Globally, about one-third of edible food is lost or wasted 1. Wasted food depletes resources used to produce the food (as a matter of definition, "produce" includes all activities to distribute, package and prepare food for human consumption at any venue, but does not include growing, transporting and processing), deprives hungry people from needed nutrition and represents the most common (>20%) and most environmentally deleterious component (in terms of greenhouse gas) of US landfills3-6. The Organization for Economic Co-operation and Development (OECD) identified reducing FW as a key to increasing global food security (OECD 2011) 7. The Obama Administration recently announced a first ever FW reduction goal for the United States of 50% by 2030. USDA and EPA will pursue this goal via partnerships with the private sector, charitable and faith-based organizations, as well as state, local and tribal governments.

In developed economies, consumers are the largest source of FW via home preparation and dining out2, 8. In order to achieve FW goals, we must anticipate how consumer FW generation and handling decisions will respond to proposed policies and interventions. However, there exist substantial barriers to measuring consumer FW: existing methods have high respondent burden and yield biased measurements of FW generation9-11. In this project we will test a smartphone application (app) for measuring household FW.

The objective of this project is to pilot test the Remote Food Photography Method (RFPM) and SmartIntake smartphone app as an approach for measuring food waste in controlled and free-living environments. The RFPM and SmartIntakeTM app constitutes a distinct improvement over self-report FI methods, such as pen-and-paper surveys and dietary recall methods that rely on subjects' ability to accurately recall the types and portion sizes of foods consumed. After training, people use the SmartIntakeTM app to capture images of their foods both before and after each eating occasion. Photos are automatically uploaded from the user's smartphone to a secure server at Pennington Biomedical. The images are analyzed to quantify food selection, food waste, and food intake using validated methods and a computer program (the Food Photography Application©) built by Pennington Biomedical. The method has been found to accurately quantify food selection, food waste, and food intake.

Importantly, the RFPM and SmartIntakeTM app eliminate one of the largest sources of error associated with self-report methods - subjects' inability to accurately estimate portion size, which accounts for ~50% of the error in self-report methods11, 13-15. FI estimation studies have observed that overweight subjects react to monitoring by underestimating the self-reported amounts of FI or eat less when monitored16, 17. Similarly, FW behavioral studies have observed that subjects start self-reporting smaller amounts of FW, however it is not clear if this is the result of increased awareness and less generation, or if it due to not appearing as wasteful individuals18. However, people respond less to the RFPM/SmartIntake app and experiment results show that the underestimation from RFPM does not vary with energy intake level12.

Many shortcomings that plague FI measurement likely hamper FW measurement. Biases in the opposite direction (e.g., cleaning your plate to show less FW) likely emerge when measuring FW and may be similarly mitigated with the proposed app. We believe using RFPM and SmartIntakeTM app as the base for a FW estimation app holds great promise to transform FW measurement. First, we note that FW associated with dining situations (i.e., plate waste) is directly estimated by the RFPM, hence only a simple transform of current outputs is required. To fully adapt RFPM for FW measurement, the procedures will be modified to estimate FW created during food preparation and during stored food purges (e.g., cleaning the fridge). The RFPM method already works in dine-out settings, which are currently omitted by some existing FW measurement methods. Additional functionality, such as estimating edible vs inedible FW (and hence calories wasted), estimating waste rate among food categories, and estimating rates of diversion from landfills, will also be explored so that multiple policy relevant metrics can be instantly calculated and stored.

Inclusion Criteria:

In order to participate in this study a candidate must:

• Be able to consent
• Be within an age range of 18-72 years, inclusive
• Have a body mass index of 18.5 - 50 kg/m2
• Have an iPhone, and be willing to use it to capture photos via an app (participants must acknowledge that cellular data will be used during the course of the project)
• Be willing to use an iPhone app (SmartIntake) and a pen-and-paper food dairy during the food intake meal and for up to five days in free-living conditions.

Exclusion Criteria:

In order to participate in this study, the candidate must not:

• Be pregnant, assessed by self-report (this is not a safety issue and study data will not be compromised if 1-3 pregnant women enroll)
• Be planning to become pregnant while enrolled in the study