NIIMBL Monitoring And Control Ontology
type: Ontology
label: Biopharma Monitoring And Control Ontology
abstract: This module contains terms and relations necessary for representing the planning and execution of monitoring and/or controlling for both laboratory and manufacturing scale.
contributor: Adlane Rebai, Millipore Sigma
contributor: Ana Nikolov, OAGi
contributor: Boonserm Kulvatunyou, NIST
contributor: Cameron Gibbs, CrownPoint Technologies, LLC
contributor: Gabriela Henning, NIST
contributor: Jan Kemper, AstraZeneca
contributor: Marie-Aude Coutouly, Millipore Sigma
contributor: Melissa Weller, CrownPoint Technologies, LLC
contributor: Milos Drobnjakovic, OAGi
contributor: Stephen J. Granite, CrownPoint Technologies, LLC
contributor: Stephen Kahmann, CrownPoint Technologies, LLC
creator: NIIMBL BD-1 Ontology Group
license: http://opensource.org/licenses/MIT
imports: BiopharmaParameter
imports: ManufacturingSystemOrganization
imports: ReferenceOntologyCandidates
version IRI: BiopharmaMonitoringAndControl
copyright: Copyright (c) 2022, 2023, 2024, 2025, 2026 Open Applications Group
maturity: Released

aliquoting process
type: Class
subclass of: material sampling process
subclass of: Restriction
  on property: hasInput
  some values from: Class
    intersection of: BFO_0000040
    intersection of: Restriction
      on property: hasQuality
      some values from: CompositionalHomogeneity
subclass of: Restriction
  on property: hasSpecifiedOutput
  some values from: Class
    intersection of: BFO_0000040
    intersection of: Restriction
      on property: BFO_0000175
      some values from: Class
        intersection of: BFO_0000040
        intersection of: Restriction
    intersection of: Restriction
      on property: hasQuality
      some values from: CompositionalHomogeneity
example: aliquoting a well-mixed harvested cell culture fluid sample into labeled 5 mL portions for distribution across analytical laboratories; division of a well-mixed serum sample into 1 mL cryovials for quality control testing and archiving
adapted from: https://goldbook.iupac.org/terms/view/A00218 and http://purl.obolibrary.org/obo/NCIT_C124326
explanatory note: 1) Because the source material is assumed to be homogeneous, the resulting aliquots represent the whole with negligible sampling error. 2) The process produces multiple portions that can be uniquely identified, stored, or used independently
first order logic axiom: AliquotingProcess(x) → MaterialSamplingProcess(x) ∧ ∃i∃h∃o1∃h1 (MaterialEntity(i) ∧ CompositionalHomogeneity(h) ∧ hasInput(x, i) ∧ hasQuality(i, h) ∧ MaterialEntity(o1) ∧ hasSpecifiedOutput(x, o1) ∧ properContinuantPartOfAtSomeTime(o1, i) ∧ CompositionalHomogeneity(h1) ∧ hasQuality(o1, h1))
is primitive: true
natural language definition: material sampling process in which a homogeneous material entity is subdivided into multiple portions of defined amount, each portion representing the composition of the source
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions
semi formal natural language axiom: if x is an 'aliquoting process' then x is a 'material sampling process' that 'has input' some compositionally homogeneous 'material entity' i and 'has specified output' some 'material entity' that is a 'proper continuant part of at some time' i and 'has quality' some 'compositional homogeneity'

