## Safety

Most African respondents associated “artificial meat” with safety, like Chinese consumers (25). That is, they consider “artificial meat” as a safe product. This is because livestock has been associated with several diseases since in many countries there are no refrigerated facilities to store meat in safe conditions. In Africa, the number of zoonotic disease outbreaks increased between 2012 and 2022 (68). The safety of animal-sourced food is therefore becoming a major concern for local populations and policy makers. However, there are still several uncertainties and limited scientific literature on the safety of “artificial meat” (15, 69).

The emphasis on safety may also stem from the 2014–2016 Ebola outbreak and the global COVID-19 pandemic. This pandemic helped raise awareness of the risk of emerging diseases related to animal exploitation. Africans could therefore perceive “artificial meat” as a way to limit these outbreaks in a context of weak health systems. However, there are still some doubts as to the safety of “artificial meat,” such as the potential addition of chemicals to the culture medium, resulting in a “chemical” meat with potential negative consequences on consumer acceptance (67, 70). Deregulation of cell lines may also occur (as in cancer cells), which may lead to unknown effects on human health (67).

## Contributions and limitations of this work and future research

The answers to the questions in any survey are likely to depend on the wording and sequencing of the questions, which may influence the conclusions of the survey. Many studies have also been conducted by “artificial meat” promoters with clear objectives to influence consumers or accelerate marketing. However, our results could still be relevant to providing useful information for understanding the drivers or barriers to “artificial meat” acceptance by African’s rapidly urbanizing citizens. More relevant information could be derived from comparing results obtained with the same experimental design across countries, such as France (19), Brazil (23) and China (25), or across similar social groups (within the same study as in this work). More importantly, relevant studies on “artificial meat” are rare in Africa which makes contextual interpretation difficult.

In addition, there is clearly a strong generational effect, particularly with regard to ethical and environmental issues of livestock production, meat consumption, and therefore the overall perception of meat substitutes, including “artificial meat.” Moreover, young, well-educated people are likely to have a clearer idea of the potential and limits of science. All these facts may explain why older people may reject “artificial meat” less as shown in this work, or more, as shown in Germany and France (41), and in Europe (71). Consequently, targeting young, well-educated people as in this survey may better indicate the trends of “artificial meat” acceptance in the future, while underestimating or overestimating this potential acceptance at present.

Generally speaking, opinion surveys must be interpreted appropriately because to their limited representative character. However, this drawback can be partially offset by the consequent size of the sample and the duplication of our survey, which makes it possible to identify the major factors likely to explain, at least in part, the variability in WTT, WTE and WTP for “artificial meat,” and to analyze different segments of the population.

Finally, due to the importance of implicit attitudes that are difficult to capture, a recent survey illustrated the inadequacy of relying on self-reported measures when seeking to capture consumer

opinions on unfamiliar products such as “artificial meat” (72). However, again, despite these limitations, comparing results obtained with the same experimental design between countries or between similar social groups (within the same study as in this work) is likely to provide useful information.

## Conclusion

Meat consumption in Africa is set to increase due to population growth, urbanization and income. This high demand will put pressure on a livestock industry that is not yet efficient in terms of productivity. Despite the small sample sizes of this survey, insights could still be gleaned from the results to get a broad view of the cultured meat perspective in the countries studied. As African countries vary in terms of consumer behavior, culture, tradition and demographics, the potential perception of the challenges facing livestock production and meat consumption is likely to vary accordingly. A strategy must therefore be tailored to each country. Price will be one of the main drivers in acceptance of “artificial meat” by several African countries. Therefore, policy makers, governments and local investors will have to make a choice to direct their financial help towards supporting more efficient meat production in order to meet the increasing demand of meat. Several options could be considered; investing in making livestock production more efficient and sustainable, investing in other animal protein alternatives such as protein from insects which are already widely consumed in many African countries, or investing in the production of meat alternatives such as plant-based meat or “artificial meat.” This choice will be reinforced by Africa’s limited investment capacity compared to richer countries.

## Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

## Author contributions

MK: survey, data analysis and draft writing. SC, M-PE-O, JL and J-FH: survey design. J-FH: supervision. All authors contributed to the article and approved the submitted version.

## Acknowledgments

The authors acknowledge the company KASI Insight for their contribution.
## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated

organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1127655/full#supplementary-material

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EDITED BY

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University of Helsinki, Finland

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Helen Vaikma,
Center of Food and Fermentation Technologies
(CFFT), Estonia
Aylin Sahin,
University College Cork, Ireland

*CORRESPONDENCE

Alissa A. Nolden

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anolden@umass.edu

RECEIVED 28 March 2023

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CITATION

D'Andrea AE, Kinchla AJ and Nolden AA (2023) A comparison of the nutritional profile and nutrient density of commercially available plant-based and dairy yogurts in the United States. *Front. Nutr.* 10:1195045. doi: 10.3389/fnut.2023.1195045

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© 2023 D'Andrea, Kinchla and Nolden. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A comparison of the nutritional profile and nutrient density of commercially available plant-based and dairy yogurts in the United States

Astrid E. D'Andrea, Amanda J. Kinchla and Alissa A. Nolden*

Department of Food Science, University of Massachusetts Amherst, Amherst, MA, United States

**Introduction:** Plant-based yogurts are sustainable alternatives to dairy yogurts, but a nutritional comparison of plant-based yogurts within the context of dairy yogurts has not yet been applied to commercially available products in the United States. Dairy yogurts provide significant dietary nutrients, and substituting plant-based yogurts may have unintended nutritional consequences. The objective of this study was to compare the macronutrient and micronutrient values of commercially available plant-based and dairy yogurts launched between 2016 and 2021.

**Methods:** Nutritional information for yogurts were collected through Mintel Global New Products Database, and products were categorized according to their primary ingredient. Regular-style yogurts (n=612) were included in this study: full-fat dairy (n=159), low and nonfat dairy (n=303), coconut (n=61), almond (n=44), cashew (n=30), and oat (n=15). We utilized the Nutrient Rich Foods (NRF) Index, a comprehensive food guidance system that assigns a score based on the nutrient density of individual foods. This allowed us to compare the nutritional density of the yogurts based on nutrients to encourage (protein, fiber, calcium, iron, potassium, vitamin D) and nutrients to limit (saturated fat, total sugar, sodium).

**Results:** Compared to dairy yogurts, plant-based yogurts contained significantly less total sugar, less sodium, and more fiber. However, plant-based yogurts contained significantly less protein, calcium, and potassium than dairy yogurts. The yogurts were ranked from the highest to lowest nutrient density based on the NRF Index as follows: almond, oat, low and nonfat dairy, full-fat dairy, cashew, and coconut. Almond yogurts scored significantly higher than all other yogurts, indicating the highest nutrient density.

**Discussion:** The highest NRF scores were awarded to almond and oat yogurts, likely a result of their low levels of total sugar, sodium, and saturated fat. By applying the NRF model to plant-based and dairy yogurts, we have identified opportunities for the food industry to improve the formulation and nutritional composition of plant-based yogurts. In particular, fortification is an opportunity to improve plant-based yogurt nutritional properties.

KEYWORDS

nutrient composition, nutritional profile, fortification, ingredients, micronutrients, protein, plant-based yogurt, macronutrients
# 1. Introduction

The world population is rising and is estimated to reach 9.7 billion by 2050 (1). To sustain this growth, the global food supply chain will require a substantial increase in energy and resources (2). However, these resources are impacted by climate change. The effects of climate change are particularly threatening to the livestock industry: rising temperatures can induce heat stress and thus lower productivity and fertility; emerging vector-borne diseases threaten the population; and climate variability affects the availability of crops for feed (3). In addition to the potential uncertainty of available resources due to climate change, the sustainability of the agricultural food supply must be improved (4). The dairy industry is one critical food production sector with a considerable impact on the environment due to greenhouse gas emissions, use of water resources, and large land requirements (5). For example, the global dairy industry was estimated to have emitted 1,711.8 million tons of CO₂ equivalent in 2015, primarily from enteric fermentation and emissions from feed production and manure management (3). European dairy farms have been estimated to contribute to approximately 80% of the total carbon footprint of dairy products (6). Livestock is directly responsible for 38 and 4% of the United States CH₄ and NO₂ emissions, respectively (7). Despite the dairy industry's effect on the environment, global cow milk production has grown approximately 2.8% per year between 2005 and 2015 (3).

In the U.S., approximately 6.3% of all dairy consumed is in the form of yogurt (8). In 2021, consumers spent $9,246 million on yogurts and yogurt drinks (9). In comparison, the plant-based yogurt market is valued at roughly $1,600 million and is expected to grow to more than $6,500 million by 2030 (10). Within this category, plant-based yogurts have been positioned as a more sustainable alternative to dairy yogurt. Further, plant-based yogurt production emits fewer greenhouse gases and requires less land than the production of dairy yogurts (11, 12). Increased environmental consciousness has been identified as a driver for adopting a plant-based diet (13). However, despite the appeal of the environmental benefits of a plant-based diet, this may not translate to consumer motivation to purchase specific products, like plant-based yogurt. A Mintel report (9) found that among American yogurt buyers, fewer than one-half (42%) perceived plant-based yogurt as more environmentally friendly than dairy yogurt. However, "trying to eat healthier" was the primary motivation for anticipated increased household yogurt consumption (9). Therefore, it has been recommended that plant-based yogurt brands specify the health benefits of their products' ingredients to attract consumers (9). This presents a challenge: dairy yogurt is considered a nutrient-rich food that offers high quantities of desirable nutrients and, dependent on the type, relatively low amounts of fat and sugar (14). When developing plant-based yogurts, it is important to consider the overall nutritional profile since consumers may utilize these products as a direct substitute for dairy products.

Previous studies have examined the nutritional properties of commercially available plant-based yogurts in the United Kingdom (15), western United States (16), European Union (17), Norway (18), Greece (19), Ireland (20), and across multiple countries (21). Some plant-based yogurt's nutritional values were found to be significantly different from dairy yogurt's nutritional values. Significantly different macronutrients have included energy (15,

17), fat (15, 17), saturated fat (15, 19), carbohydrates (15, 17, 19), total sugar (15, 19), fiber (15, 18, 19), protein (15, 17–19), and sodium (15, 18, 19). Additionally, some macronutrients within the plant-based yogurt category were found to be significantly different from each other (16, 19). Regarding micronutrients, calcium levels in plant-based yogurts significantly differed from dairy yogurts (15).

It is important to note that only one plant-based yogurt nutrition study examined products from the U.S. market (16). While Craig and Brothers (16) provided important new insights into the comparison within the plant-based yogurt category, there was no formal comparison to dairy yogurt. The present study is the first to compare the nutritional properties of dairy and plant-based yogurts from the U.S. market. Unlike previous studies examining plant-based and dairy yogurts, here we differentiate between the fat levels of the yogurts, whereas previous studies combined full-fat, low-fat, and nonfat yogurt. This separation provides insight into comparing fat and saturated fat levels in dairy yogurts to plant-based yogurts. Additionally, we conducted nutrient profiling to assess the nutrient density of plant-based and dairy yogurt products. This profiling provides a holistic approach, taking into consideration multiple nutrients both that are desirable and those that should be limited. In this study, we utilized the Nutrient Rich Foods (NRF) Index, a comprehensive food guidance system that assigns a score to an individual food based on its nutrient density (22). While several other models have been used to assess the nutrient density of foods, the NRF was recently applied to plant-based milk (23), providing an opportunity to compare plant-based products across product categories. The NRF Index is a nutrient profiling method, and its results can inform the food industry on how products can be reformulated (24). In this regard, the plant-based industry can evaluate how the nutrient density of different plant-based yogurts compares to dairy yogurts.

One way the food industry has attempted to address nutrient deficits is through the fortification of vitamins and minerals. Studies have suggested that fortification can improve the nutrient density of plant-based yogurts (15, 25). While this strategy can be effective at increasing the nutrient content, there is also a growing trend of products with a simple ingredient list (13, 26, 27). This becomes challenging within the plant-based category because additional ingredients are needed to provide functional and sensorial properties that mimic conventional animal products (28). This study will contribute to the growing body of research on the inclusion of gums and starches and the fortification of vitamins and minerals in plant-based yogurts by examining the frequency of these additives based on ingredient lists.

As the plant-based yogurt industry continues to launch new products, the nutritional differences between the variety of plant-based and dairy yogurts must be quantified and understood. The objective of this study is to compare the macronutrient and micronutrient profiles of commercially available plant-based and dairy yogurts in the United States using Mintel Global New Products Database, a market research database. Additionally, the NRF Index is used to compare the overall nutrient density of dairy and plant-based yogurts. This information will help the food industry identify areas of opportunity in the nutritional composition of plant-based yogurts to formulate nutritionally dense products.
## 2. Methods

Mintel Global New Products Database (GNPD) was used to generate a database of dairy and plant-based yogurts. From henceforth, this paper will refer to yogurt made from cow milk as “dairy.” The database contained refrigerated yogurts, both flavored and unflavored, launched in the United States between January 2016 and January 2021.

### 2.1. Yogurt database

The final database was determined based on the inclusion and exclusion criteria (Figure 1). The database yielded 622 regular-style flavored and unflavored yogurts: 462 dairy and 160 plant-based. Yogurts were categorized according to their primary ingredient, henceforth referred to as the “base.” This was achieved through a review of the ingredient list and a supplemental web search of the product descriptions and images. Bases for dairy yogurt included full-fat (n=159), low-fat (n=288), and nonfat (n=15). Low-fat and nonfat yogurts were consolidated into one base type for statistical analysis due to the Food and Drug Administration’s (FDA) elimination of separate standards of identity for low-fat and nonfat yogurt in 2021 (29). Bases for plant-based yogurt included coconut (n=61), almond (n=44), cashew (n=30), oat (n=15), soybean (n=4), coconut and cashew blend (n=4), and flaxseed (n=2). Due to their small sample size the following yogurt bases were underpowered to analyze nutrient differences: soybean, coconut and cashew blend, and flaxseed yogurts. As a result, these products were excluded from statistical analysis.

Relevant to the present analyses, information in the final database included product, company name, description, serving size (g), ingredient list, and nutritional content. In the U.S., the Nutrition Facts Label must include the following macronutrients and micronutrients: calories, total fat, saturated fat, trans fat, cholesterol, sodium, total carbohydrate, dietary fiber, total sugars, added sugars, protein, vitamin D, calcium, iron, and potassium (30). Nutritional data in the final database thus included energy (kcal), total fat (g), saturated fat (g), carbohydrate (g), fiber (g), protein (g), total sugar (g), sodium (mg), vitamin D (mcg), calcium (mg), iron (mg), potassium (mg), and vitamin B12 (mcg). Nutrient content reported as <0.1 g or <5 mg was replaced by 0.1 g and 5 mg in the database, respectively. To account for variable serving sizes, nutrient content for all products was reported in grams, milligrams, or micrograms per 100 grams.

During the product search timeframe, the FDA approved changes to the nutrition facts and daily recommended values, which had important implications in the present analysis. In 2016, the FDA updated the Nutrition Facts label (30). Of relevance to the current analyses, these new requirements included updates to reference values used in the declaration of percent Daily Values (DV) of some nutrients (i.e., fat, total carbohydrate, dietary fiber, sodium, potassium, calcium, and vitamin D) and required declarations of micronutrient amounts (i.e., vitamin D, calcium, iron, and potassium). The first phase of modifications was required on January 1, 2020. Thus, the new Nutritional Facts label updates impacted several aspects of this study’s database. See Supplementary Table S1 for description and details on products with new and old nutrition labels and management of nutritional information for the final product database. As a result of these changes that occurred during the timeframe of the product

search, some yogurts may be missing one or more nutrients selected for analysis in the present study. Products identified in Mintel GNPD as not reporting one or more nutrients were included in the final database. A researcher performed a manual search to verify nutrients were not reported.

### 2.2. Nutrient profiling

The Nutrient Rich Foods (NRF) Index is a validated comprehensive food guidance system that assigns a score to an individual food based on its nutrient content in relation to calories (31, 32). The NRF Index’s algorithm is based on a positive sub-score for nutrients to encourage (qualifying) and a negative sub-score for nutrients to limit (limiting) (22, 24). The NRF score of an individual food is calculated by the subtraction of the negative sub-score by the positive sub-score. A higher NRF score indicates a higher nutrient density. Previous studies have used different versions of the NRF Index (23, 31, 33, 34), but it has been recommended that for each model the selection of qualifying nutrients be based on nutrients that are beneficial to health (i.e., mainly vitamins and minerals), and limiting nutrients be based on fats, sugars, and sodium (22). In this study, our NRF6.3 model was composed of a positive sub-score for six qualifying nutrients and a negative sub-score for three limiting nutrients. The qualifying nutrients and their reference amounts include protein (50 g), fiber (28 g), calcium (1300 mg), iron (18 mg), potassium (4700 mg), and vitamin D (20 mcg). The limiting nutrients and their reference amounts include saturated fat (20 g), total sugar (50 g) in accordance with previous NRF models (33, 34), and sodium (2300 mg). Each nutrient was expressed as a percentage (%DV) calculated per 100 kcal, where i = qualifying nutrients and j = limiting nutrients (Eq. 1).

$$ \text{NRF6.3} = \left( \sum_{i=1}^{6} \frac{\text{Nutrient per 100kcal}}{\text{Daily Value i}} * 100 \right) - \left( \sum_{j=1}^{3} \frac{\text{Nutrient per 100kcal}}{\text{Daily Value j}} * 100 \right) \quad (1) $$

### 2.3. Additive ingredients

To identify the use of additive ingredients, specifically, gums, starches, vitamins, and minerals, ingredient lists were examined for all products in the final product database. The frequency of each ingredient used in each base was recorded.

