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Fresh-cut romaine lettuce's high perishability challenges ready-to-eat (RTE) salad production. Selecting cultivars less prone to browning and decay is crucial for extending shelf life. Traditional quality evaluation methods using instrumentation and trained panelists are time-consuming and logistically complex. This study investigated the effectiveness of untrained volunteers in assessing fresh-cut romaine lettuce quality. Given that the average consumer in the USA is familiar with the flavor characteristics of romaine lettuce, this study proposed to investigate the value of having untrained volunteers discern the quality of fresh-cut romaine lettuce. Therefore, six romaine lettuce accessions (Green Forest, King Henry, Parris Island Cos, PI 491224, SM13-R2, and Sun Valley) were assessed for sensory quality attributes (browning, green color, decay, and overall quality) and compared with instrumentation analyses (gas composition including O2 and CO2, electrolyte leakage, and color). The results showed significant quality differences (p < 0.05) among the accessions, with some seasonal variability. Very importantly, the consumers' (n = 159) assessments revealed similar results to those produced by either instrumentation or a trained panel. The consumers provided sensory scores that allowed for the grouping of accessions based on their postharvest quality, which efficiently matched their pedigree relationship. In conclusion, ad hoc consumer panels can be an effective way to characterize the quality of romaine lettuce for RTE salads. [ABSTRACT FROM AUTHOR]

The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed. [ABSTRACT FROM AUTHOR]

The 2006 "Escherichia coli" outbreak in spinach prompted the implementation of unprecedented production strategies (Arizona Leafy Green Products Shipper Marketing Agreement) to minimize microbial risk in leafy greens. As the new procedures require assessments of physical intrusion and excrement in fields, outreach was initiated to educate adult (20-35, 35-50, and greater than 50 years) Yuma, Arizona residents about these guidelines and animal (e.g., pets, horses) stewardship. Pre- and post-assessments revealed that while sensitivity to fresh produce safety improved, adults over 35 years were more industry responsive. Citizen-based outreach programs, in communities where fresh produce is grown, could benefit field-level safety mitigation. (Contains 3 tables.)

While online grocery shopping has been rapidly expanding over the last several years, online sales of fresh produce have lagged far behind. One of the most significant contributing factors for this lag is the consumer’s inability to assess the quality of produce online. We hypothesized that this could be alleviated by machine vision technology. This study examines perceived sensory attributes derived from digital images of fresh fruits and compares them with sensory attributes obtained from the actual fruit. Digital images of fresh strawberries, cherry tomatoes, grapes, and blueberries were acquired using a high-resolution digital camera. Consumer panelists evaluated the appearance, texture, flavor, overall eating quality, and also determined purchase decision. Panel sizes ranging from 32 to 40 members (a total of 147) also conducted in situ evaluations of the different fruits. The paired t-test indicated that the mean results between pairs of image scores and in situ evaluation scores were statistically insignificant. The scores obtained for texture and overall eating quality showed some variability, but the scores for quality appearance were remarkably consistent revealing no difference across the evaluations of the various commodities. The results demonstrated that digital images can be utilized to effectively relay the appearance attributes of fresh produce. This finding is relevant for the industry, as the appropriate construction of real time images can help to build consumers’ trust in the quality of e-deliveries, nudge consumers to purchase fruits and vegetables, and increase the overall e-commerce acceptance for fresh produce. A discussion of the limitations and opportunities for improving the effectiveness of digital sensory analysis of fresh produce is provided. [ABSTRACT FROM AUTHOR]

Iceberg lettuce was evaluated for yield, microbial population, and postharvest quality either following different irrigation termination (IT) schedules or before and after a rainfall event. The results revealed that microbial population in lettuce increases after rain and with late IT.