# Ultra-processed foods are of poor nutritional quality in French consumers' representations; a categorization study of images of food products without information

Jessy Youssef, Stéphanie Chambaron, Laurence Dujourdy, Gaëlle Arvisenet

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Jessy Youssef, Stéphanie Chambaron, Laurence Dujourdy, Gaëlle Arvisenet. Ultra-processed foods are of poor nutritional quality in French consumers' representations; a categorization study of images of food products without information. Food Quality and Preference, 2025, 133, pp.105609. 10.1016/j.foodqual.2025.105609. hal-05099587v2

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# Ultra-processed foods are of poor nutritional quality in French consumers' representations; a categorization study of images of food products without information

Jessy Youssef<sup>a</sup>, Stéphanie Chambaron<sup>a</sup>, Laurence Dujourdy<sup>b</sup>, Gaëlle Arvisenet<sup>a,∗</sup>

## ARTICLE INFO

### Keywords:

- Categorization

- NOVA, Nutri-score

- Food group

- Food representations

## ABSTRACT

This study investigated how consumers categorize food according to its nutritional quality and degree of processing through two online questionnaires. Two hundred French consumers used a directed sorting task to categorize 21 images of food products by nutritional quality and degree of processing. The images contained no information about these two characteristics or any other properties. Two multinomial logit regression analyses revealed that (1) without any access to established values for degree of processing or nutritional quality, participants' categorizations of food images closely mirrored these established values; (2) participants' categorization of food products by nutritional quality was also influenced by non-nutritional properties; (3) participants linked nutritional quality to degree of processing in their categorizations. Possible reasons are proposed for this link and the cognitive mechanisms involved, such as a learned association resulting from the overly simple messages conveyed by dietary guidelines, the use of heuristics to overcome the difficulty of estimating the nutritional quality of food products in the absence of information, and the health halo effect.

## 1. Introduction

Over the past few decades and worldwide, eating habits and food choices have shifted toward more processed and less varied diets (Leite et al., 2022). More than half of the energy intake in the United States and the United Kingdom is in the form of highly processed foods (Leite et al., 2022), and these products also represent approximately half the energy intake in French adults' diets (Souchon & Braesco, 2022). Ultra-processed foods (UPFs) also account for more than one-third of calorie consumption in Australia and are increasing rapidly in lower-income countries within Asia, Africa and Latin America (Leite et al., 2022). Several definitions of UPFs have been proposed. Most of these definitions consider UPFs as products generated by an industrial process and/ or containing at least five ingredients, including substances not commonly used in culinary preparations (Monteiro et al. 2016). Several factors contribute to the enormous increase in the consumption of UPFs: the globalization of food systems, which facilitates the diffusion of products between markets and makes them more accessible for consumers; economic development, which increases the food-purchasing capacity of households (Baker et al., 2020); and the convenience of

ready-to-eat products, which are perceived as a solution for consumers' lack of time (Sarmiento-Santos et al., 2022).

The scientific community has not reached a consensus on the health consequences of this change in dietary habits. A positive association has been demonstrated between the global consumption of ultra-processed food products and increases in overweight and obesity, type 2 diabetes, cancer, and cardiovascular diseases (Dicken & Batterham, 2024; Elizabeth et al., 2020; Lane et al., 2024). In some countries, processing-based information has been included in dietary guidelines to inform consumers about the possible negative effects of a highly processed diet (Koios et al., 2022; Quinn et al., 2021). These guidelines thus inform consumers about foods that should be avoided or consumed more frequently, based on food group, nutritional content, and degree of processing. However, other authors assert that it would be excessive to consider all ultra-processed foods bad in terms of nutrition and health. According to Visioli et al. (2022), "food processing does not necessarily translate into poorer nutritional properties." Some UPFs are even considered healthy according to the UK nutrient profiling model and are viewed as beneficial because they provide protein and fiber and are fortified with micronutrients (Derbyshire, 2019). For example, high-

<footnote><sup>a</sup> Centre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro, Université Bourgogne Europe, F-21000 Dijon, France</footnote>

<footnote><sup>b</sup> Institut Agro Dijon—Service d’Appui à la Recherche, 26 bd Dr Petitjean, F-21000 Dijon, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: gaelle.arvisenet@agrosupdijon.fr (G. Arvisenet).</footnote>
fiber foods, such as whole-wheat bread, cereals, and cereal bars, that are made by recombining fractions of wheat are considered nutritious even though they are industrially processed (Visioli et al., 2023; Sanchez-Siles et al., 2022). Ultra-processed foods can even be adapted to the needs of specific populations (e.g., malnourished elderly people) (Fardet, 2018). This debate is not restricted to the scientific community but has recently been relayed to consumers by mainstream media (Callahan, 2024). Under these conditions, it is useful to explore how consumers consider UPFs and food processing, and whether they are able to distinguish between the processing and nutritional quality of food.