### 2.4. Statistical analysis

Separate one-way analysis of variance (ANOVA) models were used to compare each nutrient variable across the different yogurt bases. Tukey’s honestly significant difference (HSD) tests were used to test for significant differences in nutrient values among the yogurt bases. For the NRF Index, separate one-way analysis of variance (ANOVA) models were used to compare each score across
<figure><img src="image_22.png" /><figcaption>FIGURE 1</figcaption></figure>

Flow diagram visually depicting the creation of the final database showing step by step inclusion and exclusion criteria. "Product names and/or descriptions that included "mix," "topping," "crunch," "crisp," "streusel," "piece," "slices," or "granola." "Products with ingredient layers or described as "on the bottom." "Kefir and products with missing nutrition facts labels." "Packages with individual products combined in a secondary packaging (variety packs) were considered separate products."

the different yogurt bases. Tukey's honestly significant difference (HSD) test was used to test for significant differences in macronutrient and NRF6.3 scores among the yogurt bases, at value of *p* ≤0.001, while *p*≤0.002 was considered significant for micronutrient analysis. Statistical analysis was performed in R (version 4.1.2).

# 3. Results

## 3.1. Macronutrients

We analyzed the energy, total fat, saturated fat, carbohydrates, fiber, total sugar, and protein content across 6 different yogurt bases
(full-fat dairy, low and nonfat dairy, coconut, almond, cashew, and oat). Significant differences (*p* ≤ 0.001) among yogurt bases were observed for all macronutrients examined (Figure 2 and Supplementary Table S2). A Tukey HSD post-hoc test was conducted to reveal differences between product categories. The energy (kcal/100g) of the yogurts in the database ranged from oat yogurts' 64.2 g to coconut yogurts' 114.2 g. For energy density, full-fat dairy contained more calories per 100 g compared to low and nonfat dairy yogurt. For plant-based yogurts, coconut contained significantly more calories than both dairy groups. Oat yogurt contained significantly fewer calories than full-fat dairy but was not different than the low and nonfat dairy group. Almond and cashew plant-based yogurts were not significantly different from full-fat dairy but were significantly higher than low and nonfat dairy yogurts.

Total fat content was significantly different across product
groups. The low and nonfat dairy yogurts and oat yogurts
contained the lowest amount of total fat. Coconut and almond
yogurts contained significantly more total fat, and full-fat dairy
and cashew contained an intermediate amount of total fat.
Coconut yogurts contained significantly more saturated fat,
followed by full-fat dairy yogurts. There was no difference in

saturated fat content among low and nonfat dairy, almond, cashew,
and oat yogurts.

For carbohydrate content, we observed the least amount of
difference compared to other macronutrients. Low and nonfat yogurts
contained significantly more carbohydrates than full-fat dairy.
Coconut and almond yogurts were not significantly different from
full-fat dairy, and cashew and oat yogurts were not significantly
different from either dairy yogurt category. In other words,
we observed no significant difference in carbohydrate content among
the four categories of plant-based yogurts, which were not different
from the full-fat dairy yogurts.

The average protein content for dairy yogurts was roughly 4.2 g per 100 g. There was no difference in protein content between full-fat and low and nonfat dairy yogurts. In comparing plant-based yogurts to dairy yogurts, almond yogurts were found to have a similar protein content as full-fat dairy yogurts but were significantly less than low and nonfat dairy. All other plant-based yogurts contained significantly less protein, with coconut containing significantly less protein than all other products (approximately 1.2 g per 100 g).

The average total sugar content across all product groups ranged
from approximately 5 g to almost 10 g per 100 g. There was no

<figure><img src="image_23.png" /><figcaption>FIGURE 2
A comparison of macronutrients by yogurt base (p≤0.001; values are reported as mean+SD). Different letters within a macronutrient indicate significant differences between yogurt bases. For every macronutrient, we analyzed full-fat dairy (n=159), low and nonfat dairy (n=303), coconut (n=61), almond (n=44), cashew (n=30), and oat (n=15) yogurts.</figcaption></figure>
difference in sugar content among plant-based products (5.3 g to 6.8 g per 100 g), but they contained significantly less sugar compared to both dairy yogurt groups. There was no significant difference in sugar content among dairy yogurts (9.4 g per 100 g for full-fat and 9.3 g per 100 g for low and nonfat).

Almond yogurts contained the highest amount of fiber, which was significantly different from all other yogurt bases. Dairy yogurts contained the least amount of fiber, which was significantly different from all plant-based yogurts (less than 1.2 g per 100 g). Coconut, cashew, and oat yogurts contained intermediate fiber values.

## 3.2. Micronutrients

Analysis of variance was conducted for the following micronutrients: sodium, potassium, calcium, vitamin D, vitamin B12, and iron across 6 yogurt bases (full-fat dairy, low and nonfat dairy, coconut, almond, cashew, and oat). It is important to note that potassium and vitamin D were not required to report on the Nutrition Facts Label before the FDA's label update. Additionally, vitamin B12 is currently not required to report. Significant differences ($p \le 0.002$) among yogurt bases were observed for all micronutrients examined (Figure 3 and Supplementary Table S1). There were significant differences in the sodium content across yogurt bases. Full-fat (52 mg per 100 g) and low and nonfat dairy yogurts (53 mg per 100 g) contained significantly more sodium than all plant-based yogurts. Almond, cashew, and oat yogurts contained significantly less sodium than coconut yogurts. Coconut yogurts contained sodium levels significantly different than all other product categories. Full-fat and low and nonfat dairy yogurts contained significantly more potassium, except for cashew, which was not significantly different from low and nonfat dairy yogurts. Coconut, almond, and oat yogurts contained significantly less potassium. Both dairy yogurt bases contained no difference in the amount of calcium, which was significantly higher than plant-based yogurts. Coconut contained the most amount of calcium among the plant-based yogurts, which was significantly higher than almond, cashew, and oat. There were significant differences in vitamin D content between full-fat and low and nonfat dairy yogurts; however, there were no significant differences between plant-based yogurts. Only 35 yogurts reported vitamin B12 content; these yogurts included full-fat dairy, low and nonfat dairy, oat, and coconut. No products within the almond or cashew groups reported vitamin B12 content. Coconut contained the highest amount of vitamin B12 but was not statistically different from full-fat dairy or oat yogurt. Low and nonfat dairy contained the least amount of vitamin B12 but was not statistically different from full-fat dairy yogurt. Coconut, almond, and cashew yogurts contained significantly more iron than dairy yogurts and oat yogurts.

## 3.3. Nutrient profiling of dairy and plant-based yogurts

For our NRF6.3 model, the following nutrients had to be reported on the product's label to calculate the NRF score: protein, fiber, calcium, iron, potassium, vitamin D, saturated fat, total sugar, and sodium. Of the 612 yogurts included in this study's final database, 275 were removed from the NRF model calculations because products did not report one or more nutrient values required for the NRF

calculation (See Table 1 for the number of products in each yogurt base). The remaining 337 yogurts were assigned an average score based on their nutrient density (Table 1). NRF6.3 scores ranged from -22.26 to 15.21, and from highest to lowest were almond, oat, dairy low and nonfat, dairy full-fat, cashew, and coconut. A significant difference ($p \le 0.001$) was observed for the NRF scores across yogurt bases. Almond yogurts had a significantly higher score than all other yogurts except for oat yogurts. Coconut yogurts had a significantly lower score than all other yogurts except cashew yogurts. Mean NRF6.3 scores in relation to the mean energy density of each yogurt base were mapped (Figure 4). Coconut yogurts had the highest energy density and lowest NRF6.3 score. Oat yogurts had the lowest energy density and the second highest NRF6.3 score.

## 3.4. Additive ingredients in dairy and plant-based yogurts

Examining ingredient statements revealed a variety of gums and starches used in this study's dairy and plant-based yogurts. The frequency of the gum and starch is reported in each product base (Table 2). Yogurts may have contained a combination of multiple gums and starches. Approximately 88.7% of plant-based yogurts and 64.9% of dairy yogurts contained at least one gum. Alternatively, only 17 of the 150 (11.3%) plant-based yogurts did not contain any gums; these yogurts were all coconut yogurts except for one almond yogurt. The most used gum across all yogurts was pectin, present in 308 of the 612 yogurts (50.3%). 67 (14.5%) dairy yogurts contained carrageenan, but this gum was absent in all plant-based yogurts. Corn starch was present in 199 (32.5%) of all yogurts. Both potato and rice starch were used in some plant-based yogurts but were absent in dairy yogurts. Tapioca/cassava starch was used most often in plant-based yogurts, and a majority of cashew and almond yogurts contained this starch.

Additionally, a variety of vitamins and minerals were additive ingredients present in this study's dairy and plant-based yogurts (Table 3). The addition of calcium salts was more common in plant-based yogurts than dairy yogurts, with 36.7% of plant-based yogurts fortified with calcium while 22.9% of dairy yogurts were enriched with calcium. Calcium citrate and tricalcium/calcium phosphate were most used for both dairy and plant-based yogurts. Vitamin B12 was not present in any dairy yogurts but was used in 41.0% of coconut yogurts. 234 (50.6%) dairy yogurts were enriched with vitamin D3. Vitamin D2 was the most used form of vitamin D in plant-based yogurts. Vitamin E was present in only 3 (5%) coconut yogurts and 4 (1%) low and nonfat yogurts.

## 4. Discussion

Our findings are consistent with prior work, which demonstrates significant nutritional differences between dairy and plant-based yogurts (15, 17, 18). Despite the growing popularity of plant-based dairy alternatives, the plant-based yogurt category contains variable nutritional compositions in comparison to dairy yogurt. There is an opportunity for the plant-based industry to formulate yogurts that are more nutritionally similar to dairy yogurts. We observed that these nutritional differences in plant-based yogurts are partly due to the use of a variety of ingredients, which help to deliver desirable sensory and textural properties.
<figure><img src="image_24.png" /><figcaption>FIGURE 3</figcaption></figure>

A comparison of micronutrients by yogurt base (p≤0.002; values are reported as mean±SD). Different letters within a micronutrient indicate significant differences between yogurt bases. Some products did not report all micronutrients. The following bases (full-fat dairy, low and nonfat dairy, coconut, almond, cashew, and oat, respectively) reported sodium (159, 303, 61, 44, 30, 15); potassium (94, 213, 31, 27, 11, 15); calcium (159, 303, 60, 44, 30, 15); vitamin D (79, 258, 36, 36, 4, 13); vitamin B12 (1, 5, 25, 0, 0, 4); and iron (159, 303, 60, 44, 30, 14).

## 4.1. Macronutrients

Six studies and one review have previously examined or analyzed the nutritional properties of plant-based yogurts (15–21). In three of the five studies (15, 16, 19–21) that examined nutritional differences between plant-based yogurt bases, coconut yogurts contained the

highest number of calories (15, 16, 21). Our results are in line with Clegg and colleagues (15), as coconut yogurts were determined to contain significantly more calories than dairy yogurts.

Previous studies comparing the nutritional composition of dairy and plant-based yogurt consolidated full-fat, low, and nonfat dairy yogurt into one dairy category. As a result, this limited the
understanding of how plant-based yogurts compare with dairy yogurts. The present study separated full-fat from low and nonfat dairy yogurts, which resulted in the identification of significant differences in the fat content. For example, coconut yogurts contained significantly higher fat and saturated fat levels than both full-fat and low and nonfat yogurts. On the other hand, low and nonfat yogurts were most similar in saturated fat levels to almond, cashew, and oat yogurts. Coconut yogurts contained significantly higher fat and saturated fat levels than both full-fat and low and nonfat yogurts. This study demonstrates that combining dairy products with varying fat levels into a single category increases the variability within the category and could mask nutritional differences when comparing them to plant-based alternatives (15, 17–19).

In terms of carbohydrates, low and non-fat dairy yogurts contained the highest carbohydrate levels but were not significantly different from cashew and oat yogurts. This contrasts with a previous study, which reported coconut yogurt to have a significantly higher amount of carbohydrates than dairy yogurt (15). Here, almond yogurts contained significantly higher fiber levels than all other yogurts, which supports findings from a previous study (16). Similar

to other studies, dairy yogurt fiber levels were significantly lower than plant-based yogurts (18, 19).

Total sugar in all plant-based yogurts was found to be significantly less than full-fat and low and nonfat dairy yogurts. In comparison to previous studies, total sugar results were variable across yogurt products. For studies that compared all dairy yogurts to all plant-based yogurts, the two groups were significantly different in one study (19) which contrasts with two other studies that reported no difference (17, 18). Clegg and colleagues (15) differentiated between plant-based yogurts and found nut-based (i.e., cashew or almond) yogurts to have significantly less total sugar than dairy, coconut, and soy yogurts. The sweetness of plant-based yogurt may provide an important role in masking unpleasant sensory attributes of plant proteins (35). Ingredients such as fruit purees, syrups, and sweeteners may be added (36) to increase the sensorial acceptance of commercial plant-based yogurts. Interestingly, there was no difference in sugar content among plant-based yogurts nor among dairy yogurts. The average sugar content of all plant-based yogurts was approximately 6 g per 100 g while the average sugar content for dairy yogurts was approximately 9.5 g per 100 g. Therefore, plant-based products may provide advantages over dairy yogurts when considering the sugar content.

All studies that compared plant-based yogurts to dairy yogurt found protein levels to be significantly different (15, 17–19). Dairy yogurts contained the highest protein content, except in one review that reported the average protein in soy yogurts to be higher (21). In the present study, low and nonfat dairy yogurts contained significantly higher protein levels than all other yogurts. Additionally, plant-based proteins do not meet the definition of a complete protein because they are missing essential amino acids. To address this limitation, one strategy is to create hybrid or blended products that combine dairy and plant protein. The incorporation of animal protein would help to

<table><caption>TABLE 1 A comparison of NRF6.3 scores by yogurt base ($p \le 0.001$; values are reported as mean $\pm$SD).</caption><thead><tr><td>Base</td><td>n</td><td>Mean $\pm$SD</td></tr></thead><tbody><tr><td>Almond</td><td>27</td><td>15.21 $\pm$ 14.50<sup>a</sup></td></tr><tr><td>Oat</td><td>13</td><td>9.95 $\pm$ 19.75<sup>ab</sup></td></tr><tr><td>Dairy Low &amp; Nonfat</td><td>196</td><td>4.79 $\pm$ 14.86<sup>b</sup></td></tr><tr><td>Dairy Full-Fat</td><td>78</td><td>-6.17 $\pm$ 10.55<sup>c</sup></td></tr><tr><td>Cashew</td><td>4</td><td>-7.37 $\pm$ 8.48<sup>cd</sup></td></tr><tr><td>Coconut</td><td>19</td><td>-22.26 $\pm$ 22.00<sup>d</sup></td></tr></tbody></table>

Different letters within the column indicate significant differences between yogurt bases.

