How do food businesses provide information on allergens in non-prepacked foods? A cross-sectional survey in Switzerland

Summary Purpose This project aimed to investigate allergen information practices of food businesses selling non-prepacked foods after the implementation of the new Swiss food law in May 2017. Methods A cross-sectional telephone survey was conducted with food businesses selling non-prepacked foods in Switzerland. A short, standardised questionnaire was developed in German, based on previous research and literature. It was subsequently translated into French and Italian. Altogether, 882 businesses (restaurants, dairies, butcher shops and bakeries) were contacted, of which 387 were willing to participate. SPSS® (IBM, Armonk, NY, USA) was used for statistical analyses. Results The vast majority (86.0%) of food businesses provides oral allergen information. Only 14.0% currently provide written allergen information to the customer, either upfront or on request. The most frequently used labelling system in written allergen declaration was naming all ingredients (35.2%). A significant number (39.8%) do not place a notice on how to obtain allergen information, although this is a legal requirement in Switzerland when not providing written information upfront. Conclusion So far, not all food businesses have been complying with the new Swiss food law on allergen information of non-prepacked food. Therefore, awareness of the legal obligations around communicating allergen information as well as the verification of its implementation should be enhanced. To meet the needs of consumers and avoid reactions, some form of written allergen information should be promoted. Giving this information on request might encourage communication between customer and staff, thus providing an extra measure of verification.

A labelling system and automation comparison index for industry 4.0 system

Purpose This paper aims to put forward a labelling system capable of reflecting the level of different Industry 4.0 (I4.0)features present in a manufacturing system and further propose a comparative index to collectively estimate and compare the system automation level. Design/methodology/approach Data for the empirical study were collected from interactions with the practising managers and experts. A relationship among the six I4.0 features is developed with fuzzy cognitive maps. Findings The paper proposed a simple and easy-to-understand labelling system for I4.0 systems, which indicates the automation level in each of six dimensions of any manufacturing system. The system is further strengthened by a proposed automation comparative index (ACI), which collectively reflects the automation level on a scale of “0” to “1”. Thus, the labelling system and parameter could help in comparing the level of automation in the manufacturing system and further decision-making. Research limitations/implications Only seven industrial sectors are illustrated in the paper, but the proposed concept of the classification scheme and ACI find their applicability on a large spectrum of industries; thus, the concept can be extended to other industrial sectors. Furthermore, a threshold value of ACI is a differentiator between a I4.0 and other automated systems. Both aspects have the scope of further work. Practical implications The way and pace by which the industrial world takes forward the concept of I4.0, soon it will need a labelling system and a parameter to assess the automation level of any automated system. The scheme assesses the automation level present in a manufacturing system. It will also estimate the level of the presence of each of all six attributes of an I4.0 system. Both labelling system and ACI will be the practical tools in the hands of the practising managers to help compare, identify the thrust areas and make decisions accordingly. Originality/value To the best of the authors’ knowledge, this is the first study of its kind that proposed the labelling system and automation comparison index for I4.0 systems.

SPUCL (Scientific Publication Classifier): A Human-Readable Labelling System for Scientific Publications

To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different techniques such as probabilistic, machine learning and neural networks models. One of the most popular document classification approaches is the LDA (Latent Dirichlet Allocation), a probabilistic topic model. One of the main issues of the LDA approach is that the retrieved topics are a collection of terms with their probabilities and it does not have a human-readable form. This paper defines an approach to make LDA topics comprehensible for humans by the exploitation of the Word2Vec approach.

