Luciani Lorenzo, Nocera Antonio, Raimondi Michela, Ciattaglia Gianluca, Spinsante Susanna, Gambi Ennio, Galassi Rossana
Scuola di Scienze e Tecnologie, Divisione di Chimica, Università di Camerino, Camerino 62032, Italy.
Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona 60131, Italy.
ACS Meas Sci Au. 2025 Jul 28;5(4):443-460. doi: 10.1021/acsmeasuresciau.5c00016. eCollection 2025 Aug 20.
The contamination of natural basins by agricultural or industrial activities, and the growing need for potable water due to climate changes accelerate the drive to find versatile, fast, practical, and easy-to-use methods for water analysis. A potentially versatile technique suitable for water analysis is Raman Spectroscopy (RS). Featured by good resolution but low sensitivity, RS detects molecular vibrational modes of an analyte in water. Nitrate is an indicator of chemical and/or biological pollution, it displays Raman active vibrational modes affected by the interaction with other systems in solution, allowing a wide range of applications. Concerning Nitrate analysis in water, a general introduction to the Raman effect and the basic instrumentation were herein discussed. RS is a potential solution to wastewater analysis. This review first reports the theoretical background of the technique and its basic working principles, then, the state-of-the-art scientific contributions related to Nitrate detection are investigated with a particular interest in the instrumental setup and the chemometric techniques employed to improve its sensitivity. In the studies hereby considered, instrumental setup (for example, laser frequency, laser power, acquisition times) and different technical solutions (for example, micro- versus macro-Raman instruments) to increase the technique's sensitivity on Nitrate detection are described. Concisely, the use of deep-UV lasers, optically active Surface-Enhanced Raman Spectroscopy (SERS) or Fiber-Enhanced Raman spectroscopy (FERS) equipment, coupled with instrumental settings, i.e. acquisition time, variable temperature of acquisition, use of special sampling apparatus (cuvettes or immersion probes), or with ion exchange resins for analyte enrichment, have been reported. Remarkably, examples of large data correction of unwanted fluorescence by mathematical processing or chemical quenching were reported too, suggesting solutions for the Raman analysis of wastewaters. Finally, a short digression on Machine Learning (ML) applied to RS is proposed, showing the promising results reported in other fields. Data-driven methods could be a solution to improve the low sensitivity of the RS for Nitrate detection. Hence, an approach of ML methods for the typical RS spectra processing (spike removal, baseline correction, fluorescence curve elimination, instrumental noise correction) was hereby mentioned, suggesting an improvement in the detection capability of Nitrate ion in water.
农业或工业活动对天然流域的污染，以及气候变化导致的对饮用水需求的不断增长，加速了人们寻找通用、快速、实用且易于使用的水分析方法的进程。拉曼光谱法（RS）是一种适用于水分析的潜在通用技术。RS以分辨率高但灵敏度低为特点，可检测水中分析物的分子振动模式。硝酸盐是化学和/或生物污染的指标，它呈现出受与溶液中其他系统相互作用影响的拉曼活性振动模式，具有广泛的应用。关于水中硝酸盐分析，本文讨论了拉曼效应的一般介绍和基本仪器设备。RS是废水分析的一种潜在解决方案。本综述首先报告了该技术的理论背景及其基本工作原理，然后，研究了与硝酸盐检测相关的最新科学贡献，特别关注用于提高其灵敏度的仪器设置和化学计量技术。在所考虑的研究中，描述了仪器设置（例如激光频率、激光功率、采集时间）以及不同的技术解决方案（例如微拉曼仪器与宏拉曼仪器），以提高该技术对硝酸盐检测的灵敏度。简而言之，已报道了使用深紫外激光器、光学活性表面增强拉曼光谱法（SERS）或光纤增强拉曼光谱法（FERS）设备，结合仪器设置，即采集时间、可变采集温度、使用特殊采样装置（比色皿或浸入式探头），或使用离子交换树脂进行分析物富集。值得注意的是，还报道了通过数学处理或化学猝灭对不需要的荧光进行大数据校正的示例，为废水的拉曼分析提供了解决方案。最后，提出了关于将机器学习（ML）应用于RS的简短讨论，展示了在其他领域报道的有前景的结果。数据驱动方法可能是提高RS对硝酸盐检测低灵敏度的一种解决方案。因此，本文提到了一种用于典型RS光谱处理（去除尖峰、基线校正、消除荧光曲线、校正仪器噪声）的ML方法，表明水中硝酸根离子的检测能力有所提高。
