CN113227768A - 用于使用荧光光谱测量和机器学习来量化水中的细菌的系统和方法 - Google Patents
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Abstract
本发明提供了一种用于使用荧光光谱测量和机器学习来对高质量水中的细菌进行快速量化的系统和方法。本发明可适用于饮用水配送系统、净水厂、餐饮业、以及医药行业。
Description
发明领域
本发明涉及一种用于使用荧光光谱测量和机器学习来对高质量水中的细菌进行快速量化的系统和方法。本发明可适用于饮用水配送系统、净水厂、餐饮业、以及医药行业。
背景技术
关于水的微生物质量的信息在包括饮用水配送系统、净水厂、餐饮业以及医药行业的若干领域中是高度重要的。水微生物质量的主要重要性涉及公共健康，但它对于各种行业中的水管理系统而言也是至关重要的。
当前量化高质量水中的细菌的方法需要基于微生物学的技术，这些技术是费力、昂贵且费时的。一种这样的方法是异养菌板数(HPC)方法，该方法量化好氧嗜温细菌。水样(通常是一毫升)与含有营养成分的熔融琼脂混合(倾盘法)或者被铺撒在琼脂表面上(铺板技术)，并且取决于培养温度将板温育2-5天。另一种标准方法是膜过滤，涉及使100毫升样本通过0.45mm过滤器，然后将其置于琼脂板上。
给定水系统的高度可变性和水质的动态特征，在特定时间点获取的关于细菌数的数据可能不是相关的，除非该数据反映水的微生物质量不断恶化。
由于基于微生物学的方法的缺点是需要长时间获取关于细菌数的数据，因此已经使用诸如PCR和抗原的免疫学检测之类的技术开发出了针对特定细菌病原体的几种方法。这些方法将检测水系统中特定细菌病原体所需的时间显著地缩短到几个小时。然而，对于每一水样，这些方法都需要专业技术人员、昂贵的试剂和试剂盒，并且不提供对于水管理是基本的诸如HPC之类的关于水中细菌数的一般数据。此外，这些方法利用抓取样本，而不提供有效管理水系统所需的实时或近实时监测。因此，需要一种快速的方法来计算水中的细菌总数，这有可能实现实时或近实时的基于数据的管理。
蒸馏水在紫外线和可见光范围内缺乏荧光，但天然和加工后的水中含有细菌、悬浮的和可溶的有机和无机材料，其中一些在此范围内发出荧光。微生物具有固有的荧光特性，这些荧光特性至少与蛋白质中存在的诸如芳香族氨基酸色氨酸、酪氨酸和苯丙氨酸之类的某些荧光成分相关联。测量水样的激发发射矩阵(EEM)或荧光图提供了一种检测水中各种荧光团的存在的方法，这些荧光团可基于荧光图中的特定位置来区分开。
使用各种数学方法来分析EEM并提取有关水生荧光团存在的信息，例如，平行因子分析(PARAFAC)和自组织图(SOM)。然而，高质量的水可能会显示出非常弱的荧光，这种荧光不能与背景噪声区分开，达到的程度是检测不到荧光团的存在。因此，需要一种将能够在强噪声背景下隔离弱荧光信号并实时生成结果的灵敏且快速的方法。
因此，本发明的目的是提供一种系统和方法，该系统和方法能够对水中的细菌进行快速(最终是实时)且精确的量化，由此允许在水质恶化的情况下迅速做出技术响应。的确，本发明可被在线结合到各种水厂和配水系统。
本发明的另一目的是提供不需要昂贵且费时费力的基于微生物学的技术的解决方案。该不需要样本制备，无损，且在仅仅几分钟内产生结果。
随着本说明书的展开，本发明的其他目的和优点将显而易见。
发明内容
本发明提供了一种用于量化水中的细菌的方法，包括：获取水样；生成所述水样的激发发射矩阵(EEM)；以及通过将所述EEM与经校准数据进行相关来确定所述水样中的细菌的浓度。
根据一些实施例，经校准数据是通过确定具有已知细菌浓度的多个测试样本的EEM来获取的。EEM是通过以1-5nm步长扫描200到800nm的激发波长并以1-5nm步长检测200到800nm之间发射的荧光来生成的。在一些实施例中，EEM是通过以5nm步长扫描220到400nm的激发波长并以2nm步长检测220到410nm之间发射的荧光来生成的。
根据一个实施例，本发明提供了一种用于量化水中的细菌的系统，该系统包括：用于生成水样的激发发射矩阵(EEM)的设备以及适用于将所述EEM与所述水样的细菌浓度进行相关的逻辑电路系统。该逻辑电路系统包括或关联于数据管理和处理装置。在一些实施例中，处理装置是使用历史数据集来训练的机器学习模型。