<figure><img src="image_25.png" /></figure>
<table><caption>TABLE 2 Gums and starches present in dairy and plant-based yogurts.</caption><thead><tr><th rowspan="2">Function</th><th rowspan="2">Ingredient</th><th colspan="2">Dairy n=462</th><th colspan="4">Plant-Based n=150</th></tr><tr><th>Full-Fat (n=159)</th><th>Low &amp; Nonfat (n=303)</th><th>Coconut (n=61)</th><th>Almond (n=44)</th><th>Cashew (n=30)</th><th>Oat (n=15)</th></tr></thead><tbody><tr><td rowspan="6">Gums</td><td>Guar gum</td><td>0</td><td>5</td><td>0</td><td>1</td><td>4</td><td>1</td></tr><tr><td>Locust bean gum</td><td>41</td><td>22</td><td>26</td><td>23</td><td>26</td><td>4</td></tr><tr><td>Xanthan gum</td><td>0</td><td>29</td><td>3</td><td>14</td><td>0</td><td>0</td></tr><tr><td>Pectin</td><td>87</td><td>139</td><td>37</td><td>21</td><td>17</td><td>7</td></tr><tr><td>Agar</td><td>0</td><td>53</td><td>4</td><td>17</td><td>3</td><td>5</td></tr><tr><td>Carrageenan</td><td>1</td><td>66</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td rowspan="4">Starches</td><td>Tapioca/Cassava</td><td>10</td><td>1</td><td>9</td><td>19</td><td>26</td><td>3</td></tr><tr><td>Corn</td><td>29</td><td>150</td><td>4</td><td>5</td><td>7</td><td>4</td></tr><tr><td>Potato</td><td>0</td><td>0</td><td>3</td><td>0</td><td>0</td><td>2</td></tr><tr><td>Rice</td><td>0</td><td>0</td><td>20</td><td>0</td><td>5</td><td>0</td></tr></tbody></table>

<table><caption>TABLE 3 Vitamins and minerals present in the ingredient statement for dairy and plant-based yogurts.</caption><thead><tr><th rowspan="2">Function</th><th rowspan="2">Ingredient</th><th colspan="2">Dairy n=462</th><th colspan="4">Plant-Based n=150</th></tr><tr><th>Full-Fat (n=159)</th><th>Low &amp; Nonfat (n=303)</th><th>Coconut (n=61)</th><th>Almond (n=44)</th><th>Cashew (n=30)</th><th>Oat (n=15)</th></tr></thead><tbody><tr><td rowspan="12">Vitamins &amp; Minerals<sup>a</sup></td><td>Calcium citrate</td><td>0</td><td>4</td><td>16</td><td>11</td><td>0</td><td>4</td></tr><tr><td>Calcium/Tricalcium phosphate</td><td>1</td><td>32</td><td>14</td><td>8</td><td>0</td><td>1</td></tr><tr><td>Calcium carbonate</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>"Milk calcium"</td><td>10</td><td>22</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Calcium lactate</td><td>0</td><td>37</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Dipotassium phosphate</td><td>0</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Vitamin B12</td><td>0</td><td>0</td><td>25</td><td>0</td><td>0</td><td>4</td></tr><tr><td>Vitamin D</td><td>14</td><td>8</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Vitamin D3</td><td>23</td><td>211</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Vitamin D2</td><td>0</td><td>0</td><td>25</td><td>8</td><td>0</td><td>4</td></tr><tr><td>Vitamin E</td><td>0</td><td>4</td><td>3</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

provide all essential amino acids. However, no products in this study met this definition of a hybrid product. Moreover, there are additional benefits of developing hybrid products, including the positive impact of providing a desirable sensory appeal. Hybrid products may reduce the barriers for reluctant consumers to adopt a more sustainable diet. Indeed, a recent study reported that a blended dairy yogurt with a 25% replacement of plant-based yogurt reported no difference in liking compared to 100% dairy yogurt (37). This suggests partial replacement can help consumers shift to more sustainable options. Another opportunity to improve the protein content is a combination or blend of two or more different plant-based proteins. Four products in this study's database contained a combination of two plant-protein bases: coconut and cashew. While not included in statistical analysis, these products contained an average protein value of 1.8 g per 100 g, which

was less than all other plant-based yogurts except for coconut yogurt (1.2 g per 100 g). There are also additional strategies that could be explored for improving the protein content of plant-based yogurts, such as the utilization of pulses (21) or legumes (16).

It is evident that the previous seven studies, all published from 2020 onward, represent a growing scientific interest in the nutritional properties of plant-based yogurts. Bases included in these studies were coconut (15, 16, 19–21), almond (16, 20, 21), cashew (16, 20, 21), oat (16, 21), soy (15, 16, 20, 21), hemp (20, 21), pea (16, 21), lupin (21), and flaxseed (21). Some studies also combined bases into one category (15, 19) or categorized blends of bases (16). This diversity of bases demonstrates that a blanket statement regarding plant-based yogurts' nutrition cannot be made. The present study underscores the importance of segmenting

<footnote>aNo ingredient lists contained vitamin B2 or vitamin C.</footnote>
plant-based yogurts according to their primary ingredient. Additionally, as consumers continue to adopt a plant-based diet, it is important that these distinctions are made to recognize that not all plant-based yogurts are nutritionally equal.

## 4.2. Micronutrients and fortification of plant-based yogurts

Consumers may adopt a plant-based diet for various reasons, but perceived healthfulness has been identified as an important driver (13, 38). In addition to considering macronutrient content and energy density, it is important to compare the micronutrient content of plant-based yogurt. For example, consumers may consider dairy yogurt to be a source of some vitamins and minerals, such as calcium and vitamin B12. Results from our study differ from previous reports for some nutrients, which may be a result of the base categories. In studies that combined all plant-based yogurt bases into one category and compared to dairy yogurts (17–19), sodium content was not significantly different. In agreement with Clegg and colleagues (15), our results found significant differences in the sodium content between dairy and the different plant-based yogurt bases. However, our results indicate that both full-fat and low and nonfat dairy yogurts contain significantly higher sodium levels than plant-based yogurts. This finding contradicts findings by Clegg and colleagues (15) which reported plant-based yogurts to contain significantly more sodium than dairy yogurts.

As described above, preliminary data suggests that a diet that reduces or removes dairy may create a risk of inadequate consumption of micronutrients such as calcium, vitamin D, potassium, and vitamin B12 (25, 39). Thus, it has been recommended that plant-based dairy substitutes be fortified with micronutrients to compensate for this deficiency (25). In this section, we discuss the vitamin and mineral content alongside the number of products that enhanced their content with fortification.

Calcium levels in plant-based yogurts were significantly lower than in dairy yogurts. In our database, 36.7% of plant-based yogurts' formulations contained a calcium salt. This suggests some of the products would provide equivalent amounts of calcium, yet on average all plant-based yogurts did not provide equivalent amounts of calcium. As cow's milk is considered a food rich in calcium (14), fortification of plant-based yogurts is an opportunity for calcium content improvement. In contrast with Clegg and colleagues (15), vitamin D was significantly different amongst the yogurt bases. In this study, 24.7% of plant-based and 56.3% of dairy yogurts contained some form of vitamin D as an additive. Vitamin D fortification, like calcium, is an opportunity for plant-based yogurts to improve from a nutritional standpoint.

In the U.S., potassium is a micronutrient required to be listed on the Nutrition Facts Label (30). However, here we present the first study, both in the U.S. and internationally, to analyze and compare potassium levels in plant-based and dairy yogurts. Non-traditional diets with novel plant-based substitutes did not meet daily potassium requirements (39), so consumers who rely on plant-based yogurts to fulfill this need may find their diets lacking. Full-fat dairy yogurts and low and nonfat dairy yogurts contained an average of 171.9 mg and 157.0 mg of potassium per 100 g, respectively. Future plant-based

yogurt nutritional studies should thus include potassium. This is especially important because all plant-based yogurts had lower levels of potassium than dairy yogurts. Fortification of potassium is an opportunity to increase the potassium levels in plant-based yogurts, especially since only 9 plant-based yogurts (all coconut) were fortified with dipotassium phosphate.

Animal-based foods provide sufficient amounts of vitamin B12, whereas fortification is required to incorporate vitamin B12 to plant-based foods. Dairy yogurt is considered a good source of vitamin B12 (e.g., 0.61 mcg of vitamin B12 per 100 g of nonfat yogurt) (14). However, only 29 (19.3%) of this study's 150 plant-based yogurts were formulated with vitamin B12. Based on our micronutrient analysis, the coconut yogurts that contained vitamin B12 were found to have a significantly higher content compared to low and nonfat yogurts. Craig and Brothers (16) reported that 21.7% of the studied plant-based yogurts had vitamin B12 fortification levels that reached at least 10% DV (DV is 2.4 mcg (40)). These yogurts included coconut, coconut with another ingredient, oat, and a legume-blend. Clegg and colleagues (15) also reported on vitamin B12 content in coconut (0.38 mcg per 100 g) and soy (0.37 mcg per 100 g) yogurts. Consumers reducing dairy products from their diet may need to supplement with vitamin B12 or select plant-based dairy alternatives that include the fortification of vitamin B12. This difference highlights the importance and opportunity to improve the nutritional profile of these yogurts. While this study did not correlate the frequency of additive vitamins and minerals to micronutrient levels, further research can establish the effects of additives on micronutrient properties. Additionally, the bioavailability of plant-based ingredients is variable and is generally lower than animal-based ingredients (28). More work is needed to understand the bioavailability of these ingredients in these food matrices.

## 4.3. Application of the NRF index to dairy and plant-based yogurts

The NRF Index has previously been used to identify dairy yogurt as a highly nutrient-dense food in comparison to popular American snacks (33). However, the NRF Index has not yet been applied to plant-based yogurts. The NRF Index provides an integrative assessment of the nutrient density, expanding on the individual nutrient analysis. It is important to note the high standard deviations observed in the present study for all the NRF6.3 scores for each yogurt base, indicating high variability in the nutrient density for both dairy and plant-based yogurt bases. The NRF6.3 Index was based on a positive sub-score for protein, fiber, calcium, iron, potassium, and vitamin D; and a negative sub-score for total sugar, sodium, and saturated fat. A higher NRF score indicates a higher nutrient density. In our study, almond and oat yogurts scored higher than full-fat and low and nonfat dairy yogurts. Therefore, almond and oat yogurts are more nutritionally dense than dairy yogurts. Based on our macronutrient and micronutrient analysis, almond and oat yogurts' higher NRF scores can be attributed to their low levels of total sugar, sodium, and saturated fat. All three of these macronutrient levels were lower in almond and oat yogurts than dairy yogurts. Regarding sodium and total sugar content, almond and oat yogurts contained
significantly lower levels than dairy yogurts. Though cashew yogurts had a lower NRF score than both dairy categories, the scores were not significantly different. This indicates that cashew yogurt has a similar nutrient density to dairy yogurts. Coconut yogurt had the lowest nutrient density, likely due to its high saturated fat content and low protein levels. As almond and oat yogurts are already more nutrient dense than dairy yogurts, further product development can address any sensorial disparities compared to dairy yogurts. Coconut yogurts may need reformulation to first address their distinct nutritional profiles compared to dairy yogurts.

A previous study has used a different nutrient profiling method for plant-based yogurts (19), and a different study used the NRF Index for plant-based milk alternatives (23). Katidi and colleagues (19) utilized the Nutri-Score algorithm to assess the nutrient quality of plant-based yogurts available in Greece. They reported that most plant-based yogurts were given a higher Nutri-Score than their dairy equivalents. Drewnowski (34) utilized the NRF Index to examine the nutrient density of plant-based milk alternatives (23), which is most comparable to this study's NRF results. Drewnowski's model (NRF5.3) contained the same limiting nutrients as the present study; however, for qualifying nutrients, it included vitamin A and vitamin B12, which were not used in the present study. In our study and Drewnowski's study, the order of the NRF scores from highest to lowest was the same: almond, oat, cashew, and coconut.

While this study's NRF model provides a consistent way to compare the overall nutrient profile, which considers nutrients that should be included and limited in a food product, there are some limitations of this approach in the present study. Prior to the calculation of the NRF6.3 score, 45% of the products in the database were removed due to missing one or more macronutrients and/or micronutrients selected for the model. This was largely due to missing vitamin D and potassium values, as the FDA did not require reporting these micronutrients prior to 2020. Despite this limitation, the present analysis is the first to use the NRF Index for dairy and plant-based yogurts. The NRF Index provides an advantage when comparing the nutritional compositions of products. Rather than comparing individual nutrients of a product to another product, the NRF Index examines the overall nutrient density of a product (9).

Overall, using the NRF Index to analyze the nutritional profiles of yogurts provides valuable insight into the nutrient density of commercially available plant-based yogurts. The NRF6.3 scores provide a holistic view of the nutritional quality of the plant-based products in comparison to conventional yogurt products. This study highlights that some product categories may provide advantages regarding the overall nutrient quality, yet the industry needs to continue to consider individual macronutrients and micronutrients to improve the overall nutrient composition.

## 4.4. Additive ingredients in plant-based yogurts

Sensorial properties have been identified as barriers to plant-based dairy consumption (26). Raw plant materials are known to evoke bitter, beany, astringent, herbaceous tastes, and odors, which can be unappealing to dairy yogurt consumers (36). Specific to

plant-based yogurts, the texture was found to have a major effect on product liking (41). For example, mouthfeel at the beginning of mastication was found to have an important effect on the liking of oat yogurts (42). The poor textural profile of plant-based yogurts has been attributed to the lack of globular plant proteins' ability to mimic/recreate the molecular attributes of casein (26). The food industry and researchers have noted significant challenges in recreating the textural and mouthfeel characteristics of plant-based products (36, 43). Therefore, the addition of thickeners and structural agents (i.e., gums and starches) contributes to the viscosity of plant-based gels (44) and may create a more sensorially acceptable plant-based yogurt product.

Here, we examined the incorporation of thickening agents in both dairy and plant-based yogurts. Gums can be used in plant-based yogurts to prevent syneresis (45) and can be utilized as thickening agents (46). In our study, many plant-based yogurts used gums in their formulations; pectin and locust bean gum were the most popular. Similar to Boeck and colleagues (21) and Craig and Brothers (16), the most used gums in the plant-based yogurts were pectin (54.7%), locust bean gum (52.7%), and agar (19.3%) Like gums, starches can be used in plant-based products for gelling, binding, and stabilizing purposes (47). Similar to Boeck et al. (21), the most commonly used starch was tapioca (38.0%).

Gums and starches are included in plant-based yogurts to increase consumer acceptance by improving the sensory and mouthfeel characteristics (25, 48). However, the use of these ingredients may conflict with the recent and growing consumer trend of a "clean label" defined by a simple ingredient list and minimal processing (13, 26, 27). Overall, almond yogurt contained the second-highest total number of gums and starches in all yogurts, but the highest NRF6.3 score. Almond yogurts are the most popular plant-based yogurt purchased in the U.S. (9), suggesting that consumers may be willing to accept additive ingredients if the product is nutrient dense. Further research is needed to measure how willing consumers are to accept plant-based yogurts with a high nutrient composition but also a high additive count.

## 5. Conclusion

As consumers adopt plant-based diets for health reasons, it is important to examine the extent to which plant-based yogurt alternatives match the nutritional profile of dairy yogurts. Our analysis, based on data from Mintel GNPD for commercial products in the U.S., demonstrated that while plant-based yogurts contain less total sugar, less sodium, and more fiber than dairy yogurts, they contain less protein, calcium, and potassium than dairy yogurts. A nutrient assessment revealed that when considering key nutrients provided by dairy yogurts, oat yogurts were the most similar to low and nonfat dairy yogurts. Further, when considering the nutrient density and the energy density of commercial yogurts, oat and almond appeared to be similar or better compared to dairy yogurts, while cashew yogurts were similar to low and nonfat dairy yogurts. Coconut had the lowest nutrient density and highest energy density. Fortification with vitamins and minerals has the potential to improve the nutritional profile (e.g., calcium and vitamin B12). With the current strategy for plant-based products to be substituted for conventional dairy products, there is a need to consider the desirable nutritional benefits of dairy yogurt, such
as protein, vitamin B12, and calcium, and the minimization of nutrients such as total sugar, sodium, and saturated fat.

## Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## Author contributions

AD'A, AK, and AN contributed to the conception and design of the study, interpretation of the results, and wrote and/or contributed to all sections of the manuscript. AD'A organized the database, performed the statistical analysis, and wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.

## Funding

This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, the Center for Agriculture, Food and the Environment, and the Department of Food Science at University of Massachusetts Amherst, under project numbers MAS-00529 and MAS-00491. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the USDA or NIFA.

