Unhealthy diets are a leading risk factor for non-communicable diseases and negatively impact environmental sustainability (1, 2). Policy actions such as restrictions on marketing, taxes and subsidies, front-of-pack labelling, and regulation of health claims have been recommended to tackle dietary risk factors (3–5). However, growing evidence indicates nutrient-based nutrition classification schemes, such as the Health Star Rating (HSR) system and Nutri-score, do not align with nutrition science principles as they cannot differentiate degree of processing (6–8). Nutrient-based schemes only assess a handful of isolated nutrients or components, ignoring the more holistic concept of processing. The NOVA classification system categorises foods into four groups based on the nature, extent and purpose of processing, group 1–unprocessed or minimally processed foods, group 2–processed culinary ingredients, group 3–processed foods, and group 4–ultra-processed foods, defined as “formulations of ingredients, mostly of exclusive industrial use, that result from a series of industrial processes” (9). Ultra-processed foods are often hyper-palatable, convenient and heavily marketed, and can displace nutritious unprocessed and minimally processed foods in the diet (10). Diets that include higher proportions of ultra-processed foods are linked to several chronic diseases, including obesity, metabolic syndrome, cardiovascular disease, depression, and all-cause mortality (11, 12). The scale of their production, the intense industrial processes involved, extensive packaging, and unnecessary overconsumption also contribute considerably to greenhouse gas emissions, biodiversity loss, land degradation and increased water footprints (13, 14).

In a previous study we compared seven nutrient-based, food-based and dietary-based nutrition classification schemes (NCSs) and identified key characteristics that could be used to holistically identify foods to be encouraged (“healthy”) or discouraged (“unhealthy”) for guiding the design of policy actions (15). A holistic approach in the context of NCSs considers the food matrix (extent of degradation or preservation) and the complex synergies that exist between the nutrient and non-nutrient components [more than 26,000 bioactive components are present in foods (16)]. A reductionist approach, on the other hand, assesses foods for their content of specific nutrients with established health associations (e.g., excess sodium and hypertension). A reductionist approach used in isolation assesses a food’s health potential only as the sum of its parts, ignoring the complex structure in which nutrients are contained (17). We found that NCS’s that used a “top-down” orientation, i.e., first assessing the holistic characteristics of a food’s health potential before assessing reductionist characteristics (17) identified more foods as being unhealthy [when unhealthy was defined as “discretionary” by the Australian Dietary Guidelines (ADGs) or ultra-processed].

Food-based NCSs following a top-down approach, such as the Pan American Health Organization’s Nutrient Profiling Model (PAHO NPM) and the World Health Organization European Region’s Nutrient Profile Model (WHO-Euro NPM), can more often identify unhealthy foods (when unhealthy is defined as ultra-processed) (15). However, limitations were identified in these schemes, including ambiguous food category classification and the assessment of certain nutrients, such as total fat, which resulted in the penalisation of some whole foods (overlooking holistic concepts). Thus, there is potential to reform existing NCSs to be more fit-for-purpose in informing policy actions, a particularly timely and important task considering current plans to harmonise NCSs internationally (18, 19).

Effective nutrition classification for policy actions should help shift the food supply away from ultra-processed foods toward dietary patterns consisting mostly of unprocessed and minimally processed foods. It is proposed that such nutrition classification can be improved by applying the key characteristics of NCSs identified in our previous research. This would involve a top-down holistic approach with assessment of degree of processing as the first step and application of upper thresholds for risk nutrients as the second step. Therefore, this study aims to develop a novel food processing-based nutrition classification scheme for guiding policy actions. A secondary aim is to validate the scheme by classifying food and beverage items in the Australian food supply (face validity) and comparing them to the classifications of existing NCSs (convergent validity).

