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Data Submission Food Additives and Contaminants Part B – raw data submission Food Additives & Contaminants, Part B publishes surveillance data indicating the presence and levels of occurrence of designated food additives, residues and contaminants in foods and animal feed. Data using validated methods must meet stipulated quality standards to be acceptable and must be presented in a prescribed format for subsequent data-handling. This note describes the REQUIRED format for submission of data to enable easy and rapid entry onto the database. Entry will allow readers to access the original data set behind the paper and subscribers to the journal to download aggregated data sets across a range of papers where matrices and/or analytes are common. Food Additives & Contaminants, Part B has a restricted scope in terms of classes of food additives, residues and contaminants that are included, being based on a goal of covering those areas where there is a need to record surveillance data for the purposes of exposure and risk assessment. Accumulated data sets available from the database will assist in such analyses. The Sample spreadsheet This should contain one row for EVERY sample, including any samples which were analysed but no residues were found, if these are included in the final data set reported in the paper. Sample_id – this column is vital and should be used to give each sample a unique identifier. It may be simple, for example 1, 2, 3 .....n or contain a more complex textual string used by the original survey, for example something like “13547/27.6 AAC”. It does not matter how complex it is, as long as the same identifier is used to relate that sample to all the rows of results on the results spreadsheet for that sample. doi –this is the unique identifier given to the paper by the publishers. It will be of the form 10.1080/19393210.2012.655326 but if you do not know this, leave it blank, it will be filled in on entry. Country – please enter the country of origin of the sample, NOT the country where the analysis took place. If samples were from a range of countries, please try to be specific about the origin of each sample, in other words avoid “France/Belgium” or “South America”. This will allow more meaningful analysis of aggregated data from the database across a large number of data sets. Year – please enter the year the sample was taken, not the year of analysis. If samples were taken over more than one year, please try to be specific about the year of sampling of each individual sample. Avoid using, for example, 2007/08, as in reality any one sample can only be collected in one year and the database will only accept individual years. Matrix – Enter a general name for the matrix which was analysed, for example “Fish”. A list of examples of matrix names is given in Appendix I, but you do not have to select from this list, it is only given as an example. If your analysis is on a completely new matrix, please include this, as we are expanding the range of matrices analysed all the time. Species – Where the matrix is a specific species of plant, animal, fish etc. please complete this if it will give a more meaningful description of the matrix. For example, if the matrix is generally “Fish” but several different species of fish were sampled, it will be appropriate to name each species, using its Latin binomial name. An example would be: Matrix: Fish Species: Pampus argenteus Clearly, there will be many occasions where this is inappropriate, for example where a processed food has been analysed, such as Baby Food. In these cases, leave the column blank. Common name – As with species, if the matrix is a specific species of plant, animal, fish etc. please complete this if it will give a more meaningful description of the matrix. For example, if the matrix is generally “Fish” but several different species of fish were sampled, it will be appropriate to name each species, using its common name. An example would be: Matrix: Fish Species: Pampus argenteus Common name: Pomfret Weight – This is only relevant if the samples concern species where the sample weight may influence the analytical result so inclusion of the species weight may assist in interpretation of the data. If the weight is unknown or not relevant, leave blank. Length – Again, this is only relevant if the samples concern species where the sample length, possibly as an indicator of age, may influence the analytical result so inclusion of the species length may assist in interpretation of the data. If the length is unknown or not relevant, leave blank. Age – Again, this is only relevant where the sample age may influence the analytical result so inclusion of the sample age may assist in interpretation of the data. This may not only affect individual species, where