Measuring food waste at the individual household level has been nearly impossible because comprehensive, current data on uneaten food do not exist. By using food acquisition data, this article employs a new approach to estimating household‐level food waste via a stochastic production frontier model in which food waste is identified as input inefficiency. For households in our data, the average household wastes 31.9% of the food it buys, and this figure, using survey weights, translates to annual U.S. consumer‐level food waste valued at $240 billion. In addition, by accommodating heterogeneous wasting behavior, we find that healthier diets and higher income lead to more household food waste, whereas lower household food security, food‐assistance program participation, and larger household sizes are associated with less food waste.

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In early 2020, a novel coronavirus quickly spread across the globe. In response to the rapidly increasing number of confirmed U.S. cases, state and local governments suggested social distancing, issued stay-at-home orders, and restricted travel, fundamentally changing how individuals allocate time. Directly impacted time activities, such as work, eating food away from home, grocery shopping, and childcare significantly impact two food-related topics: household food waste and diet quality. In order to investigate these non-marginal time changes, we predict weekly time allocated to seven activities for households in the National Food Acquisition and Purchase Survey using information from the American Time Use Survey. Jointly estimating household production functions for food waste and diet quality, we find that time events that are related to fresh produce consumption, such as increased grocery store trips and time spent in FAH activities, are related to higher diet quality, but lower food waste. While time events that are associated with quick convenient meals, such as time spent in secondary childcare and work time, are also associated with lower food waste, these events decrease household diet quality. We then predict the level of household food waste and diet quality for three COVID-19 scenarios: one where the household head is likely able to work remotely, another where the household head is likely to lose their job, and a third, where the household head is likely to be considered an essential worker. Households without children that are likely able to work remotely are predicted to have lower levels of food waste and higher diet quality, while households without children in the other two COVID-19 scenarios are predicted to have only minor differences.

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A novel in vivo fluorescence spectroscopic diagnostic method has been developed in an animal model to make a quantified precancer diagnosis. In the study, 40 golden hamsters were randomly divided into four groups (groups A, B, C, and D), with group A being the control group and the other three groups being inducted at different precancer stages. A 1% Rose Bengal (RB) solution was used for the fluorescence spectroscopic diagnosis. A parameter K defined as K = I(RB)/I(auto) was introduced to reflect the amount of RB in the tissue, where I(RB) and I(auto) represent the fluorescence peak intensity of the RB in the tissue and the autofluorescence intensity of tissue at 580 nm, respectively. The average K values of the four groups were calculated and statistically analyzed by analysis of variance (ANOVA), which revealed statistically significant differences within each group as well as between groups (p< 0.001). After analysis by Clementine 11.1 C&R Tree modeling (CART), the following diagnostic criteria were set: normal, K ≤ 8.91; simple hyperplasia, 8.91 < K ≤ 41.92; mild dysplasia, 41.92 < K ≤ 70.79; moderate and severe dysplasia, K >70.79. The sensitivity and specificity to detect precancerous lesions compared with scalpel biopsy were calculated. The results of this study showed that the spectrofluorometric method mediated by RB could accurately discriminate different precancer stages.

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