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## Acknowledgments

The authors would like to acknowledge the technical support provided by Katie Hilty.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1195045/ full#supplementary-material

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<figure><img src="image_26.png" /></figure>

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EDITED BY
Eugenio Aprea,
University of Trento, Italy

REVIEWED BY
Marcia Dutra De Barcelos,
Federal University of Rio Grande do Sul, Brazil
Marjeta Čandek-Potokar,
Agricultural Institute of Slovenia, Slovenia

*CORRESPONDENCE
Jean-Francois Hocquette
 jean-francois.hocquette@inrae.fr

RECEIVED 13 September 2022
ACCEPTED 17 April 2023
PUBLISHED 20 June 2023

CITATION
Liu J, Almeida JM, Rampado N, Panea B, Hocquette É, Chriki S, Ellies-Oury M-P and Hocquette J-F (2023) Perception of cultured "meat" by Italian, Portuguese and Spanish consumers.
*Front. Nutr.* 10:1043618.
doi: 10.3389/fnut.2023.1043618

COPYRIGHT
© 2023 Liu, Almeida, Rampado, Panea, Hocquette, Chriki, Ellies-Oury and Hocquette. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Perception of cultured "meat" by Italian, Portuguese and Spanish consumers

Jingjing Liu¹, João M. Almeida², Nicola Rampado³,
Begoña Panea⁴, Élise Hocquette⁵, Sghaier Chriki¹,⁵,
Marie-Pierre Ellies-Oury¹,⁶ and Jean-Francois Hocquette¹*

¹INRAE, Université Clermont Auvergne, VetAgro Sup, UMR 1213, Recherches sur les Herbivores, Saint-Genès-Champanelle, France, ²INIAV, Quinta da Fonte Boa, Vale de Santarém, Portugal, ³Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Legnaro, Italy, ⁴Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, Zaragoza, Spain, ⁵ISARA, Lyon, France, ⁶Bordeaux Sciences Agro, Gradignan, France

The aim of this study was to investigate how consumers (n = 2,171) originated from South-Western Europe (Italy, Portugal, and Spain) perceive cultured "meat" (CM) and if their demographic characteristics (origin, gender, age, education, occupation, and meat consumption) are related to their willingness to try (WTT), to regularly eat (WTE) and to pay (WTP) for CM. We found the current respondents had an initially positive attitude towards CM: 49% of them perceived CM as "promising and/or acceptable" and 23% "fun and/or intriguing" whereas 29% considered it as "absurd and/or disgusting". In addition, 66 and 25% would be willing and not willing to try CM, respectively. However, 43% had no WTE for CM and, 94% would not pay more for CM compared to conventional meat. Age and especially occupation were good indicators of consumer acceptance of CM. Respondents of 18–30 years of age had the highest acceptance. Respondents outside the meat sector had the highest WTE and people working within the meat sector had the lowest WTE, scientists (within or outside the meat sector) had the highest WTT, people not scientists but within the meat sector had the lowest WTT. Additionally, we found that men are more likely to accept CM than women, Spanish-speaking consumers had the highest WTT and WTE, people with vegan and vegetarian diets may pay more for CM but generally no more than for conventional meat. The perceptions that CM may be more eco-friendly, ethical, safe and healthy than conventional meat, and to a lower extent, the perception that current meat production causes ethical and environmental problems are likely to be major motives for the current respondents to try, regularly eat and pay for CM. On the opposite, lower perceptions of CM benefits and of conventional meat weaknesses more generally, plus emotional resistance towards CM are main barriers to accept CM.

KEYWORDS
survey, consumer, cultured "meat", acceptance, barriers, motives
Introduction

In recent years, the animal production industry has been challenged by the need to meet the growing global demand for meat while also reducing the negative impacts of livestock and meat production. These negative effects are highlighted in the press media (such as greenhouse gas emissions (GHG), feed-food competition, low animal welfare, and potential risks for human heath). Although meat consumption may be slowing down in developed countries, it is projected that global meat consumption will continue to grow as populations and incomes increase especially in some developing countries (1). As it is expected, global meat consumption would increase by 1.4% per year (2). Approximately 31% of global human-caused GHG emissions come from agri-food production, where meat accounts for a significant proportion (3). It is estimated that more than 70 billion farm animals are raised and slaughtered for meat production per year (4). A proportion of them is raised in intensive farming conditions and slaughtered at a very young age. Additionally, animal food consumption raises a set of health issues such as the risk of colorectal cancer (5, 6).

Dietary changes can bring ethical and environmental benefits to a large extent, which is not achieved yet by our food system (7). For most humans (except vegetarians), eating meat is perceived as natural, normal and necessary (8) with unique sensory and flavor properties. Meat consumption is reinforced by ingrained dietary habits, social norms, values and policy actions (7). Moreover, vegetarianism is a personal choice, meat-free diet may not be acceptable for everyone and may not be a solution anyway.

While some authors are promoting agroecology to change our current production systems to improve their sustainability (9, 10), the development of meat alternatives has been promoted as a good solution. Market share of meat substitutes grows as consumer acceptance increases due to potential ethical, safety and so-called environmental benefits. In fact, for example, plant-based meat alternatives generally may have less GHG than conventional meat (11). However, it depends on the production process, it was reported that in one scenario, plant-based meat production had higher carbon footprint than conventional beef produced on well-managed pastures (12).

Cultured "meat" (CM) is also presented to have a potential to drastically reduce environmental externalities according to some authors (13) but not all (14). As an innovation breakthrough in food production, the basic technique of CM is tissue engineering. Satellite muscle cells and fibro-adipogenic cells are cultured in culture medium allowing their proliferation and differentiation to produce muscle fibers and fat tissues which are presented as meat when mixed together (15). Even if the production of animal-free medium seems still uncertain in terms of large scale production and cost (16), and even the biopsy procedure can still cause animal pain to some extent, the introduction of CM is likely to reduce the number of slaughtered animals. Although the process of culturing cells is hardly ever perfectly controlled and even if some unexpected safety issues may arise (17), food-borne pathogens is likely to be reduced compared to animal farming and production. As for the potential environmental benefits CM can bring, it has so far not been precisely assessed but relies more on forecasts. It will take a long time to confirm these conclusions, which makes them highly speculated (18). As it was demonstrated by Escobar et al. (14), the

calculation of the beneficial contribution of CM to the environment depends on how much reliable data is available.

Compared to other meat alternatives, CM might be the closest one to meat in terms of molecular and organoleptic properties even its composition is not known yet (19). Despite so, CM is less favored than plant- and even insect-based meat substitutes (20–22). Moreover, this may incite vegetarians who like meat but do not eat meat for animal and environment protection to eat meat again. Although it was found that vegetarians would be more likely to perceive the potential benefits of CM, they were still less interested in trying it (23).

At present, CM is not yet commercially available, except for the first commercial product (chicken bites/chicken nuggets from the American startup Eat Just) distributed in 1,880 restaurants of the same chain in Singapore after receiving approval from the Singapore Food Authority (24). Thus, the commercial availability of the first cultured meat (CM) product lays the foundation and paves the way for other such meat alternatives to enter the worldwide food market (25). Without actually being exposed to the product, the study of consumer acceptance of CM is based on hypothesis and assumptions. To date, most of the literature has examined consumer acceptance through survey studies. Although more and more studies have been conducted with representative samples, what the population would be willing to do is not precisely known since consumers' answers vary according to origin and cultural factors. However, from a practical point of view, hypothetical studies on the acceptance and/or willingness of a representative population for a product that is not yet available (such as CM) may be inherently less reliable. Herein, the study of consumer acceptance would be more relevant if we could find some indicators (i.e., motives, barriers) that would be good predictors of consumer acceptance.

The main factors that are affecting consumer perception and acceptance of CM are ethical, environmental concerns and issues related to the production process, in interaction with doubts, neophobia, fear and disgust (26). The perception of those factors varies between consumers in different sociodemographic segments. This study therefore seeks to obtain data which would enrich consumer acceptance research on CM. There are two primary aims of this study: (1) to capture perception of CM in specific consumer segments from citizens with origin from South-Western Europe, and (2) to investigate potential indicators (motives, barriers) of consumer acceptance of CM. Results will be analyzed considering published data with respondents from other countries (China, Brazil, France) using the same survey (27–29). Furthermore, compared to our previous studies, novel statistical approaches were developed to better identify barriers and motives of acceptance of CM.

Materials and methods

Questionnaire design

As mentioned above, this study is an extension in other countries of previous published research with the same questionnaire (27-29) and with additional statistical approaches. Only some of the questions below and data were used in order to obtain key information that met the purpose of this study. The
complete questionnaire can be seen in Hocquette et al. (29). The questions used in this study are presented in Table 1.

## Data collection

The survey was distributed in Italy, Spain and Portugal through social networks and on campus questionnaire dissemination. Although, we note that a small part of the data was collected from people who live in other countries but speak Italian, Spanish and Portuguese, these data was still worthwhile to be used since either people originated from these three countries or those who speak these three languages as their first language are considered to have the corresponding cultural, local and dietary backgrounds. In the end, 2,171 answers including 46.7% Italian data, 31% Portuguese data and 22.3% Spanish data were collected, and the demographic information is detailed in Tables 1, 2.

## Statistical analysis

The data was analyzed using R software (version. 4.1.1) and IBM SPSS 25 depending on the different output needs such as plotting.

The demographic variables and their categories are presented in Tables 1, 2. Some treatments of categories need to be specified such as for age, 18–30 years of age was considered as young, 31–50 years of age was considered as middle-aged and more than 51 years of age was considered as senior or old in this study. For meat consumption, people never eat meat were considered as vegans and/or vegetarians. These treatments were used in the following analysis with General Linear Model (GLM) and logistic regression modeling. Three types of statistical analyses were performed with this transformed data set.

First, variance analysis (ANOVA) was performed with the GLM procedure in SPSS as previously described (27, 29) to examine the difference in WTT, WTE, and WTP depending on respondent groups based on different demographic characteristics. As in previous studies (27), some, but not all of the assumptions of ANOVA were sometimes violated in this survey case, such as normality of distributions and homogeneity of variances. Therefore, we ran a Welch's ANOVA, which does not require the homogeneity of variance assumption, and we obtained extremely similar results compared to ANOVA which is considered as being robust (30). Based on these observations, we proceeded with ANOVA since the Welch's ANOVA does not accept interactions. Post-hoc test was performed using Bonferroni test with the Bonferroni correction for pairwise comparisons between groups with significant difference, which was determined at the level of $p < 0.05$.

Second, to identify the potential motives and barriers to the acceptance of CM, Pearson correlation analysis was thereafter performed by R software to determine the relationships between variables (WTT, WTE, WTP, and other questions regarding the perception of conventional meat production and of CM). To have an overall perception of conventional meat production and CM, respectively, two overall variables “Overall perception of conventional meat production” and “Overall perception of CM” were created. Overall perception of conventional meat production

was calculated by merging answers to two questions: (1) Does meat production cause ethical problems? and (2) Does meat production cause environmental problems? as follow $0.5 \times$ answer to question 1 + $0.5 \times$ answer to question 2 (both from a scale from 1 to 5). Overall perception of CM was calculated by merging answers to four questions: (1) How ethical do you think CM would be compared to conventional meat? (2) How eco-friendly do you think CM would be compared to conventional meat? (3) How healthy, safe and nutritional do you think CM would be compared to conventional meat? and (4) How tasty do you think CM would be compared to conventional meat? as follow $0.25 \times$ answer to question 1 + $0.25 \times$ answer to question 2 + $0.25 \times$ answer to question 3 + $0.25 \times$ answer to question 4.

Third, logistic regression was developed to identify barriers and motives to accept CM. As demonstrated by Verbeke et al. (31), since this novel product is not yet commercially available on a large scale and to be consumed frequently, it is difficult to obtain real data regarding consumer WTT and WTE. Nevertheless, consumer willingness is highly driven by the perception of the product, and emotional resistance to the concept of CM can negatively affect consumers' perception of this product and their willingness to try and eat (27, 29). Combined with relevant demographic variables, this analysis considered also the variability in emotional resistance, allowing to investigate the potential profile of CM adopters and rejectors. This modeling approach was greatly inspired by the research of Verbeke et al. (31).

Willingness to try for CM was analyzed as a discrete choice (yes-1, no-0) by combining the response categories “definitely yes” and “probably yes” as “yes” (65.5%), and “definitely no” and “probably no” as “no” for WTT (24.7%), “Unsure” was not used in this analysis; “I would be willing to regularly eat CM at restaurant/home/in ready-to-eat meals” as “yes” (56.7%) and “I do not want to regularly eat CM” as “no” (43.3%).

Binary logistic regression was used to model the discrete choice in terms of WTT and WTE. If the latent variable $z_i$ is greater than zero, the binary response $y_i$ (for WTT or WTE) for respondent $i$ takes a value of one; otherwise, $y_i$ takes a value of zero:

$$ \begin{cases} y_i = 1 & \text{if } z_i > 0 \\ y_i = 0 & \text{if } z_i \le 0 \end{cases} $$

The latent variable $z_i$ is constructed with a regression model where $x_{ki}$ represents explanatory variables that from 1 to $k$ explaining WTT and WTE for participant $i$ with $\beta_k$ as the coefficient that indicates the effect of $x_{ki}$ on $z_i$, and where $\varepsilon_i$ represents the random error for respondent $i$, as below:

$$ z_i = \beta_0 + \sum_{k=1}^{k} (\beta_k x_{ki}) + \varepsilon_i $$

In this study, $z_i$ is specified by a set of explanatory variables as $z_i = \beta_0 + \beta_1 \text{Gender[Female]}_i + \beta_2 \text{Age[> 50 years of age]}_i + \beta_3 \text{Origin[Italy]}_i + \beta_4 \text{Occupation[not scientists working within the meat sector]}_i + \beta_5 \text{Meat consumption [Never]}_i + \beta_6 \text{Income[high income]}_i + \beta_7 \text{Education[high level]}_i + \beta_8 \text{Familiarity[never heard]}_i + \varepsilon_i$.

In the current model, females, more than 50 years of age, originated from Italy, not scientist, working in the meat sector, who never consume meat (considered as vegans/vegetarians), with a high income, a high education level and who never heard about
<table><caption>TABLE 1 Questions of the CM questionnaire used in this study.</caption><thead><tr><th>Section</th><th>Question</th><th>Answer/Scale</th></tr></thead><tbody><tr><td rowspan="10">Demographic information</td><td>Gender</td><td>Female;<br/>Male;<br/>No answer</td></tr><tr><td>Age</td><td>18–30 years of age;<br/>31–50 years of age;<br/>&gt; 50 years of age</td></tr><tr><td>Origin</td><td>Italian - people originated from Italy and/or mainly Italian-speaking;<br/>Portuguese - people originated from Portugal and/or mainly Portuguese-speaking;<br/>Spanish - people originated from Spain and/or mainly Spanish-speaking, even if few respondents live in South America.</td></tr><tr><td>Education</td><td>Lower education - prior to university;<br/>Medium education - studying at university or having obtained a bachelor's degree;<br/>Higher education - having obtained a master's degree (or studying to do so) and above.</td></tr><tr><td>Income (monthly net income)</td><td>0–1,500 €; 1,500–2,000 €; 2,000–2,500 €; 2,500–3,000 €; 3,000–4,000 €; &gt; 4,000 €; No answer<br/>Income level was sorted as:<br/>Low income – &lt;2,000 €;<br/>Medium income – 2,000–3,000 €;<br/>High income – &gt; 3,000 €</td></tr><tr><td>Occupation</td><td>Not scientists, work outside the meat sector;<br/>Not scientists, work within the meat sector;<br/>Scientists, work outside the meat sector;<br/>Scientists, work within the meat sector.</td></tr><tr><td>Meat consumption</td><td>Never: vegetarian or vegan diet;<br/>Rarely: weekly or less;<br/>Regularly: several times a week;<br/>Daily or at every meal</td></tr><tr><td>Familiarity with CM</td><td>Yes, I have heard of CM;<br/>No, I never heard of CM</td></tr><tr><td rowspan="5">Societal challenges that faced by conventional meat production</td><td>Do you think the conventional meat industry cause ethical problems?</td><td>(1)-Much less;<br/>(2)-Less;<br/>(3)-Unsure;<br/>(4)-More;<br/>(5)-Much more</td></tr><tr><td>Do you think the conventional meat industry cause environmental problems?</td><td>(1)-Much less;<br/>(2)-Less;<br/>(3)-Unsure;<br/>(4)-More;<br/>(5)-Much more</td></tr><tr><td>Do you think reducing meat consumption could be a good solution to resolve above problems?</td><td>(1)-Much less;<br/>(2)-Less;<br/>(3)-Unsure;<br/>(4)-More;<br/>(5)-Much more</td></tr><tr><td>How ethical do you think CM would be compared to conventional meat?</td><td>(1)-Much less;<br/>(2)-Less;<br/>(3)-Unsure;<br/>(4)-More;<br/>(5)-Much more</td></tr><tr><td>How eco-friendly do you think CM would be compared to conventional meat?</td><td>(1)-Much less;<br/>(2)-Less;<br/>(3)-Unsure;<br/>(4)-More;<br/>(5)-Much more</td></tr><tr><td rowspan="2">Potential challenges that faced by CM</td><td>How healthy, safe and nutritious do you think CM would be compared to conventional meat?</td><td>(1)-Much less;<br/>(2)-Less;<br/>(3)-Unsure;<br/>(4)-More;<br/>(5)-Much more</td></tr><tr><td></td><td></td></tr></tbody></table>

(Continued)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Section</th><th>Question</th><th>Answer/Scale</th></tr></thead><tbody><tr><td></td><td>How tasty do you think CM would be compared to conventional meat?</td><td>(1)-Much less;<br>(2)-Less;<br>(3)-Unsure;<br>(4)-More;<br>(5)-Much more</td></tr><tr><td rowspan="2">Acceptance of CM</td><td>Would you be willing to try CM?</td><td>(1)-Definitely no;<br>(2)-Probably no;<br>(3)-Unsure;<br>(4)-Probably yes;<br>(5)-Definitely yes</td></tr><tr><td>Would you be willing to eat regularly CM?</td><td>0-No (I do not want to eat CM regularly);<br>1-Yes (I want to eat CM at restaurant/home/in ready-to-eat meals)</td></tr><tr><td rowspan="2">Perception of CM</td><td>How much would you be willing to pay for CM compared to conventional meat?</td><td>(1)-Much less than conventional meat;<br>(2)-Less than conventional meat;<br>(3)-Same as conventional meat;<br>(4)-More than conventional meat;<br>(5)-Much more than conventional meat</td></tr><tr><td>What do you think of CM?</td><td>It is promising and/or acceptable;<br>It is fun and/or intriguing;<br>It is absurd and/or disgusting</td></tr><tr><td></td><td>Do you have emotional resistance to accept CM?</td><td>(1)-Much less;<br>(2)-Less;<br>(3)-Unsure;<br>(4)-More;<br>(5)-Much more</td></tr></tbody></table>

CM served as the reference category for demographic variables of gender, age, origin, occupation, meat consumption, income, education and familiarity with CM.

