# AI-enabled spectral classification of plastic resins from E-waste via laser-induced breakdown spectroscopy for advanced sorting applications

Pallab Das, Qiang Zeng, Jean-Baptiste Sirven, Jong-Min Lee, Jean-Christophe P. Gabriel

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Pallab Das, Qiang Zeng, Jean-Baptiste Sirven, Jong-Min Lee, Jean-Christophe P. Gabriel. AI-enabled spectral classification of plastic resins from E-waste via laser-induced breakdown spectroscopy for advanced sorting applications. Resources, Conservation and Recycling, 2026, 226, pp.108660. 10.1016/j.resconrec.2025.108660. cea-05349129

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Full length article

# AI-enabled spectral classification of plastic resins from E-waste via laser-induced breakdown spectroscopy for advanced sorting applications

Pallab Das<sup>a,*</sup><sup>ID</sup>, Qiang Zeng<sup>a,1 ID</sup>, Jean-Baptiste Sirven<sup>b</sup>, Jong-Min Lee<sup>a,c,2</sup>,
Jean-Christophe P. Gabriel<sup>a,d,* ID</sup>

## ARTICLE INFO

### Keywords:

Laser-Induced Breakdown Spectroscopy (LIBS)

Machine Learning

Plastic Sorting

E-waste Plastics

Circular Economy

Neural Network (NN-MLP)

## ABSTRACT

In the framework of the circular economy (CE), efficient waste segregation is essential for sustainable recycling. Plastic waste from waste electrical and electronic equipment (WEEE) poses challenges due to complex resin structures and the presence of brominated flame retardants (BFRs). This study investigates the use of laser-induced breakdown spectroscopy (LIBS) combined with supervised machine learning (ML) for the classification of various e-waste plastics, including mixed resins and those containing BFRs. Although the primary analysis was conducted using static LIBS data, dynamic tests were also performed to simulate real-world sorting conditions. Among the classifiers, Support Vector Machine (SVM) and Neural Network Multilayer Perceptron (NNMLP) delivered the best results, reaching 92–94 % accuracy on test data and up to 96 % on unseen datasets. Furthermore, dynamic trials showed over 98 % accuracy, confirming the robustness of the approach. These findings highlight the potential of LIBS–ML systems for scalable, high-precision sorting, advancing industrial recycling strategies.

## 1. Introduction

Plastic waste has emerged as a significant environmental challenge over the past few decades. While various waste management processes, such as pyrolysis (P. Das and Tiwari, 2018, 2018), incineration (Stewart and Lemieux, 2003), and dissolution (Das et al., 2023; Miller-Chou and Koenig, 2003), have been developed and implemented as alternatives to landfilling, primary recycling, or the repurposing of plastics, is increasingly recognized as the most sustainable approach (Kassab et al., 2023; Shi et al., 2022, 2021). However, a substantial challenge in this approach is the purity of the recycled plastic resin (Das et al., 2021, 2023; Jia et al., 2022; Waszczyłko-Młkowska et al., 2024). Impurities can lead to low-quality recycled products, which reduces their market demand and ultimately undermines the viability of plastic repurposing as a sustainable solution (Das et al., Apr 2021; Yousaf et al., 2024). Lack of waste segregation, inefficient sorting of the plastic is the

reason behind poor recycling. Only 9 % of the total plastic waste generated till today has been recycled (Geyer et al., 2017). The global production of plastic in 2021 was 390.7 million tons (Mt). The generation of global plastic waste amounted to 353 Mt in 2019 and out of which 174 Mt was landfilled, 67 Mt was incinerated, and 79 Mt was released into the environment and only 29 Mt was used as recycled plastics (Alves, 2025). In 2021, Europe generated around 2.6 Mt of e-waste plastic and only 54 % were collected officially (Haarman et al., 2025). Low fraction of recycling of plastic can be attributed to the inefficient segregation and costly recycling strategies associated (Tumu et al., 2023).

The mechanical processes utilized as direct sorting tool for plastics waste are mostly based on the resin gravity (Choi et al., 2006). Processes, such as sink-froth floatation (Wang et al., 2015), cyclone separation (M.R. Gent et al., 2009), jigging separation (Phengsaart et al., 2020), are some commonly practiced separation techniques used

<footnote><sup>a</sup> SCARCE, ERIAN, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, CEA, Service D'Études Analytiques et de Réactivité des Surfaces, F-91191 Gif-sur-Yvette, France</footnote>

<footnote><sup>c</sup> School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore</footnote>

<footnote><sup>d</sup> Université Paris-Saclay, CEA, CNRS, NIMBE, LICSEN, F-91191, Gif-sur-Yvette, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: pallab.das@ntu.edu.sg (P. Das), jean-christophe.gabriel@cea.fr (J.-C.P. Gabriel).</footnote>

<footnote n="1">¹ Current address: Institute of modern physics, Chinese Academy of Sciences, 509 Nanchang Rd., Lanzhou 730000, China.</footnote>

<footnote n="2">² Current address: Department of Energy Science and Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea</footnote>
industrially. These processes are optimized for scenarios where the resin densities exhibit wide variations. However, most polymer resins possess comparable densities, with substantial overlap in their respective density ranges, as illustrated in Fig. 1S (Cardarelli, 2008; Haynes, 2016; Howatson, 2012).

Presence of additives also affects the density of plastic resins. Moreover, density separation typically utilizes separation media, such as water, saline solutions, solvents, and paramagnetic liquids, which can contribute to waste generation (M. Gent et al., 2009). The efficiency of this separation process is highly contingent on factors, such as particle size, shape, the hydrophobicity or hydrophilicity of the plastics (Pita and Castilho, 2017).

