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ALOW-COST SYSTEM FOR NON-DESTRUCTIVE INTERNAL QUALITY EVALUATION OF FRUITS

机译:水果无损内部质量评估的低成本系统

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The paper describes the design of a low-cost workstation, based on near-infrared (NIR) spectrometry, for non-destructively grading the ripeness of fruits. The system enables non-destructive measures that can be repeated several times and be applied to the whole production instead that on the few samples normally selected for destructive analysis. Destructive methods are normally applied for estimating the maturity in terms of internal sugar content and firmness. Four different set-ups have been designed and realized to estimate these parameters by measuring the NIR radiation transmitted through the fruit. They differ for some hardware components but share the general architecture and the algorithms used for processing the acquired signals. The data provided by the system are pre-processed using a noise-reducing method based on a packets-wavelet transform. In addition, an outlier detection schema has been used for identifying irregular behaviors inside each of the classes that need to be separated. A straightforward minimum distance classifier has been applied for assigning the data to their proper classes. The obtained results show that even this early version of the system allows the correct grading of peaches with a percentage of 82.5%.
机译:本文介绍了一种基于近红外(NIR)光谱的低成本工作站的设计,该工作站可以无损地分级水果的成熟度。该系统实现了非破坏性措施,该措施可以重复多次并应用于整个生产过程,而不是通常为破坏性分析选择的少数几个样本上的非破坏性措施。破坏性方法通常用于根据内部糖含量和硬度来估计成熟度。已经设计并实现了四种不同的设置,以通过测量通过水果传播的NIR辐射来估算这些参数。它们在某些硬件组件上有所不同,但共享通用的体系结构和用于处理所采集信号的算法。使用基于分组小波变换的降噪方法对系统提供的数据进行预处理。此外,离群值检测方案已用于识别需要分离的每个类内部的异常行为。一个简单的最小距离分类器已被应用来将数据分配给它们的适当类别。获得的结果表明,即使是该系统的早期版本,桃子的正确分级也可以达到82.5%的百分比。

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