Sarmiento-Santos et al. (2022) studied the subjective and objective understanding of the concept of UPF by consumers through a survey where participants had to self-assess their level of understanding of UPF definitions taken from national guidelines and to identify, among a set of product images, those that they considered as UPFs. Even though respondents were convinced they understood the concept of UPFs, their answers did not fully match the established definition. Such a mismatch was not evidenced by Ares et al. (2016) who studied conceptualization of UPFs by Uruguayan consumers with similar methodologies. Other studies aiming to check the alignment between the categorization of product processing level by consumers and by the scientific community were based on categorization tasks. In order to select images of products with different degrees of processing for a sorting task, Menegassi et al. (2019) based their choice on the NOVA classification system. This classification system, created by researchers from the University of São Paulo (Brazil), classifies food into four categories according to their degree of processing: unprocessed or minimally processed foods, culinary ingredients, processed foods, and ultra-processed foods (Monteiro et al., 2016). Each category is determined according to the extent and purpose of processing and by the function and number of ingredients included in the product. The results of the classification task in the study of Menegassi and collaborators separated unprocessed from ultra-processed foods but other criteria might also have been taken into account by the participants. Indeed, the products chosen to represent each degree of processing did not cover all the possible ranges of nutritional quality.

Two studies more recently investigated how the perception of food healthiness by consumers is linked to its degree of processing (Bolhuis et al., 2022; Hässig et al., 2023). The authors of both these studies based their estimation of product healthiness on their Nutri-Score level. Nutri-Score was implemented in France in 2017. It is based on a scientific algorithm and background. Its calculation includes, for 100 ml or 100 g of the product, elements that should be restricted (e.g., calories, saturated fats, sugar and salt) and elements that should be increased (e.g., fiber, protein, fruits, vegetables, pulses and nuts) (Mertz et al. 2023). Both these studies found a positive link between healthiness and degree of processing, showing that for consumers the concept of healthiness encompasses the degree of processing, and ultra-processed products are considered to be the least healthy. Product sampling in both studies did not represent every possible combination between NOVA and Nutri-Score. Further investigation is therefore necessary to assess whether consumers perceive a direct opposition between the degree of processing of a food product and its nutritional quality.

Most studies focusing on the link between nutritional quality and healthiness used settings where consumers were given explicit information about food nutritional quality. Consumers' understanding of front-of-pack nutrition labels (FOPLs) has been widely studied in many countries (Mazzù et al., 2021). The effect of providing nutritional information on participants' ability to rank food products in terms of nutritional quality has been investigated (Egnell et al., 2018). In contrast, relatively few studies have so far explored how consumers categorize foods based on nutritional quality without providing them with any type of scientific information or label.

The relationship between consumer categorizations of nutritional properties and degree of processing remains underexplored, leaving a critical gap in our understanding of how these perceptions interact. The

aim of our study was thus to investigate consumers' representations of the nutritional quality and degree of processing of food products in the absence of any specific information about these aspects. This was meant to reproduce the way consumers might purchase food, i.e., with no access to information, or without paying attention to available information. The first objective was to examine the extent to which French consumers' categorization of food products according to these two properties is consistent with categorizations proposed by the scientific community. Our second objective was to identify any other criteria that consumers might take into account when categorizing foods according to their nutritional quality or their degree of processing; in particular, we examined whether French consumers' categorization of nutritional quality and degree of processing are related.

## 2. Materials and methods

This study received approval from the ethical committee of INSERM (IRB00003888).

In this study, the term "established values" refers to nutritional quality and degree of processing indicators that have been proposed by the scientific community. The selection of established values was based on Nutri-Score and NOVA as explained in section 2.1. The term "perceived values" represents the same properties as assessed by the panel of participants in the study. The goal of the study was to test the alignment between these two sets of values through the categorization by participants of a set of food product images by means of two online questionnaires as explained in sections 2.2 to 2.4.

### 2.1. Classification systems for the "established values"

The Nutri-Score, a classification system validated by the scientific community, is the most widely implemented public health nutrition tool in France (French Ministry of Public Health, 2023a). It was therefore the basis of "established values" for nutritional quality used in this study.

Several classification systems exist to classify food products by degree of processing. The NIPH (developed by the National Institute of Public Health in Mexico) classifies foods according to social and cultural aspects (e.g., traditional vs. modern and artisanal vs. industrial products). The IARC-EPIC (developed in Europe by the International Agency for Research on Cancer) divides foods into three categories according to the degree of processing (unprocessed, moderately processed, and highly processed). Souchon and Braesco (2022) list several other classifications, including those proposed by the IFIC (International Food Information Council), the USP (University of São Paulo), the FSANZ (Australia and New Zealand), the UNC (University of North Carolina), and the IFPRI (International Food Policy Research Institute, Guatemala). These classifications are not algorithm-based, but generally rely on a more subjective approach (Gibney, 2023). In France, the NOVA classification system is used by several food-shopping applications (e.g. OpenFoodFacts, ScanUp) and is the most documented in scientific literature (Chen et al., 2020). It is also documented in French mainstream media (Myot, 2023). It was therefore the basis of "established values" for degree of processing.

In this study, Nutri-Score level and NOVA class were used to present food products to build the data set for the questionnaires. These "established values" were also used as independent variables for the statistical analysis, but they were never presented to the participants at any time.

### 2.2. Selecting food products and building a set of unlabeled food product images

#### 2.2.1. Selecting food products based on Nutri-Score level and NOVA class

The study was designed to include three degrees of processing based on NOVA class and three levels of nutritional quality based on Nutri-Score. The same number of products was selected for each of the three
degrees of processing retained: NOVA 1 (unprocessed or minimally processed), NOVA 3 (processed), and NOVA 4 (ultra-processed). The NOVA 2 class, which represents only culinary ingredients that cannot be considered as food products per se, was therefore excluded from our study. An equal number of products was selected for each level of nutritional quality retained: Nutri-Score A–B (high), Nutri-Score C (medium), and Nutri-Score D–E (poor).