atline monitoring process
type: Class
subclass of: monitoring process
subclass of: Restriction
  on property: BFO_0000117
  some values from: Class
    intersection of: material sampling process
    intersection of: Restriction
      on property: BFO_0000063
      some values from: Class
        intersection of: MaterialLocationChangeProcess
        intersection of: Restriction
    intersection of: Restriction
      on property: hasSpecifiedOutput
      some values from: material sample
example: Monitoring cell viability and concentration by measuring a freshly withdrawn bioreactor sample using a local analyzer within the same cleanroom suite
adapted from: https://www.hamiltoncompany.com/knowledge-base/article/bioprocess-monitoring-and-control
explanatory note: 1) The measurement is performed using analytical equipment located in a physical area that environs a planned process during which the material sampling process occurs. That planned process provides the spatial reference and is not thereby asserted to be the entity being monitored. 2) At-line monitoring differs from on-line monitoring because on-line material sampling and measurement occur through analytical equipment connected by a material-transfer arrangement to process equipment, whereas an at-line material sample is brought to separate analytical equipment. 3) FDA PAT describes at-line measurement as analysis in close proximity to the process stream. In this ontology, the physical area that environs the planned process provides the governed operational boundary for that proximity.
first order logic axiom: AtlineMonitoringProcess(x) → MonitoringProcess(x) ∧ ∃s∃t∃m∃a∃r∃p (MaterialSamplingProcess(s) ∧ MaterialLocationChangeProcess(t) ∧ MeasurementProcess(m) ∧ MaterialSample(a) ∧ PhysicalArea(r) ∧ PlannedProcess(p) ∧ hasOccurrentPart(x, s) ∧ hasOccurrentPart(x, t) ∧ hasOccurrentPart(x, m) ∧ occurrentPartOf(s, p) ∧ hasSpecifiedOutput(s, a) ∧ hasParticipantAtAllTimes(t, a) ∧ hasInput(m, a) ∧ precedes(s, t) ∧ precedes(t, m) ∧ occursIn(m, r) ∧ environs(r, p))
is primitive: true
natural language definition: monitoring process consisting of one or more measurement processes that are preceded by a material sampling process, in which material representing the entity of interest is collected from a process stream or in-process material and transferred to analytical equipment located within the same physical area
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions
semi formal natural language axiom: if x is an 'atline monitoring process' then x is a 'monitoring process' that has occurrent parts including a 'material sampling process' s, a 'material location change process' t, and a 'measurement process' m, and s is an 'occurrent part of' some 'planned process' p, and s 'has specified output' some 'material sample' a that t 'has participant at all times' and that is an 'input' of m, and s 'precedes' t, t 'precedes' m, and m 'occurs in' some 'physical area' r that 'environs' p

control strategy
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: BFO_0000110
  some values from: Class
    union of: PerformanceIndicatorSpecification
    union of: ProcessParameterSpecification
    union of: QualityAttributeSpecification
alternative label: control plan specification
alternative label: integrated control strategy
example: a control strategy for sterile filling that specifies filling-pump speed, line speed, and stopper-placement settings as process parameters; fill-volume and container-closure-integrity specifications as quality attribute specifications; and viable and nonviable particle monitoring, room pressure differential, and cleanroom temperature as facility and environmental controls; A control strategy for fed-batch cell culture includes specifying a gas controller to regulate dissolved oxygen (process parameter), a pH control loop to maintain pH within range (process parameter), and monitoring of cell viability over time as a process performance indicator. It also specifies the glycosylation profile and aggregate levels as critical quality attributes and prescribes their periodic monitoring throughout the culture using off-line analysis.
adapted from: https://database.ich.org/sites/default/files/Q10%20Guideline.pdf and ISA88
explanatory note: Per ICH: A planned set of controls, derived from current product and process understanding, that assures process performance and product quality. Control strategy can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control. Per ICH: Quality risk management is used to establish the control strategy. This can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in process controls, finished product specifications, and the associated methods and frequency of monitoring and control. The control strategy should facilitate timely feedback / feedforward and appropriate corrective action and preventive action.
first order logic axiom: ControlStrategy(x) → PlanSpecification(x) ∧ ∃y((ProcessParameterSpecification(y) ∨ QualityAttributeSpecification(y) ∨ PerformanceIndicatorSpecification(y)) ∧ hasContinuantPartAtAllTimes(x, y))
is primitive: true
natural language definition: plan specification consisting of one or more specifications of methods and technologies that prescribe the monitoring or regulation of process parameters, quality attributes, or process performance indicators of a particular process and its resulting material product
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a control strategy.
semi formal natural language axiom: if x is a 'control strategy', then x is a 'plan specification' that 'has continuant part at all times' some y, where y is either a 'process parameter specification', a 'quality attribute specification', or a 'performance indicator specification'

