Novel computational tools in bakery process data analysis: A comparative study

Juho Rousu (Corresponding Author), Laura Flander, Marjaana Suutarinen, Karin Autio, Petri Kontkanen, Ari Rantanen

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)


We studied the potential of various machine learning and statistical methods in the prediction of product quality in industrial bakery processes. The methods included classification and regression tree, decision list, neural network, support vector machine and Bayesian learning algorithms as well as statistical multivariate methods. Our data originated from two industrial bakery processes: a sourdough rye bread and a Danish pastry process. In our studies, the Naive Bayesian algorithm turned out to be the best classifier building algorithm while the partial least squares (PLS) method was the best regression method. The prediction accuracy of these models improved significantly by pruning the original set of variables. In this study, two response variables could be predicted on a level that justifies further study: rye bread pH could be predicted with high accuracy with Naive Bayesian Classifier, and Danish pastry height could be predicted with a moderately high correlation with PLS.
Original languageEnglish
Pages (from-to)45-56
JournalJournal of Food Engineering
Issue number1
Publication statusPublished - 2003
MoE publication typeA1 Journal article-refereed


  • Data analysis
  • Predictive modeling
  • Bakery processes
  • Product quality


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