contaminants may accumulate with age, but also matrices such as Basmati rice, where the number of years of storage or maturity may affect the results. If the age is unknown or not relevant, leave blank. Comments – this is a very useful column where any additional information relevant to the sample which may be useful to further interpretation of the data may be included. The Results spreadsheet This should contain one row for EVERY analytical result for each sample listed on the sample spreadsheet, including any analyses where the result was below the LOD. In this case, the result should be given as < LOD where LOD is a value, for example, < 0.05. Sample-id - this should identify the sample to which this result refers and should match to ONE row on the sample spreadsheet. There should be as many rows with the same sample_id as there are different analytes tested. For example, if 10 different trace metals were analysed in each sample, there should be ten rows for each sample in the results spreadsheet, one for each trace metal, even if one or more of them were below the LOD. These rows should be listed with the value given in the Value column as < followed by the LOD for that analysis (e.g. < 0.02). Analyte – the residue/chemical etc which was analysed for. A list of analytes already in the database is given in Appendix II. If your analyte is included in this list PLEASE use the nomenclature as given in this list to allow for aggregated analyses over many data sets. If your analyte is not in this list, then enter whatever it is, as used in the original paper. Value – the numerical result of the analysis. Where the result was not determined, (< LOD), please enter this with the numerical value of the LOD, as explained above. is_less_than – where no level was detected (i.e. the value is below the LOD), please enter < Units – enter the units for the result, for example μg/kg or mg l-1. A list of units already in the database is given in Appendix III. If you can use one of these, it will make aggregated data analysis more meaningful. If y9ur particular units are not in this list, please use whatever was given in the original paper. Uncertainty – if appropriate, enter the level of uncertainty associated with the result. Some examples already on the database are given below: ±5 ±0.08 Std deviation 87% 1.327 Appendix I – examples of matrices already in the database Alfalfa Black sticky flour-ethyl Cheese-Kasseri Egg - limed duck Almond acetate Cheese-Kefalotiri Egg - liquid Apple Brandy Cheese - processed Eggplant (aubergine) Apple, peeled Bread Cheese-Regato Egg - preserved Apple juice Bread - corn Cheese - soft Egg roll Apple sauce Bread crumbs Cherry Egg - salted duck Apricot Breaded cutlet Chestnut - stir-fried Eggs - whole, hen Apricot, dried Bread - rye Chicken Egg tart Apricot drink Bread - village type Chicken - steamed Egg white - fried Apricot juice Bread - wheat Chicken - stir-fried Egg white - liquid Apricot nectar Bread - wheat, white Chick-pea - salted, Egg white - steamed Ayurvedic formulation Bread - white roasted Egg yolk - fried Bacon Bread - whole wheat Chocolate Egg yolk - steamed Bak choi Breakfast cereals Chocolate - milk & Electrolyte - Ready-to- Baked goods Breast nuts use Banana Broccoli Chocolate - plain Energy bar Barley Brownie mix Cider - alcoholic Evaporated milk Barley flour Butter Cinnamon Feed Batter mix Buttermilk Cloves Fig Beans, green Cabbage, flowering Coffee Fish Beef Cabbage, white Coffee - green Fish ball Beef ball Cake - chocolate, iced Coffee infusion Fish canned-Mackerel Beef - bresaola Cake - fruit, uniced Compote - cherry in oil Beef - corned Cake mix Compote - grape Fish canned-Sardine Beef intestine – Cake - paper wrapped Condensed milk in oil marinated Cakes Confectionary Fish canned-Tuna in Beef jerky Cakes - various (egg – Congee oil Beef lights – chocolate - fruit) Cookies French fries marinated Candy Cookies - various Fresh ground meat Beef milts - marinated Canned/jarred adult Cordial - regular Frozen breakfast Beef - minced food Coriander Fruit drink - mixed Beef - minced, Canned/jarred baby Corn oil Fruit juice and nectar steamed food Cottonseed oil - complex Beef - minced, stir- Canned roast eel Crackers Fruit juice - mixed fried Capsule Cream Fruit nectar - mixed Beef - Pasturmas, Caraway Crustacean Garlic peppered dried beef Carrot Cucumber Golden thread – Beef - shredded, Carrot juice Curcuma shallow-fried steamed Celery Curry spice (dried Golden thread – Beef - shredded, stir- Cephalopod leaves) steamed fried Cereal Curry spice (powder) Grapefruit Beef - steak Cereal porridge Custard Grape juice Beef tripe - marinated Cereal products Dandelion Grapes Beer Cereals - mixed Dessert mix Grass silage Beer, aluminium can Cheese Dietary supplement Green bananas-ethyl Beer, bottled
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