基于机器学习的ATR-FTIR和拉曼光谱综合分析大鼠血清抑制应激

IF 4.3 2区 化学 Q1 SPECTROSCOPY
Zehua Fan , Chenyu Li , Qiran Sun , Yiwen Luo , Hancheng Lin , Bin Cong , Ping Huang
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引用次数: 0

摘要

在法医实践中,准确地确定一个人是否遭受了长期的束缚或评估束缚造成的伤害可能具有挑战性。为了解决这个问题,我们探索了一种利用衰减全反射傅立叶变换红外(ATR-FTIR)光谱和拉曼光谱结合机器学习来共同识别约束应力的新方法。我们将大鼠随机分为三个实验组:约束应激组(禁食和缺水)、对照组(禁食和缺水不受约束)和正常组。收集动物血清光谱后,建立主成分分析(PCA)模型,探讨各组间分离趋势,识别相关特征峰。随后,建立随机森林(RF)模型,将约束应激组与其他两组进行比较。分析确定了表明存在约束应力的关键物质:1161 cm−1,1167 cm−1(反对称C-O-C拉伸)和980 cm−1,976 cm−1,974 cm−1(反对称N+(CH3)3))。使用RF模型比较不同时间点的约束应激组,揭示可能有助于确定约束应激持续时间的物质:1747 cm−1(酯羰基带),1626 cm−1 (β-褶片),1211 cm−1(酰胺III,−N+(CH3)3,反对称拉伸),1180 cm−1(磷酸二酯),1128 cm−1(−C-C -), 1024 cm−1 (C-O拉伸耦合C-O弯曲)和1389 cm−1,1335 cm−1,1321 cm−1(色氨酸,α螺旋,磷脂),710 cm−1(多糖),1266 cm−1(酰胺Ⅲ),1015 cm−1 (β-胡萝卜素)。这些发现表明,当与机器学习相结合时,ATR-FTIR和拉曼光谱具有作为分析和表征约束应力的强大工具的巨大潜力,为该领域的未来研究提供了新的见解和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated analysis of restraint stress in rat serum using ATR-FTIR and Raman spectroscopy with Machine learning
In forensic practice, accurately determining whether an individual has been subjected to prolonged restraint or assessing injuries resulting from restraint can be challenging. To address this, we explored a novel approach using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and Raman spectroscopy combined with machine learning to jointly identify restraint stress. We randomly assigned rats into three experimental groups: a restraint stress group (subjected to fasting and water deprivation), a control group (subjected to fasting and water deprivation without restraint), and a normal group. After collecting the serum spectra of the animals, a principal component analysis (PCA) model was established to explore the separation trends among the groups and to identify relevant characteristic peaks. Subsequently, a random forest (RF) model was established to compare the restraint stress group with the other two groups. The analysis identified key substances that indicated the presence of restraint stress: 1161 cm−1, 1167 cm−1 (anti-symmetric C-O-C stretch) and 980 cm−1, 976 cm−1, 974 cm−1 (−N+(CH3)3, antisymmetric stretch). And the RF model was used to compare the restraint stress groups at different time points, revealing substances that may help determine the duration of restraint stress: 1747 cm−1 (ester carbonyl band), 1626 cm−1 (β-pleated sheet), 1211 cm−1 (Amide III, −N+(CH3)3, antisymmetric stretch), 1180 cm−1 (phosphodiester), 1128 cm−1 (−C–C-), 1024 cm−1 (C-O stretching coupled with C-O bending) and 1389 cm−1, 1335 cm−1, 1321 cm−1 (Trp, α helix, phospholipids), 710 cm−1 (Polysaccharides), 1266 cm−1 (Amide Ⅲ), 1015 cm−1 (β-carotene). These findings suggest that ATR-FTIR and Raman spectroscopy together, when combined with machine learning, has significant potential as a powerful tool for analyzing and characterizing restraint stress, offering new insights and directions for future research in this area.
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来源期刊
CiteScore
8.40
自引率
11.40%
发文量
1364
审稿时长
40 days
期刊介绍: Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (SAA) is an interdisciplinary journal which spans from basic to applied aspects of optical spectroscopy in chemistry, medicine, biology, and materials science. The journal publishes original scientific papers that feature high-quality spectroscopic data and analysis. From the broad range of optical spectroscopies, the emphasis is on electronic, vibrational or rotational spectra of molecules, rather than on spectroscopy based on magnetic moments. Criteria for publication in SAA are novelty, uniqueness, and outstanding quality. Routine applications of spectroscopic techniques and computational methods are not appropriate. Topics of particular interest of Spectrochimica Acta Part A include, but are not limited to: Spectroscopy and dynamics of bioanalytical, biomedical, environmental, and atmospheric sciences, Novel experimental techniques or instrumentation for molecular spectroscopy, Novel theoretical and computational methods, Novel applications in photochemistry and photobiology, Novel interpretational approaches as well as advances in data analysis based on electronic or vibrational spectroscopy.
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