The food products were drawn from a preselection of sixty products belonging to three food groups (fruits and vegetables (F&V); starchy foods (S); and meat, fish and dairy (MFD)), with different degrees of processing and different Nutri-Scores. These products images were assessed by 93 people for their degree of familiarity and liking, via an online questionnaire, using five-point scales (from “I totally dislike this product” to “I like this product a lot” and from “I do not know this product at all” to “I know this product well.”). A short list of products that were familiar to consumers and not extremely liked or disliked was selected. The final set of products was obtained from this shortlist, which contained only products known by at least 80 % of the participants and disliked by less than 25 % of the participants. The 93 participants involved in the pretest took no further part in the study.

To avoid any interdependence between nutritional quality and degree of processing, each of the nine combinations of Nutri-Score level and NOVA class was represented by products selected whenever possible from each of the three food groups retained: fruits and vegetables (F&V), starchy foods (S), and meat, fish and dairy (MFD). As no unprocessed or minimally processed starches (NOVA1) have a Nutri-Score level of C or D-E, and no processed starches (NOVA3) have a Nutri-Score level of D-E, three of the potential 27 combinations proved impossible to populate. As the NOVA1 class contained only 7 products, we opted to retain the same number of products for the other NOVA classes. The Nutri-Score level D-E also contained only 7 products, so the combinations left empty were fruits and vegetables NOVA3 Nutri-Score level AB and meat, fish and dairy NOVA4 Nutri-Score levels A-B and C. The final set of 21 products (shown in supplementary material) thus comprises 8 F&V products, 6 S products and 7 MFD products. This slight imbalance does not impact the focus of the study which was to assess independently the participants' perceptions of the degree of processing and of the level of nutritional quality.

The Nutri-Scores of these 21 products were calculated via an algorithm published by the French Ministry of Public Health in 2022 (Mertz et al. 2023) and we double-checked their list of ingredients to verify their NOVA group on the basis of an article by Monteiro et al. (2016).

### 2.2.2. *Building a set of unlabeled food product images*

To obtain the “perceived values” for nutritional quality and degree of processing, the selected 21 products were represented by a set of images containing only the name of the product and the photo of the packaging. Any potential information or clues regarding nutritional or processing properties were therefore masked on these photos (see Fig. 1).

## 2.3. Participants

For an online categorization task, the consensus in the literature is that recruiting 150 participants will provide a sufficiently robust sample (Guerrero et al., 2012; Lawson et al., 2017). We therefore mandated Panelabs (a French agency specializing in panel selection) to recruit 240 participants balanced in terms of sex, age, and education level. The recruitment age was set at 20–60 to cover the major stages of adulthood.

## 2.4. Questionnaires

Two online questionnaires were created via Qualtrics XM software to collect the participants' categorization of foods (perceived values) according to their nutritional quality (NQ) and degree of processing (DP). Both the questionnaires contained the same 21 unlabeled food product images, presented in random order on each questionnaire (see supplementary material).

### 2.4.1. *Content of the questionnaires and organization of the study*

Each of the questionnaires was divided into two parts. The NQ questionnaire was composed of a directed sorting task for nutritional quality, followed by a set of questions to verify the sociodemographic balance among the participants. The DP questionnaire was composed of a directed sorting task for degree of processing, followed by the single-item food choice questionnaire (SI-FCQ) proposed by Onwezen et al. (2019). The 240 participants received links to the two questionnaires in two separate phases, one week apart. Half of the participants answered NQ followed by DP, while the other half answered DP followed by NQ.

### 2.4.2. *Directed sorting tasks*

The 21 unlabeled food product images were presented on the left-hand part of the screen in a randomized order that was different for the NQ and DP questionnaires for each participant (Fig. 1). In the NQ questionnaire, participants were asked to click on each food product image and to drag and drop it into one of three columns (labeled high,

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Online questionnaire screenshots with examples of unlabeled food product images (a) NQ questionnaire and (b) DP questionnaire.</figcaption></figure>
medium, and low), corresponding to perceived nutritional quality. In the DP questionnaire, participants followed the same protocol for the three columns of degrees of processing (labeled unprocessed/minimally processed, processed, and ultra-processed). Participants were encouraged to reply as spontaneously as possible; they were also informed that no limit was set for the number of products to be placed in the same column.

### 2.4.3. Sociodemographic questions

The sociodemographic questions referred to gender (male, female, other, or prefer not to answer), age (20–30, 31–40, 41–50, or 51–60 years old), level of education (baccalaureate and below vs. above baccalaureate), occupation, neighborhood (rural vs. periurban vs.

<caption>**Table 1**</caption>

Sociodemographic characteristics and answers to the SI-FCQs of the 200 participants.

