Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 52,
  • Issue 3,
  • pp. 329-338
  • (1998)

Identification of Modified Starches Using Infrared Spectroscopy and Artificial Neural Network Processing

Not Accessible

Your library or personal account may give you access

Abstract

The authentication of food is a very important issue both for the consumers and for the food industry with respect to all levels of the food chain from raw materials to finished products. Corn starch can be used in a wide variety of food preparation as bakery cream fillings, sauce, or dry mixes. There are many modifications of the corn starch in connection with its use in the agrofood industry. This paper describes a novel approach to the classification of modified starches and the recognition of their modifications by artificial neural network (ANN) processing of attenuated total reflection Fourier transform spectroscopy (ATR/FT-IR) spectra. Using the self-organizing artificial neural network of the Kohonen type, we can obtain natural groupings of similarly modified samples on a two-dimensional plane. Such mapping provides the expert with the possibility of analyzing the distribution of samples and predicting modifications of unknown samples by using their relative position with respect to existing clusters. On the basis of the available information in the infrared spectra, a feedforward artificial neural network, trained with the intensities of the derivative infrared spectra as input and the starch modifications as output, allows the user to identify modified starches presented as prediction samples.

PDF Article
More Like This
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)

Signal processing using artificial neural network for BOTDA sensor system

Abul Kalam Azad, Liang Wang, Nan Guo, Hwa-Yaw Tam, and Chao Lu
Opt. Express 24(6) 6769-6782 (2016)

Color image identification and reconstruction using artificial neural networks on multimode fiber images: towards an all-optical design

Nadav Shabairou, Eyal Cohen, Omer Wagner, Dror Malka, and Zeev Zalevsky
Opt. Lett. 43(22) 5603-5606 (2018)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved