The taxpayers through the United State Department of Agriculture Agriculture Research Service, Nutrient Data Laboratory have provided a USDA National Nutrient Database for Standard Reference, Release 28 of foods, food ingredients, and nutrition. The current revision is 28. Another version will be made available in 2016.
These are notes on creating this database in Ubuntu 10.4 LTS Linux using SQLite 3.6.22.
These notes apply specifically to SR28.
I had prepared detailed notes using SR23 and SR24 and tried to test the transition and discovered some differences in required in the fine documentation…
NUT_DATA has a “AddMod_Date” added as a 17th column pushing CC to 18th.
Line 37 of DERIV_CD may contain new-lines and threw sed off because I did not handle the new-lines within the quotes. As a temporary fix, hand edit the unzipped DERIV_CD.txt file to combine the long text into a single line before importing the data for DERIV_CD. DERIV_CD appears to be ok in SR28.
Google (or use another search engine) for: sr28 nutrition database
or navigate to:
Select “Datasets” in Related Topics box on left. Opens Datasets list page.
Under “Food and Nutrition”, Click link for “Nutrient Data Laboratory”.
Select USDA National Nutrient Database for Standard Reference, Release 28 link in text on right.
Note resources on this page including the PDF documentation referenced below, the links to updates for release x to release x+1, and links previous versions
Look for the words “Download the SR28 database” in bullets near the top on the right. Click Download.
This brings you to https://www.ars.usda.gov/Services/docs.htm?docid=25700 at the time this page was edited.
SR28 – Download Files
ACCESS for those that use the Microsoft database product.
ASCII – text for those that wish to use ASCII text files, as we do.
Abbreviated – a summary, de-normalized version of the database available in 2 flavors
ASCII – ASCII text version of the abbreviated database.
Excel – abbreviated database for spreadsheet users that can open Microsoft Excel 2007 formatted files.
Download the zipped ASCII versions of the file two files. There is a SR28asc.zip which is the ascii version of the separate databases. There is also a separate link that will provide a de-normalized summary version of the databases. This one will be named SR28abbr.zip
You may wish to download the Update files (sr28upd.txt) if you are interested in what has changed in the current update. Other changes can be found in the previous releases mentioned above.
Also download any patch versions if required.
Unzip the downloaded files.
If you had separately downloaded sr28_doc.pdf, the zip file version of the PDF is identical in asc, upd, and abbr zip files.
Look at the PDF.
Read the first 27 of pages to understand what has changed and what is in each of the databases.
The explanation of the file formats begins on page 27.
Table 3 on page 28 shows the names of the tables. There is a .txt file that corresponds to each of the tables.
Page 29 describes the tables and their relations. Figure 1 shows the relationships graphically.
Next come Tables 4 through 15 describing the data columns in each of the tables.
Look at one of the tables. I chose FOOTNOTE (Table 13) because it has a relatively small number of columns.
FOOTNOTE.txt ~02009~^~01~^~D~^~~^~Mix of chili pepper, other spices and salt~ ~02030~^~01~^~N~^~204~^~Total proximates do not equal 100% because piperine was subtracted from lipid value.~ ~02032~^~01~^~N~^~204~^~Total proximates do not equal 100% because piperine was subtracted from lipid value.~ ~02033~^~01~^~D~^~~^~Other phytosterols = 38.4 mg/100g; these include delta 5-avenasterol (17.7), campestanol (2.6), and other minor phytosterols (18.1 mg).~ ~02034~^~01~^~N~^~204~^~Total proximates do not equal 100% because piperine was subtracted from lipid value.~ ~02048~^~01~^~D~^~~^~Contains 5% acetic acid. Total proximates do not equal 100%.~ ~02048~^~02~^~N~^~208~^~Acetic acid is included in energy calculation.~ ~02053~^~01~^~D~^~~^~Contains 5% acetic acid. Total proximates do not equal 100%.~ ~02053~^~02~^~N~^~208~^~Acetic acid is included in energy calculation.~ ~02063~^~01~^~D~^~~^~Leaf~
Each field is “quoted” with tildes and separated with a carat. Fields with null values have adjacent tildes (02048 and 02063 are examples).
Compare the text with the description in Table 13.
Table 13.—Footnote File Format
Table 13.—Footnote File Format
Nutrient Databank number.
number. If a given footnote applies to more than one nutrient
number, the same footnote number is used. As a result, this file
cannot be indexed.
