View source for Plantlibra deliverable DWP5-6

[[heande:Database interface for risk-benefit assessment]]
[[Category:Plantlibra]]

{{deliverable
| number        = DWP5-6 (Task 5.9)
| due           = M46 (Mar 2014)
| description   = Database interface for risk-benefit assessment
| nature        = P (Prototype)
| dissemination = PP (Restricted to project partners and the Commission)
| responsible   = [[THL]]
}}

{{summary box
|question = What did Plantlibra project produce as a database interface for risk-benefit assessment of plant food supplements?
|answer =
The present deliverable (DWP5-6) includes the results of Task 5.9 which is aimed at developing
an internet based interface where users can access, combine, by plant and compound, and
discuss information on risk and benefit assessment.
A detailed description of the structure and functioning of the interface is provided in the present
document. The Opasnet wiki-based platform (www.en.opasnet.org) was used to develop the
internet based interface, named “Compound Intake Estimator”. It is a tool that allows the use to
calculate the intakes of compounds, based on food or food supplement intake. In addition, the
tool allows estimating the health risk of a compound at a defined level of intake. The risk
estimated is obtained by combining the information on compound concentration in the plant
food supplement with the specific health guidance value available for the compound. As health
guidance values, the Acceptable Daily Intake or Tolerable Daily Intake are used when available.
Otherwise, the Toxicological Threshold of Concern approach is applied. In case of compounds
having a structural alert for genotoxicity, the Margin of Exposure concept is instead used.
The interface is structured in such a way that the user can select the compound(s) of interest,
the extraction technique, and specify the level of intake. In addition, the user can also add his
own composition data. This feature is useful in case a user wants to compare the composition of
new botanical preparation with data available from literature and evaluate if any of the
compounds present might pose, or not, a health concern at a specific level of intake.
}}

= Background and objective of the work =

The use of plants and plant derivatives to maintain health has been popular throughout Europe for many centuries. The consumption of teas, digestive drinks, juices, elixirs and extracts prepared from botanicals and used for health maintenance purposes has become part of European cultural heritage. Plant food supplements are a modern-day extension of this process.

PlantLIBRA project (PLANT food supplements: Levels of Intake, Benefit and Risk Assessment) aims to foster the safe use of food supplements containing plants or botanical preparations, by increasing science-based decision-making by regulators and food chain operators. To make informed decisions, competent authorities and food businesses need more quality-assured and accessible information and better tools (e.g. metadata banks).
<ref>[http://www.plantlibra.eu/web/ PlantLIBRA website]</ref>

PlantLIBRA is structured to develop, validate and disseminate data and methodologies for risk and benefit assessment and implement sustainable international cooperation. International cooperation, on-spot and in-language capacity building are necessary to ensure the quality of the plants imported in the EU.

The ''Description of Work for PlantLIBRA project'' describes the objective of this deliverable in the following way:

'''Task 5.9: Integration of databases and guidance

Taking into account the database of WP6, THL will develop an Internet-based interface (DWP5-6)
where users can access, combine, by plant and compound, and discuss information for risk-benefit
assessment. In this context, THL will make selected case-studies available to the public
through the Mediawiki platform, when possible. THL will develop guidance for conducting risk-benefit
assessments of plants, extracts, preparations and individual products which will be made
available online as Deliverable 5-6 (DWP5-6).

= Compound intake estimator =

{{{{ns:0}}:Compound intake estimator}}

[[File:Compound intake estimator interface.png|400px]]
[[File:Compound concentration estimator interface.png|400px]]

= Compound concentration data for PlantLIBRA cases =

PlantLIBRA WP5 (HYLO) collected data about compound concentrations in three case study plants. The methods for the data collection are described elsewhere.
* Cinnamon (dried bark)
* Bitter fennel (fruit)
* Gingko (leaf)

The data were uploaded to Opasnet database. The database is a versatile data storage for all kinds of data tables related to benefit-risk assessment, health, food, and other topics. It has two parts: an open part that can be accessed using a web browser or through a machine-readable JSON interface; and a password-protected part that is technically similar but is not visible to the Internet.

