Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data

IF 2.1 3区 地球科学 Q2 LIMNOLOGY Limnology and Oceanography: Methods Pub Date : 2023-06-20 DOI:10.1002/lom3.10561
Anders Dalhoff Bruhn, Urban Wünsch, Christopher L. Osburn, Jacob C. Rudolph, Colin A. Stedmon
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Abstract

Analysis of lignin in seawater is essential to understanding the fate of terrestrial dissolved organic matter (DOM) in the ocean and its role in the carbon cycle. Lignin is typically quantified by gas or liquid chromatography, coupled with mass spectrometry (GC-MS or LC-MS). MS instrumentation can be relatively expensive to purchase and maintain. Here we present an improved approach for quantification of lignin phenols using LC and absorbance detection. The approach applies a modified version of parallel factor analysis (PARAFAC2) to 2nd derivative absorbance chromatograms. It is capable of isolating individual elution profiles of analytes despite co-elution and overall improves sensitivity and specificity, compared to manual integration methods. For most lignin phenols, detection limits below 5 nmol L−1 were achieved, which is comparable to MS detection. The reproducibility across all laboratory stages for our reference material showed a relative standard deviation between 1.47% and 16.84% for all 11 lignin phenols. Changing the amount of DOM in the reaction vessel for the oxidation (dissolved organic carbon between 22 and 367 mmol L−1), did not significantly affect the final lignin phenol composition. The new method was applied to seawater samples from the Kattegat and Davis Strait. The total concentration of dissolved lignin phenols measured in the two areas was between 4.3–10.1 and 2.1–3.2 nmol L−1, respectively, which is within the range found by other studies. Comparison with a different oxidation approach and detection method (GC-MS) gave similar results and underline the potential of LC and absorbance detection for analysis of dissolved lignin with our proposed method.

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基于机器学习辅助分解液相色谱吸收光谱数据的木质素苯酚定量
分析海水中的木质素对于了解海洋中陆地溶解有机物(DOM)的命运及其在碳循环中的作用至关重要。木质素通常通过气相色谱法或液相色谱法结合质谱法(GC-MS或LC-MS)进行定量。MS仪器的购买和维护可能相对昂贵。在此,我们提出了一种使用LC和吸光度检测来定量木质素酚的改进方法。该方法将平行因子分析(PARAFAC2)的修改版本应用于二阶导数吸光度色谱图。与手动积分方法相比,它能够在不考虑共洗脱的情况下分离分析物的单独洗脱图谱,并总体上提高了灵敏度和特异性。对于大多数木质素酚类,检测限低于5 nmol L−1,这与MS检测相当。我们的参考材料在所有实验室阶段的再现性显示,所有11种木质素酚的相对标准偏差在1.47%和16.84%之间。改变氧化反应容器中DOM的量(溶解的有机碳在22-367之间 毫摩尔 L−1)对最终木质素-苯酚组成没有显著影响。将新方法应用于卡特加特海峡和戴维斯海峡的海水样本。在这两个区域测得的溶解木质素酚的总浓度在4.3–10.1和2.1–3.2之间 nmol L−1,这在其他研究发现的范围内。与不同的氧化方法和检测方法(GC-MS)的比较给出了类似的结果,并强调了LC和吸光度检测在分析溶解木质素方面的潜力。
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来源期刊
CiteScore
4.80
自引率
3.70%
发文量
56
审稿时长
3 months
期刊介绍: Limnology and Oceanography: Methods (ISSN 1541-5856) is a companion to ASLO''s top-rated journal Limnology and Oceanography, and articles are held to the same high standards. In order to provide the most rapid publication consistent with high standards, Limnology and Oceanography: Methods appears in electronic format only, and the entire submission and review system is online. Articles are posted as soon as they are accepted and formatted for publication. Limnology and Oceanography: Methods will consider manuscripts whose primary focus is methodological, and that deal with problems in the aquatic sciences. Manuscripts may present new measurement equipment, techniques for analyzing observations or samples, methods for understanding and interpreting information, analyses of metadata to examine the effectiveness of approaches, invited and contributed reviews and syntheses, and techniques for communicating and teaching in the aquatic sciences.
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