大学化学

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基于数学分峰的荧光分析法测定邻羟基苯甲酸和间羟基苯甲酸混合物

刘一平, 杨甜, 孙绍航, 李依芊, 邵娜   

  1. 北京师范大学化学学院, 北京 100875
  • 收稿日期:2026-04-08 录用日期:2026-05-29
  • 通讯作者: 邵娜 E-mail:shaona@bnu.edu.cn Na Shao
  • 基金资助:
    国家自然科学基金面上项目(22074007)

Determination of o-hydroxybenzoic acid and m-hydroxybenzoic acid in a mixture using fluorescence analysis with mathematical peak deconvolution

Yiping Liu, Tian Yang, Shaohang Sun, Yiqian Li, Na Shao   

  1. College of Chemistry, Beijing Normal University, Beijing 100875, China
  • Received:2026-04-08 Accepted:2026-05-29
  • Contact: Na Shao E-mail:shaona@bnu.edu.cn

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摘要： 针对传统本科生教学实验“荧光分析法测定邻-羟基苯甲酸和间-羟基苯甲酸混合物”存在的实验方案不完善、测定结果误差大、数据处理训练不足等问题，本研究提出一种融合数学建模与MATLAB编程数据处理的改进实验方案。以邻羟基苯甲酸(o-HBA)和间羟基苯甲酸(m-HBA)的混合体系为分析对象，通过采集其在固定激发波长下的发射光谱，引入基于MATLAB的高斯拟合与最小二乘分峰算法，实现重叠光谱的解析与各组分的定量分析。该方案仅需在单一pH条件下完成测量，避免了传统方法因pH依赖造成的系统误差，显著提升了分析准确度。实验设计中融入了数学建模、谱图可视化与MATLAB编程，强化了学生的数据处理训练、科学研究思维与跨学科综合应用能力。本方案已在两个学期的教学实践中实施，共覆盖112名本科生，取得了良好的教学效果，具备较强的推广价值与教学改革意义。

关键词: 荧光发射光谱, 双组分分析, 数学建模, 高斯拟合, 分峰算法

Abstract: This study presents an improved experimental protocol to overcome the limitations observed in the traditional undergraduate teaching experiment “fluorescence analysis for determining ohydroxybenzoic acid and m-hydroxybenzoic acid mixtures”, which suffers from methodological imperfections, significant measurement errors, and inadequate data processing training. The enhanced approach combines mathematical modeling with MATLAB-based data processing for analyzing mixed systems of o-hydroxybenzoic acid (o-HBA) and m-hydroxybenzoic acid (m-HBA). Emission spectra were acquired at a fixed excitation wavelength, followed by spectral deconvolution using Gaussian fitting and least-squares peak deconvolution algorithms implemented in MATLAB. This method enables accurate quantitative analysis of each component while requiring only single-pH condition measurements, thereby eliminating pH-dependent systematic errors inherent in conventional approaches. The experimental design integrates mathematical modeling, spectral visualization, and MATLAB programming, effectively enhancing students’ data processing capabilities, scientific reasoning, and interdisciplinary application skills. Preliminary implementation involving 112 undergraduate students over two semesters has yielded promising results, demonstrating significant potential for widespread adoption and educational reform.

Key words: Fluorescence emission spectroscopy, Two-component analysis, Mathematical modeling, Gaussian fitting, Peak deconvolution algorithm