Journal Article

In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions

Park, Eunhee, Zhou, Bin, Teng, Zi, Turner, Ellen R., Bornhorst, Ellen R., Trouth, Frances, Luo, Yaguang, Simko, Ivan, Fonseca, Jorge M.

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Agricultural research / Agriculture / Browning / Cameras / color / Computer vision / Consumer behavior / Consumer preferences / Consumers / Correlation coefficient / Correlation coefficients / Dietary supplements / Discoloration / Food preferences / Food quality / Freshness / Image acquisition / image analysis / Image processing / Image quality / Image segmentation / industry / Information processing / International economic relations / Lactuca sativa var. longifolia / Lettuce / Machine learning / Machine vision / Marketing research / Medical screening / Medical tests / Odors / Off odor / Olfactory preferences / Packaging / Parameters / Photographic industry / Plant introductions / Polyphenols / postharvest storage / quality / Rankings / Raw materials / ready-to-eat foods / ready-to-eat salads / romaine lettuce / Salads / Sensory evaluation / Sensory properties / Shelf life / Technology application / Vegetables / Vision systems