Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Yan Ren, Yuetong Ji, Lin Teng, Heping Zhang

ABSTRACT

BACKGROUND: As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment. METHODS: In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS. RESULTS: Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%. CONCLUSIONS: To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry. More... »

PAGES

233

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12934-017-0849-8

DOI

http://dx.doi.org/10.1186/s12934-017-0849-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1099928402

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29274636


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Batch Cell Culture Techniques", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lactobacillus casei", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Spectrum Analysis, Raman", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Inner Mongolia Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.411638.9", 
          "name": [
            "Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of P. R. China, Department of Food Science and Engineering, Inner Mongolia Agricultural University, 010018, Hohhot, People\u2019s Republic of China", 
            "Key Laboratory of Dairy Products Processing, Ministry of Agriculture, Inner Mongolia Agricultural University, 010018, Hohhot, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ren", 
        "givenName": "Yan", 
        "id": "sg:person.01216126645.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01216126645.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Qingdao Institute of Bioenergy and Bioprocess Technology", 
          "id": "https://www.grid.ac/institutes/grid.458500.c", 
          "name": [
            "Single-cell Center, CAS Key Laboratory of Biofuels, and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, 266101, Qingdao, Shandong, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ji", 
        "givenName": "Yuetong", 
        "id": "sg:person.0742050462.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0742050462.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Qingdao Institute of Bioenergy and Bioprocess Technology", 
          "id": "https://www.grid.ac/institutes/grid.458500.c", 
          "name": [
            "Single-cell Center, CAS Key Laboratory of Biofuels, and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, 266101, Qingdao, Shandong, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Teng", 
        "givenName": "Lin", 
        "id": "sg:person.01371232672.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01371232672.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Inner Mongolia Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.411638.9", 
          "name": [
            "Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of P. R. China, Department of Food Science and Engineering, Inner Mongolia Agricultural University, 010018, Hohhot, People\u2019s Republic of China", 
            "Key Laboratory of Dairy Products Processing, Ministry of Agriculture, Inner Mongolia Agricultural University, 010018, Hohhot, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Heping", 
        "id": "sg:person.0666573320.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0666573320.65"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.copbio.2010.01.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003930511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jobm.200800047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004581997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep34359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005724313", 
          "https://doi.org/10.1038/srep34359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1754-6834-7-58", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010497314", 
          "https://doi.org/10.1186/1754-6834-7-58"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/mcp.m800483-mcp200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011180047"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c5an00971e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012951720"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.snb.2009.11.052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013568182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3168/jds.2013-6927", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019453735"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/b822354h", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021430036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00216-012-6073-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022631189", 
          "https://doi.org/10.1007/s00216-012-6073-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep04698", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024490554", 
          "https://doi.org/10.1038/srep04698"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jrs.1520", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024492880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jrs.1520", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024492880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1420406112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029198505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2016.04.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043507053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2016.04.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043507053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2016.04.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043507053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2016.04.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043507053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2016.04.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043507053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3791/3977", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043515362"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.vibspec.2011.08.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049865517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biotechadv.2011.03.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050461289"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10408410490884757", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051576350"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2011.11.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052396493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c0an00608d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053557342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c0an00608d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053557342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac302250t", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055003366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac902351a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055071928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac902351a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055071928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.analchem.6b01046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055079835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.analchem.6b01602", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055079992"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tim.2017.01.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083749096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.analchem.6b05051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084121404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.analchem.6b05051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084121404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbiotec.2017.03.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084527607"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-12", 
    "datePublishedReg": "2017-12-01", 
    "description": "BACKGROUND: As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment.\nMETHODS: In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS.\nRESULTS: Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%.\nCONCLUSIONS: To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s12934-017-0849-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1030599", 
        "issn": [
          "1475-2859"
        ], 
        "name": "Microbial Cell Factories", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "name": "Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell level", 
    "pagination": "233", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "60475da2e9bdbdf563a7666fd95519f0cb4aeebee3b68593cb47d7fc39dd3a54"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29274636"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101139812"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12934-017-0849-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1099928402"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12934-017-0849-8", 
      "https://app.dimensions.ai/details/publication/pub.1099928402"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T01:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000493.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186/s12934-017-0849-8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12934-017-0849-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12934-017-0849-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12934-017-0849-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12934-017-0849-8'


 

This table displays all metadata directly associated to this object as RDF triples.