inline monitoring process
type: Class
subclass of: monitoring process
subclass of: Class
  complement of: Restriction
    on property: BFO_0000117
    some values from: material sampling process
subclass of: Restriction
  on property: BFO_0000117
  some values from: MeasurementProcess
example: Monitoring the evolution of pH and dissolved oxygen in a bioreactor through continuously operating in-situ probes during a cell culture run
adapted from: https://www.hamiltoncompany.com/knowledge-base/article/bioprocess-monitoring-and-control
explanatory note: 1) In-line monitoring measures an attribute without removing or diverting material. 2) Sensors or probes may be located within equipment, mounted directly in a process stream, or otherwise integrated with equipment used in the associated planned process. 3) In-line monitoring may be continuous, periodic, or event-triggered. The distinction concerns the measurement arrangement and absence of a preceding material sampling process.
first order logic axiom: InlineMonitoringProcess(x) → MonitoringProcess(x) ∧ ∃m (MeasurementProcess(m) ∧ hasOccurrentPart(x, m)) ∧ ¬∃s (MaterialSamplingProcess(s) ∧ hasOccurrentPart(x, s))
is primitive: true
natural language definition: monitoring process consisting of one or more measurement processes that use sensors or probes integrated with the process equipment to measure an attribute without material removal or diversion before measurement
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions
semi formal natural language axiom: if x is an 'inline monitoring process' then x is a 'monitoring process' that 'has occurrent part' some 'measurement process' and no 'material sampling process' is an 'occurrent part of' x

material aliquot
type: Class
subclass of: material sample
equivalent class: Class
  intersection of: material sample
  intersection of: Restriction
    on property: isSpecifiedOutputOf
    some values from: aliquoting process
example: a 2 mL portion of purified protein reference material contained in one of several labeled vials prepared from the same homogeneous bulk lot;
adapted from: https://docs.gdc.cancer.gov/Encyclopedia/pages/Aliquot/ and https://goldbook.iupac.org/terms/view/A00218
explanatory note: 1) By assumption, aliquots are produced from a homogeneous source material, so each aliquot is considered to represent the composition of the whole with negligible sampling error. 2) In practice, aliquots are typically assigned unique identifiers (e.g., IDs, barcodes) to enable independent use and traceability. 3) In laboratory and biobanking contexts, an aliquot is typically a sub-sample taken from a primary biospecimen or analyte, prepared so that it can be distributed, stored, or analyzed independently.
first order logic definition: MaterialAliquot(x) ↔ MaterialEntity(x) ∧ ∃s(AliquotingProcess(s) ∧ isSpecifiedOutputOf(x, s))
natural language definition: material entity that is the specified output of some aliquoting process
semi formal natural language definition: every instance of 'material aliquot' is defined as exactly an instance of 'material entity' that 'is specified output of' some 'aliquoting process'

material product quality plan
type: Class
subclass of: material quality plan
subclass of: Restriction
  on property: BFO_0000110
  some values from: QualityAttributeSpecification
subclass of: Restriction
  on property: BFO_0000110
  some values from: Class
    intersection of: PlanSpecification
    intersection of: Restriction
      on property: prescribes
      some values from: MeasurementProcess
subclass of: Restriction
  on property: prescribes
  some values from: Class
    intersection of: PlannedProcess
    intersection of: Class
      union of: Restriction
        on property: BFO_0000062
        some values from: ProductProductionProcess
      union of: Restriction
        on property: BFO_0000132
        some values from: ProductProductionProcess
      union of: Restriction
        on property: isTemporallyOverlappedBy
        some values from: ProductProductionProcess
example: A material product quality plan for a particular chromatography resin prescribes synthesis process controls, intermediate and final QC testing, and release criteria to ensure the resin meets defined binding capacity, particle size distribution, and metal impurity limits. It includes in-process monitoring of pH and temperature during functionalization, validated leachables testing, and visual inspection.
explanatory note: 1) At least one process prescribed by some part of the quality plan has to be a measurement process 2) The processes embedded in the quality plan typically include testing, inspection, characterization 3) The material product quality plan typically embeds the processes done to ensure quality of intermediates and not just the final products
first order logic axiom: MaterialProductQualityPlan(x) → MaterialQualityPlan(x) ∧ ∃a (PlanSpecification(a) ∧ hasContinuantPartAtAllTimes(x, a) ∧ ∃m (MeasurementProcess(m) ∧ prescribes(a, m))) ∧ ∃q (QualityAttributeSpecification(q) ∧ hasContinuantPartAtAllTimes(x, q)) ∧ ∃p∃pp (PlannedProcess(p) ∧ ProductProductionProcess(pp) ∧ prescribes(x, p) ∧ (precededBy(p, pp) ∨ occurrentPartOf(p, pp) ∨ isTemporallyOverlappedBy(p, pp)))
is primitive: true
natural language definition: material quality plan that prescribes the processes needed to ensure that a material product meets the required quality objectives
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a material product quality plan
semi formal natural language axiom: if x is a 'material product quality plan', then x is a 'material quality plan' that 'has continuant part at all times' some 'plan specification' a that 'prescribes' some 'measurement process', 'has continuant part at all times' some 'quality attribute specification', and 'prescribes' some 'planned process' p that is 'preceded by', is an 'occurrent part of', or 'is temporally overlapped by' some 'product production process'

