Combined Laser-induced Breakdown Spectroscopy Research Articles

Exploring the viability of combined laser-induced breakdown spectroscopy and Raman spectroscopy for stratigraphic analysis of murals containing isomeric pigments: a case study on realgar and orpiment

A novel combined measurements techniques has been designed in this work, enabling the acquisition of laser-induced breakdown spectroscopy (LIBS) and Raman spectral signals at the same point on a sample. The application of this combined technique to the analysis of multi-layered mock-up blocks painted with orpiment and realgar pigments has yielded significant insights. By correlating variations in the emission line intensity of characteristic elements within the LIBS spectra with depth-specific Raman spectra, the number of laser pulses that penetrated the pigment layers has been accurately determined, thereby establishing a method to measure layer thickness. Finally, the technique wasto analysis the actual mural fragment from Mogao Cave 196, determining the types of pigment and the thickness of the pigment layers.Graphical

Advanced chemometric methodologies on single shot hyphenated LIBS data for rapid and reliable characterization of plastic classes

BackgroundPlastic Solid Waste (PSW) sorting is a procedure of paramount importance in the mechanical recycling process of plastics waste. The major limitation of the techniques relying on physical properties of plastics is the time taken for analysis and poor accuracy. Spectroscopy has been shown to be a suitable method in plastic sorting due to its atomic and molecular characterization capabilities, and ability to give results in very short time scales. However, for practical applications it is essential to translate this technique into an automatic technology, by combining it with advanced chemometric tools which can give observer independent judgement. ResultsThe indigenously developed bi-model Laser Induced Breakdown Spectroscopy (LIBS)-Raman system with single source and single detector can record the LIBS-Raman spectral signals in single-shot mode in a total time frame of 20 ms. Out of the combinations of Principal Component Analysis (PCA) and Partial Least Squares (PLS) with Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Partial Least Squares-Discriminant Analysis (PLS-DA) based classifiers, the PLS-DA based model showed the maximum classification accuracy with 95 % based on LIBS data and 100 % based on Raman data. The reliability of the model was assessed using 4-fold cross-validation which showed a sensitivity of 90.28 % and specificity of 98.29 % for predictions based on LIBS data, and 99 % sensitivity and 99.82 % specificity for predictions relying on Raman data. SignificanceThe results show how the combination of multimodal spectroscopy with chemometric analysis enhances the applicability of spectroscopic techniques for plastic sorting. The classification model successfully classified seven types of post-consumer plastic samples based on combined LIBS and Raman data. With the home-built software for automated prediction, the system takes less than a second to predict the plastic type illustrating the potential of the method for translation to regular routine industrial applications.

Chemical and Mineralogical Analysis of Samples Using Combined LIBS, Raman Spectroscopy and µ-EDXRF

Energy-dispersive X-ray fluorescence (EDXRF) analysis is one of the standard techniques for the evaluation of mineral deposits. The advantage of EDXRF is the fast delivery of information about the bulk elemental composition as well as the elemental composition of each mineral class. With micro energy-dispersive X-ray fluorescence (µ-EDXRF) analysis, information can be obtained with a micrometer resolution. However, it has some limitations. With EDXRF, light elements (e.g., lithium) cannot be detected, and the count rates for carbon, fluorine and sodium are very low. This might lead to a misinterpretation of the mineral classes and the worth of the deposit. Furthermore, the identification of the alteration phases of primary minerals is ambiguous. Here, we will present an approach to overcome the limitations of µ-EDXRF by complementing it with combined laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. In contrast to EDXRF, LIBS is able to detect all elements, including light elements. Raman spectroscopy can identify mineral phases and eventually provide additional information on their alterations and modifications. In the present paper, we show results for two different samples covering a certain chemical and mineralogical range that demonstrate the potential of the proposed combination of methods for the chemical and mineralogical analysis of geological samples.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

Characterizing plastics containing brominated flame retardants with combined LIBS and Raman spectroscopy

Waste electronic and electric equipment (WEEE) are collected in high amounts in the EU. However, in order to enable safe and effective recycling of their plastic fraction, harmful additives inside the plastics need to be identified. In this study, two spectroscopic methods, laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy were employed for characterizing different brominated flame retardants (BFRs) inside plastics from the actual WEEE stream, and also lab-made plastics. The results of this preliminary study indicate the ability of LIBS for accurate quantification of bromine content, and the prospective capability of Raman and combined Raman-LIBS for identifying different BFRs in plastics.

