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To supply the off-season melon market, Europe imports from distant markets in other countries, mainly Brazil. Cold transportation takes at least 15–20 days, thus increasing the risk of quality losses. Moreover, product deliveries, especially in international markets, can result in supply chain inefficiencies that negatively affect carbon footprint and expected freshness. Implementing quality sensors and advanced cold chain management could help to reduce these problems. The objective of this work was to monitor a real transoceanic intermodal transport of melons (Brazil to Spain), through the implementation of multi-distributed environmental sensors (15 ibuttons loggers) to evaluate the remaining shelf-life (RSHL) of melons at destination. The sensors’ location within the cargo reached a maximum variability range of 4 ◦C. Using digital sensors to track temperature variations, it was verified that in different locations in the container, the melon RSHL at the end of the journey, was nine days and 19 h in colder spots, while in the hottest spot, the RSHL was reduced to five days and 22 h. This fact has substantial implications for improved tracking of temperature to maintain fruit quality for market, potentially reducing waste, and contributing to higher profit margins for international food supply chains.

Quantifying sustainability is a difficult but necessary task to improve our environmental, economic, and social surroundings. This paper describes an investigation into the use of the Social Hotspot Database (SHDB) to determine the true value of fair-trade certification. The analysis has been carried out on two coffee systems produced in Colombia and consumed in Spain: i) a low-cost generic (GC) and ii) a high-quality organic fair trade certified (FTC). Risk levels in the SHDB for the cultivation sector in Colombia were adjusted to reflect compliance with fair-trade criteria. Surprisingly, the results showed that the social risks per functional unit (FU) (1 kg) of the FTC were significantly higher than FTC. This incongruence is caused by the fact that the lower risk values associated with the fair-trade designation are largely offset by its significantly higher economic cost. This is so even though these costs are dedicated to mitigating the risks that are then penalized by the same expenditure (e.g., low salaries, poverty). A solution to this artifact may be to conceive the fair-trade coffee as a multifunctional system and apply an economic allocation approach to evaluate separately its functions as beverage and as a contributor to social wellbeing.

The main objective of this study is to apply the Product Social Impact Assessment (PSIA) handbook to a case study. The method used to quantify the social impacts is to identify the stakeholders and the social topic, indicators, and then to apply the impact analysis method. Social studies identify the retailing stage as the phase with the greatest social risk in the life cycle followed by the product transformation phase. However, the infeasibility of focusing this study on the multiple companies involved in the commercialization, led the processing stage company to be chosen as the central part of the study. The preliminary evaluation of the social topics of the stakeholders, presents an average of +0.88 points on the level scale (-2 to +2), which positions the company beyond the generally acceptable situation, in continuous improvement.

The main objective of this investigation is to analyse the validity of multiregional input-output methodology to evaluate the environmental performance of peach production using a life cycle approach. The analysis is based on a detailed sectorial economic foreground inventory applicable to the region of Murcia (Southern Spain), following the principles of ISO 14040 and incorporating the methodological decisions of Environdec Product Category Rule for fruits and nuts. Total climate change emissions for 1 kg of peaches were calculated at 1.2 kg CO2 eq, 39.2 % of which correspond to economic activity in the sectors directly affected by the expenses and a further 60.8 % to indirect emissions from induced effects. Most of the total carbon footprint (63 %) is generated by the core stage, primarily crop production and refrigerated storage, both activities being characterized by their high economic intensity and environmental factors.

