Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

Abstract

Greenhouse-grown butter lettuce (Lactuca sativa L.) can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN) models to predict the remaining shelf life (RSL) under fluctuating postharvest temperatures. A box of 12 -24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R 2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining) storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R 2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.

Big data in food safety: An overview

“…(Table 3). Several of these technologies have been used in food safety applications (Beaudequin et al, 2015;Bouzembrak and Marvin, 2016;Marvin et al, 2016;Esser et al, 2015;Lin and Block, 2009) and have also been proposed as tool in big data handling in food safety .…”

Recent Advances in the Modeling and Predicting Quality Parameters of Fruits and Vegetables during Postharvest Storage: A Review

“…In another study, neural network models were used to predict the shelf-life of greenhouse lettuce by Lin and Block (2009). Using two-stage ANN models, an R 2 of 0.61 could be achieved for predicting the remaining shelflife.…”

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”