Ultrasound technology has been increasingly explored as an eco-friendly method to improve the microbial safety of leafy greens. However, its effect on produce quality is critical, and considerable knowledge gaps remain in this area. The present study examined the response of leafy greens to ultrasound treatment as shown by tissue damage and sensory quality, using a novel multifrequency, multimode, modulated (MMM) system to address the issue of nonuniform ultrasound field distribution. Iceberg lettuce, romaine lettuce, spinach, loose leaf lettuce and Lollo Rosso were subjected to different ultrasonication durations (1-16 min) in a MMM tubular treatment unit at 34 kHz and subsequently stored at 1 °C for three weeks. Sensory evaluations by a trained panel and electro-conductivity rate measurements were conducted to assess produce quality over time. Ultrasound treatment at an acoustic power density below 80 W/L had no significant effect (P > 0.05) on the overall sensory quality of leafy greens during 14 days of storage. Even though the electro-conductivity rate, an indicator of tissue damage, increased in ultrasound-treated samples compared to control, it did not result in perceptible changes in sensory attributes.

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.