Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters th...

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Bibliographic Details
Published in:European food research & technology 2011-08, Vol.233 (2), p.267-274
Main Authors: Mutlu, Ayse C., Boyaci, Ismail Hakki, Genis, Huseyin E., Ozturk, Rahime, Basaran-Akgul, Nese, Sanal, Turgay, Evlice, Asuman Kaplan
Format: Article
Language:English
Subjects:
Absorption
Agriculture
Analytical Chemistry
Baking
Biological and medical sciences
Biotechnology
Bread
Cereal and baking product industries
Chemical analysis
Chemical properties
Chemistry
Chemistry and Materials Science
Dough
Flour
Food industries
Food products
Food quality
Food Science
Forestry
Fundamental and applied biological sciences. Psychology
Gluten
I.R. radiation
I.R. spectroscopy
Infrared spectroscopy
Moisture content
Neural networks
Original Paper
Principal components analysis
Proteins
Quality control
Reflectance
Rheology
Sedimentation
Sedimentation & deposition
Spectrum analysis
Studies
Triticum aestivum
Wheat
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Summary:In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination ( R 2 ) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.
ISSN:1438-2377
1438-2385
DOI:10.1007/s00217-011-1515-8