苹果汁中嗜酸耐热菌免疫分离及振动光谱法鉴定
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摘要
中国是世界上最大的浓缩苹果汁生产国,近年来其产量急剧增长,而以嗜酸耐热菌为代表的安全性指标是其国际化发展的“绿色技术壁垒”之一。因此,解决嗜酸耐热菌污染问题是保证我国苹果及果汁产业快速、健康发展的关键,而首要的就是要建立嗜酸耐热菌的快速检测与鉴定方法。目前,嗜酸耐热菌的快速分离是对其进行快速检测与鉴定的关键技术瓶颈,并且现有的嗜酸耐热菌鉴定方法具有一定的局限性。
为寻求高效、快速的嗜酸耐热菌分离以及鉴定方法,本论文首先通过将嗜酸耐热菌多克隆抗体偶联到磁性微球表面制备出免疫磁性微球,建立基于免疫磁性微球的苹果汁中嗜酸耐热菌快速分离方法;另外,本论文还采用振动光谱技术(近红外、红外和拉曼)结合多元统计方法建立快速,简单,方便的鉴定嗜酸耐热菌的方法,研究获得微生物细胞近红外,红外和拉曼光谱的方法以及光谱的处理技术,建立嗜酸耐热菌鉴定模型,为浓缩苹果汁生产企业提供一个切实快速有效的嗜酸耐热菌检测与鉴定方法,以保证产品的质量。
本论文取得的主要研究结果如下:
(1)以嗜酸耐热菌(Alicyclobacillus acidoterrestris DSM3922)灭活菌体作为抗原,采用皮下多点注射法免疫2只新西兰白兔,得到了嗜酸耐热菌多克隆抗体,并对抗体进行了纯化,最终得到抗体两份:R1抗体(27.6 mg/mL,4.6 mL,效价1:40000)及R2抗体(10.8 mg/mL,5 mL,效价1:10000);采用化学包埋法制备得到壳聚糖磁性微球,并对其进行了活化,所得到的活化后的壳聚糖磁性微球固定抗体能力为2.48 mg抗体/g微球;将嗜酸耐热菌多克隆抗体偶联到磁性微球表面得到了免疫磁性微球,该免疫磁性微球对培养液中嗜酸耐热菌吸附率为68.5±1.7%,对苹果汁中嗜酸耐热菌吸附率为70.1±4.7%。
(2)得到了1株嗜酸耐热菌,1株酵母和5株细菌标准菌的近红外漫反射光谱,并对光谱数据进行了分析,构建了基于近红外光谱的嗜酸耐热菌鉴定方法。结果表明:光谱鉴别指数Dy1y2值范围为1.61±1.05-10.97±6.65,重现性良好;采用主成分分析法确定5400-4000cm-1范围为微生物细胞近红外光谱的信息富集区;建立的线性判别分析模型鉴定准确率为100%,人工神经网络模型预测结果平均相对误差为5.04%。
(3)采用硝酸纤维素滤膜和单反射水平衰减全反射红外附件建立了一种简单快速的样品制备方法,获得了1株嗜酸耐热菌和7株常见细菌的红外光谱,建立了基于红外光谱的嗜酸耐热菌鉴定方法。结果表明:采用本论文建立的样品制备方法采集细菌细胞红外光谱切实可行;利用主成分分析可以将所研究的菌株成功分为不同的类别;所建立的鉴定模型都获得了较高的准确率,其中线性判别模型准确率为100%,人工神经网络模型平均相对误差为1.32%。另外对得到的8株嗜酸耐热菌菌株的红外光谱采用多元统计方法进行了分析,结果表明利用主成分分析方法可以将所研究的菌株成功分为不同的类别;所建立的线性判别模型对不同菌株的鉴别准确率达到93.75%。
(4)得到了1株嗜酸耐热菌和5株常见细菌的拉曼光谱,对微生物细胞的拉曼谱峰进行了归属分析,并构建了基于拉曼光谱的嗜酸耐热菌鉴定方法。结果表明:微生物细胞拉曼光谱的主要谱峰来自于蛋白质和碳水化合物的分子振动信息,核酸成分的谱峰较弱,脂类成分的信息不明显;获得拉曼光谱所需细胞悬液的最小浓度为1011 CFU/mL,得到的光谱的信噪比达到250以上;重现性分析结果表明光谱鉴别指数Dy1y2值范围为20.91±16.17-39.50±59.26,重现性良好;主成分分析结果表明拉曼光谱可以识别微生物细胞的主要成分的差异,所建立的线性判别分析模型鉴定准确率达到90%以上。另外对得到的8株嗜酸耐热菌菌株的拉曼光谱采用多元统计方法进行了分析,结果表明利用主成分分析方法可以将所研究的菌株成功分为不同的类别;所建立的线性判别模型对不同菌株的鉴别准确率达到85%。
China is the largest concentrated apple juice producing country and its yield increases rapidly. The safety issues, especially Alicyclobacillus species, are“green technical barriers”of international trade. Control of Alicyclobacillus is the key to development of apple juice industry, and the most important aspect is to establish rapid methods to detect and identify Alicyclobacillus species. Currently, rapid separation and enrichment of Alicyclobacillus species is the critical technical bottleneck; furthermore, the existing methods for detection of microorganisms have tremendous limitations.
