Meat- and plant-based products induced similar satiation which was not affected by multimodal augmentation.

Little is known about how plant-based products influence satiation compared to corresponding meat-based products. As augmented reality (AR) intensifies sensory experiences, it was hypothesized to improve satiation. This study compared satiation between intake of meatballs and plant-based balls and plant-based balls intensified with AR for visual, olfactory, and haptic sensory properties. Intake order of the meatballs, plant-based balls, and augmented plant-based balls, eaten on separate days, was randomized. Satiation was measured from twenty-eight non-obese adults as ad libitum intake of the balls and extra snacks, and as subjective appetite sensations. Liking and wanting to eat the products were also investigated. There were no differences between the products in satiation. Before tasting the augmented plant-based balls were less liked than the meatballs (p = 0.002) or plant-based balls (p = 0.046), but after eating the first ball or eating the ad libitum number of balls the differences in liking disappeared. Wanting evaluations were similar for each product and decreased during eating (p < 0.001). A group of participants susceptible to AR was found (n = 11), described by decreased intake when augmentation was applied. Among the sub-group, wanting to eat the augmented balls was lower before tasting (p = 0.019) and after eating the first ball (p = 0.002) and appetite was less suppressed after eating the balls ad libitum (p = 0.01), when compared to non-susceptible participants. We conclude that meatballs and plant-based balls were equal in inducing satiation, and multisensory augmentation did not influence satiation. However, the augmentation decreased liking evaluations before tasting. Further studies are needed to explore differences between consumer groups in susceptibility to augmentation.

A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry.

The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than 3% when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least 95% or 99% of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit.