奶粉基粉是配方奶粉的基础原材料，其产地影响到终端乳制品的品质。该研究提出了一种电子鼻技术与一维拉普拉斯卷积核相结合的基粉奶源地判别方法。通过电子鼻采集样本数据，经过时域信号对齐，尝试使用不同阶数的一维拉普拉斯卷积核进行特征提取，并对比了统计数字特征、快速傅里叶变换、离散余弦变换等其他特征提取方法，最后使用偏最小二乘及可视化进行可分性分析。实验结果发现快速傅里叶变换、离散余弦变换、二阶的一维拉普拉斯卷积核相对于原始特征均有效提升了可分性，偏最小二乘的R2效应量从0.61分别提升到0.95、0.96和1.00。一维拉普拉斯卷积核特征提取方法能够准确区分产自国内和国外（澳大利亚）的基粉，在案例研究中取得了最优判别效果，说明其能够有效提取到电子鼻各通道序列信号的时间响应特征。该方法能够完成中澳两国奶粉基粉样本的区分工作，为快速鉴定奶粉基粉来源提供技术支撑。

Dehydrated milk powder serves as the fundamental raw material for formula milk, and its place of origin affects the quality of terminal dairy products. In this paper, a method for discriminating the source of dehydrated milk powder by employing electronic nose technology and utilizing a one-dimensional Laplacian convolution kernel is proposed. Sample data is collected using an electronic nose, and after temporal signal alignment, various orders of one-dimensional Laplacian convolution kernels are applied to extract features. Additionally, other feature extraction methods such as statistical numerical features, fast Fourier transform, and discrete cosine transform were compared. Subsequently, partial least squares and visualization were used for separability analysis. Experimental results revealed that the fast Fourier transform, discrete cosine transform, and second-order one-dimensional Laplacian convolution kernels effectively improved separability relative to the original features. The R2 effect size of partial least squares was increased from 0.61 to 0.95, 0.96, and 1.00. The one-dimensional Laplacian convolution kernel feature extraction method was able to accurately distinguish between domestically produced and foreign (Australia) base powder. In a case study, it achieved the best discrimination effect, indicating that it can effectively extract time response features of electronic nose channel sequence signals. This method can facilitate the differentiation work of dehydrated milk powder samples in China and Australia, providing technical support for the rapid identification of dehydrated milk powder sources.

国家重点研发计划（2023YFD1000400）；国家自然科学基金重大研究计划重点项目（91746202）；国家自然科学基金重点项目（71433006）；国家自然科学基金面上项目（62376249）；国家自然科学基金青年项目（61806177）