Data on composition were collected running a systematic search on Pubmed, using information available from previous assessments of authoritative bodies (e.g. EFSA, EMA, etc.) and the ePlantLIBRA database, which was developed in this project. ePlantLIBRA is based on eBASIS from the EuroFIR Network.
<ref>[http://eplantlibra.eurofir.org/ ePlantLIBRA website], Eurofir, accessed 15th April, 2014.</ref>
eBASIS was produced by the EuroFIR network and is now being further developed in a co-operation between the PlantLIBRA Project and EuroFIR AISBL. EuroFIR is funded by the European Commission under the ''Food Quality and Safety Priority'' of the FP7 for Research and Technological Development (CT).

eBASIS is a unique database that contains critically evaluated published data on the content and biological effects of bioactive constituents in plant based foods and an up-to-date list of plant and plant part names in 15 EU languages. Bioactive Compounds are defined as inherent non-nutrient constituents of food plants and edible mushrooms with anticipated health promoting, beneficial and/or toxic effects when ingested.
<ref>[http://ebasis.eurofir.org/Default.asp eBASIS] (Bioactive Substances in Food Information System), Eurofir, accessed 15th April, 2014.</ref>

In an early stage there was a plan to store the compound data only in the ePlantLIBRA database and access that dynamically through a machine-readable interface from Opasnet. However, this plan was abandoned for several reasons. The main reason was that ePlantLIBRA database is actually only a part of the larger eBASIS database, which contains also proprietary, non-public information. It would have been a large work to build an additional security functionality to allow access to some but not all parts of the database. In addition, the tool we are describing here needs a moderate amount of data, which can easily be copied and uploaded to another system (Opasnet database) for this purpose. And thirdly, when new compound concentration data becomes available (either from ePlantLIBRA or elsewhere), it is straightforward to upload also the new data to Opasnet database as a part of Compound intake estimator. Thus, the use of Compound intake estimator is not dependent on ePlantLIBRA database but can benefit from its future development.

= Case study results =

[[File:Concentrations of compounds in cinnamon essential oil.png|400px]] 
[[File:Exposure to compounds in cinnamon essential oil.png|400px]] 

The following figures demonstrate some output graphs from the model. The graphs give a user-friendly way to get an overview of the compounds in a product. It is especially interesting to compare concentrations and levels of concern of different compounds. As can be seen from the pair of graphs of cinnamon dried bark essential oil, most of the product is cinnamaldehyde, which is only marginally above a guidance value and thus not likely a concern. In contrast, safrole seems to be somewhat of concern or at least a compound to pay attention to, as it goes beyond a guidance value by a factor of 10 - 80.

[[File:Concentrations of compounds in bitter fennel product.png|400px]] 
[[File:Exposure to compounds in bitter fennel product.png|400px]] 

Bitter fennel is a different story. The clearly largest concern is estragole, which goes beyond the BMDL10 by a factor of 50 - 600. Interestingly, this compound is present in low concentrations when trans-anethole is present at high concentrations and vice versa. Thus, in the first case estragole might be causing a clear risk and fennel products would therefore need a a benefit-risk assessment. There is also another interesting compound, namely apiole, which goes above the level of concern by a factor of 10 - 200, even when its concentration is so low (0.1 %) that it is not even shown on the graph. With this compound, the first question in mind is whether there is actually evidence supporting its much higher putative potency compared with the other compounds.

= Discussion =

Concentration and intake data are not easily understood by consumers. There are dozens or hundreds of compounds in a single PFS product. It is therefore very difficult to get any idea which compounds are important and whether a consumer should be interested in further information about these compounds. There is a need for tools visualising these data to promote informed discussion and consumer decisions. The model presented here offers a solution to this need.

