## 3 Methods and Materials
### Shelf Life Prediction Method
#### 3.1.2 Bacterial Growth Prediction Method  
The prediction of the remaining shelf life (SL) of a vegetable among the ones studied represents the crucial functionality of the developed DSS. It is carried out based on the growth model developed in [23], which, in turn, is founded on the predictive model proposed by [9]. ComBase (Combined Database for Predictive Microbiology) is an online predictive food microbiology platform with the main objective of describing and predicting how certain microorganisms survive and grow when conditioned by a series of factors inherent to the environment to which they are exposed, namely temperature, pH, or water activity [23]. The method adopted by the DSS to predict horticultural products' shelf life is, in fact, a system that allows predicting the time that the infective dose of a certain growing bacterium takes to be reached, starting from a particular initial dose. Similar procedures were conducted by [PERSON] et al. [24] that developed a simplified approach to predict food safety through the maximum specific bacterial growth rate as a function of extrinsic and intrinsic parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Product** & **Minimum pH Value** & **Average pH Value** \\ \hline Lettuce & 5.8 & 6.0 \\ Onion & 5.3 & 5.9 \\ Carrot & 4.9 & 6.4 \\ Cabbage & 5.2 & 6.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Potential of hydrogen intervals for each horticultural product. Bacterial concentration values are given in terms of Colony Forming Units (CFU) per gram of product, with each bacterium having its own initial CFU and infective CFU doses. Table 3 indicates initial and infective CFU doses for each one of the relevant bacteria according to [13; 22], the growth analysis of which is available on [23]: _Aeromonas hydrophilia; Bacillus cereus; Listeria monocytogenes; Salmonella; Shigella flexneri; Staphylococcus aureus_ and, therefore, considered on the development of the DSS. The time interval that is a DSS main output represents the period that includes the moment that the vegetable in study enters the retailer's warehouse (the instant at which the respective bacterial doses will assume the value of the initial CFU established for each bacterium), to the moment in which the infective CFU value of each bacteria colony under study is reached. In a particular horticultural product, different species of bacteria multiply. The shelf life of that product will be dictated by the bacteria colony that most rapidly reaches its infective dose. In other words, the bacterial growth (under the same storage conditions) of the different bacteria present in a same vegetable is studied. The time that each bacterium increasing colony needs to reach its infective dose are compared. The shortest of these times will, then, represent the shelf life of the studied horticultural product, given that, once the infective dose of any bacteria is reached, the product is no longer marketable as the food safety is compromised. Table 4 outlines which bacteria mostly develop in each of the vegetables under study [14; 25]. Thus, within the purpose of predicting the time inherent to bacterial growth for each of the horticultural products under analysis, the specific bacteria mentioned in Table 4, which growth in the respective vegetables had already been reported in the literature, will be studied. The time until the infective dose of a bacterium replicating in a food item is reached will vary depending on the storage conditions of that product. Thus, the remaining shelf life of a product will essentially be dictated by its intrinsic and extrinsic factors: water activity, pH, and temperature. In addition to these factors, and as seen in Figure 3, two other inputs that influence bacterial growth are considered by the chosen predictive model [23], both of which are also mentioned above: the initial physiological state and the initial bacterial dose.