<table><thead><tr><th>Characteristics</th><th>Categories</th><th>Number of participants</th><th>%</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Male</td><td>90</td><td>45</td></tr><tr><td>Female</td><td>110</td><td>55</td></tr><tr><td rowspan="4">Age</td><td>20–30</td><td>31</td><td>15.5</td></tr><tr><td>31–40</td><td>56</td><td>28</td></tr><tr><td>41–50</td><td>56</td><td>28</td></tr><tr><td>51–60</td><td>57</td><td>28.5</td></tr><tr><td>Education</td><td>Baccalaureate and below</td><td>106</td><td>53</td></tr><tr><td rowspan="2">Occupation</td><td>Above baccalaureate</td><td>94</td><td>47</td></tr><tr><td>Farmer</td><td>0</td><td>0</td></tr><tr><td rowspan="10">Living area</td><td>Executive and higher intellectual profession</td><td>33</td><td>16.5</td></tr><tr><td>White-collar worker</td><td>71</td><td>35.5</td></tr><tr><td>Student</td><td>5</td><td>2.5</td></tr><tr><td>Craftsperson, merchant and entrepreneur</td><td>7</td><td>3.5</td></tr><tr><td>Intermediate profession</td><td>24</td><td>12</td></tr><tr><td>Blue-collar worker</td><td>15</td><td>7.5</td></tr><tr><td>Retired</td><td>3</td><td>1.5</td></tr><tr><td>Other</td><td>42</td><td>21</td></tr><tr><td>Urban</td><td>91</td><td>45.5</td></tr><tr><td>Rural</td><td>65</td><td>32.5</td></tr><tr><td rowspan="6">Diet</td><td>“I usually eat animal products”</td><td>119</td><td>59.5</td></tr><tr><td>“I limit my consumption of animal products”</td><td>73</td><td>36.5</td></tr><tr><td>“I don’t eat meat, but I eat fish”</td><td>3</td><td>1.5</td></tr><tr><td>“I don’t eat meat or fish”</td><td>5</td><td>2.5</td></tr><tr><td>“I don’t eat meat, fish or any animal product (eggs, dairy products, etc.)”</td><td>0</td><td>0</td></tr><tr><td>Yes</td><td>32</td><td>16</td></tr><tr><td>Use of food applications</td><td>No</td><td>168</td><td>84</td></tr><tr><td>SI-FCQ</td><td></td><td>Mean</td><td>Median</td><td>Standard deviation</td></tr><tr><td></td><td>Health</td><td>5.9</td><td>6</td><td>1.04</td></tr><tr><td></td><td>Mood</td><td>4.46</td><td>5</td><td>1.58</td></tr><tr><td></td><td>Convenience</td><td>4.84</td><td>5</td><td>1.44</td></tr><tr><td></td><td>Sensory appeal</td><td>5.90</td><td>6</td><td>0.98</td></tr><tr><td></td><td>Natural content</td><td>5.78</td><td>6</td><td>1.10</td></tr><tr><td></td><td>Familiarity</td><td>4.61</td><td>5</td><td>1.44</td></tr><tr><td></td><td>Weight control</td><td>5.15</td><td>5</td><td>1.46</td></tr><tr><td></td><td>Environmental protection</td><td>5.05</td><td>5</td><td>1.34</td></tr><tr><td></td><td>Animal welfare</td><td>5.39</td><td>5.5</td><td>1.43</td></tr><tr><td></td><td>Ethical concerns</td><td>4.18</td><td>4</td><td>1.16</td></tr><tr><td></td><td>Price</td><td>4.92</td><td>5</td><td>1.45</td></tr></tbody></table>

urban), and diet (Table 1). Participants were also asked if they used an application to help them choose foods when shopping. If so, they were asked to name the application used.

### 2.4.4. Single-Item Food Choice Questionnaire

The participants completed the Single-Item Food Choice Questionnaire (SI-FCQ). On this questionnaire (Onwezen et al., 2019), participants must indicate the level of importance they attach to 11 food motivations on a 7-point scale ranging from “not at all important” to “very important.” The food motivations refer to health, mood, convenience, sensory appeal, natural content, price, weight control, familiarity, environmental protection, animal welfare, and ethical concerns.

### 2.5. Statistical analyses

Two multinomial logit regression analyses were conducted to investigate consumers' food categorization and to examine the factors that influenced the “perceived values” for nutritional quality and degree of processing. This statistical method extends the model used for typical binary logistic regression to a nominal categorical outcome variable with more than two categories, as in our study. It can be used with questions where respondents can choose from several options in no particular order, such as those regarding brand preferences and product choices (Agresti, 2007; Hosmer et al., 2013).

We used RStudio 2024.04.2 + 764 and R 4.2.1 with the R packages: “tidyverse” for data visualization and manipulation, “nnet” and “MASS” and “car” to run and check multinomial log-linear models, and “gtsummary” to create publication-ready analytical and summary tables.

In the first linear regression model, analyzing the data from the NQ questionnaire, perceived nutritional quality (PNQ) was the dependent variable. The independent variables were the Nutri-Score (reference: Nutri-Score AB), food group (reference: MFD), NOVA class (reference: NOVA1) and the perceived degree of processing (PDP, obtained from the DP questionnaire; reference: unprocessed).

In the second linear regression model, analyzing the data from the DP questionnaire, perceived degree of processing (PDP) was the dependent variable. The independent variables were the NOVA class (reference: NOVA1), food group (reference: MFD), Nutri-Score (reference: Nutri-Score AB), and the perceived nutritional quality (PNQ, obtained from the NQ questionnaire; reference: high PNQ).