In order to establish efficient and rapid methods to separate, enrich and identify Alicyclobacillus species, polyclonal antibody against Alicyclobacillus acidoterrestris (DSM3922) was connected to magnetic microspheres to prepare immune magnetic microspheres, a method for separating and enriching Alicyclobacillus species in apple juice based on immunomagnetic microspheres was established; further, vibrational spectroscopy (namely, near infrared, mid-infrared and Raman) combined with multivariate analysis was employed to identify Alicyclobacillus species and identification models based on multivariate analysis were established. A rapid, simple and convenient method to identify Alicyclobacillus species was provided for apple juice industry.
The main results of this thesis:
(1) Polyclonal antibody against Alicyclobacillus acidoterrestris DSM3922 was obtained by performing immune tests on two rabbits: R1, 27.6 mg/mL, 4.6 mL, titer 1:40000; R2, 10.8 mg/mL, 5 mL, titer 1:10000. Chitosan magnetic microsphere was prepared by chemical embedding and activated, which could stabilize polyclonal antibody at 2.48 mg antibody/g microsphere; Polyclonal antibody was coupled with magnetic microsphere to prepare immune magnetic microsphere. Immune magnetic microsphere was used to separate Alicyclobacillus acidoterrestris from medium and apple juice, the absorption rate was 68.5±1.7% and 70.1±4.7%, respectively.
(2) Bacterial powders of Alicyclobacillus strain, one yeast and five bacteria strains were prepared for Fourier transform near-infrared (FT-NIR) spectral collection. FT-NIR spectral determination was done using a diffuse reflection-integrating sphere. Reduction of data was performed by principal component analysis (PCA) and two identification models based on linear discriminant analysis (LDA) and artificial neural network (ANN) were established to identify bacterial strains. The reproducibility of the method was satisfied (Dy1y2: 1.61±1.05-10.97±6.65). The wavenumber of 5400-4000cm-1 is the information-rich range for the FT-NIR spectra of microorganism and high identification accuracy was achieved in both the LDA model (accuracy rate: 100%) and the ANN model (average relative error: 5.04%).
(3) A simple and rapid sample preparation method using nitrocellulose membrane filter (NMF) and a single reflection horizontal attenuated total reflection (HATR) accessory was developed, mid-infrared (mid-IR) spectra of Alicyclobacillus strain and seven other representative bacterial strains were collected and two identification models based on LDA and ANN respectively were established to identify and distinguish Alicyclobacillus strain from others. The sample preparation method was feasible and the microorganisms studied were successfully separated into different groups by PCA. High identification accuracy was achieved in both LDA model (accuracy rate: 100%) and ANN model (average relative error: 1.32%). In addition, Fourier transform infrared (FT-IR) spectroscopy was used and tested on eight Alicyclobacillus strains. The stains could be clearly separated into different groups by PCA. High identification accuracy (93.75%) was achieved using LDA model.
(4) Raman spectra of Alicyclobacillus strain and five other representative bacterial strains were collected using a Raman microspectrometer. Reduction of data was performed by PCA and an identification model based on LDA was established to identify bacterial strains. Results showed that the main bands found in Raman spectra of microorganisms originated from proteins and carbohydrates, only several weak peaks were from nucleic acids and fatty acids. The minimal bacterial concentration for collecting Raman was 1011 CFU/mL and the S/N was higher than 250. The reproducibility of the method was satisfied (Dy1y2: 20.91±16.17-39.50±59.26) and high identification accuracy was achieved in LDA model (accuracy rate: 90%). Further, Raman spectroscopy was used and tested on eight Alicyclobacillus strains. The stains could be separated into different groups by PCA successfully. High identification accuracy (85%) was achieved using LDA model.
