Concentrations of beneficial nutrients in fish

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Citation of this page: Anna Karjalainen, Jouni T. Tuomisto, Patrycja Gradowska: Concentrations of beneficial nutrients in fish. Opasnet 2009. [2]. Accessed 08 Oct 2024.
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Question

What are the concentrations of beneficial nutrients in fish (such as omega-3 fatty acids (EPA + DHA + fatty acid 18:3 n-3)) in fish.R↻ Concentrations of sea and freshwater fish species are studied separately.

Answer

Note! The first version of results only uses Data 3, for practical reasons (other nutrients than omega-3 are not yet needed).

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Rationale

Data

Concentrations of beneficial nutrients in fish extracted from , where they are indicated as mass units/100 g fish analysed.

Concentrations of omega-3 fatty acids in fish extracted from , where they are indicated as mass units/100 g fish analysed. Criteria for choosing the fish preparation method used prior the analyses:

1. one that is presumed as a common one used by the Finnish consumers (roasting), and
2. also as available for most of the fish in the database.

The data for omega-3 fatty acids was reported as mean values of the fish species. R↻

• Omega-3 fatty acids, mg/g, as sum of n-3 PUFA (polyunsaturated fatty acids) precursors ALA (alphalinolenic acid), EPA (eicosapanthenic acid), and DHA (docosahexaenoic acid). DPA (docosapentaenoic acid) not available in the database.
• Omega-3 fatty acids, mg/g, as DHA and EPA only.
  • Vitamin D, ug/g
  • Vitamin E, mg/g
  • Selenium, ug/g
  • Iodine, ug/g
  • Group B vitamins, mg/g, as sum of B1 (tiamin), B2 (riboflavin), B6 (pyridoxin), B12 (cyanokobalamin) and niacin as its eqvivalents NE. R↻

The most commonly consumed fish species are described: Farmed salmon, wild salmon, herring, white fish, sprat, perch, flounder, pike-perch, bream, pike, vendace and burbot. Concentration data for imported fish in Finland is not available in many cases. R↻

Data 1

mass units/100 g fish analysed

Fish species	Omega-3-PUFA contents, mg/g	Vitamin D, ug/g	Vitamin E, mg/g	Selenium, ug/g	Iodine, ug/g	Group B vitamins, mg/g,
Baltic herring (Clupea harengus membras)	12.13	0.17	0.03	0.18	0.45	0.17
Herring (Clupea harengus)	12.13	0.17	0.03	0.18	0.45	0.17
Vendace (Coregonus albula)	12.77	0.13	0.03	0.22	0.76	0.00
Whitefish (Coregonus lavaretus)	10.71
Pike (Esox lucius)	6.75	0.03	0.02	0.22	0.34	0.08
Rainbow trout (Onchorhynchus mykiss)	20.52
Shrimp (Pandalus sp.)	2.49
Perch (Perca fluviatilis)	7.17
Saithe (Pollachius virens)	3.87
Atlantic salmon (Salmo salar)	17.42
Pike-perch (Sander lucioperca)	4.06
Tuna (Thunnus thynnus)	32.67

Data 2

• There are no separate data for farmed and for wild salmon. Therefore they are assumed to be the same.
• There are no data for vendace(sea), so assumption is that the parameters are the same as vendace(inland)
• There are no data for bream(sea), so assumption is that the parameters are the same as bream(inland)
• There are no data for wild salmon(inland), so assumption is that the parameters are the same as wildsalmon(sea)

First, the mercury data was used to form lognormal distributions for each species with parameters median and geometric standard deviation. Secondly, the proportion of methylmercury from total mercury was taken into account. Methylmercury proportion is assumed to follow triangular distribution (author judgement) with parameters (min=0.81, mode=0.93,max=0,98). R↻

The omega-3 distributions chosen by author judgement. Concentration distributions were formed according to amount of data. a) If 3 or more data points → triangular distribution, b) if 2 data points → uniform distribution, c) if 1 data point → normal distribution R↻

Sprat: omega-3 concentration(sprat) = omega-3 concentration(herring) closely related species, assumed to have similar composition

Units: mg/kg in fresh weight

Fish species	Omega-3 concentration
Farmed salmon (sea+inland)	1.553e+004
Wild salmon	1.516e+004
Herring(sea)	9790
White fish(sea)	7830
Sprat(sea)	9790
Perch(sea)	3030
Flounder(sea)	4220
Pike-perch(sea)	4060
Bream(sea)	4930
Pike(sea)	2920
Vendace(sea)	7510
Burbot(sea)	2010
Wild salmon(inland)	1.504e+004
White fish(inland)	7830
Perch(inland)	3030
Pike-perch(inland)	4060
Bream(inland)	4930
Pike(inland)	2920
Vendace(inland)	7510
Burbot(inland)	2010

Data 3

Since there are only mean concentration values available exponential distribution has been chosen to model the concentration of DHA and concentration of EPA in various sea and inland fish species.

