Prediction of the colorimetric parameters and mass loss of heat-treated bamboo: Comparison of multiple linear regression and artificial neural network method

In this study, the colorimetric parameters (L*, a*, b*) and mass loss of heat-treated bamboo were investigated, and the obtained results were modeled by using two methods: multiple linear regression (MLR) and artificial neural network (ANN). First, bamboo samples were exposed to heat treatment at different temperatures (110 degrees C, 140 degrees C, 170 degrees C, and 200 degrees C) and durations (15, 30, 45, 60, 75, 90, and 115 minutes) in a laboratory oven. Then, the colorimetric parameters (L*, a*, b*) and mass loss of each sample were measured after each period of heat treatment. All data were modeled by using two methods separately for each parameter and the performances of these proposed methods were compared. It was found that color change and mass loss increased with increasing temperature and duration of heat treatment. Mean absolute percentage error (MAPE) values of all obtained MLR ranged from 0.64% to 10.63%, while the all MAPE values of ANN were found to be lower than 1.5%. Based on these results, it can be said that MLR and ANN could be used to evaluate the changes on the selected properties of heat-treated bamboo samples. On the other hand, it should be emphasized that the ANN gave more accurate results than the MLR method because of its learning capability.