# Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning
###### Abstract  
Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems. a Article  
Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], L.  
coal properties [10]. [PERSON] et al. identified coal and carbonaceous shale based on visible and NIR spectroscopy [11]. NIR spectroscopy is an effective and efficient technique to measure the chemical compositions of samples and has been widely used in the agricultural, petrochemical and pharmaceutical industries in the past few decades [12; 13; 14; 15]. NIR has also been used to identify geographical origins. For example, [PERSON] et al. used NIR spectroscopy and SPA-LDA simultaneously to classify the geographical origin and quality of tea [16]. [PERSON] et al. classified the geographical origin of honey samples by NIR spectroscopy [17]. [PERSON] et al. identified the adulterations and geographical origins of Chinese herbs by NIR spectroscopy and chemometrics [18]. Many traditional learning methods including Support Vector Machine (SVM) [19], Back Propagation Neural Network (BPNN) [20], Random Forest (RF) [21], etc., are combined with the NIR spectra to discriminate the geographical origin and quality of food and herbs [22]. However, compared with the NIR spectra of the above organics, there is more redundant information and noise in the NIR spectra of coal samples. Moreover, the data set is small, and inhomogeneity may exist. Considering the problems mentioned above, the traditional classification methods cannot meet the requirements of accurate and effective identification [23; 24; 25; 26]. In this study, we employ a novel classification algorithm, Broad Learning (BL), which was recently proposed by [PERSON] et al. [24]. BL is well known for its superior performance in solving several classification problems. Unlike the deep learning-based algorithms [27], there is no layer-to-layer coupling in the network structure. It has the advantage of a simple structure and low computational cost. The BL can be used in complex classification problems with less parameters to compute. Moreover, the incremental learning systems of BL can efficiently remodel the model in a broad, expansive way, and the retraining process is unnecessary if the network is deemed to be expanded. In order to further improve the performance of the BL model, we employ Particle Swarm Optimization (PSO) to optimize the structure of the BL model. Given its advantages of being powerful and easy to implement, the PSO algorithm has become universally applicable to various optimization problems [28].