Common plastics THz classification via artificial neural networks: A discussion on a class of time domain features

Cacciari, Ilaria; Corradi, Giacomo

doi:10.1016/j.optmat.2021.111134

Cited by 4 publications

(4 citation statements)

References 72 publications

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“…Optical sensors are used for the characterization of plastic stream in a continuous manner where air jets allow for separation. Optical sensors may be subdivided in molecular spectroscopies and atomic spectroscopies [102], the prevalently used Raman spectroscopy (RS) [103], Fourier-transform infrared spectroscopy (FTIR) [96], near-infrared spectroscopy (NIRS) [104], and terahertz spectroscopy (THz) [105], and elemental spectroscopies such as laser-induced breakdown spectroscopy (LIBS) [106] and X-ray fluorescence spectroscopy (XRFS) [102].…”

Section: Primary and Secondary Recyclingmentioning

confidence: 99%

Recent Advancements in Plastic Packaging Recycling: A Mini-Review

et al. 2021

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Today, the scientific community is facing crucial challenges in delivering a healthier world for future generations. Among these, the quest for circular and sustainable approaches for plastic recycling is one of the most demanding for several reasons. Indeed, the massive use of plastic materials over the last century has generated large amounts of long-lasting waste, which, for much time, has not been object of adequate recovery and disposal politics. Most of this waste is generated by packaging materials. Nevertheless, in the last decade, a new trend imposed by environmental concerns brought this topic under the magnifying glass, as testified by the increasing number of related publications. Several methods have been proposed for the recycling of polymeric plastic materials based on chemical or mechanical methods. A panorama of the most promising studies related to the recycling of polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS) is given within this review.

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Section: Primary and Secondary Recyclingmentioning

confidence: 99%

Recent Advancements in Plastic Packaging Recycling: A Mini-Review

et al. 2021

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“…Only the training subset was used for weight updating. During the training phase, the “quality of learning” was evaluated on both training and testing datasets (the latter is not part of the training phase i.e., weight updating), calculating the corresponding learning curves [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

et al. 2022

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Here, the potential of laser-induced breakdown spectroscopy (LIBS) in grading calcareous rocks for the lime industry was investigated. In particular, we developed a system equipped with non-intensified detectors operating in scanning mode, defined a suitable data acquisition protocol, and implemented quantitative data processing using both partial least squares regression (PLS-R) and a multilayer perceptron (MLP) neural network. Tests were carried out on 32 samples collected in various limestone quarries, which were preliminarily analyzed using traditional laboratory X-ray fluorescence (XRF); then, they were divided into two groups for calibration and validation. Particular attention was dedicated to the development of LIBS methodology providing a reliable basis for precise material grading. The congruence of the results achieved demonstrates the capability of the present approach to precisely quantify major and minor geochemical components of calcareous rocks, thus disclosing a concrete application perspective within the lime industry production chain.

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“…Most of the approaches perform an extraction of features either with statistical methods [5], [6] or linear feature mapping [7], [8]. Rather uncommon is the use of raw data [9], time-domain features [10], and filtered data. Support vector machines (SVM) and neural networks are widely used for automatic learning of relationships in measurement data [5]- [11].…”

Section: Introductionmentioning

confidence: 99%

“…Common issues of most of the presented methods are the size of the dataset and the evaluation method. The amount of data used for training and testing of the algorithms is low and often consists of just 3-25 THz-TDS traces per category [5], [7], [8], [10]. In consequence, the low amount of data may not show the generalization ability of the presented methods.…”

Section: Introductionmentioning

confidence: 99%

Material Classification for Terahertz Images Based on Neural Networks

Kubiczek

Balzer

2022

IEEE Access

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Terahertz time-domain spectroscopy enables the extraction of electrical properties from materials. An extraction of the complex permittivity can be carried out with measurements in transmission or reflection geometry enabling the identification of materials. To perform an exact identification, the sample thickness and the angle of incidence of the terahertz radiation must be known. However, when those parameters are unknown and additionally the materials show strong absorbances, a precise differentiation between materials is challenging. A promising approach is the use of a neural network for automated material classification of terahertz images from different materials. Here, we show that a trained neural network can differentiate between 16 3D printed dielectric materials with a high accuracy of 98 % from measurements taken in transmission mode. As the constitution of the dataset has a big impact on the accuracy, various data preparations were investigated as well as the number of traces needed for achieving a well-trained network was determined. Finally, the trained neural network was evaluated with different sample thicknesses, revealing the huge impact of the materials absorbance on the extrapolation ability. This approach can be used in security application to classify harmful substances as well as for the automated generation of material maps.

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Common plastics THz classification via artificial neural networks: A discussion on a class of time domain features

Cited by 4 publications

References 72 publications

Recent Advancements in Plastic Packaging Recycling: A Mini-Review

Recent Advancements in Plastic Packaging Recycling: A Mini-Review

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

Material Classification for Terahertz Images Based on Neural Networks

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