在一些实施例中，该方法和系统可适用于在饮用水配送系统、净水厂、餐饮业或医药行业中监测水质。在一些实施例中，该系统可被结合到在线监测系统中。
附图说明
图1示出了系数热图，该热图显示用于预测公式的每一激发发射对的乘法系数。
图2示出了训练样本集(左)和验证样本集(右)的双蒸水预测和真实大肠杆菌浓度之间的相关性。
图3示出了训练样本集(左)和验证样本集(右)的饮用水预测和真实细菌浓度之间的相关性。
本发明的实施例的详细描述
本发明的系统和方法有用地提供数学建模方法，该建模方法利用水荧光测量来提取与水中的细菌总数相关的数据。数据使用基于诸如偏最小二乘回归(PLSR)等方法的算法来处理，这些方法通过机器学习能分析复杂的激发发射矩阵(EEM)数据并将这些数据与高质量水样中的细菌数进行相关。
目前发现，测量水样的激发发射矩阵(EEM)或荧光强度图提供了一种快速且精确地量化高质量水样中的细菌的方法。具体而言，训练样本集的荧光强度和真实细菌浓度(以每毫升CFU数计(CFU/ml))之间的线性回归被用来获取预测公式。该预测公式然后被用来确定水样中的细菌浓度。该模型使用验证样本集(此处是近乎20％的样本)来测试。进行两项研究：1)确定双蒸水中的大肠杆菌浓度；以及2)确定用作饮用水的天然地下水中的细菌密度。
在第一方面，本发明涉及一种用于量化水中的细菌的方法，包括：获取水样；生成所述水样的激发发射矩阵(EEM)；以及通过将所述EEM与经校准数据进行相关来确定所述水样中的细菌的浓度。
在一些实施例中，经校准数据是通过确定具有已知细菌浓度的多个测试样本的EEM来获取的。细菌浓度可使用量化好氧嗜温细菌的异养菌板数(HPC)方法来确定。
水样由荧光分光光度计使用200-800nm范围内的激发波长和200-800nm范围内的每个激发波长处的发射光谱来扫描。每个激发/发射波长处的强度除以纯非荧光水的拉曼散射强度，以便对数据进行归一化并将机器/灯不稳定性的影响最小化。
在一些实施例中，EEM是通过以1-5nm的步长扫描200到800nm的激发波长并以1-5nm的步长检测200到800nm之间发射的荧光来生成的。根据一些实施例，EEM是通过以5nm步长扫描220到400nm的激发波长并以2nm步长检测220到410nm之间发射的荧光来生成的。
另一实施例涉及一种用于量化水中的细菌的系统，该系统包括：用于生成水样的激发发射矩阵(EEM)的设备以及适用于将所述EEM与所述水样的细菌浓度进行相关的逻辑电路系统。
根据一些实施例，该逻辑电路系统包括或关联于数据管理和处理装置。在另外一些实施例中，该装置是使用历史数据集来训练的机器学习模型。
在一些实施例中，该方法和系统可适用于在饮用水配送系统、净水厂、餐饮业或医药行业中监测水质。
本发明提供了一种系统和方法，该系统和方法用于对细菌浓度进行实时监测以使得能够进行有效的水管理以确保水的质量和安全性。根据一些实施例，该系统可被结合到水厂或配水系统中的在线监测系统中，由此提供对水的微生物质量的准确评估。
本发明将在以下示例中进一步描述和图示。
示例
示例1：
找出用于确定细菌浓度的预测公式
含有已知细菌浓度(计数)的水样由荧光分光光度计(岛津，RF-5301PC，日本京都)使用200-800nm激发波长范围来扫描，并且每个激发波长处的发射光谱在220-410nm范围内被获取，以用于生成激发发射矩阵(EEM)。每个激发/发射波长处的强度除以纯非荧光水的拉曼散射强度，以便对数据进行归一化并将机器/灯不稳定性的影响最小化。
主成分分析(PCA；JMP Pro 13，SAS研究所，美国卡里)用于排除主要异常值(通常<5％的样本)。样本被指派被描述为因变量的其已知细菌浓度，而每一对激发/发射波长处的荧光强度被定义为自变量，由此产生3775个不同的自变量。样本然后被随机分成训练集(80％)和验证集(20％)。该划分意味着预测公式将基于80％的数据并在剩余20％的数据上进行测试以评估模型质量。JMP偏最小二乘(PLS)算法被用来获取预测载荷公式，该公式是给予在激发/发射波长的每一组合处的荧光强度(即，自变量)的权重的列表。该公式被应用于验证数据集以预测细菌计数(即，因变量)。为了测试该模型的预测质量，计算实际值和预测值之间的线性回归并且该线性回归用分别作为对相关性和偏差的度量的R2和均方根误差来表征。
图1示出了系数热图，该热图显示用于预测公式的每一激发发射对的乘法系数。