Indirect sorting has been carried out using various spectroscopic methods, such as infrared spectroscopy (IR) (Jiang et al., 2021; Signoret et al., 2020), Raman spectroscopy (Roh et al., 2017; Allen et al., 1999), X-ray fluorescence spectroscopy (XRF) (Chaine et al., 2023; Michel et al., 2020), and Laser-Induced Breakdown Spectroscopy (LIBS) (Gajarska et al., Nov 2021; Shameem et al., 2017; Zeng et al., 2021). Even though indirect techniques such as IR and Raman spectroscopy are effective for resin identification, their implementation in large-scale,

time-sensitive operations remains challenging. These methods typically require multiple stages, including data acquisition, processing, modeling, and decision-making, and their performance is often limited by analysis time as well as sensitivity to the physical appearance and surface condition of plastics, which can hinder overall sorting efficiency (Neo et al., 2022). Qualitatively, these analytical techniques offer significant advantages over mechanical sorting methods. IR and Raman spectroscopy are good at analyzing the molecular structure of the polymer through vibrational analysis. On the other hand, XRF is effective in detecting embedded heteroatoms (Cl/Br) (Jia et al., 2022), metals and metal based additives at the elemental level (Jia et al., 2022; Xia et al., 2021; Zeng et al., 2021). In contrast, LIBS allows exceptionally rapid data acquisition producing spectra within microseconds (µs) and delivering results in seconds far faster than conventional indirect techniques, which typically require tens of seconds to several minutes (Fernandes Andrade et al., 2021; Hussain Shah et al., 2020). It can detect metal additives and contaminants in polymers; however, achieving high analytical precision demands mitigation of matrix effects and signal instability, requiring rigorous calibration and advanced data-processing methodologies (Michael and Reinhard,

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** a. The schematic of the static LIBS pilot, b. Static LIBS with different components, c. Workflow for preparing the spectral data for machine learning. The process includes: (1) Data collection from six classes of plastic samples, (2) normalization of spectral data for each sample, (3) merging of normalized data into a data matrix $R_0$, (4) transposing and labeling of the data matrix to produce $R'_0$, and (5) creation of the master dataset for machine learning, integrating labeled datasets from all classes. d. Data management scheme for training and model evaluation, e. Schematic representation of the inverted dynamic LIBS pilot that integrates a conveyor belt.</figcaption></figure>
2003). LIBS technology is based on the principle of atomic emission spectroscopy, where a pulsed laser having an energy range between 10–1000 millijoules (mJ) per pulse precisely focused onto the sample surface using optical system, generating a high temperature micro-plasma through the localized material ablation. As the plasma cools, atoms and ions from the plasma emits light with different frequencies. The emission is transmitted to the intensified charge-coupled device (ICCD) spectrometer through optical fiber which create unique elemental spectra corresponding to the material (Gallot-Duval, 2024; Zhang et al., 2015; Alvarez-Llamas et al., 2025). Over the past two decades, LIBS has seen growing interest in plastic sorting, with notable progress in recent years fueled by the AI revolution and enhanced computational power. While LIBS is already well-established in industrial sectors, such as metallurgy (Sirven et al., 2025; Zhang, 2014), geology (panya et al., 2021), astrogeology (Maurice et al., 2016), and mining (Hao et al., 2021) supported by commercial solutions from companies like Steinert Lss | Libs, Ablatom, or lumtek, its deployment at scale for plastic waste sorting is still in the early stages of development, presenting both a challenge and an opportunity for innovation.

S. Michael and Reinhard (2003) (Michael and Reinhard, 2003) used LIBS for the identification of the presence of Br and toxic metals, such as Cd, Cr, Hg, Pb and Sb, in the end-of-life waste electrical and electronic (EOL-WEEE) in the wavelength (λ) region of 200–900 nm. They can quantify successfully the elements present in the EOL-WEEE plastics and measure the limit of detection for each targeted element. In most previous studies, the plasma emission spectra of the plastic samples were analyzed for special spectral features, such as 725.7 nm chlorine line, 486.12 nm of H(β) line and the 247.86 nm of Carbon line (Sattmann et al., 1998). Anzano', et al. (2000) (Anzano' et al., 2000) used statistical correlation (linear and rank) to compare the laser induced plasma (LIP) spectra of plastics in 200–800 nm spectral window to identify plastic type compared with library spectra and achieved a probabilistic identification of >80 %. Anzano, et al. (2011) (Anzano et al., 2011) applied chemometric techniques, such as principal component analysis (PCA), analyzing the hierarchical cluster data derived from LIBS spectra, using 10 laser shots per point for each sample. PCA can reduce the dimensionality of the data and offer transformation with reduced number of variables (Grégoire et al., 2011). Hierarchical cluster analysis successfully grouped the four polymers into two distinct clusters, potentially enabling faster and simpler identification and classification. While clustering and dimensionality reduction are effective unsupervised learning techniques for analyzing plastic samples, advanced supervised learning algorithms can further improve accuracy in identifying specific plastic types based on LIBS spectra. Boueri, et al. (2011) (Boueri et al., 2011) utilized an Artificial Neural Network (ANN) with LIBS spectra to classify plastic resins and quantify specific elements. They boosted classification efficiency by employing feature selection, reducing the input variables to major spectral lines, including molecular bands like CN and C2, and elemental signals from Ti, Mg, Cl, and F. Vahid Dastjerdi, et al. (2016) (Vahid Dastjerdi et al., 2016) evaluated the sorting capability of LIBS for separating polyvinyl chloride (PVC) from other polymers, such as polypropylene (PP), polystyrene (PS), and polymethyl methacrylate (PMMA). They used the relative intensities of Cz/C and N/C from single-shot LIBS spectra as input features for a Support Vector Machine (SVM) classifier. Various kernel functions including radial basis function (RBF), polynomial, and linear were applied for classification. The method achieved a separation accuracy of 90.5 %. Costa, et al. (2017) (Costa et al., 2017) carried out LIBS based polymer classification from the waste plastics from WEEE. They have classify plastic resins, such as ABS, PS, PE, PC and PA, by selecting the spectral emission lines at C (247), H (656), N(742, 744, 747) and O(777) as well as molecular band of C2(516) using classification models, such as k-nearest neighbors (KNN) and Soft independent modelling of class analogy (SIMCA) with an average accuracy achieved around 98 % for KNN and 92 % for SIMCA. Junjuri and Gundawar (2019) (Junjuri and Gundawar, 2019), Junjuri, et al. (2019) (Junjuri et al., 2019) successfully classified common

