Tomato quality based on colorimetric characteristics of digital images

ABSTRACT Results of evaluations using optical evaluation methods may be correlated with tomato quality and maturation. In this context, the objective of this study was to evaluated the correlation between tomato colorimetric and physico-chemical variables, clustering them as a function of maturation stages, using multivariate analysis. The experiment was conducted using 150 fruits and three maturation stages (immature, light red and mature). The physico-chemical variables were evaluated through traditional methods. The colorimetric variables were assessed on images in RGB color model taken with a digital camera. The correlation between colorimetric and physico-chemical variables was analyzed using the Pearson’s coefficient. Principal components analysis and k-means clustering method was applied to three data set: RGB isolated variables; colorimetric variables calculated by relation between the RGB bands (colorimetric indexes); and physico-chemical variables. The colorimetric variables present higher explanatory capacity of the maturation variation than physico-chemical variables. The colorimetric indexes presented higher performance in clustering (accuracy of 0.98) tomatoes as a function of maturation.

摘要：采用光学评估方法所得的检测结果，或与番茄品质及成熟度存在关联。为此，本研究旨在借助多变量分析手段，探究番茄色度学变量与理化变量间的相关性，并依据成熟阶段对样本进行聚类分组。本实验共使用150颗番茄果实，涵盖3个成熟阶段：未成熟、浅红及完全成熟。理化变量通过传统实验方法开展检测；色度学变量则基于数码相机拍摄的RGB色彩模式（RGB color model）图像进行分析评估。色度学变量与理化变量间的相关性采用皮尔逊相关系数（Pearson’s coefficient）进行分析。本研究对三类数据集分别应用主成分分析（Principal components analysis）与k-means聚类方法（k-means clustering method）：一是独立RGB变量数据集，二是通过RGB波段比值计算得到的色度学指标数据集，三是理化变量数据集。结果表明，相较于理化变量，色度学变量对成熟度差异的解释能力更强；在基于成熟阶段的番茄聚类任务中，色度学指标展现出更优的性能，聚类准确率可达0.98。