## 5 Conclusions  
This study employs an ultrasonic pretreatment to improve veneer dyeing performance and develops the IDBO-SAT Fusion-Gated BiLSTM model to predict dye formulations and ultrasonic pretreatment power. The key conclusions are as follows:  
1. The ultrasonic pretreatment increased the gaps between wood fibers, disrupting the structure of cellulose, hemicellulose, and lignin, a phenomenon which resulted in an enhanced dye uptake.
2. By combining pre- and post-dyeing veneer spectral similarity metrics with spectral characteristic parameters and spectral reflectance data obtained after veneer dyeing, the prediction accuracy of dye formulations and ultrasonic pretreatment power was improved. The proposed IDBO-SAT Fusion-Gated BiLSTM model demonstrated significant improvements over traditional machine learning models in metrics such as \(R^{2}\), \(RPIQ\), \(RPD\), \(RMSE\), and \(MAE\). Notably, \(RPD\) and \(RPIQ\) values showed more than a 100.8% increase over conventional models, while maintaining high computational efficiency with an 83.94% reduction in runtime relative to CNNs.
3. An analysis using the SHAP algorithm identified key spectral features, including reflectance in the 400-500 nm range and at 701.81 nm, which influence predictions of dye concentrations and ultrasonic pretreatment power.  
In conclusion, the IDBO-SAT Fusion-Gated BiLSTM model offers significant advantages in predicting dye formulations and ultrasonic pretreatment power, thereby contributing to the optimization of wood dyeing technology. However, it is important to note that these findings are specific to the reactive dyes used in this study; if other types of dyes are applied, the model may require retraining and the results may differ.  
The supporting information can be downloaded at: [[https://sensing.konicaminolta.us/wp-content/uploads/Specim-FX10-Technical-Datasheet-04.pdf](https://sensing.konicaminolta.us/wp-content/uploads/Specim-FX10-Technical-Datasheet-04.pdf)]([https://sensing.konicaminolta.us/wp-content/uploads/Specim-FX10-Technical-Datasheet-04.pdf](https://sensing.konicaminolta.us/wp-content/uploads/Specim-FX10-Technical-Datasheet-04.pdf)) (accessed on 29 January 2025).  
Conceptualization, X.G.; methodology, X.G. and X.C.; software, X.C.; validation, X.C.; data collection, X.C.; writing--original draft preparation, X.C.; writing--review and editing, X.G., X.C., Z.H., and H.C.; visualization, X.C.; project administration, X.G.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (32171691); Harbin Science and Technology Bureau Manufacturing Innovation Talent Project (CXRC20221110393).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this study are not publicly available due to confidentiality and privacy concerns. Access to the data can only be granted upon request and with the permission of the appropriate parties. Please contact the corresponding author for further information.  
**Acknowledgments:** Thanks to the School of Computer and Control Engineering, Northeast Forestry University.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest to report regarding the present study.