The free-linking task: a graph-inspired method for generating non-disjoint similarity data with food products

"Free sorting", in which subjects are asked to sort a set of items into groups of "most similar" items, is increasingly popular as a technique for profiling sets of foods. However, free sorting implies an unrealistic model of sample similarity: that similarity is purely binary (is/is not similar) and that similarity is fully transitive (similarities {A, B} and {B, C} imply {A, C}). This paper proposes a new method of rapid similarity testing -- the "free-linking" task -- that solves both problems: in free linking, subjects draw a similarity graph in which they connect pairs of samples with a line if they are similar, according to the subject s individual criteria. This simple task provides a more realistic model of similarity which allows degrees of similarity through the graph distance metric and does not require transitive similarity. In two pilot studies with spice blends (10 samples, 58 subjects) and chocolate bars (10 samples, 63 subjects), free linking and free sorting are evaluated and compared using DISTATIS, RVb, and the graph parameters degree, transitivity, and connectivity; subjects also indicated their preferences and ease-of-use for the tasks. In both studies, the first two dimensions of the DISTATIS consensus were highly comparable across tasks; however, free linking provided more discrimination in dimensions three and four. RVb stability was equivalent for the two methods. Graph statistics indicated that free linking had greater discrimination power: on average subjects made similarity groupings with lower degree, lower transitivity, and higher connectivity for free linking in both studies. However, subjects did overall find free sorting easier and liked it more, indicating a higher cognitive difficulty of free linking. The free-linking task, therefore, provides more robust, realistic similarity maps at the cost of higher panelist effort, and should prove a valuable alternative for rapid sensory assessment of product sets.Agencia Estatal de Investigació

The lexicocalorimeter: Gauging public health through caloric input and output on social media

We propose and develop a Lexicocalorimeter: an online, interactive instrument for measuring the caloric content of social media and other large-scale texts. We do so by constructing extensive yet improvable tables of food and activity related phrases, and respectively assigning them with sourced estimates of caloric intake and expenditure. We show that for Twitter, our naive measures of caloric input , caloric output , and the ratio of these measures are all strong correlates with health and well-being measures for the contiguous United States. Our caloric balance measure in many cases outperforms both its constituent quantities; is tunable to specific health and well-being measures such as diabetes rates; has the capability of providing a real-time signal reflecting a population\u27s health; and has the potential to be used alongside traditional survey data in the development of public policy and collective self-awareness. Because our Lexicocalorimeter is a linear superposition of principled phrase scores, we also show we can move beyond correlations to explore what people talk about in collective detail, and assist in the understanding and explanation of how population-scale conditions vary, a capacity unavailable to black-box type methods

Aroma characterization of American rye whiskey by chemical and sensory assays

American rye whiskey is a distilled, alcoholic beverage, manufactured and consumed in the United States since before the Revolutionary War. Although other whiskeys (Scotch, Irish, bourbon) have been studied extensively in recent years, the aroma profile and potent odorants of American rye whiskey have not been reported. Two dilution analysis methods for Gas Chromatography-Olfactometry (GCO) were used to identify the potent odorants in rye whiskey: Sample Dilution Analysis (SDA) – a novel, nonextractive, direct method of analyzing alcoholic distillates – and Aroma Extract Dilution Analysis (AEDA) – a widely used and well-understood extractive method. SDA was found to provide equivalent results to AEDA while reducing analysis time and avoiding extraction bias. American rye whiskey was found to be a complex aroma system, with no-one odorant responsible for its characteristic aroma, but among the key aroma compounds identified were: 3-methyl-1-butanol, 2-phenylethanol, cis-(3S,4S)-whiskey lactone, guaiacol, syringol, and vanillin. These compounds likely mainly originated from either yeast metabolism (in the case of fusel alcohols) or lignin pyrrolysis. Odorants identified as important through dilution analysis were then quantified using Stable Isotope Dilution Analysis (SIDA). All key odorants were quantified, with concentrations ranging from 2560 ppm (3-methyl-1-butanol) to 7 ppb (ethyl cinnamate). In addition, acetaldehyde was identified as a key odorant, and quantified using external standardization. Finally, model solutions based on the quantification were constructed and compared to authentic whiskey samples using a difference test: the R-Index by Rating method. It was shown that naïve judges were unable to discriminate between different brands of commercial whiskeys. These judges were also unable in some but not all cases to discriminate between the model and the commercial whiskeys, indicating that the model and the quantification it was based on were a partial success, but that further work is necessary

Sensory science, the food industry, and the objectification of taste

Dans cet article je propose d’examiner pourquoi l’analyse sensorielle – discipline appartenant au champ des sciences de l’alimentation et qui “consiste en un ensemble de techniques permettant de mesurer précisément les réactions humaines vis à vis des aliments” –rencontre des difficultés à évaluer les propriétés sensorielles des aliments issus d’une production “artisanale”. En tant que spécialiste de l’analyse sensorielle, je me préoccupe de cette limite de la discipline qui empêche de prendre en compte des categories entières d’aliments et d’expériences de consommateurs. J’entends démontrer que les relations historiques entre l’analyse sensorielle et l’industrie alimentaire ont conduit à asseoir certaines hypothèses qui ne peuvent s’appliquer en-dehors d’une production industrielle, et qui par conséquent nuisent à l’évaluation d’aliments issus d’une production artisanale. Je commence par tracer les grandes lignes de ces liens historiques, puis j’expose les hypotheses qui, à mon sens, posent problème, pour finir par démontrer – en me basant sur mes propres recherches - la nécessité de developer des methodologies sensorielles qui puissant être appliquées largement aux aliments, quel que soit leur mode de production.In this paper I propose to examine why sensory science – a discipline within food science that “comprises a set of techniques for the accurate measurement of human responses to foods” – has historically struggled with assessing the sensory properties of “artisan foods.” As a sensory scientist, I am concerned that this limits the discipline and underserves whole categories of foods and consumer experiences. I will demonstrate that the historical relationship between sensory science and the food industry has led to certain assumptions within the discipline that do not apply outside of industrial food production, and so undermine the assessment of artisan foods. I outline this historical relationship, explain the disciplinary assumptions I believe are problematic, and briefly demonstrate – using some of my own research – the need for sensory methodologies that are broadly applicable to foods produced in different paradigms

