摘要
人体能量摄入量与能量消耗量平衡是评估健康的标准之一。不平衡的能量摄入量可能造成生物体组织细胞损伤、机体过度肥胖等后果。评估能量摄入量对人的身体健康管理具有重大意义。目前评估能量摄入量的主要方法是膳食回顾法,该方法不仅耗时长,还会增加待评估人员的负担,所以亟需开发一种简单快速的能量摄入量评估方法。能量摄入后经过体内的消化代谢,会产生代谢产物作为废弃物排出体外。废弃物如尿液等,含有大量的化学物种,可以系统性反映生物体的饮食代谢状况和疾病进程。基于高灵敏、可无损检测两个组“指纹式”分子光谱特征的表面增强拉曼光谱(SERS),采用谱峰统计、无监督和有监督聚类算法分别对尿液的SERS信号开展分析,最终实现对不同能量摄入量的聚类分析。首先尝试对能量摄入量分组分别为1500,2030和2700千卡·日-1两个组的志愿者尿液SERS谱图进行谱峰分析,发现很多有机分子的拉曼谱峰存在一定程度的重叠,所以直接对谱峰进行解析及归属存在较大难度,需要采用化学计量学的方法建立分类模型,以实现良好区分和预测效果。对比无监督的主成分分析(PCA)和有监督的正交偏最小二乘判别分析(OPLS-DA)两种算法的区分效果。首先对原始光谱数据直接进行主成分分析,发现模型中不同类别的散点分布存在较大程度的重叠,这使得组别归类效果很差,而经过一阶导数差分校正基线后,模型呈现出可分类的趋势。OPLS-DA算法通过预先设定Y的标签及正交信号矫正处理,能将X矩阵信息分解成与Y相关和不相关的两个组分,集中表达相关的信息,实现良好的分类效果。结果表明,OPLS-DA算法可以对三种不同能量摄入量水平进行很好的归类,而且每两组间也可以实现很好区分。ROC分析结果表明敏感性和特异性均达到100%。200次迭代的置换检验结果也说明了模型良好的可靠性和预测性。表明通过采集尿液的拉曼信号,经过一定的数据处理即可评估人体能量摄入量水平。该方法可以实现尿液的快速分析,测试分析时间小于2 min,操作简单,判别结果准确,在医疗健康领域具有很大应用前景。
The balance of human energy intake and energy consumption is one of the standards for maintaining health.Unbalanced intake may cause consequences such as cell damage and obesity.The estimation of energy intake is of great significance to human health management.The current method of assessing energy intake is mainly through dietary review,but it is time-consuming because of increasing the burden of the person to be evaluated.Therefore,developing a simple and fast way to estimate energy intake is urgent.After energy intake,metabolites generated by digestion and metabolism are excreted as waste.Wastes such as urine,etc.,contain many chemical species,which can systematically reflect the dietary status and disease processes.This research aims to establish a classification model based on SERS techniques,which is highly sensitive,non-destructive,and identifiable molecular fingerpring.Peak statistics,and unsupervised and supervised clustering algorithms are utilized to analyze SERS data collected from volunteer groups of energy intake with 1500,2030,2700 kcal·day-1.Since there is a certain amount of overlapping of Raman peaks in many organic molecules,it is difficult to analyze and assign SERS peaks.This study adopts a comparative analysis of an unsupervised PCA and a supervised OPLS-DA algorithm for classification and prediction.It was found that the scattering distribution of different categories in PCA has a large extent,so the model shows poor categorization.After correcting the background by first-order derivative difference,the scatter map presents the classified trend.The OPLS-DA algorithm can decompose the X matrix information into the Y-related and unrelated two components by presetting the Y’s label to achieve good classification after orthogonal signal correction processing.The results show that the OPLS-DA algorithm can be well-classified for three or each two different energy intake levels.Both the specificity and accuracy of the ROC analysis have reached 100%.The permutation test of 200 times also illustrates the model with good accuracy and predictability.The results indicate that the levels of human energy intake can be directly estimated by analyzing the SERS signal of the urine.This method can rapidly analyse urine in 2 minutes with simple manipulation and accurate discriminant results,which shows great potential in medical health applications.
作者
韩晓龙
林嘉盛
李剑锋
HAN Xiao-long;LIN Jia-sheng;LI Jian-feng(Astronaut Scientific Research and Training Center of China,Beijing 100094,China;State Key Laboratory of Physical Chemistry of Solid Surfaces,College of Chemistry and Chemical Engineering,Xiamen University,Xiamen 361005,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第2期489-494,共6页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划项目(2019YFD0901100)资助。
关键词
表面增强拉曼光谱
能量摄入
尿液分析
主成分分析
正交偏最小二乘判别分析
Surface enhanced Raman spectroscopy
Energy intake
Urine analysis
Principal component analysis
Orthogonal partial least-squares discriminant analysis
