The food industry has recently been under unprecedented pressure due to major global challenges, such as climate change, exponential increase in world population and urbanization, and the worldwide spread of new diseases and pandemics, such as the COVID-19. The fourth industrial revolution (Industry 4.0) has been gaining momentum since 2015 and has revolutionized the way in which food is produced, transported, stored, perceived, and consumed worldwide, leading to the emergence of new food trends. After reviewing Industry 4.0 technologies (e.g. artificial intelligence, smart sensors, robotics, blockchain, and the Internet of Things) in Part I of this work (Hassoun, Aït-Kaddour, et al. 2022. The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 1-17.), this complimentary review will focus on emerging food trends (such as fortified and functional foods, additive manufacturing technologies, cultured meat, precision fermentation, and personalized food) and their connection with Industry 4.0 innovations. Implementation of new food trends has been associated with recent advances in Industry 4.0 technologies, enabling a range of new possibilities. The results show several positive food trends that reflect increased awareness of food chain actors of the food-related health and environmental impacts of food systems. Emergence of other food trends and higher consumer interest and engagement in the transition toward sustainable food development and innovative green strategies are expected in the future.

The fourth industrial revolution in the food industry-part II: Emerging food trends.

Center for Epidemiological Research in Nutrition and Health, School of Public Health, University of Sao Paulo, São Paulo, São Paulo, Brazil Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, Burwood, Victoria, Australia

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Ultra-processed foods should be central to global food systems dialogue and action on biodiversity

Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training (n = 8986) and test datasets (n = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach (R2 = 0.84 vs. R2 = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.

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An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods

http://manuscript.elsevier.com/S0308814622012055/pdf/S0308814622012055.pdf

Deep learning accurately predicts food categories and nutrients based on ingredient statements.

AIM
To assess the nutritional quality of plant-based meat analogues in Australia, compared to equivalent meat products, and to assess levels of micronutrient fortification in meat analogues.


METHODS
This cross-sectional study used nutrition composition data for products collected in 2021 from major supermarkets in Australia. Nutritional quality was assessed using the Health Star Rating, energy (kJ), protein (g), saturated fat (g), sodium (mg), total sugars (g), and fibre content (g) per 100 g, and level of food processing using the NOVA classification. Proportion of products fortified with iron, vitamin B12 and zinc were reported. Differences in health star rating and nutrients between food categories were assessed using independent t-tests.


RESULTS
Seven hundred ninety products (n = 132 plant-based and n = 658 meat) across eight food categories were analysed. Meat analogues had a higher health star rating (mean 1.2 stars, [95% CI: 1.0-1.4 stars], p < 0.001), lower mean saturated fat (-2.4 g/100 g, [-2.9 to -1.8 g/100 g], p < 0.001) and sodium content (-132 mg/100 g, [-186 to -79 mg/100 g], p < 0.001), but higher total sugar content (0.7 g/100 g, [0.4-1.1 g/100 g], p < 0.001). Meat analogues and meat products had a similar proportion of ultra-processed products (84% and 89%, respectively). 12.1% of meat analogues were fortified with iron, vitamin B12 and zinc.


CONCLUSION
Meat analogues generally had a higher health star rating compared with meat equivalents, however, the nutrient content varied. Most meat analogues were also ultra-processed and few are fortified with key micronutrients found in meat. More research is needed to understand the health impact of these foods.

The nutritional profile of plant-based meat analogues available for sale in Australia.

Unhealthy diets are underpinned by the over-consumption of packaged products. Data describing the ingredient composition of these products is limited. We sought to define the ingredients used in Australian packaged foods and beverages and assess associations between the number of ingredients and existing health indicators. Statements of ingredients were disaggregated, creating separate fields for each ingredient and sub-ingredient. Ingredients were categorised and the average number of ingredients per product was calculated. Associations between number of ingredients and both the nutrient-based Health Star Rating (HSR) and the NOVA level-of-processing classification were assessed. A total of 24,229 products, listing 233,113 ingredients, were included. Products had between 1 and 62 ingredients (median (Interquartile range (IQR)): 8 (3–14)). We identified 915 unique ingredients, which we organised into 17 major and 138 minor categories. ‘Additives’ were contained in the largest proportion of products (64.6%, (15,652/24,229)). The median number of ingredients per product was significantly lower in products with the optimum 5-star HSR (when compared to all other HSR score groups, p-value < 0.001) and significantly higher in products classified as ultra-processed (when compared to all other NOVA classification groups, p-value < 0.001). There is a strong relationship between the number of ingredients in a product and indicators of nutritional quality and level of processing.

Deconstructing the Supermarket: Systematic Ingredient Disaggregation and the Association between Ingredient Usage and Product Health Indicators for 24,229 Australian Foods and Beverages.

BACKGROUND Dietary guidelines recommend limiting the intake of added sugars. However, despite the public health importance, most countries have not mandated the labeling of added sugar content on packaged foods and beverages, making it difficult for consumers to avoid products with added sugar, and limiting the ability of policymakers to identify priority products for intervention. OBJECTIVE To develop a machine learning approach for the prediction of added sugar content in packaged products using available nutrient, ingredient, and food category information. DESIGN The added sugar prediction algorithm was developed using k-Nearest Neighbors (KNN) and packaged food information from the US Label Insight dataset (n = 70,522). A synthetic dataset of Australian packaged products (n = 500) was used to assess validity and generalization. Performance metrics included the coefficient of determination (R2), mean absolute error (MAE), and Spearman rank correlation (ρ). To benchmark the KNN approach, the KNN approach was compared to an existing added sugar prediction approach that relies on a series of manual steps. RESULTS Compared to the existing added sugar prediction approach, the KNN approach was similarly apt at explaining variation in added sugar content (R2 = 0.96 vs. 0.97 respectively) and ranking products from highest to lowest in added sugar content (ρ = 0.91 vs. 0.93 respectively), while less apt at minimizing absolute deviations between predicted and true values (MAE = 1.68 g vs. 1.26 g per 100 g or 100 mL respectively). CONCLUSIONS KNN can be used to predict added sugar content in packaged products with a high degree of validity. Being automated, KNN can easily be applied to large datasets. Such predicted added sugar levels can be used to monitor the food supply and inform interventions aimed at reducing added sugar intake.

Tazman Davies

Papers

An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods

Deconstructing the Supermarket: Systematic Ingredient Disaggregation and the Association between Ingredient Usage and Product Health Indicators for 24,229 Australian Foods and Beverages.

A Machine Learning Approach to Predict the Added Sugar Content of Packaged Foods.

Prevalence of missing nutrition label and ingredients list information on e-shops of major chain supermarkets in Hong Kong