Identification of meat species by combined laser-induced breakdown and Raman spectroscopies

We study the effect of complementary spectral information based on combined LIBS (laser-induced breakdown spectroscopy) and Raman spectroscopy, including 3 options of LIBS, Raman and LIBS-Raman, on the improved classification accuracy of meat tissues of beef, mutton and pork. The BPNN (back propagation neural network) with input variables optimized by RF (random forest) was used to classify the 3 kinds of meat tissues. The model confusion matrix, Precision, Recall, Kappa, MAE (Mean absolute error), RMSE (Root mean square error) and other parameters were obtained by 10-fold cross-validation method to evaluate the 3 classification models, and the results of the three methods were compared. The results showed that the combined LIBS-Raman model has the highest classification accuracy of up to 99.42%, and superior to the other 2 separate methods in terms of model consistency and confidence degree, indicating that the combined LIBS-Raman method has significantly improved the recognition ability and classification accuracy of meat tissues, which took the advantage of utilizing the complementary spectral information obtained by both methods. Therefore, the combination of LIBS-Raman and BPNN is a fast and robust method for meat tissue identification.

Research on anthracnose grade of Camellia oleifera based on the combined LIBS and THz technology

BackgroundAnthracnose of Camellia oleifera is a very destructive disease that commonly occurs in the Camellia oleifera industry, which severely restricts the development of the Camellia oleifera industry. In the early stage of the Camellia oleifera suffering from anthracnose, only the diseased parts of the tree need to be repaired in time. With the aggravation of the disease, the diseased branches need to be eradicated, and severely diseased plants should be cut down in time. At present, aiming at the problems of complex experiments and low accuracy in detecting the degree of anthracnose of Camellia oleifera, a method is proposed to detect the degree of anthracnose of Camellia oleifera leaves by using terahertz spectroscopy (THz) combined with laser-induced breakdown spectroscopy (LIBS), so as to realize the rapid, efficient, non-destructive and high-precision determination of the degree of anthracnose of Camellia oleifera.ResultsMn, Ca, Ca II, Fe and other elements in the LIBS spectrum of healthy and infected Camellia oleifera leaves with different degrees of anthracnose are significantly different, and the Terahertz absorption spectra of healthy Camellia oleifera leaves, and Camellia oleifera leaves with different degrees of anthracnose there are also significant differences. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and linear discriminant analysis (LDA) are used to establish the fusion spectrum anthracnose classification model of Camellia oleifera. Among them, the Root mean square error of prediction (RMSEP) and the prediction determination coefficient R2p of THz-LIBS-CARS-PLS-DA of prediction set are 0.110 and 0.995 respectively, and the misjudgment rate is 1.03%; The accuracy of the modeling set of THz (CARS)-LIBS (CARS)-SVM is 100%, and the accuracy of prediction set is 100%, after preprocessing of the multivariate scattering correction (MSC), the accuracy of the THz-LIBS-MSC-CARS modeling set is 100%, and the accuracy of prediction set is 100%; The accuracy rate of THz-LIBS-MSC-CARS-LDA of modeling set is 98.98%, and the accuracy rate of the prediction set is 96.87%.ConclusionThe experimental results show that: the SVM model has higher qualitative analysis accuracy and is more stable than the PLS-DA and LDA models. The results showed that: the THz spectrum combined with the LIBS spectrum could be used to separate healthy Camellia oleifera leaves from various grades of anthracnose Camellia oleifera leaves non-destructively, quickly and accurately.

Quantitative analysis of ceria co-doped with samarium and gadolinium using laser-induced breakdown spectroscopy.