This model will make it easy to compare two critical things: exposure to compounds relative to health-based guidance values, and concentrations in the product. By comparing these two graphs, it was possible to identify from each example product two critical compounds that would warrant further scrutiny. The reasons for need of further work were different with each compound, as described in the previous section.

The compound intake estimator model can be used online simply with a web browser. It can easily be copied to other projects or uses in Opasnet. It can also be used on one's own computer, but that requires some skills of R software. It is a small task to add new data sets to the model when they become available. It is open source and thus it can be taken and developed further. It is a part of a large web workspace Opasnet that has several independent users and continuous, long-term maintenance. This flexibility and durability is important when considering the lifespan of the model. Even after the end of the PlantLIBRA project, the model will be maintained and kept available for users.

=== EFSA data collection and PFS ===

The critical thing with the compound intake estimator model is the availability of relevant compound concentration data about PFSs. EFSA has recently studied availability of such data. The EFSA contract CFT/EFSA/DCM/2011/03 provided EFSA with an updated food composition database covering approximately 1750 foods in combination with additional FoodEx2 facet descriptors included in the EFSA FoodEx2 classification system. A model for data transfer, compatible with the EuroFIR technical annex and CEN Food Data Standard, and the EFSA data structure was developed and tested. Fourteen national food database compiler organisations supplied initial food lists mapped to the EFSA food list. Eight of them contained data about food supplements and their composition. Datasets compatible with EFSA's data structure were produced based on the models.
<ref name="roe">M.A. Roe, S. Bell, M. Oseredczuk, T. Christensen, S. Westenbrink, H. Pakkala, K. Presser and P.M. Finglas, 2013. Updated food composition database for nutrient intake. EFSA supporting publication 2013:EN-355, 21 pp. Available online: [http://www.efsa.europa.eu/publications] [http://www.efsa.europa.eu/en/supporting/pub/355e.htm] [http://www.efsa.europa.eu/en/supporting/doc/355e.pdf]</ref>

Therefore, there exists a large amount of potentially useful PFS compound concentration data in EFSA. However, EFSA has not made this data available for open use, and this will not probably happen before the end of PlantLIBRA project. In any case, it is important to acknowledge and remember this possibility and potential for improvement of the model described here.

{| {{prettytable}}
|+ '''Supplement data provided by compilers.<ref name="roe"/>
|----
!Country
!Number of supplements listed
|----
|Finland
|92
|----
|France
|449
|----
|Italy
|126
|----
|Netherlands
|90<sup>2</sup>
|----
|Serbia
|8
|----
|Slovenia
|95
|----
|Sweden
|122
|----
|UK
|103
|}

<sup>2</sup>Does not include supplements for children.

= See also =

* {{#l:Compound intake estimator.ppt}} Presentation about the tool
* [[:heande:Composition of cinnamon dried bark essential oil]]
* [[:heande:Composition of bitter fennel essential oil]]
* [[:heande:Composition of plant-based food supplements]]

= References =

<references/>

= Appendix 1: Guidance values and other inputs =

:'' Move the tables about guidance value to this location.

