Agreement Between Visual and Model-Based Classification of Tomato Fruit Ripening

Abstract

Highlights Human visual classification and predictive models often disagree when only color indices are used. The degree of agreement is improved significantly when predictive models are cultivar-specific. The degree of agreement can be improved when firmness and carotenoid contents are considered. Abstract. Traditionally, the ripening stage of tomato fruit is determined by the observed percentage of red color on the fruit surface based on color charts provided by USDA standards. However, multiple observers can assign different ripening stages to the same tomato fruit due to subjectivity and/or inaccurate evaluations. This practical challenge has not been extensively discussed in the literature, so we assessed the degree of agreement between human visual classification and model-based prediction using physicochemical properties such as color (L*, a*, b*, hue, and chroma), firmness, and carotenoid contents. In our exploratory data analyses, we clearly observed increasing a* and decreasing L*, hue, and firmness with respect to ripening stage, but the rate of change seemed different from cultivar to cultivar. To assess the degree of agreement, cross-validations were used to compare thirty linear regression models with various combinations of the predictors. The cross-validations indicated that predictions from a cultivar-specific model agreed well with human visual classifications. When the cultivar-specific model was considered with the color indices, we achieved up to 95.5% accuracy. When firmness, lycopene, and ß-carotene were added to the model, the accuracy increased to 96.8%. These results suggest the reliability of non-destructive methods for auto-sorting systems. Keywords: ß-carotene, Color index, Firmness, Fruit color, Lycopene, Ripening, Tomato.