Compound intake estimator

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Compound intake estimator calculates intakes of compounds based on food or food supplement intake, compound concentration in food, and guidance values for the compound.

Question

How to estimate intakes and compare them to guidance values?

Answer

The concern indicator is calculated for each compound separately. If the intake is smaller than the threshold of concern, the indicator is < 1 and there is no concern. The threshold of concern is acceptable daily indake (ADI), benchmark dose (BMDL10), or threshold of toxicological concern (TTC), depending on what data is available for each compound.

Intake information Fraction of essential oil in the supplement (%): Amount of supplement per day (g /d): Show intermediate results?: No Yes Do you want to add own compound data?: No Yes Additional compound information Name(s) of additional compound(s): Concentration(s) of the compound(s) (mg of compound /g product): + Show code- Hide code library(OpasnetUtils) library(ggplot2) objects.latest("Op_en6007", code_name = "answer") # Fetch official draft objects from [[OpasnetUtils/Drafts]] (in this case, ograph) ################## USER INPUTS N <- 10 name <- "Cinnamon essential oil" # 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)) # extraction is a list of extraction techniques to consider. (by default, all extraction techniques) extraction <- Ovariable("extraction", data = data.frame(Result = 1)) # addcomp is 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. addcompound <- ifelse(addcompound == ' ' | !add, NA, addcompound) addcomp <- Ovariable("addcomp", data = data.frame(Compound = addcompound, Result = addconc)) # compound.conc is the concentration of compounds in the critical constituent compound.conc <- Ovariable(name = "compound.conc", ddata = "Op_en6193.example_data") ############## ACTUAL OUTPUT MODEL objects.latest("Op_en6193", code_name="initiate") # Fetches the ovariables needed in the model. # Ovariables downloaded: guidance.values, compound.intake, food.risk compound.intake <- EvalOutput(compound.intake, N = N) #compound.intake@output <- compound.intake@output[!is.na(compound.intake@output$compound.intakeResult) , ] # For an unknown reason a few NA results show up. food.risk <- EvalOutput(food.risk, N = N) if(intermediates) { cat("Intakes of different compounds (mg /kg BW /d)\n") oprint(summary(compound.intake)) ograph(compound.intake, x = "Compound") } cat("Risks relative to guidance values (ADI, TDI, BMDL10, or TTC). Values < 1 are of minimal concern and are not shown in the graph.\n") oprint(summary(food.risk)) test <- unique(food.risk@output[food.risk@output$food.riskResult >= 1 , "Compound"]) temp <- food.risk@output[food.risk@output$Compound %in% test , ] 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))

Example data(%)

Obs	Extraction.technique	Compound	Result	Reference
1	Superheated water extraction, 100 °C	2-Carene	0.08; 0.1; 0.12	Jayawardena & Smith, 2010
2	Superheated water extraction, 100 °C	Z-Cinnamaldehyde	1.63; 2.1; 2.57	Jayawardena & Smith, 2010
3	Superheated water extraction, 100 °C	Cinnamaldehyde	81.14; 83.7; 86.26	Jayawardena & Smith, 2010
4	Superheated water extraction, 100 °C	Eugenol	0.45; 0.8; 1.15	Jayawardena & Smith, 2010
5	Hydrodistillation	1,8-Cineole	0.2	Chericoni et al, 2005
6	Superheated water extraction, 100 °C	Cinnamyl acetate	5.47; 7.2; 8.93	Jayawardena & Smith, 2010

Rationale

The idea of the tool is to look at consumption of a particular PFS product, and estimate whether any of the compounds in the product cause concern. The estimation follows this equation:

compound intakes = product intake (g /day) * concentration of each compound in the product (mg /g) / 60
    (60 kg is the assumed body weight of the person)

concern indicator = compound intakes (mg /kg /d) / threshold of concern (mg /kg /d)

Calculations

You need to run the code below only if you update the data tables on this page.

+ Show code- Hide code library(OpasnetUtils) ################## PRE-DEFINED INPUTS # food.conc is a temporary object about the concentration of a compound in food. # 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", "compound.conc", "fr.const", "extraction", "addcomp")), formula = function(...) { food.conc <- compound.conc * 10 * fr.const / 100 # compound.conc from w/w% to mg/g; fr.const from % to fraction food.conc <- food.conc * extraction # remove extractions that are not considered. # addcomp must not contain columns that are not in food.conc. It is multiplied by 1 to make # the result column name "Result". food.conc@output <- orbind(food.conc, addcomp * 1) test <- colnames(food.conc@output) != "Extraction.technique" food.conc@marginal <- food.conc@marginal[test] food.conc@output <- food.conc@output[test] # unit: mg /g out <- food.amount / 60 * food.conc # scale to 60 kg person 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 # cramer.values are the thresholds of toxicological concern for each Cramer class 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) # 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")), formula = function(...) { # guidance.risk is intake compared with guidance values. out <- compound.intake / guidance.values removepoorguidance <- function(dat, goodg) { # Remove inferior guidance values # Compounds that have good guidance values (goodg). hasgoodg <- 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") return(out) } ) objects.store(compound.intake, guidance.values, food.risk) cat("The following objects were successfully stored: compound.intake, guidance.values, food.risk\n")

Using different data on different layers: [1].

