Modeling the alcoholic fermentation of xylose by Pichia stipitis using a qualitative reasoning approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1994-03

AUTHORS

F. Guerrin, J. -P. Delgenès, R. Moletta

ABSTRACT

Qualitative Reasoning is a set of Artificial Intelligence theories, methods, and techniques that provide an answer to modeling problems in domains in which one can have a clear notion of how a system is functioning without being able to express it as classical mathematical equations, and where is posed the problem of using jointly quantitative and qualitative data, as well as processing a big amount of complex knowledge. SIMAO (‘a System to Interpret Measurements And Observations’) is an attempt to deal with such problems. Although primarily devised for heterogeneous data interpretation in hydroecology, it was thought possible to use SIMAO in a wider context, like biotechnological processes. Starting from specific problems posed by a batch fermentation, the D-xylose conversion into ethanol by the yeast Pichia stipitis, this paper describes how was built and used a SIMAO model aimed at predicting the fermentation issue from initial conditions, i.e. set-points values and substrate concentration. More... »

PAGES

115-122

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00369466

DOI

http://dx.doi.org/10.1007/bf00369466

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002894676


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Biometrics and Artificial Intelligence Station, INRA (Institut National de la Recherche Agronomique), BP 27-31326, Castanet-Tolosan Cedex, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Guerrin", 
        "givenName": "F.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Biotechnological Laboratory for Food Industry Environment, INRA, Bd du G\u00e9n\u00e9ral De Gaulle, 11100, Narbonne, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Delgen\u00e8s", 
        "givenName": "J. -P.", 
        "id": "sg:person.011220121075.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011220121075.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Biotechnological Laboratory for Food Industry Environment, INRA, Bd du G\u00e9n\u00e9ral De Gaulle, 11100, Narbonne, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Moletta", 
        "givenName": "R.", 
        "id": "sg:person.01162322134.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01162322134.15"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf01982895", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001817019", 
          "https://doi.org/10.1007/bf01982895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01982895", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001817019", 
          "https://doi.org/10.1007/bf01982895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0385-6380(88)90008-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002511386"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0385-6380(88)90008-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002511386"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-11018-6_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006839114", 
          "https://doi.org/10.1007/3-540-11018-6_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00500493", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009162697", 
          "https://doi.org/10.1007/bf00500493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00500493", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009162697", 
          "https://doi.org/10.1007/bf00500493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0141-0229(88)90001-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016628925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0141-0229(88)90001-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016628925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01078656", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025347316", 
          "https://doi.org/10.1007/bf01078656"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01078656", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025347316", 
          "https://doi.org/10.1007/bf01078656"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0141-0229(86)90136-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026743846"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0141-0229(86)90136-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026743846"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1574-6968.1986.tb01194.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036791738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1574-6968.1986.tb01194.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036791738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-3800(91)90177-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038361764"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-3800(91)90177-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038361764"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1099/00221287-106-2-277", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060362893"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1994-03", 
    "datePublishedReg": "1994-03-01", 
    "description": "Qualitative Reasoning is a set of Artificial Intelligence theories, methods, and techniques that provide an answer to modeling problems in domains in which one can have a clear notion of how a system is functioning without being able to express it as classical mathematical equations, and where is posed the problem of using jointly quantitative and qualitative data, as well as processing a big amount of complex knowledge. SIMAO (\u2018a System to Interpret Measurements And Observations\u2019) is an attempt to deal with such problems. Although primarily devised for heterogeneous data interpretation in hydroecology, it was thought possible to use SIMAO in a wider context, like biotechnological processes. Starting from specific problems posed by a batch fermentation, the D-xylose conversion into ethanol by the yeast Pichia stipitis, this paper describes how was built and used a SIMAO model aimed at predicting the fermentation issue from initial conditions, i.e. set-points values and substrate concentration.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf00369466", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297453", 
        "issn": [
          "1615-7591", 
          "1615-7605"
        ], 
        "name": "Bioprocess and Biosystems Engineering", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "name": "Modeling the alcoholic fermentation of xylose by Pichia stipitis using a qualitative reasoning approach", 
    "pagination": "115-122", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00369466"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "0dc91a19e10891b20810be5c873d3c27a4120cd9f26d0998e8ccd5d0b3b3af1f"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1002894676"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00369466", 
      "https://app.dimensions.ai/details/publication/pub.1002894676"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T08:52", 
    "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/v90/0000000374_0000000374/records_119747_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF00369466"
  }
]
 