Sensory science, the food industry, and the objectification of taste

Dans cet article je propose d’examiner pourquoi l’analyse sensorielle – discipline appartenant au champ des sciences de l’alimentation et qui “consiste en un ensemble de techniques permettant de mesurer précisément les réactions humaines vis à vis des aliments” –rencontre des difficultés à évaluer les propriétés sensorielles des aliments issus d’une production “artisanale”. En tant que spécialiste de l’analyse sensorielle, je me préoccupe de cette limite de la discipline qui empêche de prendre en compte des categories entières d’aliments et d’expériences de consommateurs. J’entends démontrer que les relations historiques entre l’analyse sensorielle et l’industrie alimentaire ont conduit à asseoir certaines hypothèses qui ne peuvent s’appliquer en-dehors d’une production industrielle, et qui par conséquent nuisent à l’évaluation d’aliments issus d’une production artisanale. Je commence par tracer les grandes lignes de ces liens historiques, puis j’expose les hypotheses qui, à mon sens, posent problème, pour finir par démontrer – en me basant sur mes propres recherches - la nécessité de developer des methodologies sensorielles qui puissant être appliquées largement aux aliments, quel que soit leur mode de production.In this paper I propose to examine why sensory science – a discipline within food science that “comprises a set of techniques for the accurate measurement of human responses to foods” – has historically struggled with assessing the sensory properties of “artisan foods.” As a sensory scientist, I am concerned that this limits the discipline and underserves whole categories of foods and consumer experiences. I will demonstrate that the historical relationship between sensory science and the food industry has led to certain assumptions within the discipline that do not apply outside of industrial food production, and so undermine the assessment of artisan foods. I outline this historical relationship, explain the disciplinary assumptions I believe are problematic, and briefly demonstrate – using some of my own research – the need for sensory methodologies that are broadly applicable to foods produced in different paradigms

The Great is the Enemy of the Good: Hedonic Contrast in a Coursed Meal

This study investigates whether hedonic contrast occurs between foods served in different courses within a meal. In particular, does the hedonic value of an appetizer affect the hedonic value of the subsequently eaten main course? Hedonic contrast is known to occur in laboratory settings, but so far it has not been demonstrated in ecologically valid, real-world meal situations. To that end, this study was conducted in an ecologically valid setting - a training restaurant in a culinary school. Two groups of subjects (Ns = 35 and 29) were served the same pasta main course after either a good or mediocre bruschetta appetizer. The pasta was rated worse (and hedonically negative, M = -9.4) by subjects eating the good appetizer than by subjects eating the mediocre one (who judged it as hedonically positive, M = 17.4). This suggests that the hedonic value of an appetizer can influence the degree to which a diner likes the main course of a meal. Implications for the phenomenon of hedonic contrast and for meal services in restaurant settings are discussed

You\u27ll Spoil Your Dinner: Attenuating Hedonic Contrast in Meals Through Cuisine Mismatch

Previous research (Lahne & Zellner, 2015) has shown that hedonic contrast occurs in a multi-coursed meal such that good appetizers reduce the hedonic evaluation of an entrée. This paper extends that finding by examining whether hedonic contrast between courses served in a real restaurant meal can be attenuated or eliminated through a categorical mismatch of cuisine (Italian vs Thai). Subjects (N = 143) ate a meal in a University teaching restaurant in which the cuisine of the appetizer (soup) was manipulated so that it either matched (Italian minestrone) or did not match (Thai tom kha) the main course (Italian pasta aglio e olio). Subjects reported on their affective response to the meal. When the cuisine matched, hedonic contrast occurred: good minestrone caused subjects to like the same pasta – and the entire meal – significantly less. However, when the cuisine did not match there was no evidence of contrast: good tom kha did not depress liking ratings for the pasta dish, and in fact the overall meal was rated as better with the good appetizer. Thus, hedonic contrast can be attenuated by a mismatch of cuisine category. This research has important implications for restaurants, in that it both provides further evidence that main courses may be negatively affected by appetizers that are “too good”, and that actively varying the cuisine categories of dishes between menu sections may ameliorate this effect

Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning

This paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. “Relevant” terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of Food Science, investigates human responses to food products and differentiates “descriptive” terms for flavors from “ordinary”, non-descriptive language. Within the field, descriptors are generated through Descriptive Analysis, a method wherein a human panel of experts tastes multiple food products and defines descriptors. This process is both time-consuming and expensive. However, one could leverage existing data to identify and build a flavor language automatically. For example, there are thousands of professional and semi-professional reviews of whisk(e)y published on the internet, providing abundant descriptors interspersed with non-descriptive language. The aim, then, is to be able to automatically identify descriptive terms in unstructured reviews for later use in product flavor characterization. We created two systems to perform this task. The first is an interactive visual tool that can be used to tag examples of descriptive terms from thousands of whisky reviews. This creates a training dataset that we use to perform transfer learning using GloVe word embeddings and a Long Short-Term Memory deep learning model architecture. The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.99. We tested for overfitting by comparing the training and validation loss for divergence. Our results show that the language structure for descriptive terms can be programmatically learned