Ceria doped with low-valence lanthanide cations has been introduced for use as an electrolyte in solid oxide fuel cells (SOFCs). Improving the performance of SOFCs using doped ceria requires an increase in ion mobility across the solid electrolyte. Recently, ceria doped with multiple low-valence lanthanide ions has been found to show better ion mobility than that of the singly doped one. In this work, the feasibility of laser-induced breakdown spectroscopy (LIBS) for stoichiometric analysis of doubly doped ceria, SmxGd0.1-xCe0.9O2-δ, was investigated. The three lanthanide elements pouring out plenty of emission lines made identifying the well-resolved single emission line of the dopants (Sm and Gd) rarely feasible. However, the spectral feature of the dopants could be extracted from the unresolved spectra successfully by partial least squares-regression (PLS-R). The PLS-R model performance calibrating the LIBS spectra to the concentration of Sm or Gd was dependent on the contribution of the matrix element (Ce) to the latent variable chosen for modeling. The residual feature of Ce in the latent variable could be reduced further by smoothing LIBS spectra using a moving average. The best model showed dependable detection limit (0.83 mol% of Sm) and accuracy (0.24 mol% of Sm) performances. Spectral denoising by moving average and PLS-R modeling based on LIBS spectra can be used as a rapid and reliable methodology for the multiply doped ceria and assist the manufacturing and recycling processes of SOFCs.

Refractory residues classification strategy using emission spectroscopy of laser-induced plasmas in tandem with a decision tree-based algorithm

The recycling of refractory scraps began to be forged just over a decade ago. Until then, virtually all refractory scraps were disposed off in landfill sites without any application. Over these past few years, a growing interest and a gain steady momentum of the circular economy, the emergent framing around waste and resource management that promotes the notions of their productive cycling, has been the driving force towards the “zero waste” culture across the spectrum of refractory users and producers. In this way, the circular economy, operated following strategies such as, but not limited to, reusing, recycling, and remanufacturing, has played the pillar role in the different essential value chains of the refractory industry to the entering the new era of secondary raw material supply. In any case, prior to starting any sustainable process, it is really necessary to know the wastes and to classify them. In this context, the present research focused on a refractory residue-classification strategy based on combined laser-induced breakdown spectroscopy (LIBS) and a decision tree algorithm for a qualitative analytical performance. This tandem approach allowed the categorization of a rich set of residues in up to 10 different refractory groups. By choosing original LIBS emission intensities and intensity ratios involving the most relevant constituent elements (Al, Mg, C ‒through its related-species CN‒, Si and Zr) of various refractory wastes, a decision tree with multiple nodes that decided how to classify inputs was designed and trained. Categorization performed from LIBS emission spectra of "blind" refractory residues showed that LIBS data combined with this supervised machine learning algorithm provided good refractory scraps-classification performance, with a classification accuracy of up to 75%. However, some more than justified decisions of the algorithm on allegedly misclassified residues showed that scores for the decision tree could found to be far superior to those obtained. The results achieved support the strategy designed for its industrial implementation, either directly in the iron and steel industry, as the major end-user of refractories, in the refractory waste management industry, or in both.

Diagnosis and staging of multiple myeloma using serum-based laser-induced breakdown spectroscopy combined with machine learning methods.

Diagnosis and staging of multiple myeloma (MM) have been achieved using serum-based laser-induced breakdown spectroscopy (LIBS) in combination with machine learning methods. 130 cases of serum samples collected from registered MM patients in different progressive stages and healthy controls were deposited onto standard quantitative filter papers and ablated with a Q-switched Nd:YAG laser. Emissions of Ca, Na, K, Mg, C, H, O, N and CN were selected for malignancy diagnosis and staging. Multivariate statistics and machine learning methods, including principal component analysis (PCA), k-nearest neighbor (kNN), support vector machine (SVM) and artificial neural network (ANN) classifiers, were used to build the discrimination models. The performances of the classifiers were optimized via 10-fold cross-validation and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). The kNN, SVM and ANN classifiers achieved comparable discrimination performances with accuracies of over 90% for both diagnosis and staging of MM. For diagnosis of MM, the classifiers achieved performances with AUC of ∼0.970, sensitivity of ∼0.930 and specificity of ∼0.910; for staging of MM, the corresponding values were AUC of ∼0.970, sensitivity of ∼0.910 and specificity of ∼0.930. These results show that the serum-based LIBS in combination with machine learning methods can serve as a fast, less invasive, cost-effective, and robust technique for diagnosis and staging of human malignancies.