Investigating the healthiness of food products on promotion: market brands and own brands

PurposeThe purpose of this study is to investigate the proportionality of market brand (MB) foods versus supermarket own brand (OB) foods sold on promotion and to compare their healthiness.Design/methodology/approachAn existing dataset containing nutritional information about a variety of foods on promotion (n = 6,776) from 48 stores across 8 retail chains in Northern Ireland (NI) was reanalysed. Product healthiness was measured using a score aligned to the Food Standards Agency's Front of Pack nutrient labelling system. MBs and OBs were considered as a whole and in their respective subsets–international/national and regional MBs, and premium, mid-market and value tiered OBs.FindingsResults found a balance in favour of health (52.4% amber/green versus 47.6% red) across retailers' promotions in NI. Further, OB products were often found to be superior to MBs with regards to overall healthfulness, and regional brands were found to be less healthy than international/national brands.Research limitations/implicationsFindings rationale further retail research to compare nutritionally OB and MB product types, and further consumer research regarding important attributes of OBs.Practical implicationsRetailers should communicate the comparative healthiness of their OBs in comparison to MB alternatives, in addition to communicating comparative price savings. There is opportunity for retailers to increase visibility of mid-market and value OB tiers, and for regional MBs to improve the nutritional profile of products in line with the consumer trend for health.Originality/valueThis study provides a contribution by using data on OBs and MBs on promotion, and by investigating the nutritional differences between different tiers of OB and MB products.

Two Dimensions of Nutritional Value: Nutri-Score and NOVA

Front-of-pack labels can improve the ability of consumers to identify which foods are healthier, making them a useful public health tool. Nutri-Score is a front-of-pack labelling system adopted by several European countries. This system ranks foods according to their nutritional quality, but does not consider other dimensions such as the degree of food processing. The aim of this study is to compare the nutritional quality (as assessed by Nutri-Score) and the ultra-processing (as assessed by the NOVA classification) of foods in the Open Food Facts database. A simple correspondence analysis was carried out to study the relationship between the two systems. Ultra-processed foods (NOVA 4) were found in all Nutri-Score categories, ranging from 26.08% in nutritional category A, 51.48% in category B, 59.09% in category C, 67.39% in category D to up to 83.69% in nutritional category E. Given the negative effect that the consumption of ultra-processed foods has on different aspects of health, front-of-pack labelling with Nutri-Score should at least be accompanied by complementary labelling indicating the level of processing, such as the NOVA classification.

How Do Different Nitrogen Application Levels and Irrigation Practices Impact Biological Nitrogen Fixation and its Distribution in Paddy System?

Background and aimsBiological nitrogen fixation (BNF) in paddy systems is impacted by nitrogen application levels and irrigation strategies, but the extents to which these factores influence BNF and its distribution in soil and rice were still largely unclear. This study investigates this influence.MethodsAn airtight transparent growth chamber 15N-labelling system were used to investigate how different nitrogen application levels (0, 125, 187.5 and 250 kg N ha-1) and irrigation strategies (flooding irrigation or intermittent irrigation) impact the amount of BNF and its distribution in soil and rice. ResultsNitrogen application at 125 ~ 250 kg N ha-1 reduced the amount of BNF by 81% - 86%. The inhibition effect of nitrogen application on BNF at a soil depth of 1-15 cm was greater than that at 0-1 cm. Relative to the continuous flooding irrigation, intermittent irrigation enhanced rice growth and promoted the transfer of fixed nitrogen from 0-1 cm soil layer to rice, but it did not change the total amount of BNF. ConclusionsThis study indicated that BNF supplied little nitrogen for rice production at the high nitrogen application levels, but the intermittent irrigation could promote utilization of biologically fixed nitrogen.

Front-Of-Pack Nutrition Labelling: A Position Statement of the European Academy of Paediatrics and the European Childhood Obesity Group

Background: Due to the growing risk of obesity and related diseases in the population of children, effective preventive measures are of great importance. Front-of-pack (FOP) nutrition labelling may contribute to health promotion by increasing consumer awareness on the nutritional qualities of packaged foods and purchasing decisions, and it may stimulate food providers to improve the composition of products. Summary: Appropriate labelling should enable customers to make healthy choices quickly and intuitively. Key Messages: The European Academy of Paediatrics and the European Childhood Obesity Group makes an appeal to European Union legislators to immediately introduce a mandatory, uniform, and interpretative FOP nutrition labelling system.