该预测公式被计算为乘以其各自的系数(a)的每一激发发射对的所有荧光强度(X)与误差(b)之和，如以下公式所示：
示例2：
确定双蒸水中的大肠杆菌浓度
为了最初通过测量激发发射矩阵(EEM)来测试快速且精确地量化水样中的细菌的能力，制备了含有递增的大肠杆菌浓度(0-108CFU/ml)的54个双蒸(非荧光)水样。包含43个水样(80％)中的细菌浓度及其同源EEM的数据集被用作如在示例1中描述的训练集。每一细菌浓度用5次重复完成。导出的模型被用来预测11个样本(20％验证集)中的细菌数。
图2示出了预测细菌浓度与真实浓度之间的相关性。左面示出了43个样本的训练集(模型)，右面示出了11个样本的验证集。
该模型能够检测验证数据中的低至100CFU/ml的浓度下的大肠杆菌细胞数。该模型以0.61log10每毫升CFU数的均方根误差(RMSE)正确地预测细菌浓度并计及超过90％的变异。
示例3：
确定用作饮用水的天然地下水中的细菌密度
在本地和国家饮用水配送系统的关键控制点中应用本发明的方法将使得能够对水的微生物质量进行实时监测并因此将允许在水质暂时恶化的情况下迅速做出反应。
为了检查该方法的有效性，具有已知微生物含量(由国际标准方法确定的HPC)的69个饮用水(地下水)样本由分光荧光计读取以获得EEM，如示例1中描述的。水样被分成两组，这两组包含用于训练的51个样本(74％)以及作为验证集的18个样本(26％)。对来自51个样本的数据执行建模并确定18个验证样本中的细菌计数(HPC)。
图3示出了从51个样本的训练集(左面)和18个样本的验证集(右面)获取的预测细菌浓度数据和真实浓度数据之间的相关性。
该模型使得能够计算总体数据中的低至200CFU/ml的浓度下的HPC。预测细菌浓度的RMSE是20CFU/ml，并且该模型计及超过90％的变异。
尽管本发明的实施例已经通过图示来描述，但将理解，本发明可以在进行许多变化、修改和适配的情况下实施，而不超出权利要求书的范围。
Claims (10)
1.一种用于量化水中的细菌的方法，包括：
i)获取水样；
ii)生成所述水样的激发发射矩阵(EEM)；以及
iii)通过将所述EEM与经校准数据进行相关来确定所述水样中的细菌的浓度。
2.如权利要求1所述的方法，其中所述经校准数据是通过确定具有已知细菌浓度的多个测试样本的EEM来获取的。
3.如权利要求1或2所述的方法，其中所述EEM是通过以1-5nm的步长扫描200到800nm的激发波长并以1-5nm的步长检测200到800nm之间发射的荧光来生成的。
4.如权利要求3所述的方法，其中所述EEM是通过以5nm的步长扫描220到410nm的激发波长并以2nm的步长检测220到410nm之间发射的荧光来生成的。
5.如权利要求1所述的方法，所述方法用于在饮用水配送系统、净水厂、餐饮业或医药行业中监测水质。
6.一种用于量化水中的细菌的系统，所述系统包括：
a.用于生成水样的激发发射矩阵(EEM)的设备；以及
b.适用于将所述EEM与所述水样的细菌浓度进行相关的逻辑电路系统。
7.如权利要求6所述的系统，其中所述逻辑电路系统包括或关联于数据管理和处理装置。
8.如权利要求7所述的系统，其中所述处理装置是使用历史数据集来训练的机器学习模型。
9.如权利要求6所述的系统，所述系统用于在饮用水配送系统、净水厂、餐饮业或医药行业中监测水质。
10.如权利要求6所述的系统，所述系统被结合到在线监测系统中。
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| IL291606A (en) * | 2021-03-22 | 2022-10-01 | Kando Env Services Ltd | Non-contact monitoring of liquid properties in wastewater transport systems |
| EP4352718A1 (en) * | 2021-06-07 | 2024-04-17 | The State of Israel, Ministry of Agriculture & Rural Development, Agricultural Research Organization (ARO) (Volcani Institute) | In-line early warning system of water contamination with organic matter |