plastics (PP, LDPE, HDPE, PS, PET) using LIBS, finding that full-spectrum features delivered greater accuracy than using only selected spectral lines (C, H, O, CN). However, these controlled findings didn't account for the complexity inherent in real-world e-waste plastics. Previous work typically used limited datasets that failed to capture the heterogeneity of real-world waste, often excluding mixed polymers and hazardous additives. Critically, these studies also neglect the exploration of scalable, industrially viable ML solutions necessary for comprehensive, real-time classification.

This study builds up the framework for the LIBS based fast plastic sorting system by analysing the effectiveness of different ML classifiers, optimizing the hyper parameters of the models, streamlining the data collection and data processing steps and identifying the factors influencing the effective training of classifier algorithms. In addition, the effectiveness of the classifiers on sorting spectral data obtain from the dynamic LIBS reveals the robustness of the ML based LIBS sorting. The system in the long run will solve crucial problems with material sorting in relation to waste disposal and recycling, particularly for complex plastic waste sorting. The results obtained in the LIBS are unique and encouraging for future development and will help reach CE goals.

## 2. Experimental method and data processing

E-waste samples were collected from the local recyclers. The e-waste contains various electrical and electronic equipment, such as computers, laptops, monitors, routers, keyboards & mouse, phones, printers, paper shredder, television, fans, and other household electrical equipment (Fig. 2S). The equipment was dismantled manually and the electrical components, such as printed circuit boards and metallic components, were separated from the plastic body. The plastic components were then carefully examined to identify the resin identification codes or plastic types and were separated accordingly based on manufacturer-provided information. All sample resins were qualified using FTIR-ATR spectroscopy to ensure quality of the training database. Samples for LIBS spectral acquisition were prepared by meticulously cleaning the plastic components to ensure the elimination of external contamination. The material was then diced into rectangular specimens having a surface area of approximately 10 cm². Samples were categorized into six categories of ABS, ABS-BFR, PC, PC+ABS, PP and HIPS/PS. BFR laden ABS plastics contain around 8–12 wt. % of Bromine and tetrabromobisphenol-A (TBBPA) is the most abundant BFR used in these types of plastics. More details can be found in our previous work (Das et al., 2023).

An in-house LIBS setup was specified and customized, consisting of a fully integrated LIBS system with engineering design and control software. The schematic of the setup is shown in Fig. 1a,b. It includes a customized light collection, emission, and focusing unit, along with an integrated optics and mechanical module, all synchronized through control software and a data acquisition system. The in-house integration of the setup was outsourced to EINST Technology Pte Ltd (Singapore). Major components of the setup are: (i) Andor iStar 320T CCD detector (Model: DH320T-25-03), with an array format of 1024 × 255 with 26 µm pixels, with 960 × 255 active pixels, sensitivity 2 to 10 e-/count and >99 % sensor linearity. (ii) Andor Imaging Spectrograph (Model: KYMERA-328I-B1 (Single slit input, double slit & CCD output) with 4 optical gratings (with resolution of G1:0.89 – 0.62 nm, G2: 0.09 – 0.06 nm, G3: 0.06 – 0.04 nm and G4: 0.05 – 0.04 nm) with manual slit cover aperture 6 × 8 mm. (iii). Lamp pumped solid state laser (Make: Quantel, Model: Ultra 100, Nd:YAG, 1064 nm) with a repetition rate of ≤ 20 Hz, energy per pulse of 100 mJ with a pulse duration of 8 ns. LIBS data were collected at G1 grating within the wavelength range of 200–700 nm. The dimension of laser head is 51 × 170 × 76 (mm). (iv) The system includes a linear motorized XY stage and a manually adjustable vertical Z-axis (Model: PA050), with an integrated camera to aid in aligning the sample's vertical position for optimal focus. The LIBS is mounted on a workstation table and the laser was covered with a large laser safety
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** FTIR-ATR spectra of six resin classes: a. ABS, b.ABS-BFR, c.PC, d.PC-ABS, e.PP, and f.HIPS+PS. Baseline-corrected spectra highlight characteristic peaks for functional groups such as C≡N stretching, C = O stretching, C = C aromatic stretching, and C—H stretching, shown in shaded regions. Molecular structures of representative monomers are shown alongside each spectrum to illustrate the link between spectral features and polymer compositions.</figcaption></figure>

enclosure.