Data about guidance values

Guidance values(mg /kg-BW /d)

Obs	Compound	Guidance	Result
1	Cinnamaldehyde	ADI	0.7
2	Safrole	BMDL10	1.9-5.1
3	α-Terpineol	ADI	1.2
4	4-Terpineol	ADI	1.2
5	Benzylbenzoate	ADI	5
6	Cinnamaldehyde	ADI	0.7
7	Eugenol	ADI	2.5
8	Linalool	ADI	0.5
9	Coumarin	TDI	0.1

Cramer values(µg /kg-BW /d)

Obs	Cramer class	Threshold of toxicological concern
1	1	30
2	2	1.5
3	3	1.5

Values 30 and 1.5 µg /kg /d come from EFSA (2012)

[show]Cramer classes of compounds

Cramer classes of compounds(-) Obs Compound Cramer class Notes 1 1,8-Cineole 3 2 2-Carene 1 3 2-Methoxycinnamaldehyde 1 4 3-Phenylpropyl acetate 1 5 4-Terpineol 3 6 α-Terpineol 3 7 Aromadendrene 1 8 Aromatic compound Class could not be assigned 9 Benzaldehyde 1 10 Benzenepropanal 1 11 Benzenepropanol 1 12 Benzenepropanol acetate 1 13 Benzylbenzoate 1 14 Borneol 1 15 Cadinene 1 16 Calamenene 2 17 Camphene 1 18 Camphor 3 19 Carvacrol 1 20 Carveol isomere 1 Carveol (2-methyl-5-prop-1-en-2-ylcyclohex-2-en-1-ol) is 1. Isomere is not specified so the class was assigned to the general compound 21 Caryophyllene 1 22 Caryophyllene alcohol 3 23 Caryophyllene oxide 3 24 Chavicol 1 25 Cinnamaldehyde 1 26 Cinnamic acid 1 27 Cinnamyl acetate 1 28 Cinnamyl alcohol 1 29 Cis-P-menth-2-en-1-ol 3 30 Cis-ß-Ocimene 1 31 Coumarin 3 32 Cryptone 2 33 Cymene isomere 1 cymene is 1. Isomere is not specified so the class was assigned to the general compound 34 E-Anethole 1 35 e-Cadinene + Pioncarvone Class could not be assigned 36 E-Cinnamyl acetate 1 37 E-o-Methoxycinnamaldehye 1 38 Epoxy-6.7-Humulene + Mw=220 Class could not be assigned 39 Eugenol 1 40 Eugenol acetate 3 41 Humulene epoxide II 3 42 Isoledene 1 43 Limonene 1 44 Linalool 3 45 Methoxy eugenol 3 46 Methyl cinnamaldehyde 1 47 Methyl eugenol 1 48 Monoterpenes Class could not be assigned 49 N-Hexadecanoic acid 1 50 N-Octadecanoic acid 1 51 P-Cymene 1 52 P-Cymene-8-ol 3 53 Para-Methoxy cinnamic aldehyde 1 54 Phenyl cyclohexene 2 55 Phenylmethyle isovalerate 1 56 Sabinene 1 57 Safrole 3 58 Sesquiterpene epoxide Class could not be assigned. Sesquierpene is class 3, but don't now if something changes with epoxide 59 Sesquiterpenol Mw=220 Class could not be assigned 60 Sesquiterpenol Mw=222 Class could not be assigned 61 Spathulenol 3 62 Styrene 1 63 Terpinen-4-ol 3 64 Terpinolene 1 65 Terpinolene isomere 1 terpinolene is 1 , isomere not known. Isomere is not specified so the class was assigned to the general compound 66 Tetradecanal 1 67 Trans-2-methoxycinnamaldehyde 1 68 Trans-P-menth-2-en-1-ol 3 69 Trans-Pinocarveol 1 70 Trans-Piperitol 1 71 Trans-ß-Ocimene 1 72 Z-Cinnamaldehyde 1 73 ß-Bisabolene 1 74 ß-Cubebene 1 75 ß-Elemene 1 76 ß-Myrcene 1 77 ß-Phellandrene 1 ß-Phellandrene + 1,8-Cineole: 1+3 78 1,8-Cineole 3 A new line was created for 1.8-Cineole based on the row above. 79 ß-Pinene 1 80 α-Cadinol 3 81 α-Calacorene 2 82 α-Copaene 1 83 α-Cubebene 1 84 α-Famesene Class could not be assigned 85 α-Gurjunene 1 86 α-Humulene 1 87 α-Muurolol 3 88 α-P-Dimethylstyrene 1 89 α-Phellandrene 1 90 α-Pinene 1 91 α-Selinene 1 92 α-Terpinene 1 93 α-Terpineol 3 94 α-Thujene 1 95 γ-Cadinene 1 96 γ-Terpinene 1 97 δ-Cadinene 1 98 Δ3-Carene 1 99 α-Muurolene 1 100 Rutin 1 Test compound, not real value 101 Estragole 1 Test compound, not real value

See also

• heande:Composition of cinnamon dried bark essential oil
• heande:Composition of plant-based food supplements
• Compound intake estimator