D = footnote adding information to the food description;
M = footnote adding information to measure description;
N = footnote providing additional information on a nutrient value.
If the Footnt_typ = N, the Nutr_No will also be filled in.
3-digit identifier code for a nutrient to which footnote applies.
For example NDB_No is a 5 digit numeric (as characters) field including leading zeros.
Footnt_Txt is a 200 character field
Nutr_no, if not blank is a 3-digit identifier of the nutrient to which the footnote applies.
You can similarly do each of the other tables.
There are 3 steps required for each table:
1. Edit the .txt file to remove the tildes.
2. Create the tables in an SQLlite database
3. Import the data from the edited .txt files
The next step is create, define and load the database. SQLite3 is available on the Ubuntu system but may not be initially loaded.
This can be tested by typing sqlite3 at a command prompt. If the program starts, use .quit to end it (;.quit if you are in the middle of SQL). .help will provide a command summary of the .commands. Ubuntu will tell you to use “sudo apt-get install sqlite3” if the program is not yet present. Using apt-get will install the program if necessary. Other Linux system distributions may offer similar help but may have different installation tools. SQLite3 is controlled by a combination of .commands and SQL.
Additional help for sqlite in man pages, info, and web links for sqlite3 at https://www.sqlite.org/docs.html.
In a previous edition, it was necessary to edit the text file for DERIV_CD to combine a long line with a new-line in the text string.
Old text:[Line 37 of DERIV_CD contains new-lines. Hand edit the unzipped DERIV_CD.txt file to combine the long text into a single line before importing the data for DERIV_CD. Delete a new-line at the end of line 38 and end of line 37.]
If the procedures below choke, this is could be the problem. Check and edit the reported problem lines.
You should be able to copy-and-paste the text from “here” to “there” (excluding here and there) and paste it into your Linux command prompt.
here mkdir temp sed -e 's/~//g' ABBREV.txt > temp/ABBREV.txt sed -e 's/~//g' DATA_SRC.txt > temp/DATA_SRC.txt sed -e 's/~//g' DERIV_CD.txt > temp/DERIV_CD.txt sed -e 's/~//g' FOOD_DES.txt > temp/FOOD_DES.txt sed -e 's/~//g' LANGDESC.txt > temp/LANGDESC.txt sed -e 's/~//g' NUT_DATA.txt > temp/NUT_DATA.txt sed -e 's/~//g' SRC_CD.txt > temp/SRC_CD.txt sed -e 's/~//g' DATSRCLN.txt > temp/DATSRCLN.txt sed -e 's/~//g' FD_GROUP.txt > temp/FD_GROUP.txt sed -e 's/~//g' FOOTNOTE.txt > temp/FOOTNOTE.txt sed -e 's/~//g' LANGUAL.txt > temp/LANGUAL.txt sed -e 's/~//g' NUTR_DEF.txt > temp/NUTR_DEF.txt sed -e 's/~//g' WEIGHT.txt > temp/WEIGHT.txt ls -l temp rm SR28.db sqlite3 SR28.db .tables CREATE TABLE ABBREV(NDB_No INTEGER, Shrt_Desc, Water FLOAT, Energ_Kcal FLOAT,Protein FLOAT, Lipid_Tot FLOAT, Ash FLOAT, Carbohydrt FLOAT, Fiber_TD FLOAT, Sugar_Tot FLOAT, Calcium FLOAT, Iron FLOAT, Magnesium FLOAT, Phosphorus FLOAT, Potassium FLOAT, Sodium FLOAT, Zinc FLOAT, Copper FLOAT, Manganese