## 3 Materials and Methods  
_Plant materials._ Plants of butterhead lettuce cvs Cortina and Prior were grown under experimental conditions in Agassiz or obtained from a commercial greenhouse in Pitt Meadows, both located within 100 km east of Vancouver, British Columbia, Canada. The experimental plants were harvested from 8 experiments. Butterhead lettuce cv. Prior was used: 6 experiments involved living lettuce (with trimmed roots) and another 2 experiments used butter lettuce (with no roots). These experiments covered various pre-harvest growing conditions and post-harvest temperature combinations (details not shown). Six commercial harvests were obtained directly from the commercial greenhouse. There were 8 experiments involving cvs. Cortina and Prior, including living and butter lettuce. The same postharvest procedures were applied to experimental and commercial materials. Storage experiments were conducted in Agassiz. Living lettuce retains a partial root system, while butter lettuce has the root system removed upon harvest. Each head of living or butter lettuce was wrapped individually with a plastic sleeve. There were 12, 18 or 24 heads in a box. Each box was considered as an experimental unit. There were a total of 198 boxes, including both experimental and commercial harvests. The air temperature of each box was recorded by a portable Hobo Temperature Data Logger, model H08 (Onset Computer Corp. Bourne, MA 02532, USA). Temperatures were averaged over a 24-hour period until shelf life was terminated. Total shelf life of each head is defined as the number of days between date of harvest and the date a lettuce head shows incipient yellowing or decay. Each head of lettuce was visually inspected 3 times a week. The incipient signs of yellowing and decay caused a lettuce head to be considered as unmarketable by the industry in British Columbia. The remaining shelf life (RSL) of each box was the average of 12, 18 or 24 heads, and used as output for shelf life prediction. At the end of each storage experiment, the RSL was back calculated by using the difference between the total shelf life (\(n\)) and the number of days in storage until current day (\(c\)). In training and testing of simple and 2-stage NN models, a total of 198 data cases were used, representing 198 boxes of lettuce harvested during 1998-2001. Routinely, 80% of 198 cases were randomly selected for training NN models and the remaining 20% were put aside and later used for testing the models. A good fit for NN models was determined by high R\({}^{2}\) values and low average errors (%) in testing. Randomization of the selected training cases was repeated at least once. The testing was arbitrarily stopped when R\({}^{2}\) of two consecutive results were within 10% or less. Further, the same experimental data used for establishing NN models was used to train regression models, and identical commercial data was used to validate both 2-stage NN and regression models. _Simple NN models._ In simple NN modeling daily storage temperatures were used directly to model RSL, while future temperatures were assumed not yet available and ignored. A simple NN model was established by using past daily temperatures with two manipulations. First, the last daily temperature _t_(\(c\)) and backward preceding \(m\) daily temperatures (_pm_) expressed as _t_(\(c\)), _t_(\(c\)-1), _t_(\(c\)-2),...., _t_(\(c\)-(_m_-1)) were used as input _pm_ nodes for the NN model. Secondly, daily temperatures before _t_(\(c\)-_m_) were totalled as temperature_prior_sum (_ps_). The values of _ps_ and _pm_ were used as inputs for modeling RSL. In BPNN, we generated a set of vectors \(T\) from the given data set containing a number of units, in which each unit has its own history of storage temperatures and its own total shelf life (\(n\)). We then performed the training procedures accordingly. For each unit, we ran the given day \(c\) from day 1 to day \(n\), and generated \(n\) number of \(T\) vectors (e.g.