A total of 7,322 food items were assessed in the combined dataset, 3,002 from AUSNUT and 4,320 from Mintel (Table 3). Models 1 and 2 classified the majority of items as unhealthy, 73.3 and 68.2%, respectively. Model 1 classified 1,958 items as healthy, of which 1,447 were NOVA group 1 or 2 foods and ingredients, and 511 were NOVA group 3 foods; and 5,364 as unhealthy, of which 570 were NOVA group 3 foods, and 4,794 NOVA group 4 foods (total number of ultra-processed items in the dataset). Model 2 differed by the number of MUPs used to identify healthy/unhealthy foods. For Model 2, 790 (16.5%) of NOVA group 4 foods were classified into sub-group 4.1 (contained only one MUP), and of these, 367 (46.5%) moved to the healthy category (representing only 7.7% of all NOVA group 4 foods).

Face validity was assessed by evaluating the resulting healthy/unhealthy classifications within different food categories and over individual products. The majority of items in the categories of eggs, fish and seafood products, fruit and vegetables, and meat and meat products, were classified as healthy for both models (Figure 2). Over 50% of food items were classified as unhealthy in the food categories of: bread and bakery products; cereal and cereal products; confectionery; convenience foods; dairy; non-alcoholic beverages; sauces, seasonings, and spreads; snack foods; special foods; and sugar, honey, and related products. Of the 367 sub-group 4.1 food items that moved to the healthy category for Model 2, the highest number were in the cereal and cereal products category (n = 78), the dairy category (n = 75), and the convenience food category (n = 51) (Supplementary Figure 1).

A complete list of the classifications for the 7,322 food and beverage items for models 1 and 2 is presented in Supplementary material 2, along with a list of the sub-group 4.1 items classified as healthy, and 99 “healthy” classifications that were considered anomalies. Examples include frozen puff pastry sheets; high fat meals (e.g., “Mac and cheese croquettes” and “Fettucine with chicken and cream sauce”); meals with processed meats as ingredients (e.g., “Ham and cheese quiche” and “One-pan brekky with beef chipolata sausages”); dairy- or coconut-based desserts (e.g., “Chocolate flavoured coconut milk mousse” and “dairy dessert, flavours other than chocolate”); pâtés (e.g., “Chicken and Madeira Pâté” and “Duck and Shiraz Pâté”); frozen potato products (e.g., “potato wedges, purchased frozen, deep fried or fried” and “potato gem, purchased frozen, baked or roasted”); mayonnaises (e.g., “Garlic Aioli Mayonnaise” and “Tartare Sauce”); and 2 alternative meat products composed largely of starches (“Lightly battered prawns” and “Lightly crumbed scallops”).

For NOVA group 3 items, a high percentage of pizzas (100%), processed meats (90.9%), breads (75%), soups (64.7%), crisps and snacks (49.2%), and cheeses (48.1%) exceeded sodium thresholds (Table 4). And a high percentage of NOVA group 3 spreads (64.8%), ice cream and ice edibles (80%), and fruit and vegetable juices (42.9%) exceeded free sugar thresholds. All NOVA group 3 cakes, muffins and pastries, confectionery, pizza, beverage mixes, cordials, and sugar products were classified as unhealthy. For sub-group 4.1 items, a high percentage of sauces (68.9%), processed meats (61.9%), cheeses (60.7%), and crisps and snacks (51.4%) exceeded sodium thresholds, and a high percentage of fruit and vegetable juices (79.2%), and nuts and seeds (63.6%), cakes, muffins and pastries (51.9%), and sauces (42.2%) exceeded free sugar thresholds (Table 5). Less than 15% of sub-group 4.1 noodles, pasta, pre-prepared salads and sandwiches, milk, and vegetables were classified as unhealthy by Model 2.