= Appendix 2: Computer code for the tool =

=== Compound intakes and risks ===

{{hidden|
<pre>
<rcode label='Intakes and risks' embed=1 graphics=1
variables="
	name:name|description:Name of plant food supplement|type:text|default:Cinnamon essential oil|
category:Intake information|
	name:food.amount|description:Amount of supplement per day (g /d)|type:text|default:1|
	name:fr.const|description:Fraction of essential oil in the supplement (%)|type:text|default:10|
	name:oil.amount|description:Amount of essential oil consumed directly per day (g /d)|type:text|default:0|
	name:intermediates|description:Show intermediate results?|
		type:selection|options:FALSE;No;TRUE;Yes|default:FALSE|
	name:extract|description:Do you want to limit to some extraction techniques?|type:selection|
		options:FALSE;No, analyze all techniques;TRUE;Yes, let me choose|default:FALSE|
	name:add|description:Do you want to add own compound data?|
		type:selection|options:FALSE;No;TRUE;Yes|default:FALSE|
	name:addcompound|description:Name(s) of additional compound(s)|default:' '|
category:Additional compound information|
category_conditions:add;TRUE|
	name:addconc|description:Concentration(s) of the compound(s) (mg of compound /g product)|default:0|
	name:extracted|description:Which extraction techniques to consider?|type:checkbox|options:
		1;Not known;
		2;Hydrodistillation;
		3;Solvent extraction;
		4;Steam distillation;
		5;Supercritical fluid extraction, 120 bar, 40 °C;
		6;Supercritical fluid extraction, 90 bar, 40 °C;
		7;Supercritical fluid extraction, 90 bar, 50 °C;
		8;Superheated water extraction, 100 °C;
		9;Superheated water extraction, 150 °C;
		10;Superheated water extraction, 200 °C;
		11;Solid phase|
		default:1;2;3;4;5;6;7;8;9;10;11|
	category:Extraction technique|
	category_conditions:extract;TRUE
">

library(OpasnetUtils)
library(ggplot2)

################## SETUP MODEL

openv.setN(100) # Sets the number of model iterations to 100.

# Fetches the ovariables needed in the model from [[Compound intake estimator]].
# Ovariables downloaded: guidance.values, compound.intake, food.risk, extraction, concplot.

objects.latest("Op_en6193", code_name="initiate") 

# compound.conc is the concentration of compounds in the critical constituent.
# Data comes from [[Compound intake estimator]].

compound.conc <- Ovariable(name = "compound.conc", ddata = "Op_en6193.example_data")

################## USER INPUTS

# name is the name of product. It is used in the output graphs.

# food.amount is the amount that the consumer consumes the defined product (g /day)

food.amount <- Ovariable("food.amount", data = data.frame(Result = food.amount))

# fr.const is the fraction of a critical constituent in the food or food supplement (given as percentage)

fr.const <- Ovariable("fr.const", data = data.frame(Result = fr.const))

# oil.amount is the amount that the consumer consumes essential oil in addition to the product (g /day)

oil.amount <- Ovariable("oil.amount", data = data.frame(Result = oil.amount))

# extraction is a list of extraction techniques to consider. (by default, all extraction techniques)

if(exists('extracted')) {
	extraction@data <- extraction@data[extracted , ] # Order of extraction techniques must not change!
} else {
	extraction@data <- data.frame(extractionResult = 1) # Remove column Extraction.techniques
}

# addcomp are user-defined additional compounds for a supplement, if they are not in the table.
## addcompound is the name and addconc is the concentration of the additional compound.
# addcomp and addcompound can be vectors.

addcompound <- ifelse(! exists('addcompound') || addcompound == ' ', NA, addcompound)
addconc <- ifelse(! exists('addconc') || addconc == ' ', 0, addconc)

addcomp <- Ovariable("addcomp", data = data.frame(Compound = addcompound, Result = addconc))


############## ACTUAL MODEL

food.risk <- EvalOutput(food.risk)

################ PRINTING MODEL OUTPUTS

if(intermediates) {

	cat("Concentrations of compounds in ", name, " (mg /kg DW)\n")

	oprint(summary(compound.conc, marginals = c("Compound")))

	cat("Intakes of different compounds (mg /kg BW /d)\n")

	oprint(summary(compound.intake, marginals = c("Compound")), digits = 3)

