Gennari Fulvia, Pagano Mario, Toncelli Alessandra, Lisanti Maria Tiziana, Paoletti Riccardo, Roversi Pio Federico, Tredicucci Alessandro, Giaccone Matteo
Dipartimento di Fisica "E. Fermi", Università di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy.
Institute of Research on Terrestrial Ecosystems (IRET), National Research Council (CNR), Via Madonna del Piano 10, 50019, Sesto Fiorentino, Italy.
Heliyon. 2023 Sep 7;9(9):e19891. doi: 10.1016/j.heliyon.2023.e19891. eCollection 2023 Sep.
The development of new non-invasive approaches able to recognize defective food is currently a lively field of research. In particular, a simple and non-destructive method able to recognize defective hazelnuts, such as cimiciato-infected ones, in real-time is still missing. This study has been designed to detect the presence of such damaged hazelnuts. To this aim, a measurement setup based on terahertz (THz) radiation has been developed. Images of a sample of 150 hazelnuts have been acquired in the low THz range by a compact and portable active imaging system equipped with a 0.14 THz source and identified as Healthy Hazelnuts (HH) or Cimiciato Hazelnut (CH) after visual inspection. All images have been analyzed to find the average transmission of the THz radiation within the sample area. The differences in the distribution of the two populations have been used to set up a classification scheme aimed at the discrimination between healthy and injured samples. The performance of the classification scheme has been assessed through the use of the confusion matrix on 50 samples. The False Positive Rate (FPR) and True Negative Rate (TNR) are 0% and 100%, respectively. On the other hand, the True Positive Rate (TPR) and False Negative Rate (FNR) are 75% and 25%, respectively. These results are relevant from the perspective of the development of a simple, automatic, real-time method for the discrimination of cimiciato-infected hazelnuts in the processing industry.
目前，开发能够识别有缺陷食品的新型非侵入性方法是一个活跃的研究领域。特别是，一种能够实时识别有缺陷榛子（如被象鼻虫感染的榛子）的简单且无损的方法仍然缺失。本研究旨在检测此类受损榛子的存在。为此，开发了一种基于太赫兹（THz）辐射的测量装置。通过配备0.14太赫兹源的紧凑型便携式主动成像系统，在低太赫兹范围内获取了150颗榛子样本的图像，并在目视检查后将其识别为健康榛子（HH）或象鼻虫感染榛子（CH）。对所有图像进行分析，以找到样本区域内太赫兹辐射的平均透射率。利用这两种群体分布的差异建立了一种分类方案，旨在区分健康样本和受损样本。通过对50个样本使用混淆矩阵来评估分类方案的性能。误报率（FPR）和真阴性率（TNR）分别为0%和100%。另一方面，真阳性率（TPR）和假阴性率（FNR）分别为75%和25%。从开发一种简单、自动、实时的方法来区分加工行业中被象鼻虫感染的榛子的角度来看，这些结果具有重要意义。