material quality plan
type: Class
subclass of: PlanSpecification
example: A material quality plan for a particular chromatography resin prescribes synthesis process controls, intermediate and final QC testing, and release criteria to ensure the resin meets defined binding capacity; A material quality plan for incoming acetaminophen API prescribes supplier approval, receipt inspection, quarantine, CoA review, identity and composite QC testing, release criteria, status labeling, and batch documentation to ensure suitability before tablet production.
first order logic axiom: MaterialQualityPlan(x) → PlanSpecification(x)
is primitive: true
natural language definition: plan specification that prescribes the processes needed to ensure that a raw material, process intermediate material, material product, or production consumable meets the required quality objectives
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a material quality plan
semi formal natural language axiom: if x is a 'material quality plan' then x is a 'plan specification'

material sample
type: Class
subclass of: BFO_0000040
equivalent class: Class
  intersection of: BFO_0000040
  intersection of: Restriction
    on property: isSpecifiedOutputOf
    some values from: material sampling process
example: a 10 mL portion of bioreactor culture collected during mid-log phase for cell-viability analysis; a portion of harvested supernatant contained in a labeled vial and taken from a chromatography pool for impurity testing; a powder portion taken from the middle of an API drum for composition-uniformity testing; a tablet selected from a production batch during compression for hardness testing
first order logic definition: MaterialSample(x) ↔ MaterialEntity(x) ∧ ∃s (MaterialSamplingProcess(s) ∧ isSpecifiedOutputOf(x, s))
natural language definition: material entity that is the specified output of a material sampling process
semi formal natural language definition: every instance of 'material sample' is exactly an instance of 'material entity' that 'is specified output of' some 'material sampling process'

material sampling plan
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: prescribes
  some values from: material sampling process
contributor: Merck KGaA
alternative label: sample plan
alternative label: sampling strategy
example: Material sampling plan that prescribes in-process sampling on Days 5, 7, and 9 for glycan profiling of the expressed monoclonal antibody; Material sampling plan that prescribes post-harvest sampling to assess product titer and host cell protein levels before downstream purification; Material sampling plan that prescribes manual collection of 20 tablets per batch for disintegration and hardness testing in the QC lab; Material sampling plan that prescribes taking an aliquot from every third eluate fraction during protein A chromatography for titer and impurity analysis (HCP and DNA)
adapted from: https://itl.nist.gov/div898/handbook/ppc/section3/ppc33.htm
explanatory note: 1) A material sampling plan in the context of production and quality management refers to a documented strategy that defines how, when, and how often samples are to be taken from materials, products, or processes for testing or inspection. 2) Key aspects of a material sampling plan include: Frequency: Specifies how often material samples should be taken (e.g., every batch, every tenth delivery, or at regular time intervals). Sample Size: Defines the number or quantity of items to be sampled at each interval.
first order logic axiom: MaterialSamplingPlan(x) → PlanSpecification(x) ∧ ∃y(MaterialSamplingProcess(y) ∧ prescribes(x, y))
is primitive: true
natural language definition: plan specification that prescribes how, when and how often material samples are to be taken
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a material sampling plan
semi formal natural language axiom: if x is a 'material sampling plan', then x is a 'plan specification' that 'prescribes' some 'material sampling process'