## 2.1. Dataset preparation

Samples were collected from six resin classes commonly found in waste electrical and electronic equipment (WEEE), with ten samples per class. For each sample, 30 LIBS spectra were acquired from double shots at different positions using a Nd:YAG laser (5.5 J per pulse, 20 Hz, 110 W average power). This resulted in a total of 1800 spectral datapoints across all classes (Table 1). Two versions of the dataset were prepared, i. Dataset 1, which is normalized by scaling each spectrum to its maximum intensity. And ii. Dataset 2, which is normalized using the intensity at the Carbon 247 nm (Anzano et al., 2008) emission line, to standardize comparisons across samples.

<table><caption>**Table 1**

Samples and the total datapoints collected with the label.</caption><thead><tr><th>Resin class</th><th>Total sample</th><th>Total pulse/spectra per sample</th><th>Total spectral datapoints</th><th>Label/ Class</th></tr></thead><tbody><tr><td>ABS</td><td>10</td><td>30</td><td>300</td><td>0</td></tr><tr><td>ABS-BFR</td><td>10</td><td>30</td><td>300</td><td>1</td></tr><tr><td>PC</td><td>10</td><td>30</td><td>300</td><td>2</td></tr><tr><td>PC+ABS</td><td>10</td><td>30</td><td>300</td><td>3</td></tr><tr><td>PP</td><td>10</td><td>30</td><td>300</td><td>4</td></tr><tr><td>HIPS/PS</td><td>10</td><td>30</td><td>300</td><td>5</td></tr></tbody></table>

Each spectrum was labelled with its corresponding resin class (0–5) and organized into a matrix $R_0$ through the steps described in Fig. 1c, where each row represents individual spectra and columns represent spectral intensities at corresponding wavelength (nm). The matrix was transposed to conform to ML input formats, with wavelength as features and intensities as observations.

The master dataset, prepared using each normalization method, was split at the spectrum level into two subsets: a training set and an evaluation set. The evaluation set comprised a randomly selected 10 % of spectra from each class, reserved as an untouched benchmark to assess the performance of the supervised machine learning models after training. The remaining 90 % of the data was used for model development, within which a stratified 90/10 train-test split was applied to maintain class balance during training and testing. The data splitting scheme is shown in Fig. 1d. The models, listed in Table A1, were trained using grid search and 5-fold cross-validation to optimize their performance. The accuracy of each model was then evaluated by comparing its performance on the 10 % test split with the results obtained from the untouched 10 % evaluation set serving as an independent check on model robustness. This approach not only optimized model performance, but also minimized risks of overfitting and bias by ensuring balanced class representation and rigorous evaluation across folds (Ntakolia et al., 2021). To further evaluate real-world applicability, the training process was replicated using data acquired from a dynamic LIBS setup, where an automated trigger mechanism activates the laser as the sample enters the laser interaction zone on a moving conveyor belt. A 3D
rendering of the dynamic LIBS pilot under development is shown in Fig. 1e.

## 2.2. PCA and K-mean clustering

To investigate the spectral variation among different plastics, the Principal Component Analysis (PCA) and K-means clustering (Genest et al., 2013; Oskouei et al., 2012) were applied. To avoid scale-related bias, spectral data were standardized using Z-scores ($Z = [Original value (X) - mean value (μ)]/Standard deviation (σ)$). PCA simplified the data by reducing the dimensionality and by transforming correlated spectral features into uncorrelated principal components (PCs) that captured maximum variance. The proportion of variance explained by each PC, calculated by dividing its eigenvalue by the total variance, was used to decide which components to retain for the clustering analysis.

K-means clustering (Na et al., 2010), an unsupervised classification algorithm, was subsequently applied using the first two principal components as input features. The K-means algorithm partitions data points into distinct clusters by iteratively minimizing the within-cluster sum of squares (WCSS), defined as in Eq. (1).

$$WCSS = \sum_{i=1}^{k} \sum_{x_j \in C_i} \|x_j - \mu_i\|^2 \quad (1)$$

where $x_j$ represents the data points in cluster $C_i$, and $\mu_i$ is the centroid of cluster $C_i$. The clustering was performed with the number of clusters ($k$) set equal to the known number of resin types. To quantitatively evaluate the effectiveness of clustering, the Adjusted Rand Index (ARI) (Santos and Embrechts, 2009) was calculated. The ARI assesses the agreement between the true resin labels and the K-means cluster assignments, correcting for random chance, and is computed as follows (Eq. (2)):

$$ARI = \frac{Index - Expected Index}{Max(Index) - Expected index} \quad (2)$$

An ARI close to 1 indicates excellent agreement between clusters and true classes, whereas an ARI near 0 implies random clustering. Additionally, a confusion matrix was computed to visualize clustering accuracy and class-specific misclassifications numerically.

## 2.3. Machine learning classifier training

To evaluate classification performance on LIBS spectral data, eight widely used supervised learning algorithms were applied. These include Random Forest (RF) (Cutler et al., 2012), K-Nearest Neighbors (KNN) (Huang et al., 2015), Logistic Regression (LR) (Yu et al., 2015), Neural Network (Multi-Layer Perceptron, NN-MLP) (Makantasis et al., 2015), Support Vector Machine (SVM) (Mammone et al., 2009), XGBoost (Wang et al., 2022; Zhang and Gong, 2020), LightGBM (Wang et al., 2018), and Gradient Boosting Decision Trees (GBDT) (Sun et al., 2020). These models were chosen for their diverse capabilities. RF, GBDT, XGBoost, and LightGBM offer strong performance on structured, high-dimensional data with built-in regularization and missing value handling. KNN and LR serve as simple, interpretable baselines. SVM is effective in high-dimensional spaces and for modelling complex boundaries, while NN-MLP (Singh and Banerjee, 2019) excels at capturing non-linear patterns through deep architectures. All pre-processing steps, including normalization, were performed prior to data splitting to maintain consistency and prevent data leakage. A detailed summary of the hyperparameters used for each ML algorithm is provided in Table A1. Model performance was assessed using multiple metrics such as confusion matrix, Receiver Operating Characteristic (ROC), validation accuracy (%), recall (%), F1-score (%), and precision (%) to ensure comprehensive and fair evaluation of classification quality (Conciatori et al., 2024).