| CN115219472B (zh) * | 2022-08-12 | 2023-05-12 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | 一种定量识别混合水体多污染源的方法及系统 |
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| US20070037135A1 (en) * | 2005-08-08 | 2007-02-15 | Barnes Russell H | System and method for the identification and quantification of a biological sample suspended in a liquid |
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| KR20180103546A (ko) * | 2017-03-10 | 2018-09-19 | (주) 테크로스 | 샘플수 분석장치 및 방법 |
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| WO2001092859A1 (en) * | 2000-06-02 | 2001-12-06 | Medicometrics Aps | Method and system for classifying a biological sample |
| US20070111225A1 (en) * | 2005-08-10 | 2007-05-17 | California Institute Of Technology | System and method for monitoring an analyte |
| US8666673B2 (en) * | 2010-05-14 | 2014-03-04 | Biomerieux, Inc | Identification and/or characterization of a microbial agent using taxonomic hierarchical classification |
| US9670072B2 (en) * | 2014-10-29 | 2017-06-06 | Horiba Instruments Incorporated | Determination of water treatment parameters based on absorbance and fluorescence |
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| US20070037135A1 (en) * | 2005-08-08 | 2007-02-15 | Barnes Russell H | System and method for the identification and quantification of a biological sample suspended in a liquid |
| CN106102757A (zh) * | 2013-11-26 | 2016-11-09 | 以色列国家农业部、农村发展农业研究组织·沃尔卡尼中心 | 极香蓍草中抗神经炎和保护性化合物 |
| CN107408152A (zh) * | 2015-03-06 | 2017-11-28 | 普凯尔德诊断技术有限公司 | 使用快速固有荧光法的含有抗性基因的细菌的免试剂鉴定 |
| KR20180103546A (ko) * | 2017-03-10 | 2018-09-19 | (주) 테크로스 | 샘플수 분석장치 및 방법 |
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| US20210389243A1 (en) | 2021-12-16 |
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