## 3. Results

### 3.1. Characterization of the participants

A timed pretest confirmed that neither of the questionnaires could be completed in less than 90 s, and that most people required 10–15 min to complete one questionnaire (NQ or DP). The cutoff times for each questionnaire to be included in the analysis were therefore set at 90 s and 20 min. Any participant outside these time constraints on one of the questionnaires was excluded. After this step, the results from the remaining 200 participants were retained for the statistical analysis. The sociodemographic characteristics of the participants remained reasonably well-balanced for age, gender, and education level (see Table 1).

### 3.2. Factors explaining the participants representations of the degree of processing

Table 2 shows the results of the multinomial logistic regression predicting the participants' categorization of products according to their degree of processing. All four independent variables examined (Nutri-Score, NOVA class, food group, and perceived nutritional quality) significantly influenced the categorization of products according to their degree of processing. The odds ratio estimates the relative odds of the occurrence of each perceived degree of processing, given the variable of
<caption>**Table 2**</caption>

Results of the multinomial logistic regression showing the effects of the NOVA class, food group, Nutri-Score and perceived nutritional quality on the categorization of products' degree of processing (PDP).

<table><thead><tr><th rowspan="2">Characteristics</th><th colspan="3">Processed</th><th colspan="3">Ultra-processed</th></tr><tr><th>OR</th><th>95% CI</th><th>P value</th><th>OR</th><th>95% CI</th><th>P value</th></tr></thead><tbody><tr><td>NOVA</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NOVA 1</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>NOVA 3</td><td>1.38</td><td>1.16–1.65</td><td>&lt;0.001</td><td>2.92</td><td>2.19–3.89</td><td>&lt;0.001</td></tr><tr><td>NOVA 4</td><td>4.14</td><td>3.38–5.06</td><td>&lt;0.001</td><td>11.2</td><td>8.34–14.9</td><td>&lt;0.001</td></tr><tr><td>Group</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MFD</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>F&amp;V</td><td>1.77</td><td>1.48–2.10</td><td>&lt;0.001</td><td>3.55</td><td>2.72–4.63</td><td>&lt;0.001</td></tr><tr><td>S</td><td>2.41</td><td>1.93–3.01</td><td>&lt;0.001</td><td>8.45</td><td>6.22–11.5</td><td>&lt;0.001</td></tr><tr><td>Nutri-Score</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>A–B</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>C</td><td>2.31</td><td>1.91–2.79</td><td>&lt;0.001</td><td>3.56</td><td>2.75–4.60</td><td>&lt;0.001</td></tr><tr><td>D–E</td><td>2.03</td><td>1.66–2.48</td><td>&lt;0.001</td><td>4.22</td><td>3.20–5.56</td><td>&lt;0.001</td></tr><tr><td>PNQ</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>High</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>Medium</td><td>1.52</td><td>1.28–1.80</td><td>&lt;0.001</td><td>1.36</td><td>1.06–1.76</td><td>0.016</td></tr><tr><td>Low</td><td>1.91</td><td>1.56–2.34</td><td>&lt;0.001</td><td>4.09</td><td>3.17–5.29</td><td>&lt;0.001</td></tr></tbody></table>

PNQ: Perceived Nutritional Quality; PDP: Perceived Degree of Processing; OR: odds ratio; CI: confidence interval;

Reference for the dependent variable: high PDP; references for the independent variables: Nutri-Score A–B, NOVA 1, MFD, High PNQ

interest (Nutri-Score, NOVA, food group, PNQ). An odds ratio (OR) greater than 1 means that the odds of the outcome of interest increase when the independent variable increases by one unit.

NOVA 4 products were more often classified as ultra-processed than NOVA 1 products (OR = 11.2) and NOVA 3 products (OR = 4.08, calculated by dividing Ultra-processed OR value for Nova 4 by that for Nova 3, Table 2). NOVA 3 products were no more likely to be classified as processed than NOVA 1 products (OR of the NOVA 3 level for the modality “processed products” is 1.38), but they were more often considered ‘ultra-processed’ (OR = 2.92) than “processed”.

Compared with products with a Nutri-Score of A–B, those with a Nutri-Score of D–E were more likely to be categorized as ultra-processed (OR = 4.22). Products with a Nutri-Score of C were more likely to be considered processed than those with a Nutri-Score of AB (OR = 2.31). Products perceived by the participants as being of poor nutritional quality (low PNQ) were more often classified as ultra-processed than products considered to be of high nutritional quality (OR = 4.09). The relationship between Nutri-Score and perceived degree of processing (PDP) was of the same order of magnitude as the relationship between perceived nutritional quality (PNQ) and perceived degree of processing.

The results for food group seem to indicate that starches (S) were more frequently classified by the participants as ultra-processed. However, this result cannot be further interpreted because the sample of products used in our study contained more ultra-processed starches than ultra-processed MFD items.

### 3.3. Factors explaining participants' representations of nutritional quality

Table 3 shows the results of the multinomial logistic regression predicting the participants' categorization of products by nutritional quality. All four independent variables examined (Nutri-Score, NOVA, food group, and perceived degree of processing) significantly affected the categorization of products. The odds ratio estimates the relative odds of the occurrence of each category of nutritional quality, given the variable of interest (Nutri-Score, NOVA, food group, PDP).

