提出了一种基于红外光谱分析技术的物质识别算法以解决定性、定量的进行红外光谱分析时所遇到的难题。通过将欧氏距离相似性判定和光谱分析传统方法的结合，完成了该算法的理论分析并开发了相应的物质分析软件。通过物质分析实验验证了该算法的可行性，实验结果表明该算法应用于测试混合物光谱分析时分析准确度优于96%。通过分析实验结果表明可知：该算法能够程序化地实现物质的现场快速检测,并保持很高的精确度，能够满足食品安全领域的光谱分析需求。

The substance recognition algorithm is presented which is based on infrared spectroscopy in order to solve the problem during qualitative and quantitative infrared spectroscopy. By the combination of the Euclidean distance and traditional spectral similarity judgment methods , the theoretical analysis of the algorithm and the development of the software are completed. The experimental results show that the accuracy is better than 96% when the algorithm is applied to test the mixture. The algorithm can programmatically achieve rapid detection of substances in the field and maintain high accuracy to meet the needs of spectroscopy in field of food security.