# 3. Results and discussion

## 3.1. FTIR-Based verification of plastic samples

All plastic samples used in this study were rigorously analyzed to ensure sample integrity and quality. FTIR-ATR (Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance) analysis was conducted on 10 samples from each category. The spectral peaks corresponding to each resin class were identified, providing a basis for classification based on the molecular structure and bond characteristics of the polymers.

The spectra, with baseline correction applied for each sample class, is presented in Fig. 2. Signature peaks within the characteristic wavenumber ranges for each polymer were identified, and the number of samples that matched these signature ranges was determined. Table 2S summarizes the IR wavenumber ranges corresponding to each resin class and indicates the number of samples that qualified for polymer verification based on these criteria. These peaks are linked to the functional groups and molecular vibrations specific to each polymer. For ABS-BFR, the presence of brominated flame retardants is confirmed by strong C-Br stretching peaks in the range of 600–700 cm⁻¹ across all samples, while the nitrile group (C≡N) stretching is consistent with ABS, albeit detected in 8 out of 10 samples. Peaks in the aromatic (1490–1600 cm⁻¹) and aliphatic (2850–2960 cm⁻¹) C-H stretching regions confirm the contributions of styrene and butadiene, respectively, while broad O-H stretching (~3400–3600 cm⁻¹) indicates the phenolic groups from TBBPA (BFR).

For ABS, distinct peaks corresponding to C≡N stretching (~2237–2250 cm⁻¹), aromatic ring vibrations (~1490–1600 cm⁻¹), and aliphatic backbone vibrations (~2850–2960 cm⁻¹) validate the molecular composition. Similarly, PC can be identified by a strong carbonate C = O stretching peak (~1720–1780 cm⁻¹) and characteristic C-O stretching (~1150–1250 cm⁻¹) from its aromatic ester groups. The weak aromatic C-H stretching (~3025–3100 cm⁻¹) and C-H deformation vibrations (~700–900 cm⁻¹) further confirm its structure. PC-ABS blends exhibit combined peaks from both components, such as the nitrile group (C≡N) stretching of ABS and the C = O stretching of PC.

HIPS+PS spectra confirm the presence of styrene and butadiene units through aromatic C = C stretching (~1600, 1490 cm⁻¹), C = C stretching of butadiene (~1640–1680 cm⁻¹), and out-of-plane C-H bending of aromatic (~690–760 cm⁻¹) and Alkenic C-H out-of-plane bending (~900–990 cm⁻¹). For PP, the prominent aliphatic C-H stretching (~2940–2960 cm⁻¹) and methyl bending (~1365–1390 cm⁻¹) are distinct. Peaks associated with isotactic polypropylene, such as C-H rocking (~970–995 cm⁻¹) and -CH₂ wagging (~830–890 cm⁻¹), indicate its crystalline structure.

While variations in peak presence and intensity across samples suggest both molecular consistency and some degree of heterogeneity, the overall data clearly demonstrate the value of specific IR peaks in identifying and characterizing polymer resins. These findings provide a solid foundation for polymer identification and quality control, both in scientific research and industrial applications.

## 3.2. Data collection for ML

Fig. A1 presents the normalized LIBS spectra for six plastic resin classes: ABS, ABS-BFR, PC, PP, PC+ABS, and HIPS/PS. For each class, 300 spectra were collected from 30 distinct laser spots form due to double ablation by laser across 10 individual samples. The left column (Fig. A1a) shows spectra normalized by scaling each individual spectrum to its maximum intensity, facilitating visual comparison of spectral patterns across resin types. The right column (Fig. A1b) shows the same spectra but normalized with respect to the carbon emission line at 247 nm. These two normalization approaches form the foundation for generating Dataset 1 and Dataset 2, respectively. Normalizing to carbon peak helps reduce intensity variability and makes elemental patterns
and distinct class-specific differences (David and Cremers, 2013; Frick-
e-Begemann et al., 2023).

3.3. PCA and K-mean clustering

PCA and K-means clustering were applied to gain insights into the
distribution and consistency of the two datasets. The primary purpose of
employing these unsupervised methods was to evaluate the inherent