Products with Nutri-Scores of C or D–E were more often categorized as low nutritional quality than those with Nutri-Scores of A–B (OR = 1.74 and 4.15). Starches (OR = 3.44) and fruits and vegetables (OR = 2.40) were more often categorized as low nutritional quality than meat, fish and dairy, the reference group. In other words, of the three food

<caption>**Table 3**</caption>

Results of the multinomial logistic regression showing the effects of the Nutri-Score, NOVA class, food group and perceived degree of processing on the categorization of products' nutritional quality.

<table><thead><tr><th rowspan="2">Characteristics</th><th colspan="3">Medium PNQ</th><th colspan="3">Low PNQ</th></tr><tr><th>OR</th><th>95% CI</th><th>P value</th><th>OR</th><th>95% CI</th><th>P value</th></tr></thead><tbody><tr><td>Nutri-Score</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>A–B</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>C</td><td>1.40</td><td>1.16–1.68</td><td>&lt;0.001</td><td>1.74</td><td>1.40–2.17</td><td>&lt;0.001</td></tr><tr><td>D–E</td><td>1.33</td><td>1.09–1.63</td><td>0.005</td><td>4.15</td><td>3.29–5.23</td><td>&lt;0.001</td></tr><tr><td>Group</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MFD</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>F&amp;V</td><td>1.61</td><td>1.34–1.92</td><td>&lt;0.001</td><td>2.40</td><td>1.94–2.99</td><td>&lt;0.001</td></tr><tr><td>S</td><td>0.83</td><td>0.67–1.04</td><td>0.11</td><td>3.44</td><td>2.68–4.40</td><td>&lt;0.001</td></tr><tr><td>NOVA</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NOVA 1</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>NOVA 3</td><td>1.36</td><td>1.14–1.63</td><td>&lt;0.001</td><td>2.21</td><td>1.78–2.76</td><td>&lt;0.001</td></tr><tr><td>NOVA 4</td><td>1.88</td><td>1.54–2.31</td><td>&lt;0.001</td><td>2.10</td><td>1.66–2.66</td><td>&lt;0.001</td></tr><tr><td>PDP</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Unprocessed</td><td>—</td><td>—</td><td></td><td>—</td><td>—</td><td></td></tr><tr><td>Processed</td><td>1.52</td><td>1.28–1.80</td><td>&lt;0.001</td><td>1.87</td><td>1.52–2.29</td><td>&lt;0.001</td></tr><tr><td>Ultra-processed</td><td>1.40</td><td>1.09–1.80</td><td>0.009</td><td>4.18</td><td>3.24–5.40</td><td>&lt;0.001</td></tr></tbody></table>

PNQ: Perceived Nutritional Quality; PDP: Perceived Degree of Processing; OR: odds ratio; CI: confidence interval;

Reference for the dependent variable: high PNQ; references for the independent variables: NOVA 1, Nutri-Score A–B, MFD, unprocessed PDP

groups studied, MFD was considered to have the highest nutritional quality.

With respect to the degree of processing, NOVA 4 products were more likely to be considered poor in terms of nutritional quality than NOVA 1 products (OR = 2.10). The same was true for NOVA 3 products compared with NOVA 1 products (OR = 2.21). In addition, products perceived as ultra-processed were more often categorized as low nutritional quality than products perceived as unprocessed (OR = 4.18).

Starches were less likely to be considered high (OR = 0.83) and more likely to be considered poor (OR = 3.44) in terms of nutritional quality than MFD. This is all the more noticeable since our product sampling included 3 starches of Nutri-Score AB and one of Nutri-Score DE, while only 2 MFD had a Nutri-Score AB and 3 had a Nutri-Score DE. Considering this repartition, this effect of food group on PNQ seems quite strong, and we can reasonably interpret it further in the discussion section.

## 4. Discussion

### 4.1. Relationship between consumer categorization and established categories

Our first objective was to compare French consumers' categorization of food by nutritional value and degree of processing with the categories established by the scientific community. Our results showed that participants' categorization of products as ultra-processed was aligned quite well with that established by the scientific community (NOVA). Products belonging to the NOVA 1 and NOVA 4 classes were clearly differentiated by the participants, and the NOVA 4 class was a strong predictor of participant categorization of products as ultra-processed. Food processing encompasses a wide range of unit operations (Forde & Decker, 2022), from those usually carried out at home and practiced for millennia (e.g., peeling, cooking) to those more typical of modern large-scale industrial food production (e.g., twin-screw extrusion). The difference between the three levels of processing appeared to be understood to some extent by the participants in our study, but NOVA 3 products were categorized twice as often as ultra-processed than as unprocessed/minimally processed. This result suggests that participants tended to over-estimate the degree of processing of NOVA 3 products.

The results for participant categorization of products by nutritional
quality appear to follow a similar trend. Participants identified quite well as being of low NQ products classified as Nutri-Score D-E. But Nutri-Score C products were more evenly distributed among the three categories, (high, medium, and low NQ) suggesting that participants had blurred representations of the medium level of nutritional quality.

