First lady’s waste-saving food bank initiative expanded

They later hand out “debit cards” to families with a pre-installed virtual currency for food they need. Cardholders then visit the banks, which are in the …

First lady Emine Erdoğan’s food bank initiative to cut food waste is expanding its network, as the number of food banks delivering food to needy households increased to 69. By spreading food banks across the country, the initiative aims to save TL 30 billion yearly. Currently, 32 provinces host food banks. Apart from depositing food, people can leave clothes and cleaning materials for delivery to families in need. The initiative, with the motto “sharing is gaining,” sets up food banks in cooperation with municipalities and nongovernmental organizations (NGO) that host the banks.

It brings together food producers or donors with people in need. Farmers, food producers, suppliers, retailers, restaurants and wholesalers joined the initiative for discarded but edible food to be delivered to the banks. Food banks aim to “recycle” 60% of the discarded food. The number of banks will increase to 81 by the end of 2019. Already, 32 municipalities host food banks, saving 30% in recycling food in six months.

The banks are supplied with items nearing expiration dates, items with packaging errors and import and export surplus.

They work on a supply and demand basis. Donors first declare the amount of the donation to the banks, and food bank workers visit families in need to determine their primary needs. They later hand out “debit cards” to families with a pre-installed virtual currency for food they need. Cardholders then visit the banks, which are in the form of supermarkets, and can obtain 30-40 different items, from food to cleaning materials.

Food banks currently exist in many cities, from İzmir and Istanbul to Çorum and Hatay. The western city of Manisa has the highest number with 19 food banks.

Food waste costs Turkey TL 214 billion every year. According to official figures, six million loaves of bread go to waste every day, which is equivalent to the cost of construction for at least 80 hospitals and 500 schools, officials say.

Several NGOs already run similar food banks, and they delivered food worth TL 24 million in the past year, as well as clothing donations worth TL 14.3 million.

Related Posts:

  • No Related Posts

Diners are delivery platform-loyal, study says

The study concluded that customers are platform-loyal, with Uber Eats, Grubhub and DoorDash capturing 82% of guests’ orders via delivery among …

Dive Brief:

  • Roughly half (51%) of consumers have used a restaurant’s website to place an online order in the past month, while 38% have used an online order aggregating service like Grubhub, according to Toast’s Restaurant Success in 2019 industry report.
  • Twenty-nine percent of guests surveyed have used a restaurant or food ordering service like LevelUp some time in the last month.
  • The study concluded that customers are platform-loyal, with Uber Eats, Grubhub and DoorDash capturing 82% of guests’ orders via delivery among those surveyed. In the past year, guests ordered the most on Uber Eats.

Dive Insight:

Online food orders have increased in recent years on both third-party services as well as restaurant-specific apps. Diners tend to place a few online orders each month and seem to enjoy having a variety of options when it comes to how they place that order and how it gets to their dining table, according to the study. This means that restaurants may have to consider offering in-house mobile app ordering as well as partnering with a third-party aggregator in order to truly cover their digital bases.

Customers’ preference for placing orders directly through a restaurant’s site bodes well for restaurants looking to skip paying third-party commission rates. A few restaurants have spearheaded online ordering through a mobile app. Chipotle has offered a variety of promotions to improve its app downloads, including offering free food. It recently opened “Chipotlanes” as a dedicated way for diners to pick up app-based orders without having to wait inside. Cava and Blaze Pizza are testing similar concepts to help expedite online orders.

Other chains are banking on apps to build customer loyalty, particularly through rewards programs. Bakery chain Paris Baguette has launched a mobile app that features rewards for orders placed through the app, while Starbucks’ app has a variety of features tied to its loyalty program, including online ordering and information about traceability for its coffee beans. Dunkin’ recently began accepting multi-tender payments through its mobile app. The coffee chain also partners with Grubhub, recently using the service’s geofencing to optimize its NYC deliveries.