For the assessment of convergent validity, most food categories in both Model 1 and Model 2 classified food items in closest alignment to NOVA (Figure 3). In all food categories, except for cereal products and special foods, the PAHO NPM classified more items as unhealthy compared to both models. This difference is explained by a high number of NOVA group 3 items in the subcategories of cheese, crisps and snacks, dips, nuts and seeds, and processed fish and seafood being classified as unhealthy by the PAHO NPM due to total fat and saturated fat criteria Supplementary Figure 2. Both models were fair to substantially aligned (although only at the 3.5 cut-off for the HSR) to all other NCS’s when pairwise agreement using Cohen’s Kappa was assessed (Table 6). Model 1 had highest agreement with NOVA, with almost perfect agreement. Model 2 had the highest pairwise agreement with the PAHO NPM, with substantial agreement. The HSR and ADGs had lower agreement with both models, but slightly higher agreement with Model 2 compared to Model 1.

This study aimed to develop a model NCS combining level of processing and nutrient thresholds, and to test the validity against items in the Australian food supply and against existing NCSs. The resulting two versions of the model combine the NOVA classification system with criteria for added sodium and free sugars, producing a binary output of either healthy or unhealthy. The intended application of the model NCSs is specific to policy purposes where a binary judgement of individual food items is required, for example front-of-package warning labels, restrictions to marketing of unhealthy food to children, taxes, or regulation of nutrition and health claims.

A higher proportion of food categories consistent with dietary patterns that are associated with positive health outcomes and included as recommended in most national dietary guidelines (60, 61), such as fruits, vegetables, and eggs were classified as healthy. And the clear majority of food categories consistent with dietary patterns associated with adverse health outcomes and not recommended in national dietary guidelines, such as confectionery, snack foods, and convenience foods were classified as unhealthy. Therefore, the models produced health assessments of food items reflective of current nutrition evidence when applied to the Australian food supply, suggesting good face validity.

The added free sugar and sodium thresholds set for the models reliably identified NOVA group 3 food items that are typically high in added sugars and salt, for example, processed meats are not usually classified as ultra-processed as the additives used have a preservation purpose. However, 91% (40/44) of the NOVA group 3 processed meats in the dataset exceeded the sodium threshold and were therefore classified as unhealthy. Only a modest number of products in the muffins, cakes and pastries, confectionery, pizza, cordials, and dessert addition sub-categories were classified as NOVA group 3 (most were NOVA group 4 ultra-processed), however 100% exceeded the added sodium or free sugar thresholds. Furthermore, over 75% of biscuits, ice creams and edible ices, and popcorn exceeded nutrient thresholds. A high percentage of NOVA group 3 breads, cheeses, and soups exceeded the sodium threshold. Although usually encouraged in dietary guidelines, there is wide variability in the sodium content of these foods, and those in the higher range can contribute significantly to dietary intake. In Australia, 15% of average daily sodium intake is derived from bread products alone (31). NOVA group 1 fruit juices are automatically classified as healthy, despite containing a significant amount of free sugars, thus juice products might need to be considered as an exemption to this criterion in the model’s application to policy.

Two versions of the model were tested in this study, differing in how they treat NOVA group 4 (ultra-processed) foods. Model 1 automatically classifies all ultra-processed foods as unhealthy, whereas Model 2 considers the presence of only 1 MUP to be healthy unless the added sodium and free sugar content exceeds thresholds. The difference this made to the types of products classified as healthy and unhealthy was explored, with particular interest in those ultra-processed foods moving to the healthy classification in Model 2. Of the 4,794 ultra-processed foods in the dataset, 16.5% contained only one MUP (sub-group 4.1), and of these 47.1% were under the set thresholds for sodium and free sugars. This resulted in only 7.7% (n = 367) of items in the dataset moving to the healthy classification for Model 2. Over half of these food items were cereals, dairy, or convenience foods. Common examples of items include gluten-free pastas, grated cheeses containing anti-caking agents, or ready-to-eat or ready-to-heat packaged meals, consisting of mostly a combination of whole foods such as meats and vegetables.