<figure><img src="image_5.png" /><figcaption>Fig. 3. Results of PCA followed by K-means clustering for LIBS spectral datasets. Panels a1–a3 correspond to Dataset 1, while b1–b3 show results for Dataset 2. Subplots a1 and b1 display PCA score plots labeled by resin type; a2 and b2 illustrate the corresponding K-means cluster distributions; and a3 and b3 show the explained variance ratio for each principal component. Panels c1–c2 present the confusion matrix and true/false positive rates for Dataset 1, where-as d1–d2 depict the same metrics for Dataset 2.</figcaption></figure>
separability and clustering structure within the spectral data prior to applying supervised classification methods. Understanding the inherent spectral variations and overlaps through PCA helps to identify the limitations of linear dimensionality reduction methods, while K-means clustering assesses the potential of unsupervised classification to accurately discriminate resin classes based solely on spectral similarity. Fig. 3a1-d2 illustrates the application of Principal Component Analysis (PCA) and subsequent K-means clustering (unsupervised classification) to two differently normalized spectral datasets: (I) dataset 1 and (II) dataset 2. The PCA score plots (Fig. 3a1, 3b1) with true class labels reveal distinct spectral patterns and partial separation among resin types. Certain resins, particularly PC and ABS, exhibit relatively well-defined clusters, whereas resin classes, such as ABS-BFR, PC-ABS, and PSHIPS, display significant spectral overlaps, complicating their clear separation in PCA space. K-means clustering results based on the PCA scores (Fig. 3a2 and 3b2) further emphasize this limitation. Although visually distinct clusters corresponding to some resin types can be identified, overlapping clusters indicate that unsupervised methods alone may not sufficiently discriminate between spectrally similar types. This limitation is quantitatively reflected in the low ARI scores = 0.106 for dataset 1 and 0.198 for dataset 2, indicating weak agreement between the K-means clustering output and the true class labels. The challenge persists across both normalization methods, highlighting the inherent complexity of accurately classifying resin classes that share closely related spectral characteristics. These results underscore the need for supervised learning approaches to achieve reliable and accurate plastic resin classification in LIBS-based analysis.

The explained variance plots (Fig. 3a3 and 3b3) confirm that PCA is effective at reducing dimensionality, as the first few principal components and captures substantial proportion of variance (~60–70 %). However, the rapid decrease in explained variance beyond the initial components indicates that PCA alone may overlook subtle yet crucial spectral differences among resins. Therefore, while PCA offers valuable insights into the primary structure of spectral data, the observed overlap among classes underscores its limitations as a standalone classification method. This highlights the necessity of employing supervised machine learning algorithms for more accurate and robust classification.

Fig. 3c,d provides a quantitative evaluation of PCA classification performance for two datasets: (I) **dataset 1** and (II) **dataset 2**, using confusion matrices and associated True Positive Rate (TPR) and False Positive Rate (FPR) metrics (Devi et al., 2021; Das et al., 2022). Confusion matrices (Fig. 3c1, 3d1) quantify the extent of misclassification observed among resin types, clearly highlighting those certain resins, particularly ABS-BFR, PC-ABS, and PSHIPS, suffer from high misclassification rates due to their overlapping spectral characteristics. This is reflected in the elevated off-diagonal values, which indicate significant confusion between spectrally similar classes.

The TPR and FPR matrices (Fig. 3c2, 3d2) further illustrate this limitation. Low TPR values combined with relatively high FPR values for ABS, ABS-BFR, and PC-ABS resins suggest inadequate separability using PCA alone. While PCA effectively reduces data dimensionality and captures significant variance within the dataset, its linear nature constrains the ability to clearly delineate closely related resin types that share overlapping spectral features. The moderate overall classification accuracy indicated by these numerical metrics confirms that PCA, when combined solely with unsupervised methods such as K-means clustering, fails to provide robust differentiation among overlapping resin classes. The presence of overlapping resin classes strongly suggests that the supervised learning methods listed in Table A1 are necessary to enhance classification accuracy by leveraging additional principal components, nonlinear transformations, and more sophisticated decision boundaries.

### 3.4. Classification using supervised machine learning

The ML classifiers listed in Table A1 are trained with both dataset and evaluated with the test set and the saved model was again evaluated

using the 10 % untouched external evaluation set. Result obtained with the test and untouched evaluation set is presented in Fig. 4 for all 8 trained models. Among them, two best performing model across the two datasets are further illustrated using and ROC and confusion matrix based on the untouched evaluation set. In case of Dataset 1 both NNMLP resulted maximum validation accuracy with untouched dataset followed by KNN with the value 96.11 % and 94 % respectively (Fig. 4a–b). Evaluation matric for the NNMLP such as ROC curve shows AUC values between 0.99 and 1.00 for all six classes, with a macro-average AUC of 1.00, indicating strong class separability. The confusion matrix further confirms this, with accuracies above 93 % for all classes and perfect prediction (100 %) for Classes 2 and 4. Only a few misclassifications occurred, mostly in Classes 0, 1, 3, and 5. Overall, the model demonstrates high reliability and generalization across all resin categories. The KNN model achieved strong performance on Dataset 1, with class-wise AUC values ranging from 0.96 to 1.00 and a macro-average AUC of 0.99, indicating high discriminative ability across all classes shown in Fig. 4b1, b2 and b3. As shown in the confusion matrix, accuracies exceed 96 % for most classes, with perfect prediction for Class 2 and Class 4 (100 %). Class 0 showed slightly lower accuracy (83.3 %), primarily due to misclassification into Class 5. Overall, the model demonstrates good generalization with minimal misclassification.

SVM and KNN are best performing algorithms for Dataset 2 with validation accuracy of 96.11 % and 93.89 % respectively (Fig. 4c–d). Both models demonstrated strong classification performance across all evaluation metrics, including precision, recall, and F1-score. The ROC curves (Fig. 4c2) show excellent class-wise AUC values, with SVM achieving AUC = 1.00 for five of six classes and a macro-average AUC of 1.00, indicating near-perfect separability. The confusion matrix (Fig. 4c3) further confirms this, with perfect or near-perfect classification in all classes except Class 3, which showed minor confusion with Class 2. Similarly, the KNN model achieved a macro-average AUC of 0.99 (Fig. 4d2), with Class-wise AUCs ranging from 0.98 to 1.00 and maintained high prediction accuracy across most classes. The confusion matrix (Fig. 4d3) reveals strong diagonal dominance, with minor misclassifications observed in Classes 2, 3, and 5. Overall, both models demonstrate reliable generalization and robustness on unseen evaluation data.