Univariate and multivariate analyses of Gd in gadolinia-doped ceria using laser-induced breakdown spectroscopy

We report laser-induced breakdown spectroscopy (LIBS) analysis of gadolinium (Gd) in gadolinium-doped ceria (GDC), the functional material used in solid oxide fuel cells (SOFCs). LIBS using laser ablation to atomize solid samples is competent in sampling the refractory ceria. Also, a few to 10 mol.% of Gd is doped in GDC to be used for SOFCs. Such a high concentration requires additional dilution after acid digestion of the sample for conventional analyses. LIBS with typical dynamic range from ppm to percent can analyze the high concentrations of Gd directly. GDCs with 1–10 mol.% of Gd were synthesized by glycine-nitrate process and the LIBS spectra were recorded. In spite of these advantages of LIBS, calibrating LIBS spectra to the concentrations of Gd is quite challenging due to rich emissions of Ce. Six prominent Gd I lines were selected for univariate calibrations and the whole spectra were used for multivariate partial least squares-regression (PLS-R) modeling. The PLS-R model considering emission intensities of both matrix and analyte elements outperformed the univariate calibrations. Our results suggest that LIBS in combination with multivariate analysis can be useful as an analysis method for the refractory lanthanide oxide materials.

Discrimination of the origin of ginseng combined laser-induced breakdown spectroscopy and machine learning algorithms

利用激光诱导击穿光谱技术结合机器学习算法, 对东北5个产地(大兴安岭、集安、恒仁、石柱、抚松)的人参进行产地识别, 建立了主成分分析算法分别结合反向传播(BP)神经网络和支持向量机算法的人参产地识别模型. 实验采集了5个产地人参共657组在200—975 nm的激光诱导击穿光谱, 经光谱数据预处理后, 对C, Mg, Ca, Fe, H, N, O等元素的8条特征谱线进行主成分分析, 原光谱数据的前3个主成分累积贡献率达到92.50%, 且样品在主成分空间中呈现良好的聚集分类. 降维后的前3个主成分以2∶1进行随机抽取, 分别作为分类算法的训练集和测试集. 实验结果表明主成分分析结合BP神经网络及支持向量机的平均识别率分别为99.08%和99.5%. 发生误判的原因是集安和石柱两地地理环境的接近而导致的H, O两元素在Ca元素离子发射谱线下的归一化强度相似. 本研究为激光诱导击穿光谱技术在人参产地的快速识别提供了方法和参考.

Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods.

The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, which calls for effective identification. In the present work, the spectral profiles of grape seeds belonging to three different cultivars were collected by laser-induced breakdown spectroscopy (LIBS). Three conventional supervised classification methods and a deep learning method, a one-dimensional convolutional neural network (CNN), were applied to establish discriminant models to explore the relationship between spectral responses and cultivar information. Interval partial least squares (iPLS) algorithm was successfully used to extract the spectral region (402.74–426.87 nm) relevant for elemental composition in grape seeds. By comparing the discriminant models based on the full spectra and the selected spectral regions, the CNN model based on the full spectra achieved the optimal overall performance, with classification accuracy of 100% and 96.7% for the calibration and prediction sets, respectively. This work demonstrated the reliability of LIBS as a rapid and accurate approach for identifying grape seeds and will assist in the utilization of certain genotypes with desirable nutritional properties essential for production rather than their being discarded as waste.

Revealing the impact of laser-induced breakdown on a gas flow

Laser-induced breakdown spectroscopy (LIBS) is an optical, and thus non-contact, but not non-invasive, measurement technique. Investigating the impact of laser-induced breakdown on a gas flow, combined LIBS and particle image velocimetry (PIV) measurements are performed. In the considered laminar air flow, the induced velocity field disturbance has an extent of about 0.7 cm with magnitudes up to 0.9 ms−1. As a further result, the combination of LIBS with other measurement techniques or high-speed LIBS measurements are found to require a minimal time delay of about 500 s in order to ensure influence of the preceding LIBS pulse on the considered gas flow of about 10 % relative velocity deviation. For a reduction to 0 % relative velocity deviation a time delay of about 20 ms is estimated for the investigated flow. Smaller time delays may occur in turbulent flows or flows with higher velocities.