Several studies have shown that consumers are able to judge the nutritional quality and degree of processing of foods when information about these food properties is provided. A nutritional front-of-pack label (e.g., Nutri-Score) can orient consumers' food choices toward the most nutritionally favorable products, suggesting that consumers understand the information conveyed by the Nutri-Score and are able to draw on this information when making food choices (Ducrot et al., 2022; Fialon et al., 2022). Similarly, it has been suggested that the NOVA principles and the definition of ultra-processing are understood (Ares et al., 2016) and consumer categorization of products by degree of processing is improved after following education intervention about NOVA principle (Nazmi et al., 2019). These studies measured consumers' ability to assess food nutritional quality and degree of processing when information about these properties was provided. Few studies have so far measured consumers' ability to correctly assess food properties in the absence of any such information. Motoki and Saito recently demonstrated that consumers could accurately estimate the caloric content of different foods presented as images (Motoki & Saito, 2023). Two other studies, one in Brazil and one in Switzerland, showed that the degree of processing attributed by consumers to a series of food products through either a categorization or a rating task was aligned with NOVA classification of the product (Hässig et al., 2023; Menegassi et al., 2019).

Our results are consistent with these findings, suggesting that French consumers are able to gauge the nutritional quality and degree of processing from food product images containing no information about these properties. Through exposure to Nutri-Score ratings over the past 6 years, French consumers may well have built mental representations of food NQ. By contrast, in France, no information about NOVA class is provided on food packaging. Somewhat surprisingly, the participants in our study were nevertheless able to estimate degree of processing quite accurately.

## 4.2. Factors other than Nutri-Score influenced representations of nutritional quality

Our second objective was to investigate whether non-nutritional cues could influence consumers' categorization of products by their nutritional quality. The odds ratios obtained in the two models of multinomial regression used in our study showed a major difference in the representations of nutritional quality and degree of processing. While participant categorization of ultra-processed foods seemed to depend mainly on the NOVA class, the categorization of nutritional quality was influenced by other factors as much as by the Nutri-Score (the maximum odds ratios for Nutri-Score, food group, and perceived degree of processing were 4.15, 3.44, and 4.18, respectively). This result might reflect the multidimensional character of participants' categorization of NQ, as their assessment was based not only on nutritional quality but also on degree of processing and food group.

### 4.2.1. Effect of degree of processing on participants' representations of nutritional quality

The participants in our study generally considered NOVA 3 and NOVA 4 products to be of lower nutritional quality than NOVA 1 products. Any form of processing appears to be associated with a negative nutritional valence. Surprisingly, participants' categorization of food products by nutritional quality was influenced more by their representations of the degree of processing of a product than by its NOVA class. In fact, perceived nutritional quality was influenced as much by the perceived degree of processing as by the Nutri-Score level of a given food product. Products perceived as ultra-processed were considered to be of poorer nutritional quality than products perceived as

being unprocessed or minimally processed. In the minds of the participants, degree of processing thus appears to be considered as an indicator of nutritional quality. Gomez (2013) previously reported from interviews that consumers tend to rely on the degree of processing (raw vs. industrialized) of food products to form judgments about their nutritional quality. Yet a meal based on minimally processed products is not necessarily better in terms of nutritional quality than a meal based on ultra-processed products (Hess et al., 2024). The link between nutrition and processing in consumer representations can therefore be an obstacle to a more nutritious diet.

Several cognitive mechanisms could explain these blurred representations of nutritional quality. To begin with, dietary guidelines increasingly convey multiple messages. In particular, the simultaneous presence of information about nutritional value and degree of processing in the same dietary guidelines can create an association between these properties in consumers' minds (Quinn et al., 2021), thus contributing to an overestimation of the similarities between these two criteria, leading to a form of assimilation bias. Having observed in a cross-cultural study a stronger association between ultra-processing and unhealthiness in Brazilian respondents than European respondents, Bolhuis and co-workers (2022) suggested that references in national dietary guidelines to industrial processing may contribute to reinforce the association between processing level and perceived healthiness.

Furthermore, nutritional quality is a complex concept (Gomez, 2013), and categorization by nutritional quality may thus be quite challenging for participants. To reduce the difficulty of the task, they may have used heuristics as shortcuts to facilitate decision-making (Wang & Begho, 2024) by categorizing products based on a more easily accessible criterion. In a free sorting task, French participants spontaneously separated food products by degree of processing and food group (Varela et al., 2022). Degree of processing has been shown to be used spontaneously by French people to categorize food products (Rozin & Holtermann, 2021). These results demonstrate that degree of processing and food group are more easily accessible to consumers than nutritional quality, which might explain why the participants in our study took these factors into account in their categorization of nutritional quality.

Another explanation for our results could be that nutritional quality and degree of processing may both be included in representations of the concept of healthiness (Lazzarini et al., 2016). People rarely discriminate between claims about healthiness or nutritional quality (Hodgkins et al., 2019; Wang & Begho, 2024); their assessments of healthiness and degree of processing can be well correlated (Hässig et al., 2023); they spontaneously take industrial processing into account when evaluating food healthiness (Bolhuis et al., 2022; Machín et al., 2020). The results of these studies suggest that “healthiness” is an all-encompassing term associating nutritional quality and degree of processing among other factors.