Smaller restaurants may not have the financial means to develop an app in-house or to retain a tech company’s services to do the job. This makes third-party aggregators a popular option because the restaurant can still offer online ordering and delivery to compete with major players in the area. Third-party aggregator services can also help a restaurant gain exposure to new customers in the region that are surfing offerings on the app.

Competition among the third-party ordering services is getting fiercer, with each service adding new features and expanding into new territories in a bid to outdo the others. Uber Eats is contemplating a dine-in option that allows users to place an order but still eat in the restaurant, while Postmates recently added 1,000 new cities to its platform to reach each state in the U.S. DoorDash raised another $600 million in its latest funding round. Online reservation service OpenTable also recently moved into the delivery realm with a new in-app feature.

Related Posts:

  • No Related Posts

A big appetite for data

Sandrine Pigat of Creme Global explains how to navigate big data by … Predictive analytics and scientific modelling are interesting areas of data …

Sandrine Pigat of Creme Global explains how to navigate big data by applying traditional predictive models to help make informed decisions about food product development, consumer health and food safety.

The evolution of novel data processing technologies is fast paced and the volume of data being generated is growing by the second. The food industry stands to benefit from this and has been testing and adapting various routes for using data science techniques to enhance the production of safe and healthy foods.

Data science requires a multidisciplinary approach and a broad range of skill sets, from mathematics and statistics, computer science and machine learning to artificial intelligence (AI). Data science also needs to have strong ties to the actual domain knowledge[1] in order to ask the right questions and select the right data. Predictive analytics and scientific modelling are interesting areas of data science and the activity in this space is growing. Applications can range from traditional methods using advanced statistics for assessing various future scenarios to machine learning techniques, including artificial intelligence.

The use of data science within food and health has become more prevalent and has been steadily complementing more traditional approaches. Predictive modelling for making informed decisions in new product development, business strategy and consumer health and safety has now demonstrated its value to stakeholders from industry, governments and research organisations on many occasions, some of which are described below.

The collecting, centralising and formatting of data via spreadsheets, hardcopies, documents, IOT or other means is the first step into digitisation of data, followed by the structuring, validating, analysing and visualising of this data. Only then is it possible to develop more advanced models that serve to inform R&D (from product design to launch), safety (including exposure and microbial food safety), consumer health and strategy.

Probabilistic Exposure Modelling

Probabilistic Exposure Modelling has been used and applied for a number of decades (Figure 1). As part of an overall risk assessment of a food contaminant, pesticide residue, additive or a novel ingredient on a population of consumers, an exposure assessment has to be carried out. As an approximation, the exposure can be quantified by the amount of food consumed multiplied by the concentration of the contaminant in this food. However, when looking at exposure within and across consumer populations, this simple calculation can become quite complex. A chemical can be present at varying levels in a large variety of foods, consumed in varying quantities, in different combinations, by different consumers in different countries/regions, and at different life stages.

Figure 1 Probabilistic Exposure Model

Therefore, exposure in a population is intrinsically variable and has a number of sources of uncertainty; this variability and uncertainty should be captured using probabilistic methods in each risk assessment scenario, as required. The results can then be expressed with confidence bounds and the scenarios can be evaluated more rigorously.

As an initial screening exercise, more simplistic methods are often applied to estimate exposure, high level consumption statistics and average or maximum chemical concentration levels. When comparing those crude exposure levels to health based thresholds, such as the acceptable daily intake (ADI) or tolerable daily intake (TDI), this can become an issue as the exposure is likely to be overestimated and will potentially exceed those limits, especially when exposure is aggregated from multiple sources.

This is where probabilistic dietary exposure modelling comes into its own to refine the exposure results for a population in a far more accurate and realistic manner by applying various mathematical techniques, such as using distributions of intakes, accounting for a range of concentrations rather than using a mean or a maximum point value, occurrence of a chemical within a food, and so on. Probabilistic data can be represented by parametric or empirical distributions, integrated in the analysis using Monte Carlo simulations.

