# USDA FDC Calorie Conversion Factors (`nexgenwatch/fdc-calorie-conversion-factors`) Actor Delivers the published USDA FoodData Central *calorie conversion factors* β€” one calorie_factor_record per row of the food_calorie_conversion_factor.csv member of the official FoodData Central full-CSV release. - **URL**: https://apify.com/nexgenwatch/fdc-calorie-conversion-factors.md - **Developed by:** [NexGen Watch](https://apify.com/nexgenwatch) (community) - **Categories:** Automation, Lead generation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $13.40 / 1,000 calorie conversion factor records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## πŸ”” USDA FDC Calorie Conversion Factors Delivers the **published** USDA FoodData Central *calorie conversion factors* β€” one `calorie_factor_record` per row of the `food_calorie_conversion_factor.csv` member of the official FoodData Central full-CSV release. Each record carries the source-native `food_nutrient_conversion_factor_id` and the three Atwater-style factors (`protein_value`, `fat_value`, `carbohydrate_value`) in **kcal per gram**, **verbatim** β€” plus the source food's `fdc_id`, attached from the small `food_nutrient_conversion_factor.csv` dimension so each factor stays tied to the specific food it was published for. Output is one `calorie_factor_record` row per result; billing is pay-per-event, the value event being one calorie conversion factor record (a $0.02 start fee per run, then $0.02 per calorie conversion factor record). Source: fdc.nal.usda.gov. No login, no API key and no CAPTCHA solving are involved: the source is read logged-out with an identified contact User-Agent. ### πŸ“Š Sample Output [![USDA FDC Calorie Conversion Factors sample output β€” a table of real calorie conversion factor record rows (food_nutrient_conversion_factor_id, protein_value, fat_value, carbohydrate_value) from run ittKzs7eOPjlzOktE on build 0.1.5](https://api.apify.com/v2/key-value-stores/80Y7h3rmVN5CWK7Mw/records/fdc-calorie-conversion-factors-sample)](https://apify.com/nexgenwatch/fdc-calorie-conversion-factors?fpr=2ayu9b) Real rows from run `ittKzs7eOPjlzOktE` on build 0.1.5 (2026-09-19), the same input as the Quick start below β€” every value is as the source published it (emails masked, long text shortened): | food_nutrient_conversion_factor_id | protein_value | fat_value | carbohydrate_value | release_date | release_checksum | |---|---|---|---|---|---| | 22721 | 4.27 | 9.02 | 3.87 | 2026-04-30 | 6a860e3e-1cb35fb7 | The run finished with the status message: `NORMAL: 1 unique rows (0 duplicates collapsed) from 2026-04-30. billable=1` ### βœ… What you get Each row is flat JSON with these fields (from the dataset schema and the sample run; a field the source does not publish for a given row is `null`): - `record_type` (string/null) β€” e.g. `calorie_factor_record` - `food_nutrient_conversion_factor_id` (string/null) β€” e.g. `22721` - `protein_value` (string/null) β€” e.g. `4.27` - `fat_value` (string/null) β€” e.g. `9.02` - `carbohydrate_value` (string/null) β€” e.g. `3.87` - `fdc_id` (string/null) β€” e.g. `746952` - `context_note` (string/null) β€” e.g. `published USDA Atwater-style calorie conversion factors (kcal per gram) that app` - `source_url` (string/null) β€” e.g. `https://fdc.nal.usda.gov/food-details/746952/nutrients` - `source` (string/null) β€” e.g. `fdc-calorie-conversion-factors` - `release_date` (string/null) β€” e.g. `2026-04-30` - `observed_at` (string/null) β€” e.g. `2026-09-19T02:19:35Z` - `release_checksum` (string/null) β€” e.g. `6a860e3e-1cb35fb7` - `license` (string/null) β€” e.g. `USDA FoodData Central β€” CC0 1.0 (public domain dedication). Cite: U.S. Departmen` - `attribution` (string/null) β€” e.g. `USDA Agricultural Research Service β€” FoodData Central (https://fdc.nal.usda.gov)` - `terminal` (string/null) β€” null in every sample row - `scope` (string/null) β€” null in every sample row - `note` (string/null) β€” null in every sample row - `reported_total` (string/integer/null) β€” null in every sample row - `raw_seen` (string/integer/null) β€” null in every sample row - `duplicates` (string/integer/null) β€” null in every sample row - `unique_found` (string/integer/null) β€” null in every sample row - `delivered` (string/integer/null) β€” null in every sample row **βœ… What you get** - `record_type` (string/null) β€” e.g. `calorie_factor_record` - `food_nutrient_conversion_factor_id` (string/null) β€” e.g. `22721` - `protein_value` (string/null) β€” e.g. `4.27` - `fat_value` (string/null) β€” e.g. `9.02` - `carbohydrate_value` (string/null) β€” e.g. `3.87` - `fdc_id` (string/null) β€” e.g. `746952` - `context_note` (string/null) β€” e.g. `published