material sampling process
type: Class
subclass of: PlannedProcess
subclass of: Restriction
  on property: hasInput
  some values from: BFO_0000040
subclass of: Restriction
  on property: hasSpecifiedOutput
  some values from: BFO_0000040
subclass of: Restriction
  on property: prescribedBy
  some values from: material sampling plan
contributor: Merck KGaA
example: material sampling process in which a technician disinfects the sampling port of a 2000 L CHO cell culture bioreactor with 70% ethanol, withdraws 15 mL of culture using a sterile syringe, discards the first 5 mL, collects the remaining 10 mL into a labeled sterile vial, and seals it for downstream analysis; material sampling process in which an operator manually collects 5 tablets from the compression chute using a stainless-steel scoop, places them into a labeled clean glass container, records the batch number and time, and seals the container for later analysis
adapted from: https://www.sciencedirect.com/topics/engineering/sampling-process
counter example: A technician places a labeled sample vial in a cold box and transports it to the analytical lab (this is sample transport and not sampling itself); An operator fills final product into vials for distribution; A lab analyst measures glucose concentration in a sample using a Nova BioProfile® FLEX analyzer (this is an analysis conducted after the sampling and not the sampling itself)
explanatory note: 1) Sampling might involve conditioning (e.g., cooling, filtering, putting in a specific container), preparing, and documenting the sample. 2) The material sampling process is designed to ensure that tests subsequently conducted on the sample yield results that are reliable and representative of the overall material or product, supporting effective quality control and decision-making.
first order logic axiom: MaterialSamplingProcess(x) → PlannedProcess(x) ∧ ∃m∃s∃p (MaterialEntity(m) ∧ MaterialEntity(s) ∧ MaterialSamplingPlan(p) ∧ hasInput(x, m) ∧ hasSpecifiedOutput(x, s) ∧ prescribedBy(x, p))
is primitive: true
natural language definition: planned process of selecting and collecting a limited amount or number of some material entity in a manner which ensures it is representative of the input material entity
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions.
semi formal natural language axiom: if x is a 'material sampling process' then x is a 'planned process' that 'has input' some 'material entity' m, 'has specified output' some 'material entity' s, and is 'prescribed by' some 'material sampling plan' p

monitoring plan
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: prescribes
  some values from: monitoring process
example: A monitoring plan for the tablet compression process prescribes in-process testing of tablet samples every 30 minutes or every 50,000 tablets. It targets weight (540–560 mg), hardness (8–12 kp), thickness (4.8–5.2 mm), and disintegration time (<15 minutes). The responsible agents are the production operator and QC technician; An environmental monitoring plan for the production area prescribes continuous monitoring of temperature (18–25 °C) and humidity (45–65%) using data loggers. The responsible agent is the facilities technician; A bioreactor monitoring plan prescribes continuous monitoring of pH, dissolved oxygen, temperature, and agitation speed during a cell culture process.
explanatory note: A monitoring plan typically specifies the measurement frequency, target metrics, and the responsible agent.
first order logic axiom: MonitoringPlan(x) → PlanSpecification(x) ∧ ∃y(MonitoringProcess(y) ∧ prescribes(x, y))
is primitive: true
natural language definition: plan specification that prescribes some monitoring process
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a monitoring plan.
semi formal natural language axiom: if x is a 'monitoring plan', then x is a 'plan specification' that 'prescribes' some 'monitoring process'