}

cat("Risks relative to guidance values (ADI, TDI, BMDL10, or TTC). Values < 1 are of minimal concern.\n")

oprint(summary(food.risk, marginals = c("Compound")))

test <- unique(food.risk@output[food.risk@output$food.riskResult >= 1 , "Compound"])

temp <- food.risk@output[food.risk@output$Compound %in% test , ]

if(nrow(temp) == 0) {
	cat("There are no compounds that exceed a guidance value.\n")
	stop("There are no compounds that exceed a guidance value.\n")
}

cat("Risks relative to guidance values (ADI, TDI, BMDL10, or TTC). Values < 1 are of minimal concern and are not shown in the 
graph. Note! BMDL10 is used to calculate margin of exposure (MOE), but instead of MOE, (10000 / MOE) is shown on this graph 
for consistency.\n")

ggplot(temp, aes(x = Compound, y = food.riskResult, fill = Guidance)) + 
	geom_boxplot() + 
	theme_grey(base_size = 24) +
	labs(title = paste("Exposure to compounds in", name), y = "Ratio (Exposure /Guidance)") +
	theme(axis.text.x = element_text(angle = 90, hjust = 1))

</rcode>
</pre>
}}

===Compound concentrations===

{{hidden|
<pre>
<rcode label='Plot concentrations' graphics=1 embed=1
variables="
	name:limit|description:Plot values above this limit (%)|default:10|
	name:allcompounds|description:Do you want to plot all compounds?|type:selection|options:TRUE;Yes;FALSE;No, choose from the list below|default:TRUE|
	name:allextractions|description:Do you want to plot all extraction techniques?|type:selection|options:TRUE;Yes;FALSE;No, choose from the list below|default:TRUE|
	name:compound|description:If not, what compounds do you want to plot?|type:checkbox|options:
		'1,8-Cineole';1,8-Cineole;
		'2-Carene';2-Carene;
		'2-Methoxycinnamaldehyde';2-Methoxycinnamaldehyde;
		'2-Methoxycinnamaldehye';2-Methoxycinnamaldehye;
		'2-Methoxycinnamaldehye cinnamalaldehyde';2-Methoxycinnamaldehye cinnamalaldehyde;
		'3-Phenylpropyl acetate';3-Phenylpropyl acetate;
		'4-Terpineol';4-Terpineol;
		'a-Copaene';a-Copaene;
		'a-Muurolene';a-Muurolene;
		'a-Terpineol';a-Terpineol;
		'Aromadendrene';Aromadendrene;
		'Aromatic compound';Aromatic compound;
		'Benzaldehyde';Benzaldehyde;
		'Benzaldeyhde';Benzaldeyhde;
		'Benzene propanol';Benzene propanol;
		'Benzenepropanal';Benzenepropanal;
		'Benzenepropanol acetate';Benzenepropanol acetate;
		'Benzylbenzoate';Benzylbenzoate;
		'Borneol';Borneol;
		'Cadinene';Cadinene;
		'Calamenene';Calamenene;
		'Camphene';Camphene;
		'Camphor';Camphor;
		'Carvacrol';Carvacrol;
		'Carveol isomere';Carveol isomere;
		'Caryophyllene';Caryophyllene;
		'Caryophyllene alcohol';Caryophyllene alcohol;
		'Caryophyllene oxide';Caryophyllene oxide;
		'Caryophyllene oxyde';Caryophyllene oxyde;
		'Chavicol';Chavicol;
		'Cinamyl acetate';Cinamyl acetate;
		'Cinnamaldehyde';Cinnamaldehyde;
		'Cinnamic acid';Cinnamic acid;
		'Cinnamyl acetate';Cinnamyl acetate;
		'Cinnamyl alchol';Cinnamyl alchol;
		'Cinnamyl alcohol';Cinnamyl alcohol;
		'Cinnamyl alcool';Cinnamyl alcool;
		'Cis-P-menth-2-en-1-ol';Cis-P-menth-2-en-1-ol;
		'Cis-ß-Ocimene';Cis-ß-Ocimene;
		'Coumarin';Coumarin;
		'Cryptone';Cryptone;
		'Cymene isomere';Cymene isomere;
		'E-Anethole';E-Anethole;
		'e-Cadinene + Pioncarvone';e-Cadinene + Pioncarvone;
