Cristina Luisa Miranda Silva

Grain processing requires sustainable and innovative alternatives. Germination, which enhances the nutritional value of grains, can also increase the microbial load, posing a challenge to food safety. In quinoa, a superfood with an exceptional nutritional profile, germination could maximize its benefits if proper methods are applied to ensure safety. The effects of different disinfection methods on mesophilic aerobic bacteria, enterobacteria, and the germination capacity of two Peruvian quinoa varieties (Rosada de Huancayo (RH) and Pasankalla (PK)) were evaluated for germinated flour production. Seven treatments were applied: four with chemical agents (200 ppm sodium hypochlorite, 4% acetic acid, 8% H₂O₂, and 70% alcohol) and three combined methods (sodium hypochlorite with ultrasound (35 kHz, 15 or 30 min), and hot water (50 °C) with H₂O₂ (2%) and acetic acid (0.1%)). All treatments significantly reduced mesophilic aerobic bacteria (1.29–4.08 log CFU/g), except alcohol (PK, RH) and sodium hypochlorite (PK). Reductions in enterobacteria (p < 0.05) ranged from 1.78 to 3.55 log CFU/g in RH; in PK, only the hot water + 2 chemical agents or 8% H₂O₂ treatments achieved significant reductions. The most effective treatment was hot water with H₂O₂ and acetic acid, which reduced bacteria and improved germination.

Ultrasound (US) treatment is an eco-friendly physical modification technique increasingly used to enhance the functionality of gluten-free flours. In this study, the impact of sonication on the techno-functional, thermal, structural, and rheological properties of a composite gluten-free flour was investigated. The flour, comprising corn starch, rice flour, and other ingredients, was treated at hydration levels of 15% and 25% (w/w) under controlled conditions (10 min of sonication at 20 °C) and compared to a non-sonicated control. Sonication reduced the water absorption capacity (WAC) and swelling power (SP) while increasing the oil absorption capacity (OAC) and water solubility (WSI). Thermal analysis revealed lower gelatinization enthalpy, indicating structural modifications induced by cavitation. Structural assessments (XRD and FTIR) confirmed minimal alterations in crystallinity and short-range order. Rheological studies demonstrated an enhanced elasticity in the gel structure, especially at 15% hydration, while a morphological analysis via SEM highlighted particle fragmentation and surface roughening. These findings demonstrate the potential of ultrasound to modify gluten-free flours for improved functionality and diverse applications in gluten-free product development.

Due to the increasing consumer demand for healthy, beneficial foods, natural fruit juices have gained popularity for their rich nutritional value and appealing flavor. However, traditional thermal processing can compromise these quality attributes. This study investigates using pulsed thermosonication, a novel mild thermal processing method, on Listeria innocua inactivation in blueberry juice, chosen for its high phenolic and anthocyanin content. Ultrasonication was applied at 60% and 100% amplitudes combined with heat treatments at 45 °C and 55 °C and compared to control heat treatments. The Weibull model effectively described the inactivation kinetics, showing that the thermosonicated samples required significantly shorter times (1 and 25 min) for a 5-log reduction compared to the heated samples (10 and 60 min). While pH, total soluble solids, and water activity remained unaffected, color parameters improved, and the best retention of phenolics and anthocyanins was observed at 100% amplitude and 45 °C. Rheological properties were unchanged. The findings demonstrate that thermosonication at milder temperatures is more effective than conventional heat treatment for microbial inactivation and quality retention in blueberry juice, suggesting it is a superior processing method for preserving fruit juices’ nutritional and sensory attributes.

The present work aims to present an overview of the postdoctoral position in general and to analyze the situation in Tunisia. An online questionnaire was designed to collect an in-depth review of the common challenges faced by PhD holders and those engaged in postdoctoral positions. PhD holders are dominated by females (72.20%). 65.18% of the participants are older than 35 years. 55% of PhD holders have accomplished at least one postdoctoral position. 43% of postdocs having accomplished a teaching activity estimated that this experience is highly beneficial. 52.5% of postdocs are satisfied or rather satisfied with their work environment. The most reported deliverables of postdocs are scientific publications and teaching materials. More than three-quarters of the PhD holders feel being in a precarious situation because of career uncertainty and low salary. Most PhD holders prefer to have an academic career and they are predisposed to have a position in the private sector and are in favor of creating other formal positions namely project manager and research engineer. Request access from your librarian to read this chapter's full text.

Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of ‘Hass’ avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit’s ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.