FLOAT, Selenium FLOAT, Vit_C FLOAT, Thiamin FLOAT, Riboflavin FLOAT, Niacin FLOAT, Panto_acid FLOAT, Vit_B6 FLOAT, Folate_Tot FLOAT, Folic_Acid FLOAT, Food_Folate FLOAT, Folate_DFE FLOAT, Choline_Tot Float, Vit_B12 FLOAT, Vit_A_IU FLOAT, Vit_A_RAE FLOAT, Retinol FLOAT,Alpha_Carot FLOAT, Beta_Carot FLOAT, Beta_Crypt FLOAT, Lycopene FLOAT, Lut_Zea FLOAT, Vit_E FLOAT, Vit_D_mcg FLOAT, Vit_D_IU FLOAT, Vit_K FLOAT, FA_Sat FLOAT, FA_Mono FLOAT, FA_Poly FLOAT, Cholestrl FLOAT, GmWt_1 FLOAT, mWt_Desc1, GmWt_2 FLOAT, GmWt_Desc2, Frefuse_Pct INTEGER); CREATE TABLE DATA_SRC(DataSrc_ID INTEGER, Authors, Title, Year INTEGER, Journal, Vol_City, Issue_State, Start_Page, End_Page); CREATE TABLE DATSRCLN(NDB_No INTEGER, Nutr_No INTEGER, DataSrc_ID INTEGER); CREATE TABLE DERIV_CD(Deriv_Cd INTEGER, Deriv_Desc); CREATE TABLE FD_GROUP(FdGrp_Cd INTEGER, FdGrp_Desc); CREATE TABLE FOOD_DES(NDB_No INTEGER PRIMARYKEY, FdGrp_Cd INTEGER, Long_Desc VARCHAR(200), Shrt_Desc VARCHAR(60), ComName VARCHAR(100), ManufacName VARCHAR(50), Survey CHAR(1), Ref_desc VARCHAR(60), Refuse INTEGER, SciName VARCHAR(60), N_Factor FLOAT, ProFactor FLOAT, FatFactor FLOAT, CHO_Factor FLOAT); CREATE TABLE FOOTNOTE(NDB_No INTEGER, Footnt_No INTEGER, Footnt_Typ, Nutr_No INTEGER, Footnt_Txt); CREATE TABLE LANGUAL(NDB_No INTEGER, Factor_Code VARCHAR(5)); CREATE TABLE LANGDESC(Factor_Code VARCHAR(5), Description VARCHAR(140)); CREATE TABLE NUTR_DEF(Nutr_No INTEGER, Units, Tagname, NutrDesc, Num_Dec INTEGER, SR_Order INTEGER); CREATE TABLE NUT_DATA(NDB_No INTEGER, Nutr_No INTEGER, Nutr_Val FLOAT, Num_Data_Pts FLOAT, Std_Error FLOAT, Src_Cd, Deriv_Cd, Ref_NDB_No INTEGER, Add_Nutr_Mark, Num_Studies INTEGER, Min FLOAT, Max FLOAT, DF INTEGER, Low_EB FLOAT, Up_EB FLOAT, Stat_cmt, AddMod_Date, CC); CREATE TABLE SRC_CD(Src_Cd INTEGER, SrcCd_Desc); CREATE TABLE WEIGHT(NDB_No INTEGER, Seq INTEGER, Amount FLOAT, Msre_Desc, Gm_Wgt FLOAT, Num_DataPts INTEGER, Std_Dev FLOAT); .tables .separator ^ .import temp/ABBREV.txt ABBREV .import temp/DATSRCLN.txt DATSRCLN .import temp/LANGUAL.txt LANGUAL .import temp/LANGDESC.txt LANGDESC .import temp/FOOTNOTE.txt FOOTNOTE .import temp/NUT_DATA.txt NUT_DATA .import temp/WEIGHT.txt WEIGHT .import temp/DATA_SRC.txt DATA_SRC .import temp/DERIV_CD.txt DERIV_CD .import temp/FOOD_DES.txt FOOD_DES .import temp/NUTR_DEF.txt NUTR_DEF .import temp/SRC_CD.txt SRC_CD SELECT COUNT(*) FROM ABBREV; SELECT COUNT(*) FROM DATSRCLN; SELECT COUNT(*) FROM LANGUAL; SELECT COUNT(*) FROM LANGDESC; SELECT COUNT(*) FROM FOOTNOTE; SELECT COUNT(*) FROM NUT_DATA; SELECT COUNT(*) FROM WEIGHT; SELECT COUNT(*) FROM DATA_SRC; SELECT COUNT(*) FROM DERIV_CD; SELECT COUNT(*) FROM FOOD_DES; SELECT COUNT(*) FROM NUTR_DEF; .quit rm -r temp there
I should have mentioned that some of those files are large with perhaps a long pause between steps.
ABBREV is a cut-down version of the entire database and is made to be used stand-alone.
Relationships are established programmatic-ally as in Figure 1 of the pdf document.
Some sample queries and expected results – copy and paste the query to the sqlite> prompt.