New product development and its impact on nutrition and health

Another example of using data science, and specifically predictive models, in the food industry is to assess the impact of a dietary change on nutritional intakes and subsequently health outcomes. This change can consist of a new product formulation, a new food or ingredient, a reformulated product or a portion size change. The impact of this dietary change on consumers can be assessed by using nutritional intake modelling. As with exposure modelling, data on food consumption is required.

Food consumption surveys assess population dietary behaviour and health at national and local level in various geographies using specific survey methodologies. If available, individual consumption data can be used to model the impact of dietary changes on intakes. These food consumption databases vary in quality, size and detail but usually report information at eating event level for each representative participant within the survey. Food descriptions, consumed amounts and a diary of consumption events are recorded as well as nutrient composition data for each food. The number of consumers representing a given population can range from a couple of hundred to tens of thousands; the number of consumers surveyed is chosen to be large enough to be statistically representative of the population. The individual foods recorded as consumed can range from 500 to up to 10,000 foods, usually categorised into specific food groups. Having access to such granular databases enables very targeted analysis.

One such study[2] investigated the impact of a new milk powder in China, a country where the burden of cardiovascular disease is on the incline. Potassium has been shown to reduce systolic blood pressure in pre-hypertensive consumers. Using a scientific model, a milk powder fortified with potassium was introduced into the Chinese diet.

The underlying data used in this model consisted of individual eating event level data on food consumption and composition of foods, representing the Chinese population (China Health and Nutrition Survey – CHNS) as well as the composition data of the new milk product. The composition data used for the foods consumed in the CHNS was obtained from the Institute of Nutrition and Food Safety, China CDC (2004) and Institute of Nutrition and Food Safety, China CDC (2002). The survey includes information for 21 food groups and 1,599 foods. Anthropometric measurements, blood pressure and biomarker data were also collected in this survey. The target age group was 45 years and older, which resulted in 6,134 subjects, whose dietary intakes were monitored.

Within this age group the new milk powder was either substituted for normal milk or added on top of the normal diet via different scenarios and depending on the consumers’ potassium intakes. Potassium intake distributions were assessed at baseline and after substitution. Individual increases in intake were calculated as well as the overall shift in population intakes.

Based on findings from the literature, the increase of potassium intakes and the resulting decrease in systolic blood pressure were assessed. Individual consumers’ blood pressure within the survey data was then modified to account for the impact on blood pressure resulting from the milk substitution.

The benefit to a food company of an analysis such as the above is to assess whether a product will provide a health benefit for a targeted consumer population and to quantify the possible impact.

Another example of using data science, and specifically predictive models, in the food industry is to assess the impact of a dietary change on nutritional intakes and subsequently health outcomes.

Product stability and shelf life – predictive modellingTo ensure consumer safety and product quality and to preserve concentration levels of ingredients, such as added nutrients, shelf life testing is performed. Tests are typically carried out via durability or challenge studies that span weeks or months. During these studies, the microbial counts and quality outcomes of interest are documented at defined time points and under defined environmental conditions.

Using experimental data to predict the stability of different products, i.e. when developing new products with new formulations, is an example of an area where predictive modelling can be applied. These models can provide cost and time effective guidance on the projected shelf life and stability of a new product or a product with modified processing.

This work may consist of developing a statistical model from experimental data, for example preservatives, food additives, pH etc., and measuring the microbial stability of different products. The model will predict whether a new product formulation is stable and will estimate the probability associated with this prediction.

Similarly, predictive mathematical models can be built using product or ingredient parameters, such as colour, texture, sensory characteristics etc. The aim is to determine the shelf life of this product based on the known parameters and experimental data, which can take months or years of taking measurements for selected parameters. Decision and development times can be shortened using the insights from these mathematical models. In addition the critical parameters for predicting stability and shelf-life can be identified and separated from the non-critical parameters.

Tools that can be used to assess product shelf-life and safety from a microbial perspective are called predictive microbiological models. Predictive models have been developed for both spoilage and pathogenic organisms and growth, survival and heat inactivation models are available for use. The models usually include variables, such as temperature, pH, salt or equivalent water activity and initial contamination levels.

