S. Lau

Summary: Although there have been extensive studies on how poor fluid milk quality can lead to negative consumer sensory acceptance, there is limited information on how a poor experience with milk affects consumer choice in brick-and-mortar and online retail settings. In this study, we implemented an online survey of 725 US consumers to assess how a poor experience with fluid milk can affect consumer grocery purchasing and found that a poor experience with milk is associated with a greater likelihood of switching retail stores and with reduced loyalty, suggesting that having high-quality milk may help retailers improve customer satisfaction and increase sales.

With the increased awareness about the economic and environmental impact of food waste, many interventions along food supply chains have been proposed to mitigate food waste. Even though interventions used to target food waste usually revolve around logistics and operations management, we highlight a unique solution to address this issue, specifically for fluid milk. We target the intrinsic quality of fluid milk by evaluating interventions that will extend the product shelf life. We used data from a previous fluid milk spoilage simulation model, collected price and product information from retail stores, conducted an expert elicitation, and used hedonic price regressions to determine the private and social gains to the dairy processing plant when implementing 5 different interventions to extend shelf life. Our data suggest that the value of each additional day of shelf life is approximately $0.03 and indicate that increasing periodic equipment cleaning is the most cost-effective strategy for processing plants to achieve fluid milk shelf-life improvements, both from a firm's economic standpoint and from an environmental standpoint. Importantly, the approaches reported here will be valuable to help individual firms to generate customized facility and firm specific assessments that identify the most appropriate strategies for extending the shelf life of different dairy products.

Summary: We assessed consumer acceptance of quick response (QR) code technology to communicate (1) product shelflife and (2) shelf-life dependent pricing, by offering identical half-gallon fluid milk with both traditional printed best-by dates and with QR codes to convey best-by dates over an 8-wk time period in a retail setting. The study included 2 phases to determine (1) consumers' receptiveness to QR codes to access product shelf-life information and (2) if offering a discounted price based on shelf-life would change consumers' willingness to use QR codes and would increase sales of milk nearing its best-by date. Our findings indicate that at least some consumer segments are likely to accept QR code technology to access shelf-life information. Hence, this technology represents a strategic opportunity to better communicate shelf-life information and to reduce food waste (e.g., by encouraging sales of products close to end of shelf life through reduced prices, using dynamic pricing approaches).

Psychrotolerant gram-negative bacteria introduced as post-pasteurization contamination (PPC) are a major cause of spoilage and reduced shelf life of high-temperature, short-time pasteurized fluid milk. To provide improved tools to (1) predict pasteurized fluid milk shelf life as influenced by PPC and (2) assess the effectiveness of different potential interventions that could reduce spoilage due to PPC, we developed a Monte Carlo simulation model that predicts fluid milk spoilage due to psychrotolerant gram-negative bacteria introduced as PPC. As a first step, 17 gram-negative bacterial isolates frequently associated with fluid milk spoilage were selected and used to generate growth data in skim milk broth at 6°C. The resulting growth parameters, frequency of isolation for the 17 different isolates, and initial concentration of bacteria in milk with PPC, were used to develop a Monte Carlo model to predict bacterial number at different days of shelf life based on storage temperature of milk. This model was then validated with data from d 7 and 10 of shelf life, collected from commercial operations. The validated model predicted that the parameters (1) maximum growth rate and (2) storage temperature had the greatest influence on the percentage of containers exceeding 20,000 cfu/mL standard plate count on d 7 and 10 (i.e., spoiling due to PPC), which indicates that accurate data on maximum growth rate and storage temperature are important for accurate predictions. In addition to allowing for prediction of fluid milk shelf life, the model allows for simulation of "what-if" scenarios, which allowed us to predict the effectiveness of different interventions to reduce overall fluid milk spoilage due to PPC through a set of proof-of-concept scenario (e.g., frequency of PPC in containers reduced from 100% to 10%; limiting distribution temperature to a maximum of 6°C). Combined with other models, such as previous models on fluid milk spoilage due to psychrotolerant spore-forming bacteria, the data and tools developed here will allow for rational, digitally enabled, fluid milk shelf life prediction and quality enhancement.