## 6 Conclusions  
A decision support system (DSS) is proposed to simultaneously help reduce food waste for traditional food retailers while allowing for the selling of fresh horticultural products and promoting food safety and quality. This computational DSS allows for the predicting of the remaining shelf life of fresh horticultural products, namely lettuce, onion, carrot, and cabbage, based on its microbial growth status, governed by extrinsic (temperature and water activity) and intrinsic parameters (pH). The online predictive food microbiology tool Combined Database for Predictive Microbiology (Combase) was used to predict the time to reach the infectious doses of bacteria considered in the study. Additionally, the DSS encompasses the dynamic price of the produce that should be set each day, depending on the predicted end of the marketing period. The shelf life determined by the DSS for optimal storage conditions is, in general, conservative, ensuring food safety. The prediction of shelf life through multiple criteria is reflected in the inaccuracy of times reflected in the literature, since these intervals present large amplitudes. Furthermore, when considering the remaining shelf life of a food item, priority should always be given to the microbiological criterion over others, given that a product may present satisfactory sensory indices and, at a same time, carrying microbial doses that go beyond safety threshold, thus rendering itself harmful to the consumer. It is considered, thus, that the conservative prediction provided by the DSS is aligned with the growing demand in matters of safety and quality required by all parts of the food chain, from the producer to the consumer, including wholesalers and retailers, as well as regulatory and governmental entities. It should also be noted that small deviations between the values collected in the literature and the values estimated by the DSS were observed. In the case study of the onion, despite the obtained results being strongly aligned with the literature, other humidity scenarios closer to the optimal storage conditions should be investigated, as a humidity content set between 65% and 75% results in a drier storage environment than the one proposed by the current model. Regarding the analysis of bacterial growth in each of the studied vegetables, in the sampled cases analysed, most shelf lives are set by contaminations of _Aeromonas hydroophila_, so it is considered that this bacterial colony most influences the DSS results. The versatility in the growth requirements inherent to this bacterial colony may justify this fact, given that it has a large pH and, especially, survives in a wide range of temperatures. For the adoption of the DSS and its effective usage by its target market, it is considered that it still needs to undergo some. First, the studied water activity scenarios should be extended beyond the currently proposed minimum, average, and maximum values, undergoing adaptations whenever necessary. This refinement becomes particularly important in cases in which the difference between the water activity scenarios proves to be considerable in terms of remaining shelf life, as seen in the onion and carrot case studies at reduced storage temperatures. The effects of vegetables' regular distribution chain on bacterial growth should be quantified more effectively than by simply disregarding the lag phase, since this parameter may affect, both positively and negatively, the estimated remaining shelf life of a food product. Finally, fluctuations of extrinsic factors in the storage environment should be considered. The proposed model assumes that the temperature and relative humidity of the storage environment remains constant over time, from the moment that the horticultural batch under study enters storage until the moment it is marketed. This assumption does not translate in practice, even though these fluctuations may be residual.