		'E-Cinnamyl acetate';E-Cinnamyl acetate;
		'E-o-Methoxycinnamaldehye';E-o-Methoxycinnamaldehye;
		'Epoxy-6.7-Humulene + Mw=220';Epoxy-6.7-Humulene + Mw=220;
		'Eugenol';Eugenol;
		'Eugenol acetate';Eugenol acetate;
		'Humulene epoxide II';Humulene epoxide II;
		'Isoledene';Isoledene;
		'Limonene';Limonene;
		'Linalool';Linalool;
		'Linaool';Linaool;
		'Methoxy eugenol';Methoxy eugenol;
		'Methyl cinnamaldehyde';Methyl cinnamaldehyde;
		'Methyl eugenol';Methyl eugenol;
		'Monoterpenes';Monoterpenes;
		'N-Hexadecanoic acid';N-Hexadecanoic acid;
		'N-Octadecanoic acid';N-Octadecanoic acid;
		'P-Cymene';P-Cymene;
		'P-Cymene-8-ol';P-Cymene-8-ol;
		'Para-Methoxy cinnamic aldehyde';Para-Methoxy cinnamic aldehyde;
		'Phenyl cyclohexene';Phenyl cyclohexene;
		'Phenylmethyle isovalerate';Phenylmethyle isovalerate;
		'Sabiene';Sabiene;
		'Sabinene';Sabinene;
		'Safrole';Safrole;
		'Sesquiterpene epoxide';Sesquiterpene epoxide;
		'Sesquiterpenol Mw=220';Sesquiterpenol Mw=220;
		'Sesquiterpenol Mw=222';Sesquiterpenol Mw=222;
		'Spathulenol';Spathulenol;
		'ß-Bisabolene';ß-Bisabolene;
		'ß-Cubebene';ß-Cubebene;
		'ß-Elemene';ß-Elemene;
		'ß-Myrcene';ß-Myrcene;
		'ß-Phellandrene';ß-Phellandrene;
		'ß-Phellandrene + 1,8-Cineole';ß-Phellandrene + 1,8-Cineole;
		'ß-Pinene';ß-Pinene;
		'Styrene';Styrene;
		'Terpinen-4-ol';Terpinen-4-ol;
		'Terpiolene';Terpiolene;
		'Terpiolene isomere';Terpiolene isomere;
		'Tetradecanal';Tetradecanal;
		'Trans-2-methoxycinnamaldehyde';Trans-2-methoxycinnamaldehyde;
		'Trans-P-menth-2-en-1-ol';Trans-P-menth-2-en-1-ol;
		'Trans-Pinocarveol';Trans-Pinocarveol;
		'Trans-Piperitol';Trans-Piperitol;
		'Trans-ß-Ocimene';Trans-ß-Ocimene;
		'Z-Cinnamaldehyde';Z-Cinnamaldehyde;
		'α-Cadinol';α-Cadinol;
		'α-Calacorene';α-Calacorene;
		'α-Copaene';α-Copaene;
		'α-Cubebene';α-Cubebene;
		'α-Famesene';α-Famesene;
		'α-Gurjunene';α-Gurjunene;
		'α-Humulene';α-Humulene;
		'α-Muurolene';α-Muurolene; 
		'α-Muurolol';α-Muurolol;
		'α-P-Dimethylstyrene';α-P-Dimethylstyrene;
		'α-Phellandrene';α-Phellandrene;
		'α-Pinene';α-Pinene;
		'α-Selinene';α-Selinene;
		'α-Terpinene';α-Terpinene;
		'α-Terpineol';α-Terpineol;
		'α-Thujene';α-Thujene;
		'γ-Cadinene';γ-Cadinene;
		'γ-Terpinene';γ-Terpinene;
		'δ-Cadinene';δ-Cadinene;
		'Δ3-Carene';Δ3-Carene|
	category:Chemical compounds|
	category_conditions:allcompounds;FALSE|
	name:extraction|type:checkbox|options:
		'Hydrodistillation';Hydrodistillation;
		'Not known';Not known;
		'Solvent extraction';Solvent extraction;
		'Steam distillation';Steam distillation;
		'Supercritical fluid extraction, 120 bar, 40 °C';Supercritical fluid extraction, 120 bar, 40 °C;
		'Supercritical fluid extraction, 90 bar, 40 °C';Supercritical fluid extraction, 90 bar, 40 °C;
		'Supercritical fluid extraction, 90 bar, 50 °C';Supercritical fluid extraction, 90 bar, 50 °C;
		'Superheated water extraction, 100 °C';Superheated water extraction, 100 °C;
		'Superheated water extraction, 150 °C';Superheated water extraction, 150 °C;
		'Superheated water extraction, 200 °C';Superheated water extraction, 200 °C|
	category:Extraction technique|
	category_conditions:allextractions;FALSE
">
### Load some R packages needed to implement the code.