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.10.1007/bf00369466'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bf00369466'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00369466'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00369466'


 

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

111 TRIPLES      21 PREDICATES      37 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00369466 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nb545050a58d0425fb0898dfe2f35f22a
4 schema:citation sg:pub.10.1007/3-540-11018-6_4
5 sg:pub.10.1007/bf00500493
6 sg:pub.10.1007/bf01078656
7 sg:pub.10.1007/bf01982895
8 https://doi.org/10.1016/0141-0229(86)90136-5
9 https://doi.org/10.1016/0141-0229(88)90001-4
10 https://doi.org/10.1016/0304-3800(91)90177-3
11 https://doi.org/10.1016/0385-6380(88)90008-8
12 https://doi.org/10.1099/00221287-106-2-277
13 https://doi.org/10.1111/j.1574-6968.1986.tb01194.x
14 schema:datePublished 1994-03
15 schema:datePublishedReg 1994-03-01
16 schema:description Qualitative Reasoning is a set of Artificial Intelligence theories, methods, and techniques that provide an answer to modeling problems in domains in which one can have a clear notion of how a system is functioning without being able to express it as classical mathematical equations, and where is posed the problem of using jointly quantitative and qualitative data, as well as processing a big amount of complex knowledge. SIMAO (‘a System to Interpret Measurements And Observations’) is an attempt to deal with such problems. Although primarily devised for heterogeneous data interpretation in hydroecology, it was thought possible to use SIMAO in a wider context, like biotechnological processes. Starting from specific problems posed by a batch fermentation, the D-xylose conversion into ethanol by the yeast Pichia stipitis, this paper describes how was built and used a SIMAO model aimed at predicting the fermentation issue from initial conditions, i.e. set-points values and substrate concentration.
17 schema:genre research_article
18 schema:inLanguage en
19 schema:isAccessibleForFree false
20 schema:isPartOf Ncd947c9bc68f4d85a1cb4d504c4e5b17
21 Nd538cf715a924458983b2a392e8cd66e
22 sg:journal.1297453
23 schema:name Modeling the alcoholic fermentation of xylose by Pichia stipitis using a qualitative reasoning approach
24 schema:pagination 115-122
25 schema:productId N5b85cec78c3942dbb0175364a4202718
26 N6896ec045f3a4288b2daf55d6527f2ca
27 N9b6ae179898840eb92f5afa0915b9ecd
28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002894676
29 https://doi.org/10.1007/bf00369466
30 schema:sdDatePublished 2019-04-15T08:52
31 schema:sdLicense https://scigraph.springernature.com/explorer/license/
32 schema:sdPublisher Ne8b413a3f2b04bdfaf268c9a3614a047
33 schema:url http://link.springer.com/10.1007/BF00369466
34 sgo:license sg:explorer/license/
35 sgo:sdDataset articles
36 rdf:type schema:ScholarlyArticle
37 N11b4cf37bcf44ca8af7df22841c4ad5c rdf:first sg:person.011220121075.12
38 rdf:rest N3ab500925b3c44adb2bca38b29d632a6
39 N2bed953f72d8479ab797eba8eaed086f schema:name Biometrics and Artificial Intelligence Station, INRA (Institut National de la Recherche Agronomique), BP 27-31326, Castanet-Tolosan Cedex, France
40 rdf:type schema:Organization
41 N3ab500925b3c44adb2bca38b29d632a6 rdf:first sg:person.01162322134.15
42 rdf:rest rdf:nil
43 N5b85cec78c3942dbb0175364a4202718 schema:name dimensions_id
44 schema:value pub.1002894676
45 rdf:type schema:PropertyValue
46 N6896ec045f3a4288b2daf55d6527f2ca schema:name doi
47 schema:value 10.1007/bf00369466
48 rdf:type schema:PropertyValue
