Comparing quality parameters obtained using destructive and optical methods in grading tomatoes

ABSTRACT Optical methods for analysing fruit quality have various advantages compared to conventional methods, including not destroying the sample and the possibility of automating the quality control process. The aim of this study was to compare artificial neural networks developed from biological activity indices obtained using the biospeckle laser optical technique, and from physico-chemical variables obtained by conventional destructive techniques, through an evaluation of their precision in classifying ripe tomatoes, using as a reference an earlier classification carried out by visual inspection. A total of 150 tomatoes were used in the experiment, divided into three ripening stages. Multivariate principal component analysis was used to evaluate interaction of the variance within the groups of data obtained using the biospeckle laser technique and destructive laboratory methods. Two artificial neural networks were developed, the first generated using biological activity indices as input vectors, and the second using physico-chemical variables. The precision of the two neural networks was compared using the Kappa index and overall accuracy, and was based on a reference classification. The variation in ripening as a function of the biological activity indices was explained by the first principal component. The neural network generated from the biological activity indices showed the best performance in classifying the tomatoes into the three ripening stages, with a significant Kappa index and an overall accuracy of 67.5%.摘要：相较于传统检测方法，用于果品品质分析的光学检测技术具备诸多优势，包括不会破坏样品，且可实现品质管控流程的自动化。本研究旨在对比两类人工神经网络（artificial neural networks）：一类基于通过生物散斑激光（biospeckle laser）光学技术获取的生物活性指数构建，另一类则基于传统破坏性检测方法得到的理化变量，通过评估二者对成熟番茄的分类精度，并以目视检验（visual inspection）得到的早期分类结果作为参考基准。实验中共使用150颗番茄，划分为三个成熟阶段。采用多元主成分分析（multivariate principal component analysis），对生物散斑激光技术与破坏性实验室检测方法所获数据集组内的方差交互效应进行评估。本研究构建了两类人工神经网络：第一类以生物活性指数作为输入向量，第二类则以理化变量作为输入。以参考分类结果为基准，通过Kappa系数（Kappa index）与总体准确率（overall accuracy）对比了两类神经网络的分类精度。第一主成分可解释基于生物活性指数的番茄成熟度变化情况。基于生物活性指数构建的人工神经网络在将番茄划分为三个成熟阶段的任务中表现最优，其Kappa系数具有统计学显著性，总体准确率达67.5%。