Primary models describe changes in microbial numbers or other microbial responses over time. The model may quantify colony forming units (CFUs) per ml, toxin formation, substrate levels (which are direct measures of the response) and absorbance or impedance (which are indirect measures of the response). A mathematical equation or function describes the change in a response over time with a characteristic set of parameter values.

Secondary models describe the responses by the parameters of these primary models to changes in environmental conditions, such as temperature, pH, or water activity. Tertiary models are computer software routines that turn the primary and secondary models into ‘user-friendly’ programmes for model users in the form of software applications and expert systems. These programmes may calculate microbial responses to changing conditions, compare the effects of different conditions, or contrast the behaviour of several different microorganisms.

Once parameters have been entered into the system, a prediction can be produced. The prediction will usually be in the form of a growth curve, but parameters, such as lag time and time to reach a specified microbial level at a specified time, are also predicted.

Note that the above models can not always replace the actual experiments, but they can help steer formulations, new products being developed or process changes and can greatly increase the likelihood of success from the experimental testing.

Using experimental data to predict the stability of different products, i.e. when developing new products with new formulations, is an example of an area where predictive modelling can be applied.

Modelling product reformulation and impacts on the population

Food and Drink companies are constantly innovating their product ranges. Product reformulation and new product development play a big role in meeting consumer needs for safe and healthy products. Mandatory or voluntary targets set by public health stakeholders for ensuring healthier nutrient profiles of foods are another important reason for product development.

Within Food Drink Ireland, 15 member companies participated in a research project[3] with the aim of assessing the impact of new food and drink development and reformulation on nutrient (sodium, total fat, saturated fat, total sugar and energy) intakes in Irish consumers using anonymised data and scientific modelling. The shift in the consumption of products sold from 2005 to 2017 was incorporated into this model, taking into account the shift in consumer preference via volume sales data, the change in product composition, the discontinuation of old products and introduction of new products by the participating companies’.

As a database on consumer behaviour, the national Irish nutrition surveys were used, including The National Teens’ Food Survey (2005 – 2006), National Children’s Food Survey (2003 – 2004), National Adult Nutrition Survey (2008 – 2010) and National Preschool Nutrition Survey (2010 – 2011). These were combined with the collected nutritional data on the company products gathered for the years 2005 and 2017. For applicable food and drink products, original composition data from the surveys was replaced by the gathered company data, with given foods being represented by multiple brands. Volume sales data for a given brand and food category were used to create weighted distributions of concentrations to represent the market.

To account for the rest of the market not represented within the data, optimistic and conservative scenarios were created. The optimistic scenario assumes that other brands followed similar reformulation and consumer preference patterns, whereas the conservative scenario assumes that all other products on the market remain unchanged over time. The latter is likely to be an underestimate because other companies and retail own brands are actively reformulating and the reality is likely to be somewhere in the middle.

Data on approximately 1800 food products and over 23,000 concentration data points were collected and incorporated into the intake model. The overall findings of the project help to quantify the impact that industry actions have had on consumer intakes over a 12 year period.

The biggest impact could be seen in a reduction in total sugar intakes ranging from 3.2g/day in children to 0.8g/day in adults. The second biggest impact was observed in the reduction of saturated fat intakes, with other nutrient intake reductions being less impactful.

A future aim of this work is to measure the impact of changes in package sizes of products and to conduct further monitoring of food reformulation and new product development. The launch of the report involved the industry stakeholders as well as the Irish Food Safety Authority demonstrating the importance of collaboration for improving public health related matters.

The use of data science is unlocking information from existing data sets that was previously not available.

Conclusions

The use of data science is unlocking information from existing data sets that was previously not available. Data science is having a very positive influence on food product development by supporting more efficient and scientific decision making and replacing or complementing traditional food science methodologies. However, some challenges still remain, such as access to the right expertise to ask the right questions, understanding and applying the correct methodologies, the availability and quality of the data used and handling the uncertainties that will inevitably arise.

