Lucía Seguí

An Electrochemical Impedance Spectroscopy-Based Technique to Identify and Quantify Fermentable Sugars in Pineapple Waste Valorization for Bioethanol Production

Electrochemical Impedance Spectroscopy (EIS) has been used to develop a methodology able to identify and quantify fermentable sugars present in the enzymatic hydrolysis phase of second-generation bioethanol production from pineapple waste. Thus, a low-cost non-destructive system consisting of a stainless double needle electrode associated to an electronic equipment that allows the implementation of EIS was developed. In order to validate the system, different concentrations of glucose, fructose and sucrose were added to the pineapple waste and analyzed both individually and in combination. Next, statistical data treatment enabled the design of specific Artificial Neural Networks-based mathematical models for each one of the studied sugars and their respective combinations. The obtained prediction models are robust and reliable and they are considered statistically valid (CCR% > 93.443%). These results allow us to introduce this EIS-based technique as an easy, fast, non-destructive, and in-situ alternative to the traditional laboratory methods for enzymatic hydrolysis monitoring.

An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R2) and root mean square errors of prediction (RMSEP) were determined as R2 > 0.944 and RMSEP < 1.782 for PLS and R2 > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step.

Sustainable Innovation in Food Science and Engineering

Abstract Nowadays the sustainability of a product, a process, or a system is assessed according to three dimensions: environmental, social, and economic. Sustainability challenges occur at all stages in the food system from production through processing, distribution, and retailing to consumption and waste disposal. Consequently, the promotion of organic and local foods is not the only way; there is another possibility that may foster continuing the production hegemony, emphasizing biotechnology and technological panaceas. Increasing research is being focused on the development of healthy, quality, and safe food products adapted to consumers’ needs and more environmentally friendly processes, that is, processes consuming energy more efficiently, generating less waste, and emitting less greenhouse-effect gases, among other features. This chapter contains detailed information about some measures taken by the food industry to ensure the supply of essential nutrients to as many individuals as possible assuring global sustainability. More specifically, the contributions of some techniques employed in the development of functional foods, such as formulation and blending, cultivation and breeding, microencapsulation, edible films and coatings application, vacuum impregnation and nutrigenomics, to increase the sustainability of the feeding process, are discussed.

Application of the Software e-SAFES (Based on SAFES Methodology) to Control Disinfection in the Sugar Extraction from Sugar Beet (Beta vulgaris L.)

Microbial contamination during extraction is an important control point in the production of sugar. It is associated with significant losses of sugar, increased production of molasses and, ultimately, a decline in the quality of juice extraction. Three species of microorganisms are responsible for microbial contamination in the extraction. Bacillus stearothermofilus constitutes the bulk of microbial contamination and the main product of its metabolism is lactic acid. Its content in the raw juice is an indicator of infection rate and shows the losses of sugar (Baryga, 2006); Leuconostoc is another type of organism that metabolizes sucrose and produces glucose. Other types of microorganisms of Lactobacillus species synthesize glucose and form dextran which has a negative impact on the technological process. With the contamination, the purification of raw juice is less efficient, filtering is hampered, sediments cover the heating surface reducing heat transfer, crystallization becomes difficult, and finally sugar yield is reduced (Santos et al., 2000). Some problems have arisen regarding the addition of formaldehyde to foods to extend shelf life. Foods known to be contaminated include noodles, salted fish, tofu. In some places formaldehyde is still used illegally as a preservative in foods, which exposes people to formaldehyde ingestion (IARC Monographs, 2006). The current trend is to minimize the application of synthetic disinfectants that may adversely affect health. Consumption and contact with some unnatural disinfectants contaminated food is a health hazard. Consuming contaminated food causes abdominal pain, vomiting, diarrhea, unconsciousness, cancer or even death (Department of Health and Human Services, 2005). This is the case of formalin which use in food production is banned in some European Union countries. In fact, formalin