Moreover, the Fineli database provides concentrations of nutrients in species caught mostly in the Baltic Sea (except vendace which was caught in inland waters). Thus, the concentration data for some of the species chosen is not available. Therefore, it has been assumed that the DHA (EPA) concentration in sea and freshwater species is the same.

Units: mg/g fish

Fish species	Mean DHA concentration	Mean EPA concentration
Baltic Herring	4.82	2.27
Vendace(inland)	4.09	2.06
Vendace(sea)	4.09	2.06
Whitefish(inland)	4.93	2.63
Whitefish(sea)	4.93	2.63
Pike(inland)	2.17	0.44
Pike(sea)	2.17	0.44
Perch(inland)	2.45	0.54
Perch(sea)	2.45	0.54
Atlantic Salmon	10.02	3.45
Pike-perch(inland)	3.01	0.92
Pike-perch(sea)	3.01	0.92

Fineli

Data comes from Fineli database maintained by THL.

Concentrations of fatty acid concentrations in salmon[3] and herring[4] products (g/100 g) (2017 data)

Salmon product	Omega-3	ALA (alphalinolenic acid)	EPA	DHA	Vitamin D (µg)
Salmon, farmed	2.3	0.796	0.456	0.669	6.7
Salmon, whole	2.7	0.176	0.504	1.465	5.2
Salmon filet	4.2	0.270	0.775	2.253	8.0
Salmon filet, cold smoked	1.4	0.470	0.286	0.412	7.2
Salmon filet, fried ("paistettu")	4.7	0.367	0.842	2.449	8.7
Salmon filet, baked in oven	4.9	0.314	0.901	2.620	9.3
Herring filet	2.4	0.174	0.413	0.586	17.9
Herring, fresh	1.4	0.125	0.402	0.606	15.6
Herring, smoked	1.8	0.154	0.495	0.745	17.0

Data updated successfully!

Fineli summary(mg/ 100 g)

Obs	Fish	Exposure_agent	Result
1	Herring	ALA	154 (125 - 174)
2	Salmon	ALA	470 (176 - 796)
3	Salmon	D_vitamin	0.0075 +- 0.0015
4	Salmon	DHA	1600 +- 950
5	Salmon	EPA	630 +- 250
6	Salmon	Omega3	3400 +- 1400

Data updated successfully!

Fineli data for common fish species(several)

Obs	Kala	Fish	energia laskennallinen (kJ)	rasva (g)	proteiini (g)	rasvahapot yhteensä (g)	rasvahapot moni-tyydyttymättömät (g)	rasvahapot yksittäis-tyydyttymättömät cis (g)	rasvahapot tyydyttyneet (g)	rasvahapot trans (g)	rasvahapot n-3 moni-tyydyttymättömät (g)	rasvahapot n-6 moni-tyydyttymättömät (g)	rasvahappo 18:2 cis n-6 (linolihappo) (mg)	rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)	rasvahappo 20:5 n-3 (EPA) (mg)	rasvahappo 22:6 n-3 (DHA) (mg)	jodidi (jodi) (µg)	fosfori (mg)	seleeni (µg)	sinkki (mg)	D-vitamiini (µg)
1	Hauki	Pike	339	0.4	19	0.3	<0.1	<0.1	0.1	<0.1	<0.1	<0.1	11	8	18	30	46.7	240	22	1.1	2.1
2	Kala, keskiarvo, silakka/muikku/ahven/hauki	Average fish	413	2.8	18.2	2.3	0.9	0.7	0.7	<0.1	0.7	0.2	101	69	187	254	30	252	24.8	1.8	10.5
3	Kirjolohi, filee, kasvatettu	Rainbow trout	715	10.3	19.7	9.6	3.3	4.7	1.5	<0.1	1.8	1.4	1326	481	318	757	6	260	15.1	0.4	5.1
4	Lahna	Bream	412	3.2	17.3	2.2	0.7	1	0.5	0	0.6	0.1	35	22	199	273	7	210	28	0.5	14
5	Lohi, filee, kasvatettu	Salmon	946	16.4	20	15.1	4.7	8.1	2.3	0.1	2.3	2.2	2055	796	456	669	6.1	240	11.6	0.4	6.7
6	Muikku, järvi	Vendace	463	3.9	18.7	2.8	1.3	0.6	0.9	<0.1	1	0.3	102	135	288	300	25.9	290	22	4	9.4
7	Siika, kasvatettu	Whitefish	554	5.9	19.8	5.3	1.6	2.6	1	<0.1	1	0.7	574	222	204	394	6.2	248	24.9	0.4	14.4
8	Silakkafilee	Herring	601	8.9	16	8.1	3.4	2.6	2	0	2.4	0.9	472	174	413	586	52.4	200	27.1	2.4	15.6
9	Särki	Roach	397	2.4	18.2	1.7	0.5	0.8	0.3	0	0.5	<0.1	18	10	112	287	30	252	24.8	1.8	10

Calculations

Bayes model

• Model run 2.3.2017 [5]
• Model run 3.3.2017 with multivariate normal and predictions [6]
• Model run 19.5.2017 with only vit.param stored [7]