为寻求高效、快速的嗜酸耐热菌分离以及鉴定方法,本论文首先通过将嗜酸耐热菌多克隆抗体偶联到磁性微球表面制备出免疫磁性微球,建立基于免疫磁性微球的苹果汁中嗜酸耐热菌快速分离方法;另外,本论文还采用振动光谱技术(近红外、红外和拉曼)结合多元统计方法建立快速,简单,方便的鉴定嗜酸耐热菌的方法,研究获得微生物细胞近红外,红外和拉曼光谱的方法以及光谱的处理技术,建立嗜酸耐热菌鉴定模型,为浓缩苹果汁生产企业提供一个切实快速有效的嗜酸耐热菌检测与鉴定方法,以保证产品的质量。
本论文取得的主要研究结果如下:
(1)以嗜酸耐热菌(Alicyclobacillus acidoterrestris DSM3922)灭活菌体作为抗原,采用皮下多点注射法免疫2只新西兰白兔,得到了嗜酸耐热菌多克隆抗体,并对抗体进行了纯化,最终得到抗体两份:R1抗体(27.6 mg/mL,4.6 mL,效价1:40000)及R2抗体(10.8 mg/mL,5 mL,效价1:10000);采用化学包埋法制备得到壳聚糖磁性微球,并对其进行了活化,所得到的活化后的壳聚糖磁性微球固定抗体能力为2.48 mg抗体/g微球;将嗜酸耐热菌多克隆抗体偶联到磁性微球表面得到了免疫磁性微球,该免疫磁性微球对培养液中嗜酸耐热菌吸附率为68.5±1.7%,对苹果汁中嗜酸耐热菌吸附率为70.1±4.7%。
(2)得到了1株嗜酸耐热菌,1株酵母和5株细菌标准菌的近红外漫反射光谱,并对光谱数据进行了分析,构建了基于近红外光谱的嗜酸耐热菌鉴定方法。结果表明:光谱鉴别指数Dy1y2值范围为1.61±1.05-10.97±6.65,重现性良好;采用主成分分析法确定5400-4000cm-1范围为微生物细胞近红外光谱的信息富集区;建立的线性判别分析模型鉴定准确率为100%,人工神经网络模型预测结果平均相对误差为5.04%。
(3)采用硝酸纤维素滤膜和单反射水平衰减全反射红外附件建立了一种简单快速的样品制备方法,获得了1株嗜酸耐热菌和7株常见细菌的红外光谱,建立了基于红外光谱的嗜酸耐热菌鉴定方法。结果表明:采用本论文建立的样品制备方法采集细菌细胞红外光谱切实可行;利用主成分分析可以将所研究的菌株成功分为不同的类别;所建立的鉴定模型都获得了较高的准确率,其中线性判别模型准确率为100%,人工神经网络模型平均相对误差为1.32%。另外对得到的8株嗜酸耐热菌菌株的红外光谱采用多元统计方法进行了分析,结果表明利用主成分分析方法可以将所研究的菌株成功分为不同的类别;所建立的线性判别模型对不同菌株的鉴别准确率达到93.75%。
(4)得到了1株嗜酸耐热菌和5株常见细菌的拉曼光谱,对微生物细胞的拉曼谱峰进行了归属分析,并构建了基于拉曼光谱的嗜酸耐热菌鉴定方法。结果表明:微生物细胞拉曼光谱的主要谱峰来自于蛋白质和碳水化合物的分子振动信息,核酸成分的谱峰较弱,脂类成分的信息不明显;获得拉曼光谱所需细胞悬液的最小浓度为1011 CFU/mL,得到的光谱的信噪比达到250以上;重现性分析结果表明光谱鉴别指数Dy1y2值范围为20.91±16.17-39.50±59.26,重现性良好;主成分分析结果表明拉曼光谱可以识别微生物细胞的主要成分的差异,所建立的线性判别分析模型鉴定准确率达到90%以上。另外对得到的8株嗜酸耐热菌菌株的拉曼光谱采用多元统计方法进行了分析,结果表明利用主成分分析方法可以将所研究的菌株成功分为不同的类别;所建立的线性判别模型对不同菌株的鉴别准确率达到85%。
China is the largest concentrated apple juice producing country and its yield increases rapidly. The safety issues, especially Alicyclobacillus species, are“green technical barriers”of international trade. Control of Alicyclobacillus is the key to development of apple juice industry, and the most important aspect is to establish rapid methods to detect and identify Alicyclobacillus species. Currently, rapid separation and enrichment of Alicyclobacillus species is the critical technical bottleneck; furthermore, the existing methods for detection of microorganisms have tremendous limitations.