The logistic function used to transform $y_i$ from $z_i$ is based on the relationship between the probability $p_i$ of dependent variable $y_i$ (WTT or WTE) and the explanatory variable $x_k$ (gender, age, etc.) as below:

$$p_i = \text{prob}(y_i = 1) = \frac{e^{z_i}}{1 + e^{z_i}} = \frac{e^{\beta_0} + \sum_{k=1}^{k} \beta_k x_{ki}}{1 + e^{\beta_0} + \sum_{k=1}^{k} \beta_k x_{ki}}$$

Meanwhile:

$$\log(\frac{p_i}{1 - p_i}) = z_i = \beta_0 + \sum_{k=1}^{k} (\beta_k x_{ki}) + \varepsilon_i$$

regression coefficient ($\beta$) was estimated based on maximum likelihood estimation and is presented with odds ratio [EXP ($\beta$), OR] and significance level ($p$-value).

# Results

## Characteristics and overall answers of the respondents

### Basic demographic information of the current respondents

Of the total respondents, 56.7% were females and 42.5% were males (and 0.8% were unwilling to answer this question), of whom 46.7% were originated from Italy and/or mainly Italian-speaking, 31% were originated from Portugal and/or mainly Portuguese-speaking and 22.3% were originated from Spain and/or mainly

Spanish-speaking. The current sample was mainly middle-aged and young people (39.5 and 37.2%, respectively), more than half (53.8%) had a net monthly income of less than 2,000 €. Most of them were well-educated (98.1% pursuing and/or have obtained a degree of bachelor, master or Ph.D.), working outside the meat sector (72.8%), being meat eaters (90.1%) with various frequencies (from rarely to daily), and being familiar with CM or at least have heard about this novel food biotechnology (Table 2).

### Overall perception and willingness

Considering together all answers from the current respondents, the overall perception of conventional meat production and CM were observed as well as their WTT, WTE, and WTP CM (Table 3). In general, more than half of the current respondents believe that conventional meat production does cause a considerable amount of ethical and environmental problems (54.3, 62.6%) and reducing meat consumption could be a good solution to resolve those problems for 50.7% of the respondents. In general, they do believe CM would be more ethical and eco-friendly than conventional meat, but do not seem to be too much convinced that CM could be safer and tastier than conventional meat. This novel biotechnology does not provoke much emotional resistance (only of 32.8% of the current participants). In addition, 48.8% of them considered CM as “promising and/or acceptable,” 22.7% perceived CM as “fun and/or intriguing” and 28.5% felt CM as “absurd and/or disgusting.” Overall, the current respondents would be willing to try and regularly eat CM, but would be only willing to pay a lower price than conventional meat. A total of 65.5% of the respondents would be willing to try CM (26.7% answered “definitely yes” and 38.8% “probably yes”), 34.5% respondents were unwilling to try or were unsure about CM (10.5% answered “definitely no,” 14.2% “probably no” and 9.8% “unsure”). A total of 56.7% of respondents
<table><caption>TABLE 2 Demographic information of the current respondents (n = 2,171).</caption><thead><tr><th>Demographic</th><th>Category</th><th>Number</th><th>Percentage</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Female</td><td>1,232</td><td>56.7</td></tr><tr><td>Male</td><td>923</td><td>42.5</td></tr><tr><td rowspan="3">Age</td><td>18–30 years of age</td><td>807</td><td>37.2</td></tr><tr><td>31–50 years of age</td><td>857</td><td>39.5</td></tr><tr><td>&gt; 50 years of age</td><td>507</td><td>23.4</td></tr><tr><td rowspan="3">Origin</td><td>Italy</td><td>1,014</td><td>46.7</td></tr><tr><td>Portugal</td><td>673</td><td>31.0</td></tr><tr><td>Spain</td><td>484</td><td>22.3</td></tr><tr><td rowspan="3">Education</td><td>Low level</td><td>42</td><td>1.9</td></tr><tr><td>Medium level</td><td>1,144</td><td>52.7</td></tr><tr><td>High level</td><td>960</td><td>44.2</td></tr><tr><td rowspan="4">Occupation</td><td>Not scientists and outside the meat sector</td><td>1,025</td><td>47.2</td></tr><tr><td>Not scientists and within the meat sector</td><td>321</td><td>14.8</td></tr><tr><td>Scientists outside the meat sector</td><td>555</td><td>25.6</td></tr><tr><td>Scientists within the meat sector</td><td>270</td><td>12.4</td></tr><tr><td rowspan="7">Monthly net income</td><td>0–1,500 €</td><td>782</td><td>36.0</td></tr><tr><td>1,500–2,000 €</td><td>386</td><td>17.8</td></tr><tr><td>2,000–2,500 €</td><td>219</td><td>10.1</td></tr><tr><td>2,500–3,000 €</td><td>98</td><td>4.5</td></tr><tr><td>3,000–4,000 €</td><td>76</td><td>3.5</td></tr><tr><td>&gt; 4,000 €</td><td>89</td><td>4.1</td></tr><tr><td>No answer</td><td>521</td><td>24.0</td></tr><tr><td rowspan="4">Income*</td><td>Low income</td><td>1,168</td><td>53.8</td></tr><tr><td>Medium income</td><td>317</td><td>14.6</td></tr><tr><td>High income</td><td>165</td><td>7.6</td></tr><tr><td>No answer</td><td>521</td><td>24.0</td></tr><tr><td rowspan="4">Meat consumption</td><td>Never: vegetarian or vegan diet</td><td>214</td><td>9.9</td></tr><tr><td>Rarely: weekly or less</td><td>477</td><td>22.0</td></tr><tr><td>Regularly: several times a week</td><td>1,268</td><td>58.4</td></tr><tr><td>Daily or at every meal</td><td>212</td><td>9.8</td></tr><tr><td rowspan="2">Familiarity</td><td>Ever heard</td><td>1,660</td><td>76.5</td></tr><tr><td>Never heard</td><td>511</td><td>23.5</td></tr></tbody></table>

would be willing to regularly eat CM (at home, restaurants or in ready-made meals), which means that 43.3% of respondents did not want to regularly eat CM at all. Only 5.7% of respondents would be willing to pay more for CM than conventional meat, 31.5% would be willing to pay the same price for CM as conventional meat, 62.8% would be willing to pay only less or much less or even nothing.

# Determinants of WTT, WTE, and WTP for CM

## Determinants of WTT

A logistic regression was performed to ascertain the effects of gender, age, origin, occupation, meat consumption, income,

education and familiarity on the likelihood that participants would be willing to try and to regularly eat CM. The model correctly classified 72.6% of the current responses of WTT. No significant differences were found for WTT between males and females (p > 0.05, Table 4) based on variance analysis in general linear model; nevertheless, the odds ratio of WTT is 1.3 times (OR = 1.29) greater for males as opposed to females (p < 0.05, Table 5) in logistic regression model. There was no difference of WTT between mid-aged and old participants (p > 0.05, Tables 4, 5). Spanish-speaking participants had the highest WTT (p < 0.001, Table 4) with 2 times (OR = 2.18) more likely than Italian-speaking participants, Portuguese-speaking people were 1.7 times (OR = 1.66) more likely to try CM than Italian-speaking participants (p < 0.001, Table 5). Low educated participants

<footnote>*Classification of monthly net income into different levels.</footnote>
<table><caption>TABLE 3 Current perceptions of conventional meat production and cultured "meat" (CM) and willingness to try (WTT), willingness to eat (WTE), willingness to pay (WTP) for cultured "meat" (CM) based on 2,171 responses.</caption><thead><tr><th>Perception of conventional meat production<sup>1</sup></th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>Do you think the conventional meat industry cause ethical problems?</td><td>3.55</td><td>1.30</td></tr><tr><td>Do you think the conventional meat industry cause environmental problems?</td><td>3.73</td><td>1.28</td></tr><tr><td>Do you think reducing meat consumption could be a good solution to resolve above problems?</td><td>3.38</td><td>1.45</td></tr><tr><th colspan="3">Perception of CM<sup>1</sup></th></tr><tr><td>How ethical do you think CM would be compared to conventional meat?</td><td>3.07</td><td>1.42</td></tr><tr><td>How eco-friendly do you think CM would be compared to conventional meat?</td><td>3.09</td><td>1.35</td></tr><tr><td>How healthy, safe and nutritional do you think CM would be compared to conventional meat?</td><td>2.85</td><td>1.24</td></tr><tr><td>How tasty do you think CM would be compared to conventional meat?</td><td>2.46</td><td>1.21</td></tr><tr><td>Do you have emotional resistance to try CM?</td><td>2.78</td><td>1.44</td></tr><tr><th colspan="3">Willingness</th></tr><tr><td>Would you be willing to try CM?<sup>1</sup></td><td>3.57</td><td>1.30</td></tr><tr><td>Would you be willing to regularly eat CM?<sup>2</sup></td><td>0.57</td><td>0.50</td></tr><tr><td>How much would you be willing to pay for CM compared to conventional meat?<sup>3</sup></td><td>2.22</td><td>0.87</td></tr></tbody></table>

had the lowest WTT than medium and high educated people (*p* < 0.01, Table 4). There was no difference for WTT between different income groups (*p* > 0.05, Table 4), also, the predictive effect of income to WTT was not significant in the current logistic regression model (*p* > 0.05, Table 5). Scientists outside the meat sector had the highest WTT (*p* < 0.001, Table 4) and were 4 times (OR = 3.96) more likely to try CM than people who were not scientists but work within the meat sector that had the lowest WTT (*p* < 0.001, Table 5). Participants who were meat scientists were 2.7 times (OR = 2.66) more likely to try CM than participants who were not scientists but work within the meat sector (*p* < 0.001, Table 5). Participants who were not scientist and outside the meat sector were 2.4 times (OR = 2.41) more likely to try and eat CM than people who were not scientists but work within the meat sector (*p* < 0.001, Table 5). There was no difference for WTT among groups with different meat consumption levels (*p* > 0.05, Table 4). Nonetheless, rarely meat eaters were almost two times (OR = 1.80) more likely to try CM than vegans/vegetarians (*p* < 0.01, Table 5).

## Determinants of WTE regularly CM

The logistic regression model correctly classified 63.2% of the current responses of WTE. According to ANOVA, no significant differences were found for WTE for CM between males and females (*p* > 0.05, Table 4) but according to logistic regression model, the odds ratio of WTE is 1.3 times (OR = 1.27) greater for males as opposed to females (*p* < 0.05, Table 3). Young people had the highest WTE (*p* < 0.001, Table 4) because they were twice (OR = 2.04) more likely than mid-aged and old people to regularly eat CM, there was no difference of WTE between mid-aged and old participants (*p* > 0.05, Tables 4, 5). Spanish-speaking people had the highest WTE (*p* < 0.001, Table 4) because they

were three times (OR = 2.92) more likely than Italian-speaking people to regularly eat CM (*p* < 0.001, Table 5), Portuguese-speaking people were 1.8 times (OR = 1.75) more likely to eat CM than Italian-speaking people (*p* < 0.001, Table 5). Scientists outside the meat sector had the highest WTE (*p* < 0.001, Table 4) and were 3.7 times (OR = 3.69) more likely to eat CM than people who were not scientists but work within the meat sector that had the lowest WTE (*p* < 0.001, Table 4). Participants who were meat scientists were 1.6 times more likely to eat CM than people who were not scientists but work within the meat sector, people who were not scientists and outside the meat sector were 2.7 times (OR = 2.68) more likely to eat CM than people who were not scientists but work within the meat sector (*p* < 0.001, Table 5). Participants with the lowest income had higher WTE than medium income people (*p* < 0.05, Table 4). There was no difference for WTE among groups with different meat consumption levels (*p* > 0.05, Table 4). Participants who had heard about CM had higher WTT (1.2 times higher than people who had never heard about it) but lower WTE (*p* < 0.01, Table 4).

## WTP for CM

Since only 5.7% of the current respondents would be willing to pay for CM at a price higher than conventional meat, it is difficult to transform the data with five categories in binary responses and this makes it impossible to apply logistic regression with WTP. Based on variance analysis with general linear model, it can be seen that females, young people and Italian-speaking participants of the current respondent sample were willing to pay more than males, middle-aged and old people, and compared to Spanish- and Portuguese-speaking participants (*p* < 0.001, Table 4). Participants working outside the meat sector would be willing to pay the most and

<footnote n="1">¹Response rated as (1)-Much less, (2)-Less, (3)-Unsure, (4)-More, (5)-Much more or Response rated as (1)-Definitely no, (2)-Probably no, (3)-Unsure, (4)-Probably yes, (5)-Definitely yes.</footnote>

<footnote n="2">²Response rated as 0-No, 1-Yes.</footnote>

<footnote n="3">³Response rated as (1)-Much less than conventional meat, (2)-Less than conventional meat, (3)-Same as conventional meat, (4)-More than conventional meat, (5)-Much more than conventional meat.</footnote>
<table><caption>TABLE 4 Respondents' willingness to try (WTT), willingness to eat (WTE) and willingness to pay (WTP) for cultured "meat" (CM) according to demographic categories.</caption><thead><tr><th rowspan="2">Demographic</th><th rowspan="2">Category</th><th colspan="2">WTT<sup>1</sup></th><th colspan="2">WTE<sup>2</sup></th><th colspan="2">WTP<sup>3</sup></th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Female</td><td>3.53</td><td>1.27</td><td>0.56</td><td>0.50</td><td>2.32<sup>a</sup></td><td>0.85</td></tr><tr><td>Male</td><td>3.63</td><td>1.33</td><td>0.58</td><td>0.49</td><td>2.09<sup>b</sup></td><td>0.88</td></tr><tr><td rowspan="3">Age</td><td>18–30 years of age</td><td>3.80<sup>a</sup></td><td>1.24</td><td>0.65<sup>a</sup></td><td>0.48</td><td>2.40<sup>a</sup></td><td>0.87</td></tr><tr><td>31–50 years of age</td><td>3.47<sup>b</sup></td><td>1.31</td><td>0.54<sup>b</sup></td><td>0.50</td><td>2.16<sup>b</sup></td><td>0.85</td></tr><tr><td>&gt; 50 years of age</td><td>3.39<sup>b</sup></td><td>1.33</td><td>0.49<sup>b</sup></td><td>0.50</td><td>2.05<sup>b</sup></td><td>0.85</td></tr><tr><td rowspan="3">Origin</td><td>Italy</td><td>3.55<sup>b</sup></td><td>1.38</td><td>0.53<sup>b</sup></td><td>0.50</td><td>2.32<sup>a</sup></td><td>0.92</td></tr><tr><td>Portugal</td><td>3.47<sup>b</sup></td><td>1.22</td><td>0.54<sup>b</sup></td><td>0.50</td><td>2.09<sup>b</sup></td><td>0.84</td></tr><tr><td>Spain</td><td>3.75<sup>a</sup></td><td>1.22</td><td>0.68<sup>a</sup></td><td>0.57</td><td>2.19<sup>b</sup></td><td>0.87</td></tr><tr><td rowspan="3">Education</td><td>Low level</td><td>3.05<sup>b</sup></td><td>1.23</td><td>0.40</td><td>0.50</td><td>1.98</td><td>0.75</td></tr><tr><td>Medium level</td><td>3.53<sup>a</sup></td><td>1.34</td><td>0.57</td><td>0.50</td><td>2.24</td><td>0.90</td></tr><tr><td>High level</td><td>3.65<sup>a</sup></td><td>1.25</td><td>0.57</td><td>0.50</td><td>2.21</td><td>0.83</td></tr><tr><td rowspan="4">Occupation</td><td>Not scientists outside MS<sup>4</sup></td><td>3.53<sup>b</sup></td><td>1.28</td><td>0.61<sup>a</sup></td><td>0.49</td><td>2.30<sup>a</sup></td><td>0.86</td></tr><tr><td>Not scientists within MS</td><td>3.08<sup>c</sup></td><td>1.40</td><td>0.39<sup>b</sup></td><td>0.49</td><td>1.79<sup>c</sup></td><td>0.84</td></tr><tr><td>Scientists outside MS</td><td>3.86<sup>a</sup></td><td>1.21</td><td>0.65<sup>a</sup></td><td>0.48</td><td>2.39<sup>a</sup></td><td>0.84</td></tr><tr><td>Scientists within MS</td><td>3.70<sup>ab</sup></td><td>1.28</td><td>0.46<sup>b</sup></td><td>0.50</td><td>2.09<sup>b</sup></td><td>0.82</td></tr><tr><td rowspan="4">Income</td><td>Low income</td><td>3.59</td><td>1.29</td><td>0.58<sup>a</sup></td><td>0.49</td><td>2.22<sup>ab</sup></td><td>0.84</td></tr><tr><td>Medium income</td><td>3.58</td><td>1.32</td><td>0.49<sup>b</sup></td><td>0.50</td><td>2.13<sup>b</sup></td><td>0.88</td></tr><tr><td>High income</td><td>3.59</td><td>1.41</td><td>0.53<sup>ab</sup></td><td>0.50</td><td>2.12<sup>ab</sup></td><td>0.95</td></tr><tr><td>No answer</td><td>3.52</td><td>1.27</td><td>0.59<sup>a</sup></td><td>0.49</td><td>2.30<sup>a</sup></td><td>0.90</td></tr><tr><td rowspan="4">Meat consumption</td><td>Never</td><td>3.46</td><td>1.46</td><td>0.56</td><td>0.50</td><td>2.94<sup>a</sup></td><td>0.85</td></tr><tr><td>Rarely</td><td>3.69</td><td>1.26</td><td>0.60</td><td>0.49</td><td>2.31<sup>b</sup></td><td>0.84</td></tr><tr><td>Regularly</td><td>3.55</td><td>1.28</td><td>0.56</td><td>0.50</td><td>2.09<sup>c</sup></td><td>0.80</td></tr><tr><td>Daily</td><td>3.52</td><td>1.36</td><td>0.56</td><td>0.50</td><td>2.08<sup>c</sup></td><td>0.98</td></tr><tr><td rowspan="2">Familiarity</td><td>Ever heard</td><td>3.61<sup>a</sup></td><td>1.33</td><td>0.55<sup>b</sup></td><td>0.48</td><td>2.17</td><td>0.79</td></tr><tr><td>Never heard</td><td>3.44<sup>b</sup></td><td>1.21</td><td>0.62<sup>a</sup></td><td>0.50</td><td>2.23</td><td>0.89</td></tr></tbody></table>