+ Show code- Hide code # This is code Op_en1838/bayes on page [[Concentrations of beneficial nutrients in fish]] library(OpasnetUtils) library(rjags) library(reshape2) library(ggplot2) library(car) # For scatterplotMatrix() dat <- opbase.data("Op_en1838", subset = "Balticherring") dat$Result <- as.numeric(gsub(",", ".", as.character(dat$Result))) colnames(dat) fisl <- sort(unique(as.character(dat$Fish_species))) compl <- sort(unique(as.character(dat$variable))) dat.wide <- reshape( dat, idvar = "Sample_code", v.names = "Result", timevar = "variable", direction = "wide" ) mod <- textConnection(" model{ for(i in 1:N) { # Observation conc[i,1:C] ~ dmnorm(mu[], Omega[,]) } mu[1:C] ~ dmnorm(mu0[1:C], S2[1:C,1:C]) Omega[1:C,1:C] ~ dwish(S3[1:C,1:C],4) conc.pred[1:C] ~ dmnorm(mu[1:C], Omega[1:C,1:C]) } ") jags <- jags.model( mod, data = list( N = nrow(dat.wide), C = length(compl), conc = dat.wide[ncol(dat.wide)+(-3:0)], mu0 = rep(100,4), S2 = diag(4)/100000, S3 = diag(4)/10000 ), n.chains = 4, n.adapt = 100 ) update(jags, 100) samps.j <- jags.samples( jags, c('mu', 'Omega', 'conc.pred'), 1000 ) js <- array( c( samps.j$mu[,,1], samps.j$Omega[,1,,1], samps.j$Omega[,2,,1], samps.j$Omega[,3,,1], samps.j$Omega[,4,,1], samps.j$conc.pred[,,1] ), dim = c(4,1000,6), dimnames = list( Exposure_agent = compl, Iter = 1:1000, Parameter = c("mu","Omega1", "Omega2", "Omega3", "Omega4", "conc.pred") ) ) # Mu for all questions scatterplotMatrix(t(js[,,1])) # All parameters for Exposure_agent 1 (DHA?) scatterplotMatrix(js[1,,]) jsd <- melt(js) coda.j <- coda.samples( jags, c('mu', 'Omega', 'conc.pred'), 1000 ) plot(coda.j) ######## vit.param contains expected values of the distribution parameters from the model vit.param <- list( mu = apply(js[,,1], MARGIN = 1, FUN = mean), Omega = solve(apply(js[,,2:5], MARGIN = c(1,3), FUN = mean)) # solve matrix: precision->covariace ) objects.store(vit.param) cat("List vit.param stored.\n") if(FALSE) { concddeo.mu <- array( samps$mu, dim = c(length(compl), 1000, 4), dimnames = list(Compound = compl, Iter = 1:1000, Seed = 1:4) ) concddeo.Omega <- array( samps$Omega, dim = c(length(compl), length(compl), 1000, 4), dimnames = list(Compound1 = compl, Compound2 = compl, Iter = 1:1000, Seed = 1:4) ) concddeo.pred <- array( samps$mu, dim = c(length(compl), 1000, 4), dimnames = list(Compound = compl, Iter = 1:1000, Seed = 1:4) ) concdf.mu <- melt(concddeo.mu) scatterplotMatrix(t(concddeo.mu[,,1])) concdf.pred <- melt(concddeo.pred) scatterplotMatrix(t(concddeo.pred[,,1])) ggplot(dat, aes(x = Result, colour = Fish_species))+geom_density()+ facet_wrap(~ variable, scales = "free") ggplot(concdf.pred, aes(x = value))+geom_density()+ facet_wrap(~ Compound, scales = "free") objects.store(concddeo.mu, concddeo.Omega, concddeo.pred) cat("Arrays of concentrations of vitamin D, DHA, EPA, and Omega3) stored:\n") cat("concddeo.mu (mean), concddeo.Omega (precision matrix), concddeo.pred (predicted distribution).\n") } #if(FALSE)

Initiate ovariable conc_vit

• Initiation 19.5.2017 [8]
• Initiation 5.10.2017: salmon data taken from Fineli [9]
• Initiation 3.9.2019 with ALA [10]

+ Show code- Hide code # This is code Op_en1838/initiate on page [[Concentrations of beneficial nutrients in fish]] library(OpasnetUtils) conc_vit <- Ovariable( "conc_vit", ddata = "Op_en1838", subset="Fineli summary", dependencies = data.frame(Name = "vit.param", Ident = "Op_en1838/bayes"), formula = function(...) { require(MASS) require(reshape2) N <- openv$N vit <- data.frame( Fish = "Herring", Iter = 1:N, as.data.frame(mvrnorm(N, vit.param$mu, vit.param$Omega)) ) vit <- melt(vit, id.vars = c("Iter", "Fish"), variable.name = "Exposure_agent", value.name = "Result") return(vit) }, unit = "mg /100 g" ) objects.store(conc_vit) cat("Ovariable conc_vit stored.\n")

See also

• Previous Analytica formulas can be found from a previous version of this page.