The application of scientific modelling, data science and new technologies are quickly maturing bringing the knowledge and the expertise required to continue to grow this important toolkit that has become an integral part of many organisations in the food sector.

Sandrine Pigat, Head of Food and Nutrition, Creme Global

The Tower, Trinity Technology & Enterprise Campus, Grand Canal Quay, Dublin 2, Ireland, D02 P956

Email sandrine.pigat@ cremeglobal.com

Web cremeglobal.com

References

1. Data Science Field/.Term Diagram: Ryan Urbanowicz, PhD University of Pennsylvania, Philadelphia PA, 19104

2. Dainelli L, Xu T, Li M, Zimmermann D, Fang H, Wu Y, Detzel P. 2017. Cost-effectiveness of milk powder fortified with potassium to decrease blood pressure and prevent cardiovascular events among the adult population in China: a Markov model. BMJ Open [Internet]. 7:e017136. Available from: http://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2017-017136

3. https://www.fooddrinkireland.ie/IBEC/Press/PressPublicationsdoclib3.nsf/wvFDINewsByTitle/new-report-details-progress-of-food-and-drink-reformulation-20-02-2019/$file/The+evolution+of+food+and+drink+in+Ireland+2005+-+2017+-+Reformulation+and+Innovation+-+Supporting+Irish+diets.pdf

Related Posts:

  • No Related Posts

With Project DASH, DoorDash Uses Logistics to Rescue Over 1 Million Pounds of Surplus Food

That logistics disconnect is exactly what Project DASH (DoorDash Acts for Sustainability and Hunger) is trying to solve. Through Project DASH …
Photo: Project DASH

Much as restaurants may try to reduce their amount of food waste to cut costs, it’s nearly impossible for them to get to zero loss. Often, whatever perishable food is left at the end of the day — and doesn’t go home with employees — gets tossed in the trash.

That’s because the logistics of donating food is really tricky. In order to get leftovers to a local soup kitchen or hunger relief non-profit, they need someone to physically deliver the surplus food. Restaurants working off of already thin margins usually can’t afford to pay someone more money to do an extra task, especially if it’s cheaper just to chuck the leftovers in the trash.

That logistics disconnect is exactly what Project DASH (DoorDash Acts for Sustainability and Hunger) is trying to solve. Through Project DASH, DoorDash provides drivers that shuttle surplus food from restaurants and other businesses to hunger relief non-profits that have partnered with the third-party delivery service. The service is powered by Drive, DoorDash’s white-label fulfillment platform that lets organizations use the DoorDash fleet to deliver whatever they want, wherever they want. “Basically, DoorDash provides the logistics piece,” said DoorDash’s Head of Social Impact, Sueli Shaw, over the phone.

There are two ways for businesses to access Drive. They can either fill out the delivery details (starting point, endpoint, timing, etc.) via an online form, or they can integrate the Drive API into their own platform. DoorDash drivers will get food rescue assignments the same way they receive any other delivery job. They’ll get paid the same too. DoorDash provides in-kind grants to its nonprofit food rescue partners that cover the delivery fees.

Project DASH is currently partnered with about half a dozen food rescue organizations, including Copia and Replate, and operates in 20 states and 90 cities. Shaw said they just hit the mark of 1 million pounds of food saved so far. But the sky is the limit. “We could operate anywhere,” Shaw told me. DoorDash is currently available in 4,000 cities in the U.S. and Canada, and she said the only thing limiting Project DASH’s growth is the reach of their food rescue partners — which they’re looking to grow until they reach a “100 percent diversion rate” for surplus food.

Shaw said that DoorDash isn’t interested in developing its own food rescue branch internally. “There are a lot of initiatives right now in the space which have been around for a long time,” she told me. “I don’t want to compete — we can play a role in supporting them all.”

In my mind, Project DASH is a win-win. Helping reduce food waste and stop hunger is a good PR move for DoorDash, especially since the company has been getting a lot of flack lately for its controversial tipping policy (which it is now changing), and is hurtling towards an IPO. On the restaurant side, Project DASH provides an easy way to get rid of surplus food while providing an altruistic marketing angle.

