From Traditional to Intelligent: Advances in Wood Color Treatment Technologies
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摘要: 近年来，木材颜色处理技术取得了显著进展，多种方法共同推动了木材加工业的发展。漂白剂的使用提升了木材颜色的均匀性，为后续处理奠定了基础。真菌染色通过生物作用实现了颜色变化，天然染料则增强了木材的抗紫外线和防霉性能，延长了户外使用寿命。金属离子变色与木材成分反应，带来丰富的颜色变化，提升了装饰性。热处理改变木材结构，使颜色加深并提高了耐久性。在此基础上，智能算法尤其是机器学习技术，被应用于染色和热处理工艺，精准调整参数并预测效果，显著提升了生产效率和产品质量。这些技术集成推动了木材加工业向高效、环保和可持续方向发展。Abstract: In recent years, significant progress has been made in wood color treatment technologies, with various methods collectively advancing the wood processing industry. The use of bleaching agents has improved the color uniformity of wood, laying a solid foundation for subsequent treatments. Fungal staining achieves color changes through biological processes, while natural dyes enhance the UV resistance and anti-mold properties of wood, extending its outdoor lifespan. Metal ion color changes, through reactions with wood components, bring rich color variations and enhance decorative appeal. Heat treatment alters the wood's structure, deepening its color and increasing durability. Building on these advancements, intelligent algorithms, particularly machine learning techniques, have been applied to staining and heat treatment processes, enabling precise parameter adjustments and outcome predictions, significantly boosting production efficiency and product quality. These integrated technologies are driving the wood processing industry toward greater efficiency, environmental sustainability, and long-term viability.
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Key words:
- Wood color /
- fungal staining /
- natural dyes /
- metal ion color change /
- heat treatment /
- machine learning
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图 3 所选染色木块的扫描图像。(a)未处理。(b) Pergasol 黄。(c)果胶酶/Pergasol 黄。(d) Pergasol 红。(e)鞣酸。(f)果胶酶/鞣酸。(g)果胶酶/漆酶/鞣酸。(h) Procion 柠檬黄。(i)Procion 洋红。(j) Procion 鲜橙。(k) Procion 蓝绿。上排：未浸洗；下排：用水浸洗[18]。
Figure 3. Scanned images of selected dyed wood blocks. (A) Untreated. (B) Pergasol Yellow. (C) Pectinase/Pergasol Yellow. (D) Pergasol Red. (E) Tannin. (F) Pectinase/tannin. (G) Pectinase/ laccase/tannin. (H) Procion Lemon Yellow. (I) Procion Magenta. (J) Procion Brilliant Orange. (K) Procion Turquoise. Upper row: unleached; lower row: leached with water[18].
图 8 参考木材及用硫酸铁处理的木材图像：从左到右分别是施加10%、20%和30%溶液后的效果；从上到下分别是短时冷浴(2小时，20°C)、短时热浴(2小时，70°C)和长时冷浴(24小时，20°C)后的效果[28]。
Figure 8. Images of reference wood, and wood treated with iron sulphate: from left to right after 10 %, 20 % and 30 % solution was applied; from top to bottom: after short-term cold bath (2 h; 20℃), after short-term hot bath (2 h, 70℃), after long-term cold bath (24 h, 20℃)[28].
表 1 木材染色技术中色彩匹配和预测算法的最新进展。
Table 1. Recent Advances in Color Matching and Prediction Algorithms in Wood Dyeing Technologies.
Researcher Algorithm Type Purpose Results and Advantages Reference Guan et al. Genetic Algorithm (GA) optimizing Extreme Learning Machine (ELM) Improve accuracy of dye formula prediction Average relative deviation reduced to 0.262, significant accuracy improvement, cost-effectiveness and resource efficiency enhanced [11] Guan et al. Particle Swarm Optimization (PSO) Improve accuracy and efficiency of computer color matching Average relative deviation of 0.643%, average fit color difference of 0.720, significantly improved color matching accuracy and efficiency [48] Guan et al. Enhanced Radial Basis Function (RBF) Network Increase convergence speed and approximation accuracy of neural networks Average relative error reduced from 1.55% to 0.62%, training epochs reduced to 50, improved speed and accuracy [49] Wu et al. Particle Swarm Optimization (PSO) Improve wood dyeing accuracy Optimized model has lower color difference values, improved color matching accuracy, suitable for furniture production and interior design [50] Apraiz et al. Kubelka-Munk Theory with Self-learning Algorithms Predict dye color of stained oak accurately Accurately predicts wood color under various dye proportions and application methods, evaluates effects of varnishing on final color appearance [51] Wei et al. Adaptive Differential Evolution (ADE) Optimize prediction of wood dye formulas Optimized model's color difference ΔE00 less than 3, significantly improved prediction accuracy and optimization efficiency [52] Guan et al. Particle Swarm Optimization (PSO) Improve color matching accuracy Average fit color difference reduced from 0.8202 to
0.7287 , significantly improved color matching accuracy[53] Guan et al. Wolf Algorithm Optimized Support Vector Regression (SVR) Increase accuracy and speed of wood dye color matching Relative formula deviation reduced to 0.177, optimized model superior to traditional models, demonstrated global optimization capability [54] 
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表 2 先进预测模型在木材热处理及他颜色处理技术中的应用分析。
Table 2. Analysis of the Application of Advanced Prediction Models in Wood Heat Treatment and Other Color Processing Technologies.
Algorithm Type Research Objective Results and Advantages Reference Mo et al. Artificial Neural Networks (ANN) Predict color changes in wood High accuracy (R²>0.96), reduced time and resource usage [55] Kropat et al. Partial Least Squares Regression (PLSR) Predict visibility of chemically dyed wood Improved consistency, especially with iron acetate (R²_cv=0.92) [60] Nguyen et al. Artificial Neural Networks (ANN) Predict color changes in heat-treated and weather ed wood High accuracy (all datasets R²>0.92), reduced experimental costs [56] Van et al. Artificial Neural Networks (ANN) Predict color changes in heat-treated wood Extremely high predictive accuracy (R²>0.99), saves physical tests [57] Li et al. Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) Enhance prediction accuracy of color changes post heat treatment and weathering Significantly improved accuracy, reduced errors and variance [58] Li et al. Response Surface Methodology (RSM) Study the effect of CO2 laser on changing wood color Quantified relationship between laser settings and color results, showed chemical changes related to
color changes[59]
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