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

Author:

Hana Maha1,McClure W.F.1,Whitaker T.B.2,White M.W.3,Bahler D.R.4

Affiliation:

1. Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA

2. USDA, ARS, North Carolina State University, Raleigh, NC 27695, USA

3. Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA

4. Computer Science, North Carolina State University, Raleigh, NC 27695, USA

Abstract

Classification of flue-cured and Burley tobacco types with artificial neural networks (ANNs) were studied. Burley tobacco was further classified as either grown in the USA or grown outside the USA. The input data were in the form of near infrared (NIR) spectra, each spectrum containing 19 points. The number of flue-cured and Burley samples were 654 and 959, respectively. The number of native and non-native tobacco samples were 266 and 267, respectively. The models selected for this research were a quadratic classifier, a back-propagation network and a linear network. The results of the calibration model and the true performance for classifying tobacco species were (100%, 100%), (99.38%, 99.39%) and (95.19%, 99.26%) for the quadratic classifier, back-propagation network and linear network, respectively. The identification of native tobacco and its true performance were (100%, 100%) using a quadratic classifier, (89.12%, 88.46%) using a back-propagation network and (80.68%, 79.62%) using a linear network.

Publisher

SAGE Publications

Subject

Spectroscopy

Reference11 articles.

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