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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。
A material recognition algorithm based on infrared spectrum analysis technology is proposed to solve the problems encountered in qualitative and quantitative infrared spectroscopy analysis. By combining the Euclidean distance similarity determination with the traditional spectral analysis method, the theoretical analysis of the algorithm is completed and the corresponding material analysis software is developed. The feasibility of the proposed algorithm is verified by the material analysis experiment. The experimental results show that the proposed algorithm is more than 96% accurate when applied to the spectral analysis of test mixtures. The experimental results show that the algorithm can program the rapid detection of material in the field and maintain the high precision to meet the needs of spectral analysis in the field of food safety.