RESUMEN
The paper describes the developed hardware and software components of a computer vision system that extracts colour parameters from calibrated colour images and identifies non-destructively the different quality levels exhibited by lettuce (either whole or fresh-cut) during storage. Several colour parameters extracted by computer vision system have been evaluated to characterize the product quality levels. Among these, brown on total and brown on white proved to achieve a good identification of the different quality levels on whole and fresh-cut lettuce (P-value<0.0001). In particular, these two parameters were able to discriminate three levels: very good or good products (quality levels from 5 to 4), samples at the limit of marketability (quality level of 3) and waste items (quality levels from 2 to 1). Quality levels were also chemically and physically characterized. Among the parameters analysed, ammonia content proved to discriminate the marketable samples from the waste in both product's typologies (either fresh-cut or whole); even the two classes of waste were well discriminated by ammonia content (P-value<0.0001). A function that infers quality levels from the extracted colour parameters has been identified using a multi-regression model (R2=0.77). Multi-regression also identified a function that predicts the level of ammonia (an indicator of senescence) in the iceberg lettuce from a colour parameter provided by the computer vision system (R2=0.73), allowing a non-destructive evaluation of a chemical parameter that is particularly useful for the objective assessment of lettuce quality. The developed computer vision system offers flexible and simple non-destructive tool that can be employed in the food processing industry to monitor the quality and shelf life of whole and fresh-cut lettuce in a reliable, objective and quantitative way.