AI for Food Process Engineering

A packing line, explained to someone who has never set foot in a plant. The model flags. The lab releases.

Developed under the new AI Act model · Continuous updates

IntellCore campus app, available on iOS and Android

Edition 2026

200 hours of applied learning

8 academic credits

25 lessons + 6 workshops

Digital campus

Verifiable certificate

550 USD one payment · or 2 × 275 USD (today and after 30 days)

Enroll here

What you'll learn

Walk the line in order and without acronyms: intake, processing, packing and warehouse.
Explain lot, label and allergens under Regulation (EU) 1169/2011, and who runs the line and who releases.
Read the shift reject rate and know why the lab, not the camera, closes the point.
Interpret a vision model flags with their precision, threshold and drift.
Keep the model ingredient change as a draft alert, never as a formula change.
Write a sheet that reads without the chat, with the release signature left empty.
Apply the IntellCore AI Act Method™: classify the risk of an AI use case in your field and document it under the AI Act 2026.

Program curriculum

01 — The place

What AI is and how it reached the food plant
Intake, processing, packing and warehouse
Lot, label and allergen
Who runs the line and who releases
Your role

02 — The number that already exists

The shift reject rate
The eye and the laboratory
A shift without the model
The limit already written

03 — Where the model may propose

Vision flags lids
A new formula is a separate file
What is missing
An alert is opened, the recipe is not changed

04 — The tool

What vision counts
Drafts within the approved list
What it does not do
To the lab, not to the chat

05 — The failure

A thickener that is not on the list
No trial and no allergen
The text on the production floor
What gets blocked

06 — The file

From jar to shift
Change requested and test still missing
Who releases and who only flags
A sheet the plant can file

07 — IntellCore AI Act Method™ applied to your field

The EU AI Act 2026: risk levels, prohibited practices and obligations
Transparency, human oversight and documentation in your professional field
Classifying and auditing a real AI use case step by step
Your compliance file: certified with the IntellCore AI Act Method™

Practical application

· Final project: the shift sheet for the case lot, with the data, the thickener proposal as a draft alert, what was not checked and an empty release signature, fileable without the chat and defended before quality, R&D and audit.

Practical project: Final project: the shift sheet for the case lot, with the data, the thickener proposal as a draft alert, what was not checked and an empty release signature, fileable without the chat and defended before quality, R&D and audit.

Who is this for?

· People about to sit in front of a packing-line shift
· Food, quality and production teams without plant vocabulary
· Analysts who review model proposals

Requirements

· No prior plant experience
· No programming required

Meet your instructor

AI for Food Process Engineering

Chair, AI for Food Process Engineering

Packing lines, shift rejects, vision alerts and the shift sheet — the model flags, the lab releases.

Teaches: AI for Food Process Engineering

Edition 2026

What each week includes

Every week of the program follows the same structure.

01
Explainer video per lesson
02
Guided reading (6 h)
03
IntellCore Labs · ~90 min workshop with data
04
Verified real case
05
IntellCore Podcast
06
IntellCore Radar
07
Self-assessment
08
Practice and exam (70%, 3 attempts)
09
Extended practice (5 h)
10
IntellCore AI Act Method™ module applied to your field (30 h)
11
Applied final project (30 h)
12
6 modules × 23 h + IntellCore AI Act Method™ module 30 h + final assessment 2 h + final project 30 h = 200 h
13
Downloadable PDF summary