Fig. 4e and Fig. 4f present the evaluation matrices such as accuracy, precision, recall, and F1-score for the remaining six models across Dataset 1 and Dataset 2, respectively. While SVM, RF, and LR maintained relatively consistent performance between the test set and the untouched evaluation set, the three gradient boosting models LightGBM, XGBoost, and GBDT showed a notable decline in performance on the unseen evaluation data.

This performance drop is particularly evident in the untouched evaluation set, where boosting models often underperformed across all metrics. This degradation is likely due to overfitting, a known tendency of boosting algorithms when trained on limited or homogeneously sampled data (Zhang and Yu, 2005). Additionally, the untouched evaluation sets may have contained distributional shifts or out-of-distribution (OOD) samples do not present during training or testing, challenging the generalization capacity of these models. Unlike ensemble bagging methods like RF or linear models like LR, boosting models rely heavily on the specific patterns of the training data and can struggle to adapt to subtle variations in unseen inputs. Confusion matrix regarding the test set and the untouched evaluation set for both the dataset for each algorithm are shown in Fig. 3S – Fig. 6S and detailed evaluation matrix along with the optimized hyperparameters are listed in Table 3S and Table 4S in the supplementary information file.

The findings explicitly show that the type of input data and the normalization strategy selected have an impact on the performance of machine learning algorithms, in addition to the algorithms' architecture and optimized hyperparameters. For min-max normalization (dataset1) NNMLP and KNN performed relatively better than others, and SVM and KNN performed better when C-247 normalization was considered. The
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Comparison of evaluation metrics (Validation Accuracy (VA) %, Precision (P) %, Recall (R) %, and F1-Score (F1) %) for the trained models with respect to the *test dataset* and *untouched evaluation dataset*. Panels a and b show the best-performing models NNMLP and KNN, respectively for Dataset 1. Subpanels a2 and b2 present the ROC curves with class-wise AUC values, while a3 and b3 display the corresponding confusion matrices based on the *untouched evaluation set*. Panels c and d highlight the best-performing models NNMLP and KNN, respectively for Dataset 2. Subpanels c2 and d2 show the ROC curves with class-wise AUC values, and c3 and d3 present the confusion matrices based on *untouched evaluation set*. Panels e and f summarize the evaluation metrics for the remaining six models on Dataset 1 and Dataset 2, respectively.</figcaption></figure>

performance metrics for NNMLP, KNN, SVM, and LR improved when evaluated on the untouched dataset, suggesting better generalization in these models. In contrast, boosting algorithms such as LightGBM, XGBoost, and GBDT showed a significant drop in performance under the same conditions. This underlines the need for rigorous validation on diverse data to ensure model robustness and reliability in real-world sorting systems. Such insights are critical for developing adaptive, high-precision classification frameworks that support scalable plastic recycling and circular economy initiatives.

The promising results obtained from the ML-based classification of LIBS data laid a strong foundation for the development of an industrial-scale plastic sorting system. Building on this, the next phase of the work involves transitioning from static to a more dynamic sorting setup (Fig. 1e), wherein LIBS spectra are acquired from periodically moving plastic samples on a conveyor belt. These samples, representing different resin classes like the trained categories, are to be identified in real-time by the implemented ML model.

In this study, a dual-shot LIBS acquisition strategy was adopted, where the spectral data were generated from the second ablation event. Based on this initial knowledge, the processing speed can be calculated. Each LIBS shot required 0.022 s, resulting in a total data acquisition time of roughly 0.05 s for two shots. An additional time of 0.1 s for the resin identification time and 0.1 s for the sorting time can be allocated. Based

on the total detection time of 0.25 s, the system can identify 4 samples/sec (240 samples/min). An auto-trigger mechanism was implemented to sense the presence of a sample within laser's focal region and automatically initiate laser firing once proper alignment was achieved. Considering the sample size of 10 × 10 cm² and 2 cm gap between two consecutive sample makes it 0.12 m per sample. To maintain a constant throughput of 240 samples/min, the conveyor belt needs to operate at the speed of 0.48 m/s (~28.8 m/min). Speed of the conveyor belt is encouraging for the industrial implementation (He et al., 2018; Rao, 2020). A sample dataset was collected for six resin classes, with 5–6 samples each, using the static LIBS setup to verify the application of classifiers in the dynamic LIBS system. The two best classifiers established in the static system NNMLP and SVM were used to train models, and 10 % of untouched data was used to evaluate them. The results are encouraging, with validation accuracy exceeding 98 %. The validation metrics for the untouched dataset are shown in Fig. 5, demonstrating high classification accuracy (~98 %) for both NNMLP and SVM models (Fig. 5b–5e). At present, most dynamic approaches for plastic sorting rely on indirect optical identification techniques, primarily based on NIR, hyperspectral imaging (HSI), or visible light, and are typically restricted to a limited number of plastic types at the industrial scale using AI-assisted mechanical or robotic sorting systems. A comprehensive overview of such commercial technologies has been provided by
<figure><img src="image_7.png" /><figcaption>Fig. 5. a. normalized LIBS Spectra from the dynamic setup. b. and c. Evaluation metrics for the NNMLP and SVM model respectively. d. and e. Confusion matrix with the untouched evaluation data.</figcaption></figure>

Lubongo, et al. (2024) (Lubongo et al., 2024). The next step in system development will be to integrate the trained models with a mechanical sorting system that can separate plastics into individual bins based on the resin identified by the classifier. For a faster system to sort the identified resins, reducing the sample size and the gaps between samples can be a contributing factor. Future work will focus on addressing these issues associated with the LIBS acquisition and sorting, expanding the database to incorporate a broader range of resin classes, and subsequently developing a more industrially robust, scalable plastic sorting system.