The relationship observed in our study between perceived nutritional quality and perceived degree of processing might even result from a “halo effect.” A halo effect is a “cognitive bias in which people form an opinion about one feature of a product attribute based on their (positive or negative) bias towards another attribute” (Nicolau et al., 2020). In our study, products perceived as “bad” due to their degree of processing were also perceived as “bad” in nutritional quality. The initial negative impression about the degree of processing (more easily accessible to participants) might therefore have resulted in a negative assumption regarding nutritional quality.

### 4.2.2. Effect of food group on representations of nutritional quality

Our results also showed that participants appeared to associate a specific level of nutritional quality with each food group. Meat, fish and dairy products (MFD) were more likely than starchy foods to be classified as products of high nutritional quality. Gomez (2013) used interviews to explore the strategies used to assess food nutritional quality. This study reported that participants tended to base their judgment on
non-nutritional properties such as product group. Participants tend not to categorize products individually, but groups of products (e.g. snacks, plant-based products) according to a dichotomous healthy or unhealthy point of view. A product will be categorized not for its own characteristics, but with reference to the group to which it is assimilated. In France, the consumption of starchy foods is below the recommended amount, particularly for people on weight-loss diets (Szabo De Edelenyi et al., 2014). A study carried out in UK showed that starchy foods are indeed considered high in energy, and their consumption can be seen as an impediment to weight control (Stubenitsky & Mela, 2000). These representations of starches as “fattening foods” could explain their categorization as low nutritional quality in our study. The associations between food group and nutritional quality could also develop through exposure to nutritional messages conveyed by institutional communication tools or mainstream media. In 2002, the National Nutrition and Health Program (PNNS) still recommended consuming animal-based products (MFD) once or twice a day (INPES, 2002). Animal-based products are promoted as containing all the essential amino acids that the human body cannot produce (ANSES, 2013), and red meat is known to be rich in highly absorbable iron. This might explain why the participants often considered MFD of higher nutritional quality than other food groups. Despite more recent dietary guidelines advising consumers to limit meat consumption (French Ministry of Health and Prevention, 2019), previous dietary recommendations appear still to be firmly anchored in the minds of French consumers. People are exposed to dietary guidelines throughout their lives, and the messages conveyed by these guidelines participate in shaping mental representations that in turn lead to food categorization. Our results suggest that it is difficult to deconstruct previously formed representations.

### 4.2.3. Strengths and weaknesses

In our study, attention was particularly focused on participants' food categorizations in the absence of any informational context, which were compared with the categorization criteria established by the scientific community. We consider our study to be robust, as two hundred participants completed the sorting task.

The choice of scientific classification systems (Nutri-Score and NOVA) was crucial, and the choice of a different system might perhaps have influenced the closeness of the relationship between “established” and “perceived” properties. This is particularly true for the degree of processing, for which several classifications exist, none of which has so far achieved consensus among the scientific community. The choice of an established categorization for degree of processing was based on popularity: we chose the indicator that consumers were most likely to know. The NOVA system is sometimes criticized for being ambiguous and not sufficiently robust. One of the criticisms is that the classification is based on two dimensions: the number of ingredients and the type of industrial process. Some scientists have recommended developing a more reliable system, based on a reproducible algorithm to classify foods according to their degree of processing only.

It is impossible to perfectly balance all the factors under study in real-life conditions, using only products available on the market. In this study, the product selection method was designed to balance the NOVA and Nutri-Score categories, so that all possible combinations of the three different levels for each of these two indicators were represented by two or three products. In previous studies, product sampling did not seek to represent every possible combination between NOVA and Nutri-Score. Thus, in one study, the Nutri-Score level of each unprocessed product was better than that of each UPF product (Hässig et al., 2023). Another study sought to include a broader range of nutritional quality for UPF products, but product sampling still lacked any minimally processed products of poor nutritional quality (Bolhuis et al., 2022).

Once the NOVA and Nutri-score categories were balanced, it proved impossible to achieve the same balance for the three food groups. The resulting unavoidable imbalance in product sampling with regard to food group thus requires some degree of caution in the interpretation of

the impact of food group on the categorization of nutritional quality. Further studies will thus be necessary to consolidate the interesting results obtained here.

## 5. Conclusion

The participants' categorization of food products by degree of processing was quite well aligned with that proposed by the scientific community. For nutritional quality, however, the categorization was multidimensional. In particular, the perceived degree of processing was taken into account in the categorization of food products by nutritional quality. This blending of the concepts of nutrition and processing in consumer representations may have a negative impact on the adoption of a healthier diet. As degree of processing is a more easily understood concept than nutritional quality, there is a risk that this criterion will be used as an indirect indicator to select foods of better nutritional quality. Yet minimally processed products do not automatically guarantee adequate nutritional content. Dietary guidelines should be adapted in order to take into account this potential confusion. Food groups also impacted the categorization of nutritional quality in our study, and this result requires further investigation. Our study of this point was exploratory, as it was carried out with simple products containing few ingredients representing a single food group. Our results need to be confirmed by studies using a broader range of products, including raw foods and composite ready-to-eat foods.

## CRediT authorship contribution statement

Jessy Youssef: Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. Stéphanie Chambaron: Writing – review & editing, Project administration, Methodology, Funding acquisition, Conceptualization. Laurence Dujourdy: Writing – review & editing, Data curation, Conceptualization. Gaëlle Arvisenet: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgement

Carmela Chateau is particularly acknowledged for English proofreading.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodqual.2025.105609.

## Data availability

Data will be made available on request.

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