Abstract

Artificial neural networks (ANNs) have demonstrated their usefulness in near infrared (NIR) reflection and transmittance spectroscopy for quantitative prediction. The new approach presented here considers the use of ANNs for qualitative classification. Four forms of neural networks (a competitive network using the learning vector quantisation, LVQ learning rule; a backpropagation network using the extended delta-bar-delta, EDBD rule; a network with direct random search, DRS; and a simple competitive linear network, CL) have been tested for classification of 118 fat samples from Iberian pig carcasses into three different price groups. An ANN using the LVQ learning rule has been found to be the best in terms of classification error size. The classification ability of the LVQ network has been evaluated against discriminant analysis, one of the most used methods for NIR spectroscopic qualitative analysis.

© 1994 NIR Publications

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)

Artificial neural network for the classification of nanoparticles shape distributions

Y. Mansour, Y. Battie, A. En Naciri, and N. Chaoui
Opt. Lett. 44(13) 3390-3393 (2019)

Energetic materials identification by laser-induced breakdown spectroscopy combined with artificial neural network

Amir Hossein Farhadian, Masoud Kavosh Tehrani, Mohammad Hossein Keshavarz, and Seyyed Mohammad Reza Darbani
Appl. Opt. 56(12) 3372-3377 (2017)

Hybrid gray wolf optimizer–artificial neural network classification approach for magnetic resonance brain images

Heba M. Ahmed, Bayumy A. B. Youssef, Ahmed S. Elkorany, Adel A. Saleeb, and Fathi Abd El-Samie
Appl. Opt. 57(7) B25-B31 (2018)

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural networks in revealing adulteration of similarly looking fish fillets

Omnia Hamdy, Zienab Abdel-Salam, and Mohamed Abdel-Harith
Appl. Opt. 61(34) 10260-10266 (2022)