SELECT * FROM FD_GROUP; 100|Dairy and Egg Products 200|Spices and Herbs 300|Baby Foods 400|Fats and Oils 500|Poultry Products 600|Soups, Sauces, and Gravies 700|Sausages and Luncheon Meats 800|Breakfast Cereals 900|Fruits and Fruit Juices 1000|Pork Products 1100|Vegetables and Vegetable Products 1200|Nut and Seed Products 1300|Beef Products 1400|Beverages 1500|Finfish and Shellfish Products 1600|Legumes and Legume Products 1700|Lamb, Veal, and Game Products 1800|Baked Products 1900|Sweets 2000|Cereal Grains and Pasta 2100|Fast Foods 2200|Meals, Entrees, and Sidedishes 2500|Snacks 3500|Ethnic Foods
Specify a specific row using SQL expressions
SELECT * FROM FD_GROUP WHERE FdGrp_Cd=600 ...> ; 600|Soups, Sauces, and Gravies
Do not forget the semi-colon terminator. If you do the …> prompt will return until you do.
When you select *, you get all of the values in each row. They will be separated by the default separator | or the most recently specified .separator value.
SELECT * FROM FOOD_DES WHERE FdGrp_Cd=3500; 35001|3500|Agutuk, fish/berry with seal oil (Alaskan ice cream) (Alaska Native)|AGUTUK,FISH/BERRY W/ SEAL OIL (ICE CREAM) (ALASKA NATIVE)|||||0||6.25||| 35002|3500|Agutuk, fish with shortening (Alaskan ice cream) (Alaska Native)|AGUTUK,FISH W/ SHORTENING (ALASKA NATIVE)|||||0||6.25||| 35003|3500|Agutuk, meat-caribou (Alaskan ice cream) (Alaska Native)|AGUTUK,MEAT-CARIBOU (ALASKAN ICE CREAM) (ALASKA NATIVE)|||||0||6.25||| ...
A more complex query specifying specific selection and columns to return
SELECT FOOD_DES.NDB_No, FOOD_DES.Shrt_Desc, NUT_DATA.Nutr_No, NUT_DATA.Nutr_Val, NUTR_DEF.Units, NUTR_DEF.NutrDesc FROM FOOD_DES, NUT_DATA, NUTR_DEF WHERE ((FOOD_DES.FdGrp_Cd=3500) AND ((FOOD_DES.NDB_No>35130) AND (FOOD_DES.NDB_No<35132)) AND ((NUTR_DEF.Nutr_No>300) AND (NUTR_DEF.Nutr_No<350)) AND (NUT_DATA.Nutr_No=NUTR_DEF.Nutr_No) AND (FOOD_DES.NDB_No=NUT_DATA.NDB_No)); 35131|CORNMEAL,BLUE (NAVAJO)|301|5.0|mg|Calcium, Ca 35131|CORNMEAL,BLUE (NAVAJO)|303|2.91|mg|Iron, Fe 35131|CORNMEAL,BLUE (NAVAJO)|304|133.0|mg|Magnesium, Mg 35131|CORNMEAL,BLUE (NAVAJO)|305|354.0|mg|Phosphorus, P 35131|CORNMEAL,BLUE (NAVAJO)|306|393.0|mg|Potassium, K 35131|CORNMEAL,BLUE (NAVAJO)|307|7.0|mg|Sodium, Na 35131|CORNMEAL,BLUE (NAVAJO)|309|2.91|mg|Zinc, Zn 35131|CORNMEAL,BLUE (NAVAJO)|312|0.218|mg|Copper, Cu 35131|CORNMEAL,BLUE (NAVAJO)|315|0.758|mg|Manganese, Mn 35131|CORNMEAL,BLUE (NAVAJO)|317|11.8|μg|Selenium, Se 35131|CORNMEAL,BLUE (NAVAJO)|323|0.73|mg|Vitamin E (alpha-tocopherol) 35131|CORNMEAL,BLUE (NAVAJO)|341|0.0|mg|Tocopherol, beta 35131|CORNMEAL,BLUE (NAVAJO)|342|3.21|mg|Tocopherol, gamma 35131|CORNMEAL,BLUE (NAVAJO)|343|0.37|mg|Tocopherol, delta
In the output above, I have substituted a μ for the unknown character � that appears on the Linux console where the query was run. μ is not in the console font.