Utilization of FTIR and Machine Learning for Evaluating Gluten-Free Bread Contaminated with Wheat Flour

Akinbode A. Adedeji, Abuchi Okeke, Ahmed M. Rady

Research output: Contribution to journalArticlepeer-review


In this study, Fourier-transform infrared (FTIR) spectroscopy coupled with machine learning (ML) approaches were applied to detect and quantify wheat flour (WF) contamination in gluten-free cornbread. Samples of corn flour (CF) were contaminated with WF in the range of 0–10% with a 0.5% increment. The flour samples were baked into bread using basic bread formulation and ground into a fine particle size for homogeneity, and FTIR spectra of the ground samples were obtained and standardized before modeling. For constructing the classification model, majority voting-based ensemble learning (stack of k-nearest neighbor [KNN], random forest, and support vector classifier) was implemented to detect and quantify WF in the cornbread samples. KNN regressor was determined to be the best predictive model to quantify wheat contaminants based on the majority-vote ensemble. The optimal classification model for the test set showed an F1 score, true positive rate (TPR), and false negative rate (FNR) of 1.0, 1.0, and 0.0, respectively. For the quantification models, the coefficient of determination and root mean square error for the prediction set (R2P and RMSEP) were 0.99 and 0.34, respectively. These results show the feasibility of utilizing FTIR along with supervised learning algorithms for the rapid offline evaluation of wheat flour contamination in gluten-free products.

Original languageEnglish
Article number8742
JournalSustainability (Switzerland)
Issue number11
StatePublished - Jun 2023

Bibliographical note

Funding Information:
This research was funded by the National Institute of Food and Agriculture (NIFA) U.S.D.A. Multistate Project #: 1024529.

Publisher Copyright:
© 2023 by the authors.


  • FTIR
  • celiac disease
  • cornbread
  • ensemble learning
  • gluten
  • machine learning
  • wheat flour

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


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