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Article
Publication date: 17 June 2019

Ravipim Chaveesuk and Natthamon Konjanattham

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…

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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.

Details

British Food Journal, vol. 121 no. 8
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 13 June 2008

Prisana Suwannaporn, Anita Linnemann and Ravipim Chaveesuk

Rice consumption per capita in many Asian countries is decreasing constantly, but American and European citizens are eating more rice nowadays. A preference study among consumers…

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Abstract

Purpose

Rice consumption per capita in many Asian countries is decreasing constantly, but American and European citizens are eating more rice nowadays. A preference study among consumers was carried out with the aim of determining new rice product characteristics in order to support export of Thai rice. This paper aims to report the results

Design/methodology/approach

The research was based on both secondary and primary data collection. The secondary data included exploratory surveys of rice and its products which were conducted in some of Thailand's potential rice export markets. Exploratory primary data were collected through qualitative focus group research. A quantitative questionnaire with 1,128 consumers of target nationalities was conducted to access consumer attitudes and preferences with respect to rice and rice products.

Findings

Rice products were grouped with factor analysis and could be characterized by convenience (explained variance 33.9 per cent), grain variety (21.2 per cent), and tradition/naturalness (12.8 per cent). Rotated factor score plot of the preference for rice products among different nationalities showed a similarity in the preference for the tradition/natural products. Convenient products were preferred in higher income Asian countries and the non‐rice eating countries. These three product categories were correlated with consumers' ideas concerning the health‐supporting character of processed food.

Originality/value

Consumers' rice preferences differed greatly among nationalities. Rice exporters have to understand these different preferences in order to offer the right products to their customers. Assuming consumer preferences to be comparable to one's own country's preference can cause new product failure. This paper confirms existing differences and presents details and backgrounds of these differences.

Details

British Food Journal, vol. 110 no. 6
Type: Research Article
ISSN: 0007-070X

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