In order to establish efficient and rapid methods to separate, enrich and identify Alicyclobacillus species, polyclonal antibody against Alicyclobacillus acidoterrestris (DSM3922) was connected to magnetic microspheres to prepare immune magnetic microspheres, a method for separating and enriching Alicyclobacillus species in apple juice based on immunomagnetic microspheres was established; further, vibrational spectroscopy (namely, near infrared, mid-infrared and Raman) combined with multivariate analysis was employed to identify Alicyclobacillus species and identification models based on multivariate analysis were established. A rapid, simple and convenient method to identify Alicyclobacillus species was provided for apple juice industry.
The main results of this thesis:
(1) Polyclonal antibody against Alicyclobacillus acidoterrestris DSM3922 was obtained by performing immune tests on two rabbits: R1, 27.6 mg/mL, 4.6 mL, titer 1:40000; R2, 10.8 mg/mL, 5 mL, titer 1:10000. Chitosan magnetic microsphere was prepared by chemical embedding and activated, which could stabilize polyclonal antibody at 2.48 mg antibody/g microsphere; Polyclonal antibody was coupled with magnetic microsphere to prepare immune magnetic microsphere. Immune magnetic microsphere was used to separate Alicyclobacillus acidoterrestris from medium and apple juice, the absorption rate was 68.5±1.7% and 70.1±4.7%, respectively.
(2) Bacterial powders of Alicyclobacillus strain, one yeast and five bacteria strains were prepared for Fourier transform near-infrared (FT-NIR) spectral collection. FT-NIR spectral determination was done using a diffuse reflection-integrating sphere. Reduction of data was performed by principal component analysis (PCA) and two identification models based on linear discriminant analysis (LDA) and artificial neural network (ANN) were established to identify bacterial strains. The reproducibility of the method was satisfied (Dy1y2: 1.61±1.05-10.97±6.65). The wavenumber of 5400-4000cm-1 is the information-rich range for the FT-NIR spectra of microorganism and high identification accuracy was achieved in both the LDA model (accuracy rate: 100%) and the ANN model (average relative error: 5.04%).
(3) A simple and rapid sample preparation method using nitrocellulose membrane filter (NMF) and a single reflection horizontal attenuated total reflection (HATR) accessory was developed, mid-infrared (mid-IR) spectra of Alicyclobacillus strain and seven other representative bacterial strains were collected and two identification models based on LDA and ANN respectively were established to identify and distinguish Alicyclobacillus strain from others. The sample preparation method was feasible and the microorganisms studied were successfully separated into different groups by PCA. High identification accuracy was achieved in both LDA model (accuracy rate: 100%) and ANN model (average relative error: 1.32%). In addition, Fourier transform infrared (FT-IR) spectroscopy was used and tested on eight Alicyclobacillus strains. The stains could be clearly separated into different groups by PCA. High identification accuracy (93.75%) was achieved using LDA model.
(4) Raman spectra of Alicyclobacillus strain and five other representative bacterial strains were collected using a Raman microspectrometer. Reduction of data was performed by PCA and an identification model based on LDA was established to identify bacterial strains. Results showed that the main bands found in Raman spectra of microorganisms originated from proteins and carbohydrates, only several weak peaks were from nucleic acids and fatty acids. The minimal bacterial concentration for collecting Raman was 1011 CFU/mL and the S/N was higher than 250. The reproducibility of the method was satisfied (Dy1y2: 20.91±16.17-39.50±59.26) and high identification accuracy was achieved in LDA model (accuracy rate: 90%). Further, Raman spectroscopy was used and tested on eight Alicyclobacillus strains. The stains could be separated into different groups by PCA successfully. High identification accuracy (85%) was achieved using LDA model.
引文
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