HI

Hyperspectral