monitoring process
type: Class
subclass of: PlannedProcess
equivalent class: Class
  intersection of: PlannedProcess
  intersection of: Restriction
    on property: BFO_0000117
    some values from: MeasurementProcess
  intersection of: Restriction
    on property: BFO_0000132
    some values from: PlannedProcess
  intersection of: Restriction
    on property: prescribedBy
    some values from: monitoring plan
alternative label: process monitoring
example: UV absorbance and conductivity monitoring during column loading; continuous monitoring of temperature and humidity in a clean room; tablet weight monitoring every 30 minutes during compression using an automated tablet-testing system; During a fed-batch cell culture process, at-line nutrient monitoring is performed using a Nova BioProfile® FLEX analyzer to measure glucose, lactate, glutamine, and ammonia levels; Flow rate is continuously monitored during tangential flow filtration to ensure consistent membrane performance
adapted from: https://www.biopharminternational.com/view/monitoring-biopharmaceutical-processes-present-and-future-approaches
explanatory note: 1) Each monitoring process is an execution of a monitoring plan that prescribes it 2) If a monitoring process monitors an attribute, parameter, or indicator across multiple process parts, it should be modeled as part of the larger planned process that has those process parts as occurrent parts. This does not imply that the monitoring process is an occurrent part of each monitored process part individually. For example, a decontamination process may include mixing, heating, and adsorption as process parts. If one temperature monitoring process monitors temperature across the decontamination process, then the monitoring process is an occurrent part of the decontamination process. It is not automatically an occurrent part of each of the mixing, heating, and adsorption processes. If temperature is monitored only during heating, then the monitoring process may be modeled as an occurrent part of the heating process.
first order logic definition: MonitoringProcess(x) ↔ PlannedProcess(x) ∧ ∃m(MeasurementProcess(m) ∧ hasOccurrentPart(x, m)) ∧ ∃p(PlannedProcess(p) ∧ occurrentPartOf(x, p)) ∧ ∃mp(MonitoringPlan(mp) ∧ prescribedBy(x, mp))
natural language definition: planned process that is a part of another planned process and that consists of one or more observation or measurement processes in which a particular attribute (specifically dependent continuant or temporal region or process characteristic or process profile) of some entity of interest is measured or observed
semi formal natural language definition: every instance of 'monitoring process' is defined as exactly an instance of 'planned process' that 'has occurrent part' some 'measurement process', is 'occurrent part of' some 'planned process', and is 'prescribed by' some 'monitoring plan'

offline monitoring process
type: Class
subclass of: monitoring process
subclass of: Restriction
  on property: BFO_0000117
  some values from: Class
    intersection of: material sampling process
    intersection of: Restriction
      on property: BFO_0000063
      some values from: Class
        intersection of: MaterialLocationChangeProcess
        intersection of: Restriction
example: Monitoring host-cell protein and residual DNA levels by sending harvested or purified samples to a quality control laboratory for ELISA and qPCR testing performed on dedicated analytical platforms
adapted from: https://www.hamiltoncompany.com/knowledge-base/article/bioprocess-monitoring-and-control
explanatory note: 1) The measurement is performed in a physical area that does not environ a planned process during which the material sampling process occurs. That planned process provides the spatial reference and is not thereby asserted to be the entity being monitored. 2) Off-line monitoring differs from at-line monitoring because at-line measurement is performed in a physical area that environs the planned process during which sampling occurs, whereas off-line measurement is performed outside such an area. 3) The complete off-line monitoring process may extend beyond the temporal boundaries of the planned process during which sampling occurs.
first order logic axiom: OfflineMonitoringProcess(x) → MonitoringProcess(x) ∧ ∃s∃t∃m∃a∃r∃p (MaterialSamplingProcess(s) ∧ MaterialLocationChangeProcess(t) ∧ MeasurementProcess(m) ∧ MaterialSample(a) ∧ PhysicalArea(r) ∧ PlannedProcess(p) ∧ hasOccurrentPart(x, s) ∧ hasOccurrentPart(x, t) ∧ hasOccurrentPart(x, m) ∧ occurrentPartOf(s, p) ∧ hasSpecifiedOutput(s, a) ∧ hasParticipantAtAllTimes(t, a) ∧ hasInput(m, a) ∧ precedes(s, t) ∧ precedes(t, m) ∧ occursIn(m, r) ∧ ¬environs(r, p))
is primitive: true
natural language definition: monitoring process consisting of one or more measurement processes that are preceded by a material sampling process, in which material representing the entity of interest is collected from a process stream or in-process material and analyzed in a separate analytical area
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions
semi formal natural language axiom: if x is an 'offline monitoring process' then x is a 'monitoring process' that has occurrent parts including a 'material sampling process' s, a 'material location change process' t, and a 'measurement process' m, and s is an 'occurrent part of' some 'planned process' p, and s 'has specified output' some 'material sample' a that t 'has participant at all times' and that is an 'input' of m, and s 'precedes' t, t 'precedes' m, and m 'occurs in' some 'physical area' r that does not 'environ' p