190 TRIPLES      21 PREDICATES      59 URIs      24 LITERALS      12 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12934-017-0849-8 schema:about N45afa77f12ee460ca68429a61d4c08c7
2 N5033bb32ee9b4565baafa55ea65f3aa5
3 Nee92e05ecffd4c77bacdb807036282d7
4 anzsrc-for:06
5 anzsrc-for:0601
6 schema:author N19f2af1877ef40ad9e3401ea8ecf4541
7 schema:citation sg:pub.10.1007/s00216-012-6073-9
8 sg:pub.10.1038/srep04698
9 sg:pub.10.1038/srep34359
10 sg:pub.10.1186/1754-6834-7-58
11 https://doi.org/10.1002/jobm.200800047
12 https://doi.org/10.1002/jrs.1520
13 https://doi.org/10.1016/j.biotechadv.2011.03.007
14 https://doi.org/10.1016/j.copbio.2010.01.007
15 https://doi.org/10.1016/j.copbio.2011.11.019
16 https://doi.org/10.1016/j.copbio.2016.04.018
17 https://doi.org/10.1016/j.jbiotec.2017.03.020
18 https://doi.org/10.1016/j.snb.2009.11.052
19 https://doi.org/10.1016/j.tim.2017.01.002
20 https://doi.org/10.1016/j.vibspec.2011.08.003
21 https://doi.org/10.1021/ac302250t
22 https://doi.org/10.1021/ac902351a
23 https://doi.org/10.1021/acs.analchem.6b01046
24 https://doi.org/10.1021/acs.analchem.6b01602
25 https://doi.org/10.1021/acs.analchem.6b05051
26 https://doi.org/10.1039/b822354h
27 https://doi.org/10.1039/c0an00608d
28 https://doi.org/10.1039/c5an00971e
29 https://doi.org/10.1073/pnas.1420406112
30 https://doi.org/10.1074/mcp.m800483-mcp200
31 https://doi.org/10.1080/10408410490884757
32 https://doi.org/10.3168/jds.2013-6927
33 https://doi.org/10.3791/3977
34 schema:datePublished 2017-12
35 schema:datePublishedReg 2017-12-01
36 schema:description BACKGROUND: As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment. METHODS: In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS. RESULTS: Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%. CONCLUSIONS: To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.
37 schema:genre research_article
38 schema:inLanguage en
39 schema:isAccessibleForFree true
40 schema:isPartOf N948d7e7110f147d486e9f8b0d8b5d80a
41 Nf9882b0a34b64b0186978aeb7469b8ba
42 sg:journal.1030599
43 schema:name Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell level
44 schema:pagination 233
45 schema:productId N470daf735bb14c9684e0649363a6076c
46 N70a3d796ded244249b83b8710f69d668
47 N8a45bbdc13654746bff5f813c6cab88a
48 N95850fdac09c481eb535c9350bb6e361
49 Nf4b742195c0646f58b47fd34dc820449
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099928402
51 https://doi.org/10.1186/s12934-017-0849-8
52 schema:sdDatePublished 2019-04-11T01:55
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher Nbd69790b3cca4f238f5327c5e72aad50
55 schema:url http://link.springer.com/10.1186/s12934-017-0849-8
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N19f2af1877ef40ad9e3401ea8ecf4541 rdf:first sg:person.01216126645.75
60 rdf:rest N4aed8538a95142679416695e768f835e
61 N45afa77f12ee460ca68429a61d4c08c7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
62 schema:name Lactobacillus casei
63 rdf:type schema:DefinedTerm
64 N470daf735bb14c9684e0649363a6076c schema:name dimensions_id
65 schema:value pub.1099928402
66 rdf:type schema:PropertyValue
67 N4aed8538a95142679416695e768f835e rdf:first sg:person.0742050462.53
68 rdf:rest N75f9ff169a814dc295e1cb520eae677f
69 N5033bb32ee9b4565baafa55ea65f3aa5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Batch Cell Culture Techniques
71 rdf:type schema:DefinedTerm
72 N70a3d796ded244249b83b8710f69d668 schema:name nlm_unique_id
73 schema:value 101139812
74 rdf:type schema:PropertyValue
75 N75f9ff169a814dc295e1cb520eae677f rdf:first sg:person.01371232672.50
76 rdf:rest Nca46d99de942427192a7f1bee8b4ccb9
77 N8a45bbdc13654746bff5f813c6cab88a schema:name doi
78 schema:value 10.1186/s12934-017-0849-8
79 rdf:type schema:PropertyValue
80 N948d7e7110f147d486e9f8b0d8b5d80a schema:issueNumber 1
81 rdf:type schema:PublicationIssue
82 N95850fdac09c481eb535c9350bb6e361 schema:name pubmed_id
83 schema:value 29274636
84 rdf:type schema:PropertyValue
85 Nbd69790b3cca4f238f5327c5e72aad50 schema:name Springer Nature - SN SciGraph project
86 rdf:type schema:Organization
87 Nca46d99de942427192a7f1bee8b4ccb9 rdf:first sg:person.0666573320.65
88 rdf:rest rdf:nil
89 Nee92e05ecffd4c77bacdb807036282d7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Spectrum Analysis, Raman
91 rdf:type schema:DefinedTerm
92 Nf4b742195c0646f58b47fd34dc820449 schema:name readcube_id
93 schema:value 60475da2e9bdbdf563a7666fd95519f0cb4aeebee3b68593cb47d7fc39dd3a54
94 rdf:type schema:PropertyValue
95 Nf9882b0a34b64b0186978aeb7469b8ba schema:volumeNumber 16
96 rdf:type schema:PublicationVolume
97 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
98 schema:name Biological Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
101 schema:name Biochemistry and Cell Biology
102 rdf:type schema:DefinedTerm
103 sg:journal.1030599 schema:issn 1475-2859
104 schema:name Microbial Cell Factories
105 rdf:type schema:Periodical
106 sg:person.01216126645.75 schema:affiliation https://www.grid.ac/institutes/grid.411638.9
107 schema:familyName Ren
108 schema:givenName Yan
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01216126645.75
110 rdf:type schema:Person
111 sg:person.01371232672.50 schema:affiliation https://www.grid.ac/institutes/grid.458500.c
112 schema:familyName Teng