USDA Atwater-style calorie conversion factors (kcal per gram) that app` - `source_url` (string/null) β€” e.g. `https://fdc.nal.usda.gov/food-details/746952/nutrients` - `source` (string/null) β€” e.g. `fdc-calorie-conversion-factors` - `release_date` (string/null) β€” e.g. `2026-04-30` - `observed_at` (string/null) β€” e.g. `2026-09-19T01:40:54Z` - `release_checksum` (string/null) β€” e.g. `6a860d85-1cb35fb7` - `license` (string/null) β€” e.g. `USDA FoodData Central β€” CC0 1.0 (public domain dedication). Cite: U.S. Departmen` - `attribution` (string/null) β€” e.g. `USDA Agricultural Research Service β€” FoodData Central (https://fdc.nal.usda.gov)` - `terminal` (string/null) β€” null in every sample row - `scope` (string/null) β€” null in every sample row - `note` (string/null) β€” null in every sample row - `reported_total` (string/integer/null) β€” null in every sample row - `raw_seen` (string/integer/null) β€” null in every sample row - `duplicates` (string/integer/null) β€” null in every sample row - `unique_found` (string/integer/null) β€” null in every sample row - `delivered` (string/integer/null) β€” null in every sample row **Output** One record per unique `food_nutrient_conversion_factor_id`: `food_nutrient_conversion_factor_id`, `protein_value`, `fat_value`, `carbohydrate_value` (all verbatim), `fdc_id` (source food), `context_note`, `source_url`. Values are carried as strings exactly as published; a schema canary refuses to bill if the source header drifts. ### βš™οΈ Sample inputs **1. Quick start β€” the Store example (this is what the sample above came from)** ```json { "factorIds": [ "22721" ], "maxRecords": 10, "maxScanRows": 20000 } ``` The sample run charged exactly: 1 Γ— $0.02 apify-actor-start + 1 Γ— $0.02 calorie_factor_record = **$0.04** on the Free tier β€” every delivered row was billed. **2. A smaller, narrowed run** ```json { "factorIds": [ "22721" ], "maxRecords": 5, "maxScanRows": 20000 } ``` Caps the run at 5 rows β€” about $0.12 on the Free tier ($0.02 start + 5 Γ— $0.02). **3. A full-size run** ```json { "factorIds": [ "22721" ], "maxRecords": 2500, "maxScanRows": 20000 } ``` Up to 2500 rows (the schema default for `maxRecords`) β€” about $50.02 on the Free tier ($0.02 start + 2500 Γ— $0.02) if the source has that many. ### 🧾 JSON sample record One real record from run `ittKzs7eOPjlzOktE`, exactly as it lands in the dataset (emails masked, long text shortened): ```json { "record_type": "calorie_factor_record", "food_nutrient_conversion_factor_id": "22721", "protein_value": "4.27", "fat_value": "9.02", "carbohydrate_value": "3.87", "fdc_id": "746952", "context_note": "published USDA Atwater-style calorie conversion factors (kcal per gram) that apply ONLY within their source food's context; no calorie calculation is performed by this actor", "source_url": "https://fdc.nal.usda.gov/food-details/746952/nutrients", "source": "fdc-calorie-conversion-factors", "release_date": "2026-04-30", "observed_at": "2026-09-19T02:19:35Z", "release_checksum": "6a860e3e-1cb35fb7", "license": "USDA FoodData Central β€” CC0 1.0 (public domain dedication). Cite: U.S. Department of Agriculture, Agricultural Research Service, FoodData Central.", "attribution": "USDA Agricultural Research Service β€” FoodData Central (https://fdc.nal.usda.gov)" } ``` ### πŸ”§ How it works **Source.** The actor reads fdc.nal.usda.gov β€” endpoints: `https://fdc.nal.usda.gov`, `https://fdc.nal.usda.gov/fdc-datasets/FoodData_Central_csv_`. Public pages and feeds only; nothing behind a login. **Transport.** Plain HTTPS from the Apify platform, no proxy. robots.txt is read first and a disallowed path is never fetched. Every request carries an identified contact User-Agent. Pacing: `CRAWL_DELAY`=0.0. **Terminal states.** A run ends NORMAL, CAPPED (your cap was reached), PARTIAL (something was withheld and the message says what), GENUINE_EMPTY (the source was read and truly had nothing in scope) or BLOCKED (the source refused or changed shape β€” the run FAILS loud and bills nothing). A zero-row run is never reported as a silent success. **Charging.