The NOVA system was developed to assess diet quality at the population level, and the recommendation is to limit ultra-processed foods as a group to achieve diets with low content of these foods. Therefore, the application of the NOVA system at the individual food-level for regulatory purposes presents some challenges. Although there is no evidence on how the number of markers of ultra-processed foods affect health, we assumed that most of the food matrix of wholefoods are preserved when only one marker is used. In fact, only a small number of products were assessed as such, and our findings indicate that the approach of Model 2 could be a suitable solution to this issue when classifying foods as unhealthy for these particular policy purposes. However, the importance of the number and type of MUP to the classification of unhealthy needs to be further investigated. For example, the presence of ingredients such as colours and flavours (purely cosmetic and unnecessary) and emulsifiers [associated with changes in microbiota (62)] could be considered to also automatically classify a food as unhealthy. Furthermore, the accurate classification of ultra-processed foods is currently limited by the lack of information provided by manufacturers or regulated on labelling regarding the specific purpose/function of an industrial food substance in a food, and the industrial processing techniques or processing aids used.

Convergent validity, the extent to which a measure aligns with a closely related one, is recognised to be an important step in the validation of NCSs (45, 46). Agreement with NOVA was almost perfect (according to the Kappa coefficient) for Model 1 and substantial for Model 2, indicating only modest modification to and departure from the original holistic concept on which they are based. Substantial agreement of both models with the PAHO NPM is also a predictable outcome considering they both combine level of processing and nutrient criteria. Much lower agreement was observed for the ADGs and the HSR, particularly for the HSR when a 2.5-star rating was applied as the cut off for identifying a healthy food. The current ADGs do not incorporate the concept of level of processing into the description of discretionary foods, hence the low agreement observed was expected. However, moderate agreement with the ADGs still indicates broad alignment with the evidence base underlying this key Australian policy tool. The fair to moderate agreement (depending on what cut-off is applied) with the HSR is further evidence of the low alignment of nutrient-profiling-only approaches and the NOVA concept (6).

A key difference between the tested models and the PAHO NPM is exclusion of the fat criteria. This difference was most apparent in the cheese category, with the PAHO NPM classifying 106 of the 108 NOVA group 3 cheese items as unhealthy due to excess total or saturated fat (Supplementary Figure 2). Over half of these items were classified as healthy by Models 1 and 2, examples of which included cottage cheese, reduced fat cheddar, and buffalo mozzarella. Other food examples with misalignment were firm tofu, soy milk, seedy crackers, roasted cashews, and unflavoured salmon canned in water, all classified as unhealthy by the PAHO NPM on the basis of total fat/saturated fat, but healthy by Models 1 and 2 (Supplementary Table 3). These examples are all recommended nutritious five food group foods according to the ADGs, and not foods that would need to be targeted as “unhealthy” for policy actions. However, other examples of NOVA group 3 foods with a higher fat content classified as healthy by the tested models were not always clearly healthier. For example, tartare sauce, beetroot chips, cashew cream cheese, coconut yoghurt with raw cacao, black truffle duck paté, and fettucine with chicken and cream sauce (Supplementary material 2). On balance however the exclusion of fats criteria in Models 1 and 2 appears to overcome the PAHO NPM’s limitation of penalising whole foods, and more accurately reflects the evidence base on the food-specific effects of fats (39).

This is not the first time the NOVA concept has been incorporated into a NCS in combination with nutrient profiling. As discussed, the PAHO NPM presents a strong example of this approach and is already applied for policy purposes (e.g., informs labelling schemes in Mexico and Argentina). Although in contrast to Models 1 and 2, it assesses the nutrient content of all NOVA group 3 and group 4 foods, meaning manufacturers of ultra-processed foods are still able to manipulate nutrient content to avoid an unhealthy classification (63). The inclusion of % energy and fat criteria in the PAHO NPM also leads to a limited interpretation of what can be classified as a healthy food. The Siga scheme, developed and applied for commercial purposes, also employs a top-down approach, applying NOVA categories before overlaying nutrient criteria (43). However, the complexity of the system, which considers risk assessment of additives and the application of industrial processing techniques, may not be practical for policy purposes, potentially being overly burdensome on manufacturers, enforcement agencies, policy makers, and regulators. The Food Compass scheme, developed to guide consumer behaviour, food policy, scientific research, and industry reformulations (49), includes the NOVA classification as a minor criterion, yet also involves an impractical level of complexity, incorporating a range of vitamins, minerals, and phytonutrients that are rarely provided in the nutrition information panel.