113 schema:givenName Lin
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01371232672.50
115 rdf:type schema:Person
116 sg:person.0666573320.65 schema:affiliation https://www.grid.ac/institutes/grid.411638.9
117 schema:familyName Zhang
118 schema:givenName Heping
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0666573320.65
120 rdf:type schema:Person
121 sg:person.0742050462.53 schema:affiliation https://www.grid.ac/institutes/grid.458500.c
122 schema:familyName Ji
123 schema:givenName Yuetong
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0742050462.53
125 rdf:type schema:Person
126 sg:pub.10.1007/s00216-012-6073-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022631189
127 https://doi.org/10.1007/s00216-012-6073-9
128 rdf:type schema:CreativeWork
129 sg:pub.10.1038/srep04698 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024490554
130 https://doi.org/10.1038/srep04698
131 rdf:type schema:CreativeWork
132 sg:pub.10.1038/srep34359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005724313
133 https://doi.org/10.1038/srep34359
134 rdf:type schema:CreativeWork
135 sg:pub.10.1186/1754-6834-7-58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010497314
136 https://doi.org/10.1186/1754-6834-7-58
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1002/jobm.200800047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004581997
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1002/jrs.1520 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024492880
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.biotechadv.2011.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050461289
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/j.copbio.2010.01.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003930511
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/j.copbio.2011.11.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052396493
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.copbio.2016.04.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043507053
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.jbiotec.2017.03.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084527607
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.snb.2009.11.052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013568182
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/j.tim.2017.01.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083749096
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.vibspec.2011.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049865517
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1021/ac302250t schema:sameAs https://app.dimensions.ai/details/publication/pub.1055003366
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1021/ac902351a schema:sameAs https://app.dimensions.ai/details/publication/pub.1055071928
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1021/acs.analchem.6b01046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055079835
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1021/acs.analchem.6b01602 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055079992
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1021/acs.analchem.6b05051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084121404
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1039/b822354h schema:sameAs https://app.dimensions.ai/details/publication/pub.1021430036
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1039/c0an00608d schema:sameAs https://app.dimensions.ai/details/publication/pub.1053557342
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1039/c5an00971e schema:sameAs https://app.dimensions.ai/details/publication/pub.1012951720
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1073/pnas.1420406112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029198505
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1074/mcp.m800483-mcp200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011180047
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1080/10408410490884757 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051576350
179 rdf:type schema:CreativeWork
180 https://doi.org/10.3168/jds.2013-6927 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019453735
181 rdf:type schema:CreativeWork
182 https://doi.org/10.3791/3977 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043515362
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.411638.9 schema:alternateName Inner Mongolia Agricultural University
185 schema:name Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of P. R. China, Department of Food Science and Engineering, Inner Mongolia Agricultural University, 010018, Hohhot, People’s Republic of China
186 Key Laboratory of Dairy Products Processing, Ministry of Agriculture, Inner Mongolia Agricultural University, 010018, Hohhot, People’s Republic of China
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.458500.c schema:alternateName Qingdao Institute of Bioenergy and Bioprocess Technology
189 schema:name Single-cell Center, CAS Key Laboratory of Biofuels, and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, 266101, Qingdao, Shandong, People’s Republic of China
190 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...