** Each calorie conversion factor record is charged at the moment it is pushed (`calorie_factor_record`); a row that fails to charge is not delivered, so the dataset count always equals the charged count. **πŸ”§ How it works** **What this actor is β€” and is NOT** **It is** a faithful mirror of factors USDA already published. The values are copied exactly as they appear in the release; nothing is computed, rounded, or re-derived. **It is NOT** a calorie calculator. This actor performs **no calorie calculation**, produces **no diet product**, and makes **no claim that a food's factors apply outside their source context**. Atwater-style factors are food-specific: the factors published for one food are valid **only** for that food. A `context_note` on every record states this in plain language. No health claim of any kind is made. **The door** The only source is the **official FoodData Central full-CSV dump** β€” `https://fdc.nal.usda.gov/fdc-datasets/FoodData_Central_csv_.zip`. The 481 MB archive is **never materialized**: the actor reads the ZIP central directory over HTTP **Range** requests and streams only the two members it needs (`food_calorie_conversion_factor.csv` and the small `food_nutrient_conversion_factor.csv` dimension). The keyed FoodData Central API is **never** called β€” there is no `api_key`, `apiKey`, or `DEMO_KEY` anywhere in this actor. **Licence & attribution** USDA FoodData Central is released into the public domain under **CC0 1.0**. Cite: *U.S. Department of Agriculture, Agricultural Research Service. FoodData Central.* `https://fdc.nal.usda.gov`. **What is not done.** No login, no cookie or CAPTCHA bypass, no private or personal-account data, no browser automation. ### πŸ’° Pricing example | Event | Free | Bronze | Silver | Gold | |---|---|---|---|---| | Actor Start (`apify-actor-start`) | $0.02 | $0.02 | $0.02 | $0.02 | | Calorie conversion factor record (`calorie_factor_record`) | $0.02 | $0.018 | $0.016 | $0.0134 | Worked at the live Free-tier price: - 1 calorie conversion factor records: $0.02 start + 1 Γ— $0.02 = **$0.04** - 25 calorie conversion factor records: $0.02 start + 25 Γ— $0.02 = **$0.52** - 2500 calorie conversion factor records: $0.02 start + 2500 Γ— $0.02 = **$50.02** A run that delivers zero rows charges the $0.02 start fee only. A BLOCKED run (source refused) fails loud and charges no value event. The start fee is charged once per GB of run memory; the default run memory is 4096 MB. Yield on the sample run: `NORMAL: 1 unique rows (0 duplicates collapsed) from 2026-04-30. billable=1`. `maxRecords` is a hard ceiling on what is delivered and billed, never a target. ### βš–οΈ Legal & ToS This actor reads public, logged-out pages and feeds published by fdc.nal.usda.gov. It collects only what the source publishes to any visitor, keeps to the source's robots rules (checked on every run), identifies itself with a contact User-Agent, and does not access accounts, private data or anything behind authentication. Use the output in line with the source's terms and your local law; the intended use is B2B research and monitoring. ### ❓ FAQ **Q: Do I need an API key or a login?**\ A: No. The source (fdc.nal.usda.gov) is read logged-out; the input schema has no key field and the actor carries no secrets. **Q: Why did my run return 0 rows?**\ A: Read the run's status message. GENUINE_EMPTY means the source was read and had nothing in scope for your input; BLOCKED means the source refused and the run failed without billing a value event β€” retry later or narrow the input. A zero-row run bills the start fee only. **Q: How many rows can one run return?**\ A: Up to `maxRecords` (default 2500). Raise the cap for a bigger run; you pay per delivered row. **Q: How fresh is the data?**\ A: Every run reads the source live at run time; nothing is cached between runs. Put it on a schedule for a continuous feed. **Q: What formats can I export?**\ A: The dataset downloads as JSON, CSV, Excel, XML or RSS from the run's Dataset tab or the Apify API, and any run can push to a webhook or integration. **Q: How is this different from the other NexGen Watch actors actors?**\ A: Same output shape and billing model; this one covers fdc.nal.usda.gov. The siblings under **Related Actors** cover the other sources or slices β€” run several on one schedule for a combined feed. **Q: Are there rate limits?**\ A: The actor paces itself against the source (`CRAWL_DELAY`=0.0) and honours its robots rules; there is no per-buyer limit beyond your Apify plan's concurrency. ### πŸ†˜ Troubleshooting - **Run FAILED with BLOCKED** β†’ the source refused the request or changed its page shape β†’ nothing was billed beyond the start fee; retry after a while, and if it persists open an Issue with the run id. - **Status says CAPPED** β†’ your cap (`maxRecords`) was reached β†’ raise it for a bigger run. - **Input validation error on start** β†’ a field is outside the schema's allowed values β†’ start from the Quick start block and change one field at a time. - **Run TIMED-OUT** β†’ a very wide request on a slow day β†’ raise the run timeout in Run options