## 4. Conclusion

LIBS and ML-based plastic sorting present a promising solution for plastic waste management by enabling classification based on resin type and the presence of additives or contaminants. This study demonstrates that advanced machine learning classifiers can be effectively trained on LIBS spectral data even in cases where differentiating between visually and chemically similar resins, such as PS and ABS, is challenging. Notably, the models were able to identify BFR-laden ABS with high accuracy. Classifiers like NNMLP, SVM, and KNN showed strong performance across both the test and untouched evaluation sets, highlighting their robustness in handling complex LIBS datasets without the need for dimensionality reduction or feature elimination. The high evaluation metrics obtained from the dynamic LIBS setup further validate the effectiveness of this approach. Moving forward, the integration of dynamic LIBS with a mechanical sorting system powered by AI models trained on a broader set of resin classes could significantly

enhance the recyclability of plastic waste, particularly complex e-waste plastics and support the broader goals of a circular economy.

## Claim

During the preparation of this work, PD used his private "ChatGPT" account to double-check for English spelling, grammatical errors or typos. After this, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## CRediT authorship contribution statement

**Pallab Das:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Qiang Zeng:** Writing – review & editing, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Jean-Baptiste Sirven:** Validation, Writing – review & editing.
**Jong-Min Lee:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization.
**Jean-Christophe P. Gabriel:** Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

All authors reports that financial support was provided by National Environment Agency (Singapore).
Jean-Christophe Gabriel reports that financial support was provided by the Agence Nationale de la Recherche (France).

If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

All authors acknowledge financial support from the SCARCE project, which (i) phase 1 (2018–2023) was supported by the National Research Foundation, Prime Minister’s Office, Singapore, the Ministry of National

Development, Singapore, and National Environment Agency Waste Loop R&D Initiative as part of the Urban Solutions & Sustainability – Integration Fund [Award No USS-IF-2018-4] and; (ii) Phase 2 is currently supported by the National Research Foundation, Singapore, and National Environment Agency, Singapore under its Closing the Waste Loop Funding Initiative (Award No CTRL-2023-1D-01).

J.C.G. acknowledge financial support from the French government managed by the Agence Nationale de la Recherche under the France 2030 program, under the reference ‘ANR-22-PERE-0002’, project "PLASTICS".

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.resconrec.2025.108660.

## Appendix

<figure><img src="image_8.png" /><figcaption>Fig. A.1. a. Normalized spectra (Dataset 1) and b. C-247 normalized spectra (Dataset 2) plotted for all 10 samples, with 30 laser spots analyzed for each resin type.</figcaption></figure>

<table><caption>**Table A.1**

Hyperparameters of the selected ML algorithms.</caption><thead><tr><th>Classifiers/Algorithms</th><th>Hyperparameters*</th></tr></thead><tbody><tr><td>RF</td><td>n_estimators: [100, 200, 300]; max_features: ['auto', 'sqrt', 'log2']; max_depth: [None, 10, 20, 30]; min Samples split: [2, 5, 10]; min Samples leaf: [1, 2, 4]</td></tr><tr><td>KNN</td><td>n_neighbors: [3, 5, 7, 10]; weights: ['uniform', 'distance']; metric: ['euclidean', 'manhattan', 'minkowski']; algorithm: ['auto', 'ball_tree', 'kd_tree', 'brute']</td></tr><tr><td>NN (MLP)</td><td>Learning_rate = [0.001, 0.01]; batch size = [32, 64]; num epochs = [30, 50]; hidden layer sizes = [(128, 50), (256,100)]; activation = ['ReLU', 'Tanh']; optimizer = ['Adam', 'SGD']; scheduler = ['StepLR', 'None']</td></tr><tr><td>LR</td><td>Regularized parameter (C): [0.001, 0.01, 0.1, 1, 10, 100]; solver: ['saga']; max_iter: [10,000]; penalty: ['elasticnet', 'L2']</td></tr><tr><td>SVM</td><td>Regularized parameters (C): [0.1, 0.5, 5, 10]; gamma (kernel coefficient): [0.01, 0.1, 1]; kernel: ['rbf', 'poly', 'sigmoid']</td></tr><tr><td>XGBoost</td><td>Max_depth: [3, 6, 9]; gamma: [0, 0.1, 0.5]; subsample: [0.7, 0.8, 0.9]; colsample_bytree: [0.7, 0.8, 0.9]; learning rate: [0.01, 0.1, 0.2]; n_estimators: [100, 200, 300]</td></tr><tr><td>LightGBM</td><td>Num_leaves: [31, 50, 70]; max_depth: [10, 20, 30]; learning_rate: [0.05, 0.1, 0.2]; n_estimators: [50, 100, 200]</td></tr><tr><td>GBDT</td><td>Learning_rate: [0.01, 0.1, 0.2]; n_estimators: [100, 200, 300]; max_depth: [3, 5, 7]; min Samples split: [2, 5, 10]; min Samples leaf: [1, 2, 4]</td></tr></tbody></table>

## Data availability

Data will be made available on request.

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