Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

Abstract

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

Performance of European Artificial Neural Network (ANN) Calibrations for Moisture and Protein in Cereals Using the Danish Near‐Infrared Transmission (NIT) Network

“…To overcome these constraints, the use of regression modelling using nonlinear artificial neural networks (ANN) was proposed. The use of neural networks for agricultural applications has been reported by several authors (Hervas et al 1994;Hana et al 1995Hana et al , 1997Lo 1995) but usually with relatively small numbers of samples. The idea that ANN combined with very large data sets displaying great diversity with respect to variety, and growing and harvest conditions would provide an advantage in prediction of protein and moisture in cereals was first suggested by Büchmann (1996).…”

Applying Artificial Neural Networks: Part II. Using near Infrared Data to Classify Tobacco Types and Identify Native Grown Tobacco

“…11 The output of the network was determined in the same manner as that used for the back-propagation network. The learning rate and the momentum were fixed at 0.25 and 0.9, respectively.…”

Extended Probabilistic Latent Semantic Analysis Model For Topics In Time-Stamped Images

“…Zhang et al (2002) [20] used the neural network models to predict the performance indices and optimal parameters of rough rice drying. Hama et al (1995) [21]used two artificial neural network models (a back-propagation network and a linear network) to estimate the nicotine in tobacco, and found the true performance of the back-propagation network was better than the multiple linear regression method and linear network by 35.14% and more.…”