or narrow the input; what was delivered before the timeout is still in the dataset. ### πŸ”— Related Actors - [EU Transparency Register Organisations](https://apify.com/nexgenwatch/eu-transparency-register-organisations?fpr=2ayu9b) β€” One structured organisation_record per interest representative in the EU Transparency Register β€” from the official current full XML export, keyless a… - [Euronext Oslo Regulated News](https://apify.com/nexgenwatch/euronext-oslo-regulated-news?fpr=2ayu9b) β€” One record per regulated Euronext Oslo announcement β€” issuer, title, category, topic, timestamp, official archive link. Disclosure facts only; the an… - [USDA FDC Analytical Sub-Sample Results](https://apify.com/nexgenwatch/fdc-analytical-subsample-results?fpr=2ayu9b) β€” Structured laboratory sub-sample measurement records from USDA FoodData Central, read directly from the official FDC full-CSV data dump (sub_sample_r… - [USDA FDC Sample Provenance Graph](https://apify.com/nexgenwatch/fdc-sample-provenance-graph?fpr=2ayu9b) β€” The provenance / traceability graph behind USDA FoodData Central foods β€” how a sample food links to its sub-samples and to the market acquisition it… - [USDA FNDDS Recipe Ingredient Composition](https://apify.com/nexgenwatch/fndds-recipe-ingredient-composition?fpr=2ayu9b) β€” Structured recipe composition records from USDA FoodData Central β€” which input foods compose each survey (FNDDS) or recipe (SR-legacy) food β€” read di… - [USDA FNDDS Survey Food Master](https://apify.com/nexgenwatch/fndds-survey-food-master?fpr=2ayu9b) β€” Structured FNDDS (What We Eat In America) survey food master records from USDA FoodData Central, read directly from the official FDC full-CSV dump (s… - 🏒 **About NexGenData** β€” NexGen Watch is NexGenData's fleet of more than 300 public data actors built on official sources, pay-per-result. Browse the catalog at [apify.com/nexgenwatch](https://apify.com/nexgenwatch?fpr=2ayu9b). ### ⭐ Found this useful? If this actor saved you a manual check, a quick **[review on the Apify Store](https://apify.com/nexgenwatch/fdc-calorie-conversion-factors?fpr=2ayu9b)** helps other teams find it. Feature request or a source that changed? Open it from the **Issues** tab β€” every one is read. # Actor input Schema ## `factorIds` (type: `array`): food_nutrient_conversion_factor_id values, e.g. \["22721"]. At least one of factorIds / fdcIds is REQUIRED β€” the member is never emitted in full. ## `fdcIds` (type: `array`): Source-food fdc_id values, e.g. \["167512"]. Matched against the fdc_id attached from the parent conversion-factor dimension. ## `maxRecords` (type: `integer`): Max calorie-factor records delivered (buyer bound; one charge per delivered record). Hard-capped at 5000. ## `maxScanRows` (type: `integer`): Hard scan bound over the streamed food_calorie_conversion_factor.csv member. Default 20000. ## `releaseUrl` (type: `string`): Pin a specific FoodData_Central_csv\_.zip URL. Default: newest release. ## `userAgent` (type: `string`): Override the transparent crawler UA. ## `searchText` (type: `string`): Optional. Keep only rows where any field contains this text (case-insensitive, substring). Applied after the source is read and BEFORE the maxRecords cap, so the cap counts matches and only delivered matches are billed. Zero matches = EMPTY_SELECTION, nothing charged beyond the start fee. ## `fieldMatch` (type: `object`): Optional. {"column": "text"} keeps rows whose named output column contains the text (case-insensitive); a list of texts means any of them; several columns must all match. Column names are the dataset fields shown in the sample output. Applied BEFORE the maxRecords cap and before billing. ## Actor input object example ```json { "factorIds": [ "22721" ], "fdcIds": [], "maxRecords": 10, "maxScanRows": 20000 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "factorIds": [ "22721" ], "maxRecords": 10, "maxScanRows": 20000 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgenwatch/fdc-calorie-conversion-factors").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "factorIds": ["22721"], "maxRecords": 10, "maxScanRows": 20000, } # Run the Actor and wait for it to finish run = client.actor("nexgenwatch/fdc-calorie-conversion-factors").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "factorIds": [ "22721" ], "maxRecords": 10, "maxScanRows": 20000 }' | apify call nexgenwatch/fdc-calorie-conversion-factors --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,nexgenwatch/fdc-calorie-conversion-factors" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/2TXL7LT3WmrEPO1yd/builds/MlAZu7PfDiZqWp9oh/openapi.json