The binary classification of the developed models results in less flexibility regarding foods and beverages in the “grey zone” of healthiness. For example, Food Compass and the HSR are ranking scales that enable foods to sit at a mid-point for healthiness. However, nutrition policy actions require a method to unambiguously identify unhealthy foods, the regulation of which will likely result in the greatest improvement to food environments (20, 64). The application of the models to policies such as restrictions to marketing is relatively straightforward, as foods classified as unhealthy will not be eligible for the specific marketing activity. The models could be applied to a warning style FOPL by labelling unhealthy products with one or more of three statements, based on the criteria on which the product failed, i.e., “Ultra-processed food,” “High in free sugars,” and/or “High in sodium” (65). Given the diversity of foods in the marketplace, no NCS will always correctly identify healthy and unhealthy foods in absolute terms. However, an underlying principle in the development of the models is that it is preferable, in policy terms, to favour the most accurate classification of unhealthy foods so that healthy foods are not incorrectly identified for regulation. Any anomalies that may occur are most likely to represent potentially unhealthy foods as healthy (e.g., some dairy desserts, high-fat meals, crumbed fish, and puff pastry). These anomalies are minimal, and the actual foods would not be labelled as healthy but would just not be subject to taxation, warning labels or restrictions to marketing.

Potential difficulties that could be encountered in the model’s use are determining MUPs and added free sugar content. A standard list of ingredients considered MUPs would need to be developed and updated regularly for any new or novel products, specific to the national food supply. The reporting of added free sugars is not a requirement on the nutrition information panel in Australia and many other countries, and thus the free sugar content needs to be estimated in most jurisdictions. A standard estimation approach was used in this study, however, for greater accuracy food regulations would need to be modified to include free sugars. The simplicity of the model however enables the upper nutrient thresholds and the categories they apply to (cheeses, foods, and liquids) to be adjusted based on the specific policy application and national context.

The development of the model NCS was based on a strong foundation of principles evolved through rigorous analysis of existing schemes (15). The models were applied to a large dataset comprising a diverse range of food and beverage products available in the Australian food supply and potentially subject to regulation. Convergent validity was also rigorously assessed against four existing NCSs with differing conceptual bases. Nevertheless, some limitations in the analysis should be acknowledged. Some variables were estimated, including free sugars, and the number of MUPs (for AUSNUT data) for the model NCS, and fibre and fruit/vegetable/nut/legume content for existing NCSs. However, robust procedures were developed and recorded for each estimation. Our analysis only assesses the model NCS against the Australian food supply, product types available in other marketplaces may produce different results, thus assessment against national food supplies would be recommended and any adjustments made before application to policy. The validation of a NCS should also involve predictive validity, that is, the ability of the model to predict health outcomes, thus this should be prioritised as the next step.

This study found the incorporation of a top-down holistic approach to nutrition classification, considering degree of processing as a first step, produces a NCS that may prevent ultra-processed foods from being promoted or evading regulation. The novel NCS represents an improvement on existing models by avoiding an overly strict interpretation of what is considered an “unhealthy” food. This analysis also indicates strong face and convergent validity against existing NCSs. Thus, a model NCS combining level of processing and nutrient criteria presents a valid alternative to existing methods to classify the health potential of individual foods for policy purposes.

The data analyzed in this study is subject to the following licenses/restrictions: Restrictions apply to the availability of the data described in this manuscript, which were used under license from Mintel, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of Mintel. Requests to access these datasets should be directed to SD, s.dickie@deakin.edu.au.

SD: conducted the research, analysed the data, and wrote the manuscript. ML, JW, and PM: provided supervision and reviewed and edited the manuscript. All authors contributed to the conception and design of the research and read and approved the final manuscript.