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朱乐,顾鹏,张胜,孔丽琴,冯耀泽.基于水分子结构变化的不同温度下细菌浓度的检测[J].华中农业大学学报,2020,39(3):120-126
基于水分子结构变化的不同温度下细菌浓度的检测
Detecting bacterial concentration at different temperature based on changes in molecular structure of water
投稿时间:2019-12-09  
DOI:
中文关键词:  样本温度  细菌浓度  水分子结构  检测机理  近红外光谱  模型预测  多级同时成分分析(MSCA)
英文关键词:temperature of samples  bacterial concentration  molecular structure of water  detection mechanism  near infrared spectroscopy  model prediction  multistage simultaneous component analysis(MSCA)
基金项目:国家自然科学基金项目(61705074) 
作者单位E-mail
朱乐 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室,武汉 430070 191299249@qq.com 
顾鹏 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室,武汉 430070  
张胜 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室,武汉 430070  
孔丽琴 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室,武汉 430070  
冯耀泽 华中农业大学工学院/农业农村部长江中下游农业装备重点实验室,武汉 430070 feng@mail.hzau.edu.cn 
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中文摘要:
      为探究样本温度、细菌浓度对水分子结构的改变从而明确细菌浓度检测的机理,以金黄色葡萄球菌悬浊液样本为例在水的特征吸收波段(1 334~1 539 nm)对不同温度下不同浓度细菌进行表征和检测。首先,通过对比不同预处理方法,得到最佳预处理方法,然后利用多级成分分析分别建立一级模型(温度与光谱的关系模型)和二级模型(细菌浓度与光谱的关系模型)。结果表明,Savitzky-Golay 卷积平滑结合连续小波变换的预处理方法最优。一级模型可以精确表征样本温度,其校正相关系数(correlation coefficient,RC)和校正均方根误差(root mean square error,RMSEC)分别为0.997和0.37℃。对温度贡献较大的波长对应水溶剂化层OH-(H2O)1,4和超氧化物O2-(H2O)4、游离水和游离OH-(S0)及具有1个氢键的水分子(S1),该模型预测相关系数RP和预测均方根误差RMSEP分别为0.997和0.37℃。二级模型可以较好表征样本浓度,其校正相关系数RC和校正均方根误差RMSEC分别为0.887和0.891 log CFU/mL。对细菌浓度贡献最大的波长对应H2O非对称伸缩振动V3,具有1个氢键的水分子(S1)、具有3个氢键的水分子(S3)及具有4个氢键的水分子(S4),预测相关系数RP和预测均方根误差RMSEP分别为0.869和0.858 log CFU/mL。本研究所建模型均具有较好的检测精度和适应性,表明水的结构特性对细菌悬浊液温度、细菌浓度检测起重要作用。
英文摘要:
      The absorption band of water (1 334-1 539 nm) at different temperatures of Staphylococcus aureus suspension was detected and characterized to investigate the effects of changes in the temperature of samples and bacterial concentration on the molecular structure of water and to clarify the mechanism of detecting bacterial concentration. Different spectral pretreatment methods were compared to obtain an optimal one. Multi-level component analysis (MSCA) was used to establish a first-level model (quantitative spectra-temperature relationship,QSTR) and a second-level model (quantitative spectra-concentration relationship,QSCR) for the suspension of Staphylococcus aureus. The results showed that Savitzky-Golay convolution smoothing and continuous wavelet transform was the best pretreatment method. The QSTR can accurately characterize the temperature of samples,and its corrected correlation coefficient (RC) and corrected root mean square error (RMSEC) are 0.997 and 0.37℃,respectively. The wavelengths that contribute the most to temperature correspond to the water solvation shell,OH-(H2O)1,4 and superoxide,O2-(H2O)4,free water and free OH- (S0) or trapped water and water molecules with 1 hydrogen bond (S1). RP and RMSEP in the prediction set for the Staphylococcus aureus were 0.997 and 0.37℃. The QSCR can characterize the concentration of bacterial suspension with RC of 0.887 and RMSEC of 0.891 log CFU/mL for calibration. The wavelengths that contribute the most to the concentration of bacteria correspond to V3,H2O asymmetric stretching vibration,water molecules with 1,3 and 4 hydrogen bonds (S1,S3 and S4). For the prediction of Staphylococcus aureus samples based on the secondary level model,RP of 0.869 and RMSEP of 0.858 log CFU/mL were obtained. The models built in this study have good detection accuracy and adaptability,indicating that the structural characteristics of water play an important role in detecting the temperature and bacterial concentration of bacterial suspensions.
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