa, José Ricardo"

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    Differential Extraction and Preliminary Identification of Polyphenols from Ugni candollei (White Murta) Berries
    (2024) Fuentes Jorquera, Natalia Andrea; Canales Muñoz, Roberto Iván; Perez Correa, José Ricardo; Pérez Jiménez, Jara; Mariotti Celis, María Salomé
    Ugni candollei, commonly known as white murta, is a native Chilean berry with a polyphenol composition that has been underexplored. This study aimed to establish a comprehensive profileof white murta polyphenols using ultra-performance liquid chromatography electrospray ionizationOrbitrap mass spectrometry (UPLC-ESI-ORBITRAP MS). Additionally, it compared the efficacy ofconventional extraction methods with emerging techniques such as deep eutectic solvent (DES)extraction and hot pressurized water extraction (HPWE). The analysis tentatively identified 107 phenolic compounds (84 of them reported for the first time for this cultivar), including 25 phenolic acids,37 anthocyanins, and 45 flavonoids. Among the prominent and previously unreported polyphenolsare ellagic acid acetyl-xyloside, 3-p-coumaroylquinic acid, cyanidin 3-O-(6′-caffeoyl-glucoside, andphloretin 2′-O-xylosyl-glucoside. The study found HPWE to be a promising alternative to traditionalextraction of hydroxybenzoic acids, while DES extraction was less effective across all categories. Thefindings reveal that white murta possesses diverse phenolic compounds, potentially linked to variousbiological activities.
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    Reliable calibration and validation of phenomenological and hybrid models of high-cell-density fed-batch cultures subject to metabolic overflow
    (2024) Ibáñez Espinel, Francisco; Puentes Cantor, Hernán Felipe; Barzaga Martell, Lisbel; Saa Higuera, Pedro; Agosin Trumper, Eduardo; Perez Correa, José Ricardo
    Fed-batch cultures are the preferred operation mode for industrial bioprocesses requiring high cellular densities. Avoids accumulation of major fermentation by-products due to metabolic overflow, increasing process productivity. Reproducible operation at high cell densities is challenging (> 100 gDCW/L), which has precluded rigorous model evaluation. Here, we evaluated three phenomenological models and proposed a novel hybrid model including a neural network. For this task, we generated highly reproducible fedbatch datasets of a recombinant yeast growing under oxidative, oxygen-limited, and respiro-fermentative metabolic regimes. The models were reliably calibrated using a systematic workflow based on pre-and post-regression diagnostics. Compared to the best-performing phenomenological model, the hybrid model substantially improved performance by 3.6- and 1.7-fold in the training and test data, respectively. This study illustrates how hybrid modeling approaches can advance our description of complex bioprocesses that could support more efficient operation strategies