An advanced spectroscopic approach (LIBS in combination with AAS): a steadfast means to probe the elemental composition of monosodium glutamate

The elemental composition of monosodium glutamate (MSG) was probed by engaging a laser-induced breakdown spectroscopy (LIBS) analytical technique. The LIBS spectra of MSG were acquired in air pressure with an appropriate detector (LIBS2000 + , Ocean Optics) with an optical resolution of ~0.06 nm in the spectral range from 220 to 720 nm. Analyses of the acquired LIBS spectra revealed the presence of Ag, Ca, Mg, S and Na in the sample. To maximize the signal to noise ratio in the resulting LIBS spectra, the experimental parameters, such as pulse energy and gate delay, were optimized. The accomplishment of local thermodynamic equilibrium supposition was confirmed by the McWhirter’s criterion and in view of the relaxation time, as well as the diffusion length of the atoms in the plasma. For the determination of relative concentrations of detected elements in MSG, we used three LIBS-based approaches, i.e. by setting the concentration of Na as the calibration, by setting the sum of all concentrations as the calibration and by the calibration curve method. The same sample was also subjected to the more standard analytical tool of atomic absorption spectroscopy, and its results were cross-compared with the findings of LIBS to confirm its validity. It was highly encouraging to find that both results were in sound agreement.

Potential use of surface-assisted LIBS for determination of strontium in wines.

Laser-induced breakdown spectroscopy (LIBS) is a well-established technique for elemental analysis and has been widely used for qualitative and quantitative analysis of different solid samples. LIBS is also well-known for not requiring sample preparation, but the analysis of liquids is actually a great challenge. In the present work, a novel approach of elemental analysis of liquids with an organic matrix has been performed, to the best of our knowledge, making a liquid-to-solid matrix conversion by drying wine samples on aluminum and silicon wafers, which have demonstrated an increase in the analytical performance of LIBS. A red wine from Slovakia (not blended with any other variety or wine from other regions or adulterants) was prepared according to the procedure consisting of drying 2 ml of wine dropped on a solid wafer having a flat surface area of about 25 cm2. Surface-assisted LIBS in combination with the conversion of liquid into solid avoids the difficulties and limitations of working with liquid samples by LIBS, improving the limit of detection (LOD), repeatability, and sensitivity. The quantification of strontium (Sr) has been realized by addition of the known concentration of SrCl2·6H2O in wine before the drying process. Sr is an important element among those usually used as markers for identification of the soils on which the vines grow. Two ionic (407.771 nm and 421.552 nm) and two neutral (460.733 nm and 481.188 nm) Sr lines were used to plot the calibration curves in order to study the LODs and the matrix effects for the analysis of Sr in the tested wines and for different wafer materials. This direct surface-assisted LIBS measuring method has been successfully applied for the determination of Sr in a red wine sample from Slovakia, and the obtained results with two kinds of substrates (Al and Si) were compared. Finally, a validation sample has been employed to test the accuracy of the established calibration curves.

Elmar K. Jessberger (1943–2017)

Elmar K. Jessberger (1943–2017)

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods

Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC=0.990, sensitivity=0.970 and specificity=0.956. For MM, the corresponding values are AUC=0.986, sensitivity=0.892 and specificity=0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.

A hybrid LIBS-Raman system combined with chemometrics: an efficient tool for plastic identification and sorting.

Classification of plastics is of great importance in the recycling industry as the littering of plastic wastes increases day by day as a result of its extensive use. In this paper, we demonstrate the efficacy of a combined laser-induced breakdown spectroscopy (LIBS)-Raman system for the rapid identification and classification of post-consumer plastics. The atomic information and molecular information of polyethylene terephthalate, polyethylene, polypropylene, and polystyrene were studied using plasma emission spectra and scattered signal obtained in the LIBS and Raman technique, respectively. The collected spectral features of the samples were analyzed using statistical tools (principal component analysis, Mahalanobis distance) to categorize the plastics. The analyses of the data clearly show that elemental information and molecular information obtained from these techniques are efficient for classification of plastics. In addition, the molecular information collected via Raman spectroscopy exhibits clearly distinct features for the transparent plastics (100% discrimination), whereas the LIBS technique shows better spectral feature differences for the colored samples. The study shows that the information obtained from these complementary techniques allows the complete classification of the plastic samples, irrespective of the color or additives. This work further throws some light on the fact that the potential limitations of any of these techniques for sample identification can be overcome by the complementarity of these two techniques. Graphical Abstract ᅟ.