library(OpasnetUtils)
library(ggplot2)

################ SETUP THE MODEL

openv.setN(100) # Set model iterations

# Fetches the ovariables needed in the model from [[Compound intake estimator]].
# Ovariables downloaded: guidance.values, compound.intake, food.risk, extraction, concplot

objects.latest("Op_en6193", code_name="initiate") 

### Download the data from the database. You can also see the raw data on this page below,
### or access the database via this link: http://heande.opasnet.org/wiki/Special:Opasnet_Base?id=Heande3857

# compound.conc is the concentration of compounds in the critical constituent.
# Data comes from [[Compound intake estimator]].

compound.conc <- Ovariable(name = "compound.conc", ddata = "Op_en6193.example_data")

################ USER INPUTS

### Select only those rows that fulfill the criteria of compound and extraction method set by the user (if any).

dat <- compound.conc@data
if(!allcompounds) {dat <- dat[dat$Compound %in% compound , ]}
if(!allextractions) {dat <- dat[dat$Extraction.technique %in% extraction , ]}
compound.conc@data <- dat

### Create an information object (called ovariable) out of the data for easy handling.

compound.conc <- EvalOutput(compound.conc)

### Print a summary table of the output.

oprint(summary(compound.conc), digits = 3)

### Use the graph formatting for several graphs. The graphs only differ by the range of concentration that is shown on Y axis.

concplot(compound.conc@output$compound.concResult >= limit)
concplot(compound.conc@output$compound.concResult <= 10 & compound.conc@output$compound.concResult > 1)
concplot(compound.conc@output$compound.concResult <= 1 )

</rcode>
</pre>
}}

===Model initiation===

:''This is the code for the actual compound intake estimator model. This part of the model is stored online in Opasnet, and it is automatically fetched when the other parts of the model are run.

{{hidden|
<pre>
<rcode name="initiate" label="Initiate model" embed=1>

library(OpasnetUtils)

################## PRE-DEFINED INPUTS

# compound.conc is the concentration of a compound in the critical constituent (or food, if fr.const == 1) 
# fr.const is the fraction of the critical constituent in the whole food
# extraction is a criteria to select specific extraction techniques
# addcomp is an additional compound not in the product. Data about its concentration in food is given by the user.

# compound.intake is the intakes of the compounds via food or food supplement, each given as mg /kg /d.

compound.intake <- Ovariable("compound.intake", 
	dependencies = data.frame(Names = c(
		"food.amount", # amount of food supplement consumed
		"compound.conc", # concentration of compounds in essential oil
		"oil.amount", # amount of essential oil consumed in addition to the supplement product
		"fr.const", # fraction of essential oil in the supplement product
		"extraction", # Which extraction methods the user wants to consider
		"addcomp" # concentrations of compounds in the essential oil (missing from default data)
		# conc is the temporary ovariable for compound.conc used in calculations
	)), 
	formula = function(...) {

		conc <- compound.conc * 10 * extraction 
		# remove extractions that are not considered.
		# compound.conc from w/w% to mg/g

#		oapply would be an alternative to remove extra indices, but there is a bizarre error.
#		compound.conc <- oapply(compound.conc, cols = "Extraction.technique", fun = mean)

		test <- colnames(conc@output) != "Extraction.technique"
		conc@marginal	<- conc@marginal[test]
		conc@output 	<- conc@output[test] # unit: mg /g
		# removes index Extraction.technique, which is no longer needed.

