HI

START

Dr Maria Ricci, riccimaria@gmail.com

A possible solution to the necessary revolution in the food industry

Hyperspectral Imaging in food industry

Information

Challenges

Technique

Examples

Context

Index

Context

SECTION 01

What do we want from our food?

Minimal food waste

Authenticity and Compliance

Safety

Quality

Which aspects are relevant for each category?

Minimal food waste

Authenticity and Compliance

Safety

Quality

Reference

Precise, rapid and objective inspection systems throughout the entire food process is important to ensure the customers' satisfaction. It is therefore necessary to find accurate, reliable, efficient and non-invasive alternatives to evaluate quality and quality-related attributes of food products.

How do we normally inspect food?

Technique

SECTION 02

Spectroscopy

Imaging

Concept of hyperspectral Imaging

By integrating two classical optical sensing technologies of imaging and spectroscopy into one system, hyperspectral imaging can provide both spatial and spectral information, simultaneously. Therefore, hyperspectral imaging has the capability to rapidly and non-invasively monitor both physical and morphological characteristics and intrinsic chemical and molecular information of a sample.

Concept of hyperspectral Imaging

Reference

The hardware of an HSI system is fundamental in the acquisition of hyperspectral image data. A typical HSI system consits on:1. light sources2. wavelength dispersion devices3. detectors

Acquisition of hyperspectral images

Reference

Reference

There are three different sensing mode, depending on the relative position of light source and detector:

trasmittance

reflectance

interactance

Information

SECTION 03

Reference

Reference

Because the data volume of a hyperspectral image is usually very large and suffers from collinearity problems, chemometric algorithms are required for mining detailed important information. These are the typical steps of a full algorithm for analyzing hyperspectral image.

Hyperspectral image processing methods

Reference

Reference

Reference

Examples

SECTION 04

+ info

Challenges

SECTION 05

• Redundancy of information renders the classification challenging to achieve: improvements on the classification algorithms in terms of accuracy and speed are required
• Quite dispersive: extremely high number of applications and possible parameters
• Difficult trade-off between efficiency and broadness of the applicability

• Possible lack of good reference data for classification purposes mainly for missing standards in sample preparation and data treatment
• There are little studies directly performed at the idustrial sites but rather in laboratories

Where does the devil hide?

Do you have any question?

Thanks for your attention!