当前位置: X-MOL 学术Limnol. Oceanogr. Methods › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Lignin phenol quantification from machine learning-assisted decomposition of liquid chromatography-absorbance spectroscopy data
Limnology and Oceanography: Methods ( IF 2.7 ) Pub Date : 2023-06-20 , DOI: 10.1002/lom3.10561
Anders Dalhoff Bruhn 1 , Urban Wünsch 1 , Christopher L. Osburn 2 , Jacob C. Rudolph 2, 3 , Colin A. Stedmon 1
Affiliation  

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.

中文翻译:

通过机器学习辅助的液相色谱吸光度光谱数据分解来定量木质素苯酚

海水中木质素的分析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或液相色谱结合质谱(GC-MS 或 LC-MS)进行定量。MS 仪器的购买和维护费用相对较高。在这里,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数吸光度色谱图。尽管存在共洗脱,但它仍能够分离分析物的单独洗脱曲线,与手动积分方法相比,总体提高了灵敏度和特异性。对于大多数木质素酚,检测限低于 5 nmol L达到-1 ,这与MS检测相当。我们的参考材料在所有实验室阶段的重现性显示,所有 11 种木质素酚的相对标准偏差在 1.47% 到 16.84% 之间。改变氧化反应容器中的 DOM 量(溶解的有机碳在 22 至 367 mmol L -1之间)不会显着影响最终的木质素酚组成。新方法应用于卡特加特海峡和戴维斯海峡的海水样本。两个区域测量的溶解木质素酚的总浓度在4.3-10.1和2.1-3.2 nmol L -1之间分别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) 进行比较得到了相似的结果,并强调了采用我们提出的方法分析溶解木质素的 LC 和吸光度检测的潜力。
更新日期:2023-06-20
down
wechat
bug