La presente Tesis está orientada al análisis de la supervisión multidistribuida de tres procesos agroalimentarios: el secado solar, el transporte refrigerado y la fermentación de café, a través de la información obtenida de diferentes dispositivos de adquisición de datos, que incorporan sensores, así como el desarrollo de metodologías de análisis de series temporales, modelos y herramientas de control de procesos para la ayuda a la toma de decisiones en las operaciones de estos entornos. En esta tesis se han utilizado: tarjetas RFID (TemTrip®) con sistema de comunicación por radiofrecuencia y sensor de temperatura; el registrador (i-Button®), con sensor integrado de temperatura y humedad relativa y un tercer prototipo empresarial, módulo de comunicación inalámbrico Nlaza, que integra un sensor de temperatura y humedad relativa Sensirion®. Estos dispositivos se han empleado en la conformación de redes multidistribuidas de sensores para la supervisión de: A) Transportes de producto hortofrutícola realizados en condiciones comerciales reales, que son: dos transportes terrestre de producto de IV gama desde Murcia a Madrid; transporte multimodal (barco-barco) de limones desde Montevideo (Uruguay) a Cartagena (España) y transporte multimodal (barco-camión) desde Montevideo (Uruguay) a Verona (Italia). B) dos fermentaciones de café realizadas en Popayán (Colombia) en un beneficiadero. Estas redes han permitido registrar la dinámica espacio-temporal de temperaturas y humedad relativa de los procesos estudiados. En estos procesos de transporte refrigerado y fermentación la aplicación de herramientas de visualización de datos y análisis de conglomerados, han permitido identificar grupos de sensores que presentan patrones análogos de sus series temporales, caracterizando así zonas con dinámicas similares y significativamente diferentes del resto y permitiendo definir redes de sensores de menor densidad cubriendo las diferentes zonas identificadas. Las metodologías de análisis complejo de las series espacio-temporales (modelos psicrométricos, espacio de fases bidimensional e interpolaciones espaciales) permitieron la cuantificación de la variabilidad del proceso supervisado tanto desde el punto de vista dinámico como espacial así como la identificación de eventos. Constituyendo así herramientas adicionales de ayuda a la toma de decisiones en el control de los procesos. Siendo especialmente novedosa la aplicación de la representación bidimensional de los espacios de fases en el estudio de las series espacio-temporales de variables ambientales en aplicaciones agroalimentarias, aproximación que no se había realizado hasta el momento. En esta tesis también se ha querido mostrar el potencial de un sistema de control basado en el conocimiento experto como es el sistema de lógica difusa. Se han desarrollado en primer lugar, los modelos de estimación del contenido en humedad y las reglas semánticas que dirigen el proceso de control, el mejor modelo se ha seleccionado mediante un ensayo de secado realizado sobre bolas de hidrogel como modelo alimentario y finalmente el modelo se ha validado mediante un ensayo en el que se deshidrataban láminas de zanahoria. Los resultados sugirieron que el sistema de control desarrollado, es capaz de hacer frente a dificultades como las variaciones de temperatura día y noche, consiguiendo un producto con buenas características de calidad comparables a las conseguidas sin aplicar ningún control sobre la operación y disminuyendo así el consumo energético en un 98% con respecto al mismo proceso sin control. La instrumentación y las metodologías de análisis de datos implementadas en esta Tesis se han mostrado suficientemente versátiles y transversales para ser aplicadas a diversos procesos agroalimentarios en los que la temperatura y la humedad relativa sean criterios de control en dichos procesos, teniendo una aplicabilidad directa en el sector industrial ABSTRACT This thesis is focused on the analysis of multi-distributed supervision of three agri-food processes: solar drying, refrigerated transport and coffee fermentation, through the information obtained from different data acquisition devices with incorporated sensors, as well as the development of methodologies for analyzing temporary series, models and tools to control processes in order to help in the decision making in the operations within these environments. For this thesis the following has been used: RFID tags (TemTrip®) with a Radiofrequency ID communication system and a temperature sensor; the recorder (i-Button®), with an integrated temperature and relative humidity and a third corporate prototype, a wireless communication module Nlaza, which has an integrated temperature and relative humidity sensor, Sensirion®. These devices have been used in creating three multi-distributed networks of sensors for monitoring: A) Transport of fruits and vegetables made in real commercial conditions, which are: two land trips of IV range products from Murcia to Madrid; multimodal transport (ship - ship) of lemons from Montevideo (Uruguay) to Cartagena (Spain) and multimodal transport (ship - truck) from Montevideo (Uruguay) to Verona (Italy). B) Two coffee fermentations made in Popayan (Colombia) in a coffee processing plant. These networks have allowed recording the time space dynamics of temperatures and relative humidity of the processed under study. Within these refrigerated transport and fermentation processes, the application of data display and cluster analysis tools have allowed identifying sensor groups showing analogical patterns of their temporary series; thus, featuring areas with similar and significantly different dynamics from the others and enabling the definition of lower density sensor networks covering the different identified areas. The complex analysis methodologies of the time space series (psychrometric models, bi-dimensional phase space and spatial interpolation) allowed quantifying the process variability of the supervised process both from the dynamic and spatial points of view; as well as the identification of events. Thus, building additional tools to aid decision-making on process control brought the innovative application of the bi-dimensional representation of phase spaces in the study of time-space series of environmental variables in agri-food applications, an approach that had not been taken before. This thesis also wanted to show the potential of a control system based on specialized knowledge such as the fuzzy logic system. Firstly, moisture content estimation models and semantic rules directing the control process have been developed, the best model has been selected by an drying assay performed on hydrogel beads as food model; and finally the model has been validated through an assay in which carrot sheets were dehydrated. The results suggested that the control system developed is able to cope with difficulties such as changes in temperature daytime and nighttime, getting a product with good quality features comparable to those features achieved without applying any control over the operation and thus decreasing consumption energy by 98% compared to the same uncontrolled process. Instrumentation and data analysis methodologies implemented in this thesis have proved sufficiently versatile and cross-cutting to apply to several agri-food processes in which the temperature and relative humidity are the control criteria in those processes, having a direct effect on the industry sector.