		conc@output <- orbind(conc, addcomp * 1)
		# addcomp must not contain columns that are not in conc. It must contain Compound.
		# It is multiplied by 1 to make the result column name "Result".

		out <- (food.amount * fr.const / 100 + oil.amount) * conc / 60 
		# calculates the oil intake from food supplement and then adds direct oil intake.
		# scale to 60 kg person
		# fr.const from % to fraction

		test2 <- unique(out@output$Compound[result(out) > 0]) # remove all compounds where intake = 0
		out@output <- out@output[out@output$Compound %in% test2 , ]

		return(out)
	}
)

# guidance.values is the guidance value (ADI, BMDL10, TTC or similar) for compounds. unit: mg /kg /d
# cramer is cramer classes of compounds (temporary)
# cramer.values are the thresholds of toxicological concern for each Cramer class (temporary)

cramer <- tidy(opbase.data("Op_en6193", subset = "cramer_classes_of_compounds"))
cramer$Result <- NULL
cramer$Guidance <- "TTC" # Threshold for toxicological concern

cramer.values <- tidy(opbase.data("Op_en6193", subset = "cramer_values"))
cramer.values <- merge(cramer, cramer.values)
cramer.values[["Cramer class"]] <- NULL
cramer.values$Result <- cramer.values$Result / 1000 # from ug /kg /d to mg /kg /d

guidance.values <- tidy(opbase.data("Op_en6193", subset = "guidance_values"))
guidance.values <- orbind(cramer.values, guidance.values)
guidance.values <- Ovariable("guidance.values", data = guidance.values)

safety.factor <- Ovariable("safety.factor", ddata = "Op_en6193.safety_factors") #  [[Compound intake estimator]]

# food.risk calculates "hazard quotients" or risks relative to guidance values. Value < 1 are of minimal concern

food.risk <- Ovariable(name = "food.risk",
	dependencies = data.frame(Name = c("compound.intake", "guidance.values", "safety.factor")),
	formula = function(...) {

		# guidance.risk is intake compared with guidance values.

		out <- compound.intake / (guidance.values / safety.factor)

		removepoorguidance <- function(dat, goodg) { # Remove inferior guidance values

			# Compounds that have good guidance values (goodg).
			hasgoodg <- unique(dat[dat$Guidance == goodg , "Compound"])

			# The row contains data about a chemical with a good guidance value
			# but the data is about an inferior guidance value.

			out <- dat[!(dat$Compound %in% hasgoodg & dat$Guidance != goodg) , ]

			return(out)
		}

		out <- removepoorguidance(out@output, "ADI")
		out <- removepoorguidance(out, "TDI")
		out <- removepoorguidance(out, "BMDL10")

		test2 <- unique(out$Compound[out$Result > 0]) # remove all compounds where risk = 0
		out <- out[out$Compound %in% test2 , ]

		return(out)
	}
)

extraction <- Ovariable("extraction", ddata = "Op_en6193.extraction_techniques")
extraction@data$extractionResult <- 1

### Define formatting for the concentration graphs that will be shown.

concplot <- function(condition) {
	dat <- compound.conc@output[condition, ]
	if(nrow(dat) != 0) {
		out <- ggplot(dat, 
			aes(
				x = Compound, 
				y = compound.concResult, 
				colour = Extraction.technique
			)) + 
			geom_point(size=5) +
			theme_grey(base_size = 24) +
			theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
			labs(y = "Concentration (%)")
		return(out)
	} else {
		 warning("No data to show")
	}
}

objects.store(compound.intake, guidance.values, food.risk, extraction, concplot, safety.factor)

cat("The following objects were successfully stored:
compound.intake, guidance.values, food.risk, extraction, concplot, safety.factor\n")

</rcode>
</pre>
}}