participants working within the meat sector especially those who were not scientists would pay the least for CM (*p* < 0.001, Table 4).

## Motives and barriers of CM acceptance

### Relationships between participants' acceptance and perception of conventional meat production and of CM

Figure 1 illustrates the correlations between participants' willingness to try, to regularly eat and to pay for CM and their overall perception of conventional meat and of CM.

The perception of ethical and environmental problems caused by conventional meat production has positive

correlations with the wish of consumers to reduce their meat consumption (*r* = 0.60, 0.64, *p* < 0.001). Considering that reducing meat consumption is a good solution is positively correlated with WTT, WTE, and WTP for CM (*r* = 0.29, 0.31, 0.38, *p* < 0.001). On the opposite, emotional resistance about CM is negatively correlated with the perception that CM may be ethical, eco-friendly, tasty and safe (*r* = -0.32, -0.30, -0.25, -0.31, *p* < 0.001). Emotional resistance is also negatively correlated with WTT, WTE, and WTP (*r* = -0.57, -0.42, -0.29, *p* < 0.001).

The overall negative perception of conventional meat production is positively correlated with WTT, WTE, and WTP (*r* = 0.30–0.39, *p* < 0.001). Similarly, the overall positive perception of CM (perceived as ethical, eco-friendly, tasty, and safe) is positively correlated with WTT, WTE, and WTP (*r* = 0.50–0.52, *p* < 0.001).

<footnote n="1">¹Response rated as (1)-Definitely no, (2)-Probably no, (3)-Unsure, (4)-Probably yes, (5)-Definitely yes.</footnote>

<footnote n="2">²Response rated as 0-No, 1-Yes.</footnote>

<footnote n="3">³Response rated as (1)-Much less than conventional meat, (2)-Less than conventional meat, (3)-Same as conventional meat, (4)-More than conventional meat, (5)-Much more than conventional meat.</footnote>

<footnote n="4">⁴MS, meat sector.</footnote>

<footnote>Within each demographic category, mean values with different superscript letters significantly differ from each other at the level of *p* < 0.05.</footnote>
<table><caption>TABLE 5 Binary logistic regression explaining odds ratio (OR) of respondents' willingness to try (WTT) and willingness to eat (WTE) cultured "meat" (CM) according to sociodemographic characteristics.</caption><thead><tr><th rowspan="2">Category (ref)</th><th rowspan="2"></th><th colspan="3">WTT<sup>1</sup></th><th colspan="3">WTE<sup>2</sup></th></tr><tr><th>β<sup>3</sup></th><th>OR<sup>4</sup></th><th>P-value</th><th>β</th><th>OR</th><th>P-value</th></tr></thead><tbody><tr><td>Gender (Female)</td><td>Male</td><td>0.25</td><td>1.29</td><td>&lt;0.05</td><td>0.34</td><td>1.27</td><td>&lt;0.05</td></tr><tr><td rowspan="2">Age (&gt; 50 years of age)</td><td>18–30 years of age</td><td>0.81</td><td>2.24</td><td>&lt;0.001</td><td>0.71</td><td>2.04</td><td>&lt;0.001</td></tr><tr><td>31–50 years of age</td><td>0.23</td><td>1.26</td><td>0.093</td><td>0.16</td><td>1.17</td><td>0.196</td></tr><tr><td rowspan="2">Origin (Italy)</td><td>Spain</td><td>0.78</td><td>2.18</td><td>&lt;0.001</td><td>1.07</td><td>2.92</td><td>&lt;0.001</td></tr><tr><td>Portugal</td><td>0.51</td><td>1.66</td><td>&lt;0.001</td><td>0.56</td><td>1.75</td><td>&lt;0.001</td></tr><tr><td>Occupation (Not scientists working within MS)</td><td>Scientist working within MS<sup>5</sup></td><td>0.97</td><td>2.66</td><td>&lt;0.001</td><td>0.47</td><td>1.60</td><td>&lt;0.01</td></tr><tr><td rowspan="2">Meat consumption (Never)</td><td>Scientist working outside MS</td><td>1.38</td><td>3.96</td><td>&lt;0.001</td><td>1.31</td><td>3.69</td><td>&lt;0.001</td></tr><tr><td>Not scientist working outside MS</td><td>0.88</td><td>2.41</td><td>&lt;0.001</td><td>0.99</td><td>2.68</td><td>&lt;0.001</td></tr><tr><td rowspan="3">Income (High income)</td><td>Daily</td><td>0.20</td><td>1.22</td><td>0.427</td><td>-0.05</td><td>0.96</td><td>0.838</td></tr><tr><td>Regularly</td><td>0.30</td><td>1.35</td><td>0.117</td><td>-0.01</td><td>0.99</td><td>0.997</td></tr><tr><td>Rarely</td><td>0.59</td><td>1.80</td><td>&lt;0.01</td><td>0.35</td><td>1.41</td><td>0.053</td></tr><tr><td rowspan="2">Education (High level)</td><td>Low income</td><td>-0.09</td><td>0.91</td><td>0.661</td><td>0.05</td><td>1.05</td><td>0.798</td></tr><tr><td>Medium income</td><td>0.007</td><td>1.01</td><td>0.997</td><td>-0.15</td><td>0.86</td><td>0.460</td></tr><tr><td rowspan="2">Familiarity (Never heard)</td><td>Low level</td><td>-2.81</td><td>0.76</td><td>0.362</td><td>0.19</td><td>1.02</td><td>0.946</td></tr><tr><td>Medium level</td><td>0.11</td><td>1.11</td><td>0.455</td><td>0.33</td><td>1.39</td><td>0.007</td></tr><tr><td></td><td>Heard before</td><td>0.18</td><td>1.20</td><td>0.169</td><td>-0.17</td><td>0.85</td><td>0.143</td></tr></tbody></table>

## Predicted probabilities for WTT and WTE CM

Wald Chi-Square ($\chi^2$) value in logistic regression models indicates the predictive power of explanatory variables to the dependent variable. The Wald $\chi^2$ of WTT for gender, age, origin, occupation, meat consumption level, income, education and familiarity are 6.90, 28.66, 20.91, 60.67, 9.66, 1.37, 2.03, and 1.89. The Wald $\chi^2$ of WTE for gender, age, origin, occupation, meat consumption, income, education and familiarity are 8.87, 31.9, 48.9, 82.1, 9.28, 1.94, 7.77, and 2.15. Age, origin and occupation are therefore the most influential factors in both models of WTT and WTE. Considering the effect of origin can be partly skewed due to different sample sizes (46.7, 31.0, and 22.3% of Italian-, Spanish- and Portuguese-speaking participants) and due to nested effects with gender, age and other factors, only the effects of age and occupation are analyzed further in interaction with emotional resistance.

Predicted probabilities for WTT and WTE CM are presented in Figures 2–5 for participants of different age groups and different occupations across the range (1–5) of emotional resistance. Negative effects on WTT and WTE can be observed in all figures with increasing level of emotional resistance.

Figure 2 displays the simulated impact of emotional resistance on WTT CM for young, middle-aged and old participants. For an average emotional resistance of 3, the probability of WTT CM amounts to approximately 45% for old participants, 70% for young participants and 75% for middle-aged participants.

Different trends can be seen for WTE (Figure 3). Indeed, for an average emotional resistance of three, the probability of WTE CM amounts to approximately 30% for old participants, 55% for middle-aged participants and 70% for young participants.

These results demonstrate that emotional resistance has the most impact on willingness to try and to regularly eat CM for old people, and has the least impact to try CM for middle-aged people and has the least impact to regularly eat CM for young people.

For a low emotional resistance (value of one), the resulting probabilities of WTT amounted to a maximum of approximately 90% for all the participants working in different areas; assuming emotional resistance at the highest value of five, the resulting probabilities of WTT amounted to a minimum of 10% for people who were not scientists but work within the meat sector and of 45% for people who were scientists work outside the meat sector (Figure 4).

A larger effect of emotional resistance can be seen in Figure 5, according to the work area of respondents. People working outside the meat sector regardless they were scientists or not expressed the same WTE. With those participants, emotional resistance has the least impact on their WTE. Assuming emotional resistance at the lowest value of one, the resulting probabilities of WTE amounted to a maximum of approximately 85% for the participants working outside the meat sector, of 75% for scientists working in the meat sector and of 60% for people who were not scientists but working within the meat sector. Assuming emotional resistance at the highest value of five, the resulting probabilities of WTE

<footnote n="1">¹Willingness to try (WTT), the score of WTT was converted into a binary score, the scores of 1 and 2 were converted into 0 (unwilling to try), the score of 4 and 5 were converted into 1 (would be willing to try).</footnote>

<footnote n="2">²Willingness to eat (WTE), 0-unwilling to regularly eat, 1-would be willing to regularly eat.</footnote>

<footnote n="3">³β, regression coefficient β is associated with the expected change in log odds of dependent variable (WTT or WTE) per unit change in the explanatory variable.</footnote>

<footnote n="4">⁴OR, odds ratio represents the constant effect of an explanatory variable, on the likelihood that dependent variable will change (WTT or WTE).</footnote>

<footnote n="5">⁵MS, meat sector.</footnote>
<figure><img src="image_27.png" /><figcaption>FIGURE 1</figcaption></figure>

Correlation analysis between willingness to try (WTT), willingness to eat (WTE), willingness to pay (WTP) and overall perception of conventional meat production and cultured "meat" (CM). To have an overall perception of conventional meat production and CM, respectively, two overall variables "Overall perception of conventional meat production" and "Overall perception of CM" were created. Overall perception of conventional meat production was calculated by merging answers to two questions: (1) Does meat production cause ethical problems? and (2) Does meat production cause environmental problems? as follow (0.5 x answer to question 1 + 0.5 x answer to question 2) (both from a scale from 1 to 5). Overall perception of CM was calculated by merging answers to four questions: (1) How ethical do you think CM would be compared to conventional meat? (2) How eco-friendly do you think CM would be compared to conventional meat? (3) How healthy, safe and nutritional do you think CM would be compared to conventional meat? and (4) How tasty do you think CM would be compared to conventional meat? as follow (0.25 x answer to question 1 + 0.25 x answer to question 2 + 0.25 x answer to question 3 + 0.25 x answer to question 4). Positive correlations are presented in black, negative correlations are presented in red. ***Means that the correlation is significant at *p* < 0.001 level.

amounted to a minimum of 10% for people who were not scientists but work within the meat sector and of 15% for meat scientists and of 25% for those who work outside the meat sector.

# Discussion

## Perceptions and acceptance of CM in different countries

This survey translated into Italian, Spanish and Portuguese and distributed in countries in the South-West of Europe has provided novel results in addition to previous data from France (29), China (27) and Brazil (28). Emotional resistance associated to some food externalities (i.e., disgust, neophobia) is generally cultural dependent (32). Although people with origin from the South of Europe are known to be more conservative than Northern countries, the current respondents from Italy, Spain and Portugal seem to have a quite positive attitude towards CM (49% of the current sample considered CM as "promising and/or acceptable" and 29% perceived CM as "absurd and/or disgusting") when compared to the French participants (17% for "promising and/or acceptable" and 59% for "absurd and/or disgusting") (29), but less positive than Chinese and Brazilians, 15 and 18% of their samples perceived CM as "absurd and/or disgusting," respectively (27, 28; Table 6). With different sample sizes, the same proportion of respondents (33%) from the current samples and Brazilians had higher emotional resistance towards CM, which is expectedly, lower than the French proportion (56%

and higher than the Chinese proportion (16%) (Table 6). The emotional resistance may be associated to perceived immorality of innovation, distrust of new technologies, food neophobia (33), and also concerns about the decline and collapse of tradition (i.e., conventional livestock farming, traditional grazing landscape) (29).

However, even though the majority of current consumers from South-Western Europe have a more positive view of CM, and are more willing to try and eat it (around 66% WTT, 60% WTE), they are not willing to pay a premium for it (only 6% WTP more than conventional meat) as in France, Brazil and China (27-29). This is in line with the conclusion that positive perception is not necessarily predictive of the potential WTP for CM (34). For the time being, consumers still prefer conventional meat for the same price, even if CM is available at a significant discount (35). Although consumers are willing to try CM, when it comes to WTP, most prefer not to consume it (36, 37).

In overall terms, we found that the current respondents from the South-Western Europe have a similar level of acceptance of CM to those in Brazil, which is higher than those in France (50% WTT, 20% WTE, 8% WTP more) and even in China (50% WTT, 53% WTE, 4% WTP more) (Table 6). In general, Europeans are still reluctant to accept CM (38) compared to Americans (23, 39) and Chinese (27, 40). Especially in areas with a strong agricultural tradition such as France, they are particularly concerned about the origin and production process of agri-food (41). Meat alternatives including CM would be considered as ultra-processed food with safety concerns (41). According to the European consumers in the Netherlands, Poland, Spain, and
<figure><img src="image_28.png" /><figcaption>FIGURE 2
Predicted probability of willing to try (WTT) cultured "meat" (CM) depending on emotional resistance for different age participants.</figcaption></figure>

Finland, and also the United Kingdom, CM was the least (6%) accepted protein alternative compared to plant-based (58%), single-cell (20%) and insect-based (9%) protein (42). Although sample sizes and demographics of respondents vary across studies, it seems that consumers from Germany, Italy, Spain and the Netherlands, for example, are more likely to accept CM than those from France and Belgium (29, 34, 43–46). We found that in our study, the current Spanish participants tend to accept CM more than the Italian and Portuguese ones. According to different comparative studies of consumer perception of CM, Spanish consumers had indeed higher trust and acceptance, and lower food neophobia and disgust towards CM than people from other European countries such as France, Germany, Sweden, and even UK and Brazil (47, 48), and it was reported that Spanish consumers would be ready to buy CM if it would be affordable (49). Comparing to the major European countries from the North that are leading the race such as the Netherlands, there are several CM start-ups currently operating in Spain and the Spanish government is also investing in the CM sector (50). These initiatives may boost citizen's awareness of CM from different angles. By comparison, Italy is where valuable indigenous cattle breeds are largely raised and is the country where the prestigious PDO [Protected Designation of Origin (food and wine)] and PGI [Protected Geographical Indication (food and wine)] are largely located. Italian cuisine has influenced gourmets across Europe and around the World (51). Meat consumption is therefore significant and important in Italy, despite the fact that Italians are increasingly sensitive to the negative effects of the

conventional meat sector (51). In 2020, more than half of Italian consumers stated that they would reduce their meat consumption in order to meet the principles of ethical consumption and there are 8% Italians who chose a vegetarian diet and this number is continuously growing (52). The traditional consumption of meat and the influence of emerging trends will have a decisive impact on the acceptance of CM in Italy. Moreover, we found that the current respondents from the South-Western Europe have a similar level of acceptance of CM to those in Brazil, we assume that some common points (i.e., language, culture) between Brazil and Portugal might be able to explain part of the similar acceptance of CM.

