Modeling approach for releasing a frankfurter production batch
ISSN: 0007-070X
Article publication date: 17 June 2019
Issue publication date: 9 July 2019
Abstract
Purpose
The purpose of this paper is to model the relationship between 11 frankfurter physical properties and their sensory scores to classify a release of frankfurter production batches to the market.
Design/methodology/approach
Data from 209 frankfurter batches were collected. Market batch release classifications were based on 11 physical properties via predictive and direct classification models. The predictive models under study included a regression, backpropagation neural network (BPN) and radial basis function neural network (RBFN) whereas the direct classification models were logistic regression, BPN and RBFN. Model performance was evaluated via correct classification rate.
Findings
The 11-7-4 RBFN predictive model proved superior with a 90 percent correct classification rate and 0 percent producer risk while the 11-5-1 RBFN, as a classification model, outperformed with the same level of accuracy, 90 and 0 percent, respectively. Producers prefer the less time-consuming direct classifiers for evaluation. Furthermore, the 11-5-1 RBFN direct classifier revealed that color measurement greatly influenced frankfurter batch release. Increases in redness, yellowness and brownness increased batch release probability.
Originality/value
This research attempts to establish a novel production batch release model for sausage manufacturing. Key factors can then be optimized for improving batch release probability for implementation throughout the sausage industry.
Keywords
Citation
Chaveesuk, R. and Konjanattham, N. (2019), "Modeling approach for releasing a frankfurter production batch", British Food Journal, Vol. 121 No. 8, pp. 1813-1824. https://doi.org/10.1108/BFJ-09-2018-0602
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited