Huo Juan, He Feng, Lu Changtong, Zhu Meng, Bu Yifan, Kang Di, Wang Rui, Feng Wenning, Ma Rong
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
China Tobacco Hebei Industrial Co., Ltd., Shijiazhuang 050051, China.
ACS Omega. 2025 Jan 13;10(3):2908-2918. doi: 10.1021/acsomega.4c08978. eCollection 2025 Jan 28.
This paper investigates the nonlinear relationship between tobacco harmful content tar reduction and laser perforation parameters. To find a model to demonstrate the relationship between the laser perforation parameters and the cigarette tar reduction level, an online platform based on Python Streamlit was built to collect and publish related data. After the initial analysis of the collected experimental data, the quadratic nonlinear regression model demonstrates a significant fit to the experimental data. However, although the nonlinear regression has much higher accuracy than the linear regression plane, the prediction normalized root mean squared error (NRMSE) is still high, over 10%, which indicates that the regression relationship is more complex than the simple quadratic function expression. On the other hand, the sample dataset used for modeling is very limited, which restricts its exploration and the development of a model comparable to those built with big data. To address this challenge for small sample size data in modeling this complex nonlinear relationship, a novel rational-quadratic Minkowski (RM)-based kernel was designed. This RM-kernel model acquires higher accuracy than other kernels in both SVM and Gaussian process regression. Furthermore, this new kernel also shows less sensitivity to hyperparameter change, the greater ability to capture complex relationships, and more flexibility than the RBF kernel and RQ kernel. Subsequently, the kernel-based RM regression model was successfully implemented for laser perforation parameter selection, yielding consistent results that align with human sensory test data.
本文研究了烟草有害成分焦油降低与激光穿孔参数之间的非线性关系。为了找到一个模型来证明激光穿孔参数与卷烟焦油降低水平之间的关系，构建了一个基于Python Streamlit的在线平台来收集和发布相关数据。在对收集到的实验数据进行初步分析后，二次非线性回归模型显示出与实验数据的显著拟合。然而，尽管非线性回归比线性回归平面具有更高的精度，但预测归一化均方根误差（NRMSE）仍然很高，超过10%，这表明回归关系比简单的二次函数表达式更为复杂。另一方面，用于建模的样本数据集非常有限，这限制了对其的探索以及与大数据构建的模型相媲美的模型的开发。为了应对在对这种复杂非线性关系进行建模时小样本数据的这一挑战，设计了一种新型的基于有理二次闵可夫斯基（RM）的核。在支持向量机（SVM）和高斯过程回归中，这种RM核模型都比其他核具有更高的精度。此外，这种新核对比径向基函数（RBF）核和有理二次（RQ）核，对超参数变化的敏感性更低，捕捉复杂关系的能力更强，并且具有更大的灵活性。随后，基于核的RM回归模型成功应用于激光穿孔参数选择，得到了与人类感官测试数据一致的结果。
