Characterization and Discrimination of Plastic Materials Using Laser-Induced Fluorescence

Rapid identification of marine microplastics by laser-induced fluorescence technique based on PCA combined with SVM and KNN algorithm

The laser-induced fluorescence technique has the advantage of fast and non-destructive detection and can be used to classify types of marine microplastics. However, spectral overlap poses a challenge for qualitative and quantitative analysis by conventional fluorescence spectroscopy. In this paper, a 405 nm excitation laser source was used to irradiate 4 types of microplastic samples with different concentrations, and a total of 1600 sets of fluorescence spectral data were obtained. The 726 data points contained in each sample spectrum were first analyzed by PCA, and the 4 microplastics were differentiated by their position in the PCA score plot. The classification and identification are then performed by SVM, KNN, PCA-SVM and PCA-KNN algorithms respectively. The classification accuracy of microplastics in seawater using SVM and KNN algorithms is higher than 86%. The classification accuracy can be increased to 100% by PCA combined with SVM and KNN algorithm. Concentration inversion was conducted by SVM and KNN algorithms after classification. The correlation coefficients between the predicted values and the actual values were higher than 0.8, and the RMSE was less than 0.47%, which indicated that both algorithms had good prediction results. These machine learning methods provide accurate and reliable identification results in the rapid identification of microplastic types and their concentrations without complex spectral data preprocessing and fluorescence background removal algorithms.

Experimental detection of marine plastic litter in surface waters by 405 nm LD-based fluorescence lidar

Plastic pollution has become a global challenge, affecting water quality and health. Plastics including polystyrene (PS), polyvinyl chloride (PVC), polypropylene (PP), polyethylene terephthalate (PET), and high-density polyethylene (HDPE), are significant contributors to environmental pollution. With the growing need for investigation and detection of plastics found in natural waters, we propose the use of a portable laser diode (LD)-based fluorescence lidar system for in-situ detection of plastic litters in surface waters based on excitation-emission fluorescence spectroscopic data. The experiments were carried out in a controlled environment using a fluorescence lidar system with 405 nm excitation wavelength to determine the fluorescence signals of several plastics at 470 nm emission wavelength. Simultaneous detection of PET plastic and Chlorella vulgaris were also observed to determine the fluorescence influence of chlorophyll in surface waters. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was employed to study the chemical composition of the plastics used before and after being submerged in the water. Scanning electron microscopy (SEM) and high-resolution camera microscopy were used to analyze the morphology of the submerged PET samples. This study provides a basis for a new in-situ technique using a fluorescence lidar system for submerged or transparent plastics in surface waters.

Multi-sensor characterization for an improved identification of polymers in WEEE recycling

Polymers represent around 25% of total waste from electronic and electric equipment. Any successful recycling process must ensure that polymer-specific functionalities are preserved, to avoid downcycling. This requires a precise characterization of particle compounds moving at high speeds on conveyor belts in processing plants. We present an investigation using imaging and point measurement spectral sensors on 23 polymers including ABS, PS, PC, PE-types, PP, PVC, PET-types, PMMA, and PTFE to assess their potential to perform under the operational conditions found in recycling facilities. The techniques applied include hyperspectral imaging sensors (HSI) to map reflectance in the visible to near infrared (VNIR), short-wave (SWIR) and mid-wave infrared (MWIR) as well as point Raman, FTIR and spectroradiometer instruments. We show that none of the sensors alone can identify all the compounds while meeting the industry operational requirements. HSI sensors successfully acquired simultaneous spatial and spectral information for certain polymer types. HSI, particularly the range between (1600-1900) nm, is suitable for specific identification of transparent and light-coloured (non-black) PC, PE-types, PP, PVC and PET-types plastics; HSI in the MWIR is able to resolve specific spectral features for certain PE-types, including black HDPE, and light-coloured ABS. Fast-acquisition Raman spectroscopy (down to 500 ms) enabled the identification of all polymers regardless their composition and presence of black pigments, however, it exhibited limited capacities in mapping applications. We therefore suggest a combination of both imaging and point measurements in a sequential design for enhanced robustness on industrial polymer identification.

Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry

Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.

