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Article
Publication date: 1 July 2019

Xingyi Zhao and Louise Manning

The purpose of this paper is to consider the factors that influence food plate waste in a UK university food service setting and the insinuated intention to waste food…

Abstract

Purpose

The purpose of this paper is to consider the factors that influence food plate waste in a UK university food service setting and the insinuated intention to waste food among staff and students.

Design/methodology/approach

The study conducted empirical research using an online questionnaire (n=260) at the university. The data were analysed descriptively and inferentially by IBM SPSS Statistics version 22.

Findings

Multiple factors influence the level of food plate waste including gender, different categories of food, plate size, portion size and palatability. Two recommendations to reduce plate food waste in the university food service setting include providing a variation in plate size and pricing strategy by portion rather than a whole meal, and communicating with staff and students in the food service setting.

Research limitations/implications

The research contributes, along with previous studies, by focussing here on participants’ food waste intention in food service settings and evidencing the factors of influence.

Originality/value

The research contributes to understanding on participants’ food waste intention in food service settings.

Details

British Food Journal, vol. 121 no. 7
Type: Research Article
ISSN: 0007-070X

Keywords

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Article
Publication date: 11 May 2020

Haiyang Gu, Kaiqi Liu, Xingyi Huang, Quansheng Chen, Yanhui Sun and Chin Ping Tan

Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of…

Abstract

Purpose

Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of coconut water brands.

Design/methodology/approach

PARAFAC was applied to reduce three-dimensional data of excitation emission matrix (EEM) to two-dimensional data. SVM was applied to discriminate between six commercial coconut water brands in this study. The three largest variation data from fluorescence spectroscopy were extracted using the PARAFAC method as the input data of SVM classifiers.

Findings

The discrimination results of the six commercial coconut water brands were achieved by three SVM methods (Ga-SVM, PSO-SVM and Grid-SVM). The best classification accuracies were 100.00%, 96.43% and 94.64% for the training set, test set and CV accuracy.

Originality/value

The above results indicate that fluorescence spectroscopy combined with PARAFAC and SVM methods proved to be a simple and rapid detection method for coconut water and perhaps other beverages.

Details

British Food Journal, vol. 122 no. 10
Type: Research Article
ISSN: 0007-070X

Keywords

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