In search of optimum fresh-cut raw material: Using computer vision systems as a sensory screening tool for browning resistant romaine lettuce accessions

Authors: BORNHORST, ELLEN - Orise Fellow; Luo, Yaguang - Sunny; Park, Eunhee; Zhou, Bin; Turner, Ellen; TENG, ZI - Orise Fellow; Simko, Ivan; Fonseca, Jorge; Trouth, Frances

Submitted to: Horticulturae
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/2/2024
Publication Date: 7/12/2024
Citation: Bornhorst, E.R., Luo, Y., Park, E., Zhou, B., Turner, E.R., Teng, Z., Simko, I., Fonseca, J.M., Trouth, F.J. 2024. In search of optimum fresh-cut raw material: Using computer vision systems as a sensory screening tool for browning resistant romaine lettuce accessions. Horticulturae. 10(7). Article e10070731. https://doi.org/10.3390/horticulturae10070731.
DOI: https://doi.org/10.3390/horticulturae10070731

Interpretive Summary: Ready-to-eat (RTE) salads in the USA commonly includes romaine lettuce, which is a highly perishable vegetable. Breeding of new cultivars is emerging as a potential biotechnological option to improve shelf life of fresh-cut romaine lettuce. To determine optimal breeding lines for RTE the evaluation of color is critical as discoloring evidences decay. Aiming for a practical way to evaluate color we used computer vision system (CVS) (software to acquire and analyze digital images) to determine differences among 16 cultivars of romaine lettuce during postharvest storage. Statistical information, in particular correlations considering actual human perception, showed that CVS can be very effective for screening performance of cultivars of romaine lettuce during postharvest. This study suggest that digital images analysis can be used to rapidly screen multiple cultivar of romaine lettuce, and may have potential for other food products.

Technical Abstract: 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 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 to browning index with a Pearson correlation coefficient of -0.85. Other important sensory decision parameters were also strongly or moderately correlated to 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.