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Outline
Laser-induced breakdown spectroscopy (LIBS) holds significant potential for application in the field of coal property analysis, due to its advantages of eliminating complex sample pretreatment, enabling multi-parameter synchronous detection, and offering rapid analysis. However, discrepancies in spectral responses exist among different instruments. These discrepancies cause severe accuracy degradation when a quantitative model trained on spectra acquired by a master instrument is applied to slave instruments. Therefore, this study constructed cross-instrument LIBS quantitative analysis models of coal property by integrating TrAdaBoost transfer learning with various machine-learning algorithms.
Two LIBS-based coal analyzers were designated as the master and slave instruments respectively, and LIBS spectra were collected from different numbers of coal samples on both devices. Random forest (RF), support-vector regression (SVR), and their TrAdaBoost-enhanced counterparts (TrA-RF and TrA-SVR) were employed to build quantitative analysis models. Model performance was evaluated by predicting the coal properties of unknown coal samples on the slave instrument.
The results indicated that both TrA-RF and TrA-SVR models significantly outperformed their non-transfer counterparts. The TrA-RF model achieved the highest accuracy for calorific value, ash content, and carbon content. Compared with RF model, the mean absolute errors decreased from 1.390 MJ/kg, 4.774 %, and 3.826 % to 0.654 MJ/kg, 2.338%, and 1.927%, respectively. TrA-SVR model yielded the highest accuracy for volatile matter prediction. Compared with the SVR model, the mean absolute error decreased from 2.722% (SVR) to 2.524%.
These findings demonstrate that coupling transfer learning with an appropriate base learner markedly enhances the adaptability of LIBS-based coal property models across different instruments.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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