## Potential profile of CM adopters and rejectors

According to previous published results, the profiles for potential consumers of CM can be, on average, young and well-educated people, vegetarian, and aware of the technology of CM to some extent (22, 34, 35, 53). Our results confirm these observations since we found that young and higher educated people, also people who are familiar with CM would be more willing to accept CM (summary in Table 7). Notably, we found that people who work outside the meat sector and/or work in academia as scientists would be more willing to try, to eat and to pay more for CM.

With the current respondents, gender, age, origin and occupation have significant effects on the acceptance of CM. While
<figure><img src="image_29.png" /><figcaption>FIGURE 3
Predicted probability of willingness to eat regularly (WTE) cultured "meat" (CM) depending on emotional resistance for different age participants.</figcaption></figure>

there is no doubt that people of 18–30 years of age had the highest acceptance of CM, 31–50 years middle-aged people also seem to have a higher willingness to try but not regularly eat compared to young people. Moreover, although there was no difference of WTT and WTE between females and males, we do notice that the possibility of WTT and WTE for males was significantly higher than females. Wilks et al. (54) found that the effect of age and gender are more important for acceptance than education level. This is consistent with our results. Indeed, despite that higher educated people had higher WTT than lower educated people, the predictive effect of education level is not significant in the logistic regression model.

Similarly, with the current respondents, meat consumption level is not a significant factor influencing CM acceptance, but we noticed that the current vegetarians and low meat eaters would be willing to pay more for CM than heavy meat eaters. This is in contrast with the finding that meat consumers rather than vegetarians/vegans seem to be willing to pay more for CM (34). However, it seems that there has always been controversy regarding vegetarian acceptance of CM depending on the motivations to adopt the vegetarian diet. In some studies, vegetarians were more likely to accept CM (23), but in others, vegetarians were less likely to consider the consumption of CM (55), due to concerns about such as healthiness and safety (45). Vegetarians may accept to eat CM to avoid slaughtering animals or may not accept to eat CM, because they refuse to eat any type of meat (including meat from cultured

muscle cells). Therefore, it seems difficult to conclude about any effect of meat consumption level on potential CM acceptance.

Although those more familiar with CM had a higher WTT and WTE, the predictive effect of familiarity is not significant, which would suggest that it is unreliable to predict acceptance based merely on familiarity with CM. Nonetheless, we found that people who heard about CM tend to be more likely to try CM but less likely to consume it regularly. This may suggest that regular consumption of CM is unacceptable for consumers at the present stage, even if they are willing to try but maybe just due to curiosity. Anyhow, results from the literature are not consistent in this area: Rolland et al. (44) and Siegrist and Hartmann (48) observed that the previous knowledge of CM can be a good predictor of consumer acceptance, but, however, providing too many technical details to consumers may reduce consumer acceptance (48).

Bryant et al. (43) found that people who work in the sector of animal agriculture or meat production were more likely to accept CM. The associated explanation is that farmers may believe that CM can be an effective means of meeting increasing meat demands and of transitioning away from intensive industrial productions. As the authors mentioned, this might be counter-intuitive. Indeed, we may think that farmers would be opposed to a technology that is likely to replace their own professional activity (56). This can explain why we observed in our study that people working in the meat industry had the least willingness to accept CM. These people have a stronger emotional resistance towards CM. And, even if they have the same level of emotional resistance, those working in the
<figure><img src="image_30.png" /><figcaption>FIGURE 4
Predicted probability of willing to try (WTT) cultured "meat" (CM) depending on emotional resistance for participants working in different areas. MS, meat sector.</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>FIGURE 5
Predicted probability of willingness to eat regularly (WTE) cultured "meat" (CM) depending on emotional resistance for participants working in different areas. MS, meat sector.</figcaption></figure>
meat sector have a lower WTT and WTE than those not working in the meat sector.

We also found that people working as scientists in academia were more willing to try, eat and pay more for CM. This is logical since people who work in the scientific area are likely to be more open to any technology. They might be also more aware of the principle of CM production, and they may know better the importance of technical expertise and financial investment required for innovation, so they are more willing to pay a premium. Their views on CM are more likely to be rational perceptions based on science and technology as opposed to emotional fear or disgust to something unknown on the one hand or to quick adherence to concepts disseminated on social networks on the other hand. Alternatively, scientists being mainly motivated by science and technology, their opinions regarding social consequences of the development of any technology might be less robust compared to stakeholders and politics.

## The potential motives and barriers of the current acceptance of CM

Cultured “meat” has emerged in a period where the ethical, environmental, safety issues regarding conventional meat production have been subject to growing criticisms. To reach the goals of a sustainable meat production, CM aims at guaranteeing global food security while reducing animal suffering and preserving environmental resources (15). The potential benefits of producing meat *in vitro* have been advocated by CM proponents for a long time, including by some highly influential celebrities (i.e., Bill Gates). Under the influence of these privileged people and due to socially influential activities (such as the protests against animal slaughter and referendums on animal welfare and environmental protection), it may become politically correct to accept CM. In other words, with the constant propaganda about the advantages of CM and the disadvantages of conventional meat, to address environmental, ethical and safety problems that caused by conventional meat production, citizens awareness will be boosted, and they may have to end up by accepting CM. Hence, CM is indeed perceived as a promising new field. This can be seen in the large number of articles that continue to be published in the press media despite a low scientific background (57). Although CM can avoid mass slaughter and exploitation of animals (58), once reliance on fetal bovine serum is no longer necessary, this technology is perceived as ahead of morals. However, the fact that the production process does not fit with the current European law that meat should originate from animal flesh, not from cell culture is also a moral issue. This is also the origin of emotional resistance caused by food neophobia and disgust that has a negative effect on CM acceptance (48). Even leaving aside the different nature of producing meat in a conventional or artificial context, food fraud issue also deserves caution. As demonstrated by Treich (59), CM and conventional meat may become indistinguishable as technology is constantly and rapidly updated. In this way, conventional meat could be fraudulently substituted, which would cause threaten and challenges to consumer welfare and market regulation.

Weinrich et al. (46) found that ethical concern is a strong driver that affects consumer acceptance. In fact, we found that the concern of environment is as strong as ethics to affect consumer WTT and WTE CM (correlation coefficients between ethical, environmental concern and WTT are 0.24 and 0.27; with WTE are 0.37, 0.37, *p* < 0.001, summary in Table 8). As mentioned above, the advantages of CM in terms of animal welfare and environmental protection have been promoted for a long time, despite the latter at least is controversial (14) and need to be considered in depth. According to some authors, as the production of CM would be progressively optimized, much fewer resources (60) and energy might be required and more environmental-friendly and sustainable production could be achieved (61), but this is not clear yet (14). CM also requires no management of carcass waste and may have less transport and refrigeration costs and it is expected that CM should have a longer shelf life than conventional meat (13). However, at present, a large amount of energy is still needed to produce CM (i.e., ingredients producing, bioreactor running and post- processing, etc.). Therefore, the issues involving land use, energy use and carbon opportunity cost and their precise estimation are still key to determine the environmental benefits that CM could contribute at this stage (14, 62). In addition, CM generally produces less emissions than conventional meat, but more than plant-based meat substitutes (63) and it could cause even worse environmental damage in some scenarios. Indeed, emissions from CM production consist mainly of carbon dioxide, which will remain in the atmosphere for longer than methane and nitrous oxide, the main greenhouse gases emitted by conventional meat production (64). It is clear that the CM industry has put focus on a more sustainable production with improved efficiency on cost and resource use (15). As a consequence, the current trend seems to be that public trust is being gradually built up by the support of this innovation, before those environmental benefits are actually fully achieved. That is why it is still necessary to continue to carry out consumer acceptance studies, although it is difficult to anticipate and obtain precise data of future consumer acceptance. It is key to better understand the drivers and barriers of the perception of CM.

Our research shows that the overall perceptions of conventional meat production and CM have both significant impacts on the acceptance of CM. For the current respondents, the more they consider the conventional meat production causes serious ethical and environmental problems, the more they agree that reducing meat consumption could resolve these problems, the more likely they would be willing to try, to regularly eat and to pay for CM. Likewise, the more they believe CM can be ethical, eco-friendly, tasty and safe, the more likely they would be willing to accept CM. On the contrary, if people are less convinced that CM could be more ethical, eco-friendly, tasty and/or safe than conventional meat, this novel food technology, which is not yet widely available, would provoke higher emotional resistance, which would further result in more reluctance to accept CM, especially for the first attempts to try (since WTT is more correlated with emotional resistance than WTE and WTP). However, this observation is based on the current respondents, which are composed mainly of young and middle-aged people. The respondents of our sample are indeed younger than the actual populations of the studied countries. As it was demonstrated by Mancini and Antonioli (51), today young consumers' choices, based on more ethical principles, will contribute to shape the future market of meat and also of CM.
<table><caption>TABLE 6 Perceptions and acceptance of cultured "meat" (CM) in different countries.</caption><thead><tr><th rowspan="2">Country of origin</th><th colspan="3">Perception</th><th colspan="1">Emotion</th><th colspan="3">Willingness</th></tr><tr><th>Promising and/or acceptable</th><th>Fun and/or intriguing</th><th>Absurd and/or disgusting</th><th>Resistance</th><th>WTT</th><th>WTE</th><th>Higher WTP</th></tr></thead><tbody><tr><td>Italy, Spain, Portugal</td><td>49%</td><td>23%</td><td>29%</td><td>33%</td><td>66%</td><td>57%</td><td>6%</td></tr><tr><td>France<fnref n="1" /></td><td>17%</td><td>24%</td><td>59%</td><td>56%</td><td>51%</td><td>20%</td><td>8%</td></tr><tr><td>China<fnref n="2" /></td><td>36%</td><td>49%</td><td>15%</td><td>16%</td><td>50%</td><td>53%</td><td>4%</td></tr><tr><td>Brazil<fnref n="3" /></td><td>47%</td><td>35%</td><td>18%</td><td>33%</td><td>66%</td><td>60%</td><td>6%</td></tr></tbody></table>

<table><caption>TABLE 7 Potential profile of cultured "meat" (CM) adopters and rejecters.</caption><thead><tr><th>Potential profile of CM adopters</th><th>Potential profile of CM rejectors</th></tr></thead><tbody><tr><td>Young</td><td>Old</td></tr><tr><td>Higher educated</td><td>Less educated</td></tr><tr><td>Familiar with CM technology</td><td>Not familiar with CM technology</td></tr><tr><td>Work outside the meat sector</td><td>Work within the meat sector</td></tr><tr><td>Scientist</td><td>Non-scientist</td></tr></tbody></table>

Moreover, the significant effects of age and occupation indicate that consumer acceptance of CM is highly affected by these two factors in addition to emotional resistance. According to logistic regression analysis, we do find that people different in age and occupations have different levels of emotional resistance and consequently various acceptance level of CM. Older people and people working in the meat sector, especially grassroots workers (i.e., non-scientists) are more likely to be emotionally resistant to CM and thus refuse to try and eat it. Conversely, young people and those working outside the meat sector, especially scientists, are more likely to be less emotionally resistant and more likely to accept CM.

Therefore, to give a more general conclusion based on the factors covered by our study, the negative impact of conventional

meat and the positive impact of CM on issues that concern consumers (namely ethical and environmental issues) can be the motives of acceptance of CM. Conversely, issues for which CM may have weaknesses compared to conventional meat and emotional resistance would be the main barriers to accept CM. However, while these findings may be useful, they may also biased, at least in part, by the lack of such products on the market and by the way information has been provided to respondents on the potential benefits or drawbacks of both conventional meat and CM. Overall, these findings provide insights into consumer perception and acceptance of CM that can be used by independent academia, and industries of conventional meat and CM. Not only consistent findings but also variabilities in the potential acceptance of CM by different consumer segments are important for the future communication on consumer study of CM. As it is highlighted by Faletar and Cerjak (65), the development and even success of CM in the current marketplace depends firstly on the advancement of the technology and how eco-friendly, ethically and economically the production process can be. However, it also mainly depends on the moral, ethical, economic perception consumers may have about this novel product and on their potential acceptance limited by some emotional resistance.

## Limitation concerning sampling and representativeness

In general, the sample collected for this study consisted of a slightly more female, young, middle-aged and Italian population. It is certainly the most rigorous and correct approach to collect demographically representative data for a questionnaire-type study. Nevertheless, are survey data that are less strictly representative can be effective to convey some useful information? In recent years, there has been a proliferation of research studies on CM, using sample data that are either representative of the local population (43) or not, which seem to be more often not representative (22, 31). However, through our research, as well as previous research and review articles (48), the perceptions and acceptance of CM among consumers with different origins and backgrounds, while not identical, but basically move up and down on the same trends. One of the essences of survey-type questionnaires is to reflect a certain trend through a large sample of data. Undoubtedly, the trend reflected by an extremely biased sample will also be extremely biased, which is definitely not our case. But it cannot be guaranteed that the results demonstrated by a strictly representative sample

<table><caption>TABLE 8 Potential motives and barriers of cultured "meat" (CM) acceptance.</caption><thead><tr><th>Potential motives of CM acceptance</th><th>Potential barriers of CM acceptance</th></tr></thead><tbody><tr><td>Concerns about environmental impacts of livestock</td><td>Emotional resistance</td></tr><tr><td>Ethical concerns</td><td>Lower perception of CM benefits in terms of environmental and ethical impacts</td></tr><tr><td>A better understanding of CM technology and less knowledge in meat production</td><td>Less knowledge about CM technology and more knowledge in conventional meat production</td></tr><tr><td>A better understanding of science and technology in general</td><td>Lower education level and less understanding of science and technology</td></tr><tr><td>Perception that CM may be tasty, safe and healthy</td><td>Perception that CM may be not tasty, safe and healthy</td></tr></tbody></table>

<footnote n="1">¹Data from Hocquette et al. (29).</footnote>

<footnote n="2">²Data from Liu et al. (27).</footnote>

<footnote n="3">³Data from Chriki et al. (28).</footnote>
are completely accurate and representative. One of the purposes of this study is to reflect the overall consumer perception and acceptance of CM in three countries from South-Western Europe. Despite for the limitation of sampling and representativeness, comparing results obtained with the same experimental design between countries or between similar consumer segments could provide useful information.

## Conclusion

Consumer acceptance is critical for the success of the CM industry. This study sheds light on how consumers from Italy, Spain, and Portugal perceive CM and their acceptance. In comparison with the previous data, the current Italian-, Spanish-, and Portuguese-speaking and/or originated people seem to have a more positive attitude towards CM especially compared with French samples. About a quarter of people have a negative view (absurd and/or disgusting) or emotional resistance towards CM. According to the current participants, the acceptance of CM tends to be higher for 18-30 years-old people and for respondents who work outside the meat sector especially scientists, and people who already heard about CM and with a higher acceptance for males. By comparison with respondents originated from Italy and Portugal, Spanish respondents seem also to have a higher propensity to accept CM. The high predictive effects of age and especially occupation indicate that these two factors can be good indicators of consumer acceptance of CM, which tends to be larger among young people and people working outside the meat sector.

Issues arising from conventional meat production that can be addressed by CM, for example with regard to animal welfare and the environment, can be among the major motives for the current respondents to try and regularly eat CM. For instance, according to proponents of the CM industry, the perceptions that CM may be more eco-friendly, ethical and healthy than conventional meat could motivate consumers to consume CM. On the opposite, price, the emotional resistance induced by CM and the negative impacts of CM for consumers (in terms of safety and healthiness for example) would be the barriers for the current respondents to accept CM.