Project DASH may be a smart PR strategy for DoorDash, but that doesn’t mean it’s not also a smart way to curb waste. It could be an effective piece of the food waste puzzle when used with end of day food discount services like Karma or Too Good To Go, as well as foodservice inventory management tools like Winnow.

Working together, these technologies could help restaurants get closer to the ever-elusive goal of zero food loss.

Thanks for subscribing! Please check your email for further instructions.

Share this:

Related

Related Posts:

  • No Related Posts

Impossible Foods Hires Ravi Thakkar as Vice President of Product Management

Investors include Khosla Ventures, Bill Gates, Google Ventures, Horizons Ventures, UBS, Viking Global Investors, Temasek, Sailing Capital, and Open …

REDWOOD CITY, Calif.–Impossible Foods announced today the hiring of Ravi Thakkar in the all-new position of Vice President of Product Management.

Thakkar will build and lead a team of product managers at Impossible Foods. The Redwood City, Calif-based food tech startup currently sells the award-winning Impossible Burger, and its scientists are working on a full range of delicious, nutritious, plant-based meat and dairy foods.

Thakkar joins Impossible Foods from Apple, where he served as iPhone Product Manager in the Worldwide Product Marketing division. Before Apple, Thakkar held several product leadership roles at Motorola Mobility, a mobile devices company acquired by Google.

“I am deeply passionate about the mission at Impossible Foods. The profound impact we can have on animal welfare and the planet is limitless,” Thakkar said. “It is a privilege to build a world-class product management organization to make the global food system sustainable.”

Scorching demand

Impossible Foods has experienced tremendous growth since the launch of the award-winning and “shockingly good” Impossible™ Burger 2.0 in January 2019. The Impossible Burger is now sold in more than 15,000 restaurants in the United States and Asia.

Growth has come from every sales category where Impossible Foods does business — independent restaurants, large restaurant chains such as White Castle, Qdoba and Red Robin, and non-commercial outlets such as theme parks, museums, stadiums and college campuses nationwide. Earlier this month, Burger King introduced the Impossible Whopper to all 7,200 US restaurants.

In addition to an increasing number of outlets that sell the Impossible Burger, many restaurants are expanding the number of items made from the versatile plant-based meat; average per-store volume is increasing. Sales have increased more than four-fold in Asia over the last four months.

Last month, Impossible Foods announced a co-manufacturing collaboration with global food provider OSI Group, one of the largest food producers in the world. OSI has already begun to produce the Impossible Burger, adding short-term capacity to Impossible Foods’ plant in Oakland, Calif. OSI will continue to expand production of Impossible Foods’ flagship product throughout 2019 and thereafter.

Apple hire is most recent executive to join startup

Thakkar has a track record of launching successful products at global scale. He has an MBA from Northwestern University’s Kellogg School of Management and a Bachelor of Science in Computer Engineering from the University of Illinois at Urbana-Champaign. Thakkar’s experience spans product management, engineering, marketing, sales, operations and other functions.

“Ravi brings a wealth of product experience and leadership from high-tech companies,” said Sheetal Shah, Senior Vice President for Product and Operations. “It is so important that we build a strong product management function as we scale the business and there’s no one better than Ravi to lead it.”

Impossible Foods’ executive team includes executives with an unusually diverse range of backgrounds, including government, academia and the food sector, as well as “hypergrowth” startups Dropbox, Google and Tesla.

About Impossible Foods:

Based in California’s Silicon Valley, Impossible Foods makes delicious, nutritious meat and dairy products from plants — with a much smaller environmental footprint than meat from animals. The privately held company was founded in 2011 by Patrick O. Brown, M.D., Ph.D., Professor Emeritus of Biochemistry at Stanford University and a former Howard Hughes Medical Institute investigator. Investors include Khosla Ventures, Bill Gates, Google Ventures, Horizons Ventures, UBS, Viking Global Investors, Temasek, Sailing Capital, and Open Philanthropy Project.

Related Posts:

  • No Related Posts