49 N749386096ed4408e84ef53f57fc5523c schema:name Biotechnological Laboratory for Food Industry Environment, INRA, Bd du Général De Gaulle, 11100, Narbonne, France
50 rdf:type schema:Organization
51 N9252a36c366c4275b22ca2c2498aa61d schema:name Biotechnological Laboratory for Food Industry Environment, INRA, Bd du Général De Gaulle, 11100, Narbonne, France
52 rdf:type schema:Organization
53 N9b6ae179898840eb92f5afa0915b9ecd schema:name readcube_id
54 schema:value 0dc91a19e10891b20810be5c873d3c27a4120cd9f26d0998e8ccd5d0b3b3af1f
55 rdf:type schema:PropertyValue
56 Nb545050a58d0425fb0898dfe2f35f22a rdf:first Nb9fc516ac73b414ba2bec170feb6ae12
57 rdf:rest N11b4cf37bcf44ca8af7df22841c4ad5c
58 Nb9fc516ac73b414ba2bec170feb6ae12 schema:affiliation N2bed953f72d8479ab797eba8eaed086f
59 schema:familyName Guerrin
60 schema:givenName F.
61 rdf:type schema:Person
62 Ncd947c9bc68f4d85a1cb4d504c4e5b17 schema:volumeNumber 10
63 rdf:type schema:PublicationVolume
64 Nd538cf715a924458983b2a392e8cd66e schema:issueNumber 3
65 rdf:type schema:PublicationIssue
66 Ne8b413a3f2b04bdfaf268c9a3614a047 schema:name Springer Nature - SN SciGraph project
67 rdf:type schema:Organization
68 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
69 schema:name Information and Computing Sciences
70 rdf:type schema:DefinedTerm
71 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
72 schema:name Artificial Intelligence and Image Processing
73 rdf:type schema:DefinedTerm
74 sg:journal.1297453 schema:issn 1615-7591
75 1615-7605
76 schema:name Bioprocess and Biosystems Engineering
77 rdf:type schema:Periodical
78 sg:person.011220121075.12 schema:affiliation N749386096ed4408e84ef53f57fc5523c
79 schema:familyName Delgenès
80 schema:givenName J. -P.
81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011220121075.12
82 rdf:type schema:Person
83 sg:person.01162322134.15 schema:affiliation N9252a36c366c4275b22ca2c2498aa61d
84 schema:familyName Moletta
85 schema:givenName R.
86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01162322134.15
87 rdf:type schema:Person
88 sg:pub.10.1007/3-540-11018-6_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006839114
89 https://doi.org/10.1007/3-540-11018-6_4
90 rdf:type schema:CreativeWork
91 sg:pub.10.1007/bf00500493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009162697
92 https://doi.org/10.1007/bf00500493
93 rdf:type schema:CreativeWork
94 sg:pub.10.1007/bf01078656 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025347316
95 https://doi.org/10.1007/bf01078656
96 rdf:type schema:CreativeWork
97 sg:pub.10.1007/bf01982895 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001817019
98 https://doi.org/10.1007/bf01982895
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1016/0141-0229(86)90136-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026743846
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1016/0141-0229(88)90001-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016628925
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/0304-3800(91)90177-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038361764
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/0385-6380(88)90008-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002511386
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1099/00221287-106-2-277 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060362893
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1111/j.1574-6968.1986.tb01194.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1036791738
111 rdf:type schema:CreativeWork
 




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


...