Development of an inter-confirmatory plastic characterization system using spectroscopic techniques for waste management

Ever-accumulating amounts of plastic waste raises alarming concern over environmental and public health. A practical solution for addressing this threat is recycling, and the success of an industry-oriented plastic recycling system relies greatly on the accuracy of the waste sorting technique adapted. We propose a multi-modal spectroscopic sensor which combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy in a single optical platform for characterizing plastics based on elemental and molecular information to assist the plastic identification-sorting process in recycling industries. The unique geometry of the system makes it compact and cost-effective for dual spectroscopy. The performance of the system in classifying industrially important plastic classes counting PP, PC, PLA, Nylon-1 1, and PMMA is evaluated, followed by the application of the same in real-world plastics comprising PET, HDPE, and PP in different chemical-physical conditions where the system consumes less than 30 ms for acquiring LIBS-Raman signals. The evaluation of the system in characterizing commuting samples shows promising results to be applied in industrial conditions in future. The study on effect of physical-chemical conditions of plastic wastes in characterizing them using the system shows the necessity for combining multiple techniques together. The proposal is not to determine the paramount methodology to characterize and sort plastics, but to demonstrate the advantages of dual-spectroscopy sensors in such applications. The outcomes of the study suggest that the system developed herein has the potential of emerging as an industrial-level plastic waste sorting sensor.

Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements

Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.

Integrated Laser Sensor (ILS) for Remote Surface Analysis: Application for Detecting Explosives in Fingerprints

Here, we describe an innovative Integrated Laser Sensor (ILS) that combines four spectroscopic techniques and two vision systems into a unique, transportable device. The instrument performs Raman and Laser-Induced Fluorescence (LIF) spectroscopy excited at 355 nm and Laser-Induced Breakdown Spectroscopy (LIBS) excited at 1064 nm, and it also detects Laser Scattering (LS) from the target under illumination at 650 nm. The combination of these techniques supplies information about: material change from one scanning point to another, the presence of surface contaminants, and the molecular and elemental composition of top target layers. Switching between the spectroscopic techniques and the laser wavelengths is fully automatic. The instrument is equipped with an autofocus, and it performs scanning with a chosen grid density over an interactively-selected target area. Alternative to the spectroscopic measurements, it is possible to switch the instrument to a high magnification target viewing. The working distances tested until now are between 8.5 and 30 m. The instrument is self-powered and remotely controlled via wireless communication. The ILS has been fully developed at ENEA for security applications, and it was successfully tested in two outdoor campaigns where an automatic recognition of areas containing explosives in traces had been implemented. The strategies for the identification of nitro-compounds placed on various substrates as fingerprints and the results obtained at a working distance of 10 m are discussed in the following.

Excitation wavelength analysis of a laser-induced fluorescence technique for quantification of extra virgin olive oil adulteration

The performance of the laser-induced fluorescence (LIF) technique is greatly affected by the excitation wavelength (EW). This study aims to find an appropriate EW that can be used for analyzing extra virgin olive oil (EVOO) adulteration quantification by comparing the effect of different EWs. The EWs of 405 nm, 450 nm, and 532 nm were selected to perform the comparative experiments. By using the three EWs in the experiments, the LIF spectra of EVOO samples adulterated with peanut oil (PO) or soybean oil (SO) in different proportions, as well as the prediction models established through different multivariate analysis algorithms were analyzed. The linear discriminant analysis (LDA) was applied for qualitative analysis, while the partial least squares regression (PLSR), backpropagation neural network, and k-nearest neighbor were employed for quantitative analysis. The results show that the performance of 450 nm EW is always superior to that of 405 and 532 nm EWs in any model, with a smaller root mean square error (RMSE). Using the LDA-PLSR model, the RMSE is 1.35% for SO adulterants and 1.36% for PO adulterants, respectively.

Improved continuous locality preserving projection for quantification of extra virgin olive oil adulteration by using laser-induced fluorescence

An optimized dimensionality reduction technique is proposed as the improved continuous locality preserving projection (ICLPP), which was developed by modifying and optimizing the weighting functions and weighting factors of the continuous locality preserving projection (CLPP) algorithm. With only one adjustable parameter, this optimized technique not only enhances CLPP's capability of maintaining the continuity of the massive data, but also results in better simplicity and adaptability of the algorithm. In this paper, the performance of ICLPP is validated through quantification analysis of the adulteration of extra virgin olive oil (EVOO) with low-cost oils based on laser-induced fluorescence spectroscopy. Through cross validation and comparative studies, ICLPP, combined with the regression algorithm, is employed to predict and screen adulteration in EVOO, and is found to generally outperform other state-of-the-art dimensionality reduction algorithms, especially for prediction of adulterants at low level (<10%). It is evidenced that the ICLPP-based framework is superior in detecting adulteration by using spectral data.