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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…
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.
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.
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.
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.
The research contributes to understanding on participants’ food waste intention in food service settings.
Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of…
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.
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.
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.
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.