online monitoring process
type: Class
subclass of: monitoring process
subclass of: Restriction
  on property: BFO_0000117
  some values from: Class
    intersection of: material sampling process
    intersection of: Class
      union of: Restriction
        on property: BFO_0000063
        some values from: Class
      union of: Restriction
        on property: temporallyOverlaps
        some values from: Class
example: monitoring glucose, lactate, and ammonium by automatically diverting a bioreactor sidestream through a connected Raman flow cell and then returning or discarding the analyzed material; monitoring protein concentration during chromatography by diverting eluate through a connected UV flow cell
adapted from: https://www.hamiltoncompany.com/knowledge-base/article/bioprocess-monitoring-and-control
explanatory note: 1) Depending on the analytical arrangement, the sampled material may be returned, discarded, consumed, diluted, or mixed with reagents. 2) On-line monitoring differs from in-line monitoring because in-line monitoring does not involve a material sampling process before measurement. 3) On-line monitoring differs from at-line and off-line monitoring because material transfer and measurement occur through analytical equipment connected to equipment used in the associated planned process, rather than by bringing the sample to separately located analytical equipment. 4) Electronic transmission of measurement results does not by itself make a monitoring process on-line. The classification concerns the connected material-sampling and analytical arrangement.
first order logic axiom: OnlineMonitoringProcess(x) → MonitoringProcess(x) ∧ ∃s∃m∃a (MaterialSamplingProcess(s) ∧ MeasurementProcess(m) ∧ MaterialSample(a) ∧ hasOccurrentPart(x, s) ∧ hasOccurrentPart(x, m) ∧ hasSpecifiedOutput(s, a) ∧ hasInput(m, a) ∧ (precedes(s, m) ∨ temporallyOverlaps(s, m)))
is primitive: true
natural language definition: monitoring process consisting of one or more measurement processes that are preceded by or temporally overlapped by a material sampling process, in which material representing the entity of interest is collected from a process stream or in-process material and measured using analytical equipment connected to the process equipment
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions
semi formal natural language axiom: if x is an 'online monitoring process' then x is a 'monitoring process' that 'has occurrent part' some 'material sampling process' s and some 'measurement process' m, and s 'has specified output' some 'material sample' a, m 'has input' a, and s either 'precedes' or 'temporally overlaps' m

retention sample role
type: Class
subclass of: BFO_0000023
subclass of: Restriction
  on property: roleOf
  some values from: material sample
example: the retention sample role borne by a separately identified portion of an incoming raw-material lot retained for future confirmatory analysis; the retention sample role borne by a stored culture-medium lot sample retained for future identity, quality, or investigation testing; the retention sample role borne by a finished-drug-product sample retained from a released batch for possible examination or confirmatory testing during shelf life
adapted from: https://www.who.int/docs/default-source/medicines/norms-and-standards/guidelines/quality-control/trs929-annex4-guidelinessamplingpharmproducts.pdf
explanatory note: Commonly includes requirements on retained quantity (e.g., enough for confirmatory analyses) and on separate identification, packaging, sealing, and storage.
first order logic axiom: RetentionSampleRole(x) → Role(x) ∧ ∃s (MaterialSample(s) ∧ roleOf(x, s))
is primitive: true
natural language definition: role that is held by a material sample set aside for future testing, examination, identification, or investigation
primitive rationale: There are insufficient constructs to create necessary and sufficient conditions.
semi formal natural language axiom: if x is a 'retention sample role' then x is a 'role' that is the 'role of' some 'material sample'

SamplePlan
type: Class
subclass of: PlanSpecification
deprecated: true
replaced by: material sampling plan

SamplingProcess
type: Class
subclass of: PlannedProcess
deprecated: true
replaced by: material sampling process

has monitored attribute
type: ObjectProperty
sub property of: topObjectProperty
domain: monitoring process
range: Class
  union of: BFO_0000008
  union of: BFO_0000020
  union of: BFO_0000144
  union of: ProcessCharacteristic
example: a pH monitoring process has monitored attribute the pH of a culture medium; a cleanroom-temperature monitoring process has monitored attribute the temperature of the cleanroom air; a bioreactor dissolved-oxygen monitoring process has monitored attribute the dissolved-oxygen concentration of the cell-culture material
natural language definition: relation from a monitoring process to a specifically dependent continuant, temporal region, process profile, or process characteristic that is measured or observed during the monitoring process