## 3 Methods and Materials
### Dynamic Pricing Method  
Dynamic pricing may be defined according to [PERSON] et al. (2017), as the attribution of different prices over time to a same product or to different products from the same category, considering the individual characteristics of each product or changes in its qualitative state. The task of setting dynamic prices on horticultural products, as perishable goods, based on multiple criteria proves to be arduous and inaccessible to all small retailers, as it requires them to obtain information, in real time, about variables such as rate of deterioration, time of onset of deterioration, demand, price elasticity of demand, or unit storage cost, that are parameters considered in numerous pricing models. To simplify the pricing model to adopt, and considering the target market of the developed tool, variables such as those mentioned above are disregarded. Therefore, a pricing model is adopted in which the qualitative state, expressed in terms of remaining shelf life, is taken as the only criterion to be considered in the dynamic pricing of the marketed vegetables. Assuming the remaining shelf life of a horticultural product as the only criterion to consider in the calculation of dynamic selling prices, the price to be set each day will assume a linear decrease, as a function of the number of remaining shelf-life days, where there is a constant price decrease as the level of stocks decreases, as proposed by [PERSON] and [PERSON] (2018). Thus, the price to be set each day will steadily decrease as the end of the predicted commercialization period approaches. The proposed pricing model uses the following assumptions:  
When the horticultural batch under study enters storage (\(t=0\)), that is, at the time when the lag phase ends and bacterial proliferation begins, the selling price to be charged is at its maximum, without any discount applied. This condition is similar to the model proposed by [PERSON] et al. (2019), given that the horticultural batch under study is considered to be in the fullness of its microbiological characteristics. At this moment, the ideal and desirable sale scenarios are given, as selling products at this date implies maximum food safety for the consumer and maximum profit for the retailer;  
At the end of the calculated commercialization period (\(t_{\text{f}}\)), i.e., at the moment immediately prior to reaching the infective dose of any of the bacteria growing in the studied vegetable, the selling price to be charged is as small as possible. Thus, the product is sold at cost price, in order to avoid losses for the retailer. This represents the sale scenario that must be avoid. In this case, it is assumed that food safety is at its lowest and the retailer's profit is null;  
The calculated end of sale date is never prior to the date that horticultural products under study enter the warehouse, and vice versa. In other words, it is considered that the studied produce batch goes through its normal distribution chain arriving at the retailer's warehouse in proper microbial conditions representative of food safety. From the assumptions stated above, the dynamic selling price for any given time \(t\) to be considered in the proposed DSS is given by Equation (8). \[\text{SP}(t)=\text{SP}(0)-[\text{SP}(0)-\text{SP}(t_{\text{f}})]\;[t/\text{SL}] \tag{8}\]