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## Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

## Author contributions

ÉH, JL, M-PE-O, SC, and J-FH: conceptualization. JA, NR, and BP: data collection. JL: curation, formal analysis, and writing—original draft. JL and J-FH: investigation and methodology. M-PE-O, SC, and J-FH: supervision. JL, M-PE-O, SC, and J-FH: validation. JL, JA, NR, BP, ÉH, SC, M-PE-O, and J-FH: writing—review and editing. All authors read and agreed to the published version of the manuscript.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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EDITED BY
Jean-François Hocquette,
INRA UMR1213 Unité Mixte de Recherche sur
les Herbivores (UMRH), France

REVIEWED BY
Antti Knaapila,
University of Helsinki, Finland
Jacques Servière,
Institut National de recherche pour
l'agriculture, l'alimentation et l'environnement
(INRAE), France

*CORRESPONDENCE
Nazanin Abbaspour
✉ naz.abbaspour@gmail.com

RECEIVED 18 April 2023

ACCEPTED 19 June 2023

PUBLISHED 04 July 2023

CITATION
Abbaspour N, Sanchez-Sabate R and
Sabaté J (2023) Non-animal-based options for
animal-based foods- towards a systematic
terminology.
*Front. Nutr.* 10:1208305.
doi: 10.3389/fnut.2023.1208305

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# Non-animal-based options for animal-based foods- towards a systematic terminology

Nazanin Abbaspour¹*, Ruben Sanchez-Sabate²,³ and Joan Sabaté¹

¹School of Public Health, Environmental Nutrition Research Group, Loma Linda University, Loma Linda, CA, United States, ²Centro de Excelencia en Psicología Económica y del Consumo (CEPEC), Núcleo Científico y Tecnológico en Ciencias Sociales y Humanidades, Universidad de La Frontera, Temuco, Chile, ³Núcleo de Investigación en Educación, Ciencias Sociales y Patrimonio, Universidad Adventista de Chile, Chillán, Chile

The market has seen a rapid increase in animal-free products intended to replace animal-based foods due to concerns for human health and environmental sustainability. However, there is a lack of consistent terminology for these products, with various terms being used interchangeably, creating ambiguity. To address this issue, we propose a systematic nomenclature that defines the most commonly used terms, namely alternative, substitute, replacement, and analog, along with examples of each. In this nomenclature, a substitute primarily serves a culinary purpose, while a replacement is concerned with nutritional properties. An analog strives to satisfy both culinary and nutritional attributes to closely mimic animal-based foods in terms of sensory, nutritional, and functional characteristics. The term "alternative" serves as an umbrella term encompassing all possibilities. This work aims to promote a clearer understanding of such products and their intended use and facilitate a unified use of terminology across disciplines. This will also enable informed decision-making for consumers and greater transparency in the food industry. The health and environmental implications of these products are not discussed in this perspective.

KEYWORDS

alternative, substitute, replacement, analog, animal-based, plant-based

Introduction

The interest in animal-free products for animal-based foods has seen a remarkable surge in recent years driven by a combination of ethical, environmental, and health considerations. With the growth of plant-based meat, dairy, and protein market, small and large companies are turning to produce a new generation of animal-free choices that imitate the taste, texture, and appearance of traditional animal-based foods (1). The industry has experienced a rapid increase in demand (2), leading to technological advancements in creating non-animal-based products, particularly plant-based, imitating animal foods (1). In 2018, the market was valued at $4.6 billion, and some projections indicate that this figure is set to reach $85 billion by 2030 (3). As the popularity and production of these products increase, so does the need for understanding and education about them. New knowledge comes with new vocabulary or new usage of the existing vocabulary. However, the wide range of terms used to describe these products has used little or no consistency and systematicity, leading to ambiguity and uncertainty about them.

Regarding meat, our research based on the Web of Knowledge from 2000 to 2022 using "All Fields" found that the terms "meat alternative," "meat substitute," "meat analog or analogue," "imitation meat," "mock or fake meat," and "meat replacer or replacement" have
been the primary terms used. Our findings show a sharp rise in using these terms in the scientific literature over the past decade compared to their negligible use before (Figure 1A). Among different terms, “meat analog” has been increasingly used over the years, followed by “meat substitute,” “meat alternative,” and “meat replacement.” In this search formulation, some overestimation in the number of articles per term was unavoidable, as different terms may have been used within the same article. However, this does not change the remarkable increasing trend of emerging publications (4). In Figure 1B, we plotted the sum of the publication numbers for each term shown in Figure 1A and compared that with an “OR” formulation, where at least one term should exist in each publication. The difference indicates the increasing number of times several of these terms are being used within the same article. The figure shows the acceleration of this difference in the last few years. Moreover, most publications use different terms without distinction (3, 5–15). This lack of clear differentiation is also apparent in both mass and social media (15–17).

The synonymous use of words may serve some rhetorical purposes, such as enhancing readability and esthetics. However, in the realm of science, it is crucial to consistently use precise and unambiguous language to ensure clear and unified understanding. Given the abundance of non-animal-based food products available today, the lack of a distinct and purposeful differentiation represents at least a missed opportunity, calling for a well-defined language shared by different agents in the food system. In light of this, we suggest a definition for the most commonly used terms: “alternative,” “substitute,” “replacement,” and “analog,” along with examples of the products they represent. Such standardization is not only important for researchers, producers, and regulators in terms of

health, food production, and labeling, but distinguishing between different options facilitates informed decision-making.

# Terminology: foundation and definitions

## Foundation

There are different dietary approaches to reducing the consumption of animal foods. The most common is choosing whole plant foods rich in protein or using other protein sources, such as plant-based meat/dairy, algae, fungi (e.g., mycoprotein), insects, or cultured meat. The plant-based products considered here entail algae and fungi as plant-like foods or ingredients. However, other options, such as insects and cultured meat, are not considered plant-based and, therefore, not discussed.

Before addressing the basis of the proposed terminology, it should be noted that the increasing demand for plant-based products as an option to animal foods is relevant in societies that primarily consume animal-based foods, such as the Western world. However, in communities where plant foods form the staple diet and animal products are consumed only occasionally, the idea of plant-based “alternatives” would be irrelevant.

The nutritional and culinary attributes are used as the basis for our definitions of the most commonly used terms, namely “substitute,” “replacement,” “analog,” and “alternative.”

The nutritional attribute considers the nutrients present in the plant-based products compared to their animal food counterpart when a nutritionally comparable plant-based option is sought for an

<figure><img src="image_33.png" /><figcaption>FIGURE 1</figcaption></figure>

(A) The cumulative number of scientific publications reported by Web of Knowledge using "All Fields" containing the terms "meat alternative," "meat analog/anologue," "meat substitute," "imitation meat," "meat replacer/replacement," and "mock/fake meat"; (B), Inset) The sum of the number of publications containing the terms "meat alternative," "meat analog/anologue," "meat substitute," "imitation meat," "meat replacer/replacement," and "mock/fake meat" presented in (A) (SUM), and the number of publications that have used at least one of those terms (OR) from 2000 to 2022 reported by the Web of Knowledge using "All Fields."
animal-based food. Therefore, the selection is made based on the nutritional profile, which refers to the presence and quantity of key nutrients. In determining nutritional similarity, the key nutrients of a particular food, such as calcium in dairy or protein, iron, and zinc in meat, serve as the point of reference. The goal is a nutritionally comparable plant-based option to the animal-based counterpart.

The culinary attribute applies to artisanal, household, and industrial food preparation and production. The point of reference here would be the function of food, often used as an ingredient, as well as the sensory characteristics of the end product or the product itself if consumed alone. As for function in a recipe, the plant-based ingredient used in place of the animal food should have the same functional properties (e.g., thickening, foaming, emulsifying, stabilization, and gelling ability) to produce an end product with the same or very similar sensory qualities (2).

# Proposed terminology

## Substitute

A plant-based food or ingredient that can substitute the original animal-sourced food or ingredient outside or within a recipe. When used in a recipe, a substitute should be similar, if not identical, in its culinary properties to the original ingredient. As the physiochemical and biological properties of plant-based foods notably vary from those of animal foods, it is important to find those that match the functional attributes of the animal source ingredients to produce the same or similar end product. Therefore, understanding the fundamental qualities of both the original and the substituting ingredients is necessary. However, when substituting an animal-based food with a plant-based food outside a recipe, the gastronomic and sensory aspects take precedence over functionality. In both cases, the nutritional qualities are either not considered or become secondary.

## Examples

- *Bean patties*: vegetarian or vegan burger patties made primarily from mashed beans, such as black beans, kidney beans, or chickpeas, along with other ingredients such as vegetables, grains, and spices. Bean burgers do not try to mimic the taste or texture of meat. Moreover, although they can be a good source of protein, they have an overall different nutrient profile than meat. However, they can be prepared to have a similar texture to beef burgers with similar cooking methods, such as grilling or pan-frying. In addition, they can be served on a bun with toppings such as lettuce, tomato, onion, cheese, and condiments such as ketchup or mustard, providing a similar sensory and gastronomic experience to a traditional beef burger.

- *Plant-based milks* (e.g., nut milks, oat milk, coconut milk, hemp milk): the nutritional values of these products vary from dairy milk (18), except for soymilk; however, they typically have physicochemical and sensory properties similar to cow's milk (1). Therefore, they can provide a culinary function and sensory experience similar to dairy milk. For example, they can substitute dairy milk in a "latte" or hot chocolate or be consumed alone. Nevertheless, some plant-based milks may not react the same way as dairy milk in certain cases, such as those that require heating and curdling of milk (1).

- *Vegetable oil*: they can be used as a substitute for butter in baking and/or cooking. Like butter, they contribute to tenderness.

- *Nuts*: although relatively high in fats (mainly unsaturated) and protein, they have a different overall nutrient composition than meat. Nevertheless, they can provide similar functionality and sensory properties as meat in various recipes. Walnuts, for example, can substitute meat to make walnut balls instead of meatballs. Similarly, nut loaves can replace meat loaves.

- *Aquafaba*: can be used as a substitute for egg white in baking (19). It is the water in which chickpeas and other legumes are cooked and has similar foaming and binding abilities to egg white (19).

- *Agar agar*: can be used as a substitute for gelatin in dishes like jellies, puddings, custard, and fruit gummies. It is extracted from red algae and has very similar gelling and stabilizing abilities as animal-sourced gelatin (20).

## Replacement

Refers to a plant-based option with similar nutritional properties to its animal-based counterpart. The focus here is the key nutrients in the animal-based food or ingredient, and the functional and sensory attributes are secondary considerations. Therefore, the primary concern is the nutritional profile when seeking a plant-based replacement.

## Examples

- *Tofu*: a minimally processed product made from soybeans that provides high-quality plant-based protein similar to animal protein (21). Although tofu's protein content is lower than meat, it is often used to replace animal protein due to its high quality and digestibility. It is also considered a reasonable source of some key nutrients such as calcium and iron (22).

- *Tempeh*: another soy product made from partially cooked, fermented soybeans (21). It is dense and chewy and can be used in stir-fries, burritos, sandwiches, soups, and other dishes. Tempeh is less processed than texturized vegetable protein (TVP) and tofu. However, fermentation improves its protein digestibility and mineral bioavailability compared to tofu (23), resulting in nutritional values compatible with meat. While 100 g of beef (ground, 85% lean meat / 15% fat, patty, cooked, broiled) contains about 26 g protein, 15.4 g lipids, 18 mg calcium, 2.6 mg iron, and 6.3 mg zinc, 100 g of tempeh contains about 20 g of protein, 11 g lipids, 111 mg calcium, 2.7 mg iron, and 1.1 mg zinc (24). Although the nutrient composition of tempeh can vary depending on the brand, it provides equivalent amounts, and sometimes more, of the key nutrients such as protein, fat, iron, and calcium and, therefore, can be used as a replacement for meat.

## Analog

Refers to a plant-based product that intends to match both the nutritional and culinary attributes of its animal food counterpart. The aim is to re-create the original animal food in terms of appearance, texture, flavor, mouthfeel, and other sensory qualities while meeting its nutritional and functional properties. Thus, their production often requires extensive processing with a careful selection of ingredients and technologies compared to "substitutes" and "replacements."

## Examples

- *Soymilk*: nutritionally, it is the closest to dairy milk (18, 25). It is the only plant-based milk with comparable amounts of protein, minerals, and vitamins to cow's milk (18). Additionally, it is often fortified with vitamins and minerals, such as vitamin D and calcium,
to be nutritionally compatible with dairy milk (26, 27). Despite its "beany flavor" (27), it is placed under the analog category as it can also be used as a dairy substitute, therefore meeting both the nutritional and culinary criteria.

- *First-generation meat analogs:* plant-based food products designed to mimic the texture, taste, and appearance of meat. They were developed in the 1960s and 1970s and are typically made from soy, wheat, nuts, or products such as TVP. Their production was to meet the dietary needs of vegetarians and vegans who wanted to consume a meat-like product without consuming animal-based foods. However, the initial meat analogs produced through low-level processing techniques were criticized for their lack of taste and texture compared to real meat. Some examples include veggie burgers, different forms of vegetarian chicken such as nuggets and patties, vegetarian sausages or links, hot dogs, and cold cuts. Some famous producing brands include Worthington, Yves, MorningStar Farms, Loma Linda Foods, Lightlife, Tofurky, and Gardenburger.

- *Second-generation meat analogs:* a type of non-animal-based meat that aims to replicate the texture, flavor, and appearance of real meat more closely than earlier-generation products. Unlike the first-generation meat analogs, which were simply made from soy protein, wheat gluten, or nuts, second-generation meat analogs often use a combination of plant-based or plant-like ingredients, such as mycoprotein, and food technologies to mimic meat in its entirety. They require a larger number of additives and ingredients, a higher level of processing, and extensive technological advancements (28–30). Some examples include Beyond Meat and Impossible Foods. These products utilize soy, pea, and wheat as the primary protein source (31), along with coconut oil, potato starch, and other ingredients to create products that not only taste and cook like animal meat, but also resemble its nutrient profile (1).

## Alternative

In the context of the present perspective, an alternative refers to a food option that does not attempt to replicate its animal-based counterpart's nutritional and culinary qualities. An alternative is simply a different choice that may have similar physical characteristics to the original animal-based food, such as texture or form (e.g., fluid or solid), and is gastronomically desirable to the consumer. However, it is not required to be equivalent in all other properties. Hence, as the definition of the word implies, it encompasses all possible options or "alternatives," making it the broadest category.

## Examples

- Drinking apple juice instead of milk.

- Using avocado and nuts instead of cheese and salami as part of a charcuterie board.

- An entrée of pasta and vegetables instead of beef stew.

## Few notes to consider

Although sparse literature was found using the terms fake meat and/or mock meat, we recommend refraining from using these expressions as they could be considered derogatory. As we progress from alternative to replacement or substitute to analog, there is an increase in technological requirements, inputs, and processing (e.g., edamame/beans < tofu/bean patties < Beyond Meat). Therefore, the

criteria required to be met also become more stringent. While an alternative is not required to meet any specific criteria to replace the original animal food, except for being gastronomically satisfying to the consumer, the substitute must have comparable culinary properties, and the replacement must meet the nutritional qualities. The analogs, on the other hand, are expected to satisfy both nutritional and culinary characteristics and provide the same or very similar sensory experience.

Despite the industry's efforts, some nutritional properties of analogs remain different. For example, animal foods contain mainly saturated fat, while plants primarily have unsaturated fat and no cholesterol. In the case of meat, some food companies have incorporated plant-based sources of saturated fat, such as coconut oil, into their plant-based meat products to simulate the characteristics of real meat (1). Meanwhile, a plant-based analog that is identical to its animal food counterpart in all aspects has yet to be produced. Whether these efforts are desirable from the health and environmental perspectives is beyond the scope of this perspective.

It is important to note that the definitions presented here are not always mutually exclusive and can overlap (Figure 2). For instance, an analog can serve as a substitute, replacement, or alternative, but the reverse is not always the case. The term alternative has often been used in different platforms to refer to analogs (10); however, in precise terminology, it is simply a voluntary food choice and does not necessitate to fulfill the nutritional or culinary attributes of animal-sourced food. As the criteria for this category are not stringent, it encompasses a wide range of options, from those emulating the original food (analogs) to those that bear no resemblance. For instance, an alternative to meat can range from avocado, which only shares the physical characteristic of being solid, to tofu, which additionally meets some nutritional qualities, to second-generation meat analogs (e.g., Beyond Meat), which is the closest to meat not only in physical appearance and nutrient content, but also the sensory properties. Analogs, on the other hand, are innovative creations that require advanced technologies and are gaged by their all-aspect equivalence with the original food. It is worth re-mentioning that there is a varying degree of similarity to the original animal food regarding the nutritional and culinary characteristics among analog

<figure><img src="image_34.png" /><figcaption>FIGURE 2
The overlapping nature of different terms describing various animal-free options for animal foods. The "alternative" is an all-inclusive term, "substitute" (culinary properties) and "replacement" (nutritional properties) overlap to give rise to "analog," which intends to imitate animal foods in terms of sensory qualities while meeting their nutritional and functional properties.</figcaption></figure>