Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Abstract

Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixelwise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.

A Novel High-Throughput Detection Method for Plastic Debris in Organic-Rich Matrices Based on Image Fusion

“…Exploring alternative fusion methods for enhanced spatial resolution and minimized spectral distortion is crucial. Finally, spectra preprocessing using algorithms could be applied to enhance indicative peaks, and advanced algorithms such as extreme gradient boosting, known for its effectiveness in organic waste classification, could be implemented for detecting plastic debris in the future.…”

Hyperspectral Imaging Algorithms and Applications: A Review

“…HSI, combined with partial least squares regression, showed good accuracy in predicting APC levels, indicating its potential as a non-invasive tool for real-time grading of hides and improving efficiency in leather processing. In [75], the authors explore the use of HSI in combination with the XGBoost algorithm to classify various industrial organic waste materials. XGBoost outperforms traditional methods in terms of accuracy and prediction time, suggesting that this approach could be valuable for developing an online waste sorting platform, providing cost savings and accurate waste identification.…”

Machine Learning-Based Prediction of the CO2 Concentration in the Flue Gas and Carbon Emissions from a Waste Incineration Plant

“…Compared to traditional modeling approaches, such as direct testing and the IPCC factor calculation, machine learning (ML) algorithms are adept at modeling complex nonlinear processes. In recent years, ML has gained significant attention in the field of MSW management. − In incineration operations, ML models have been used to forecast the performance of a combustion boiler when processing waste plastics and to predict the gas composition in medical waste incineration plants. , Zhu et al developed an extreme gradient boosting (XGBoost) model for predicting CO 2 emissions in thermal power plants using a few key observable factors. However, compared to thermal power plants, the fuel composition in waste incineration plants is more diverse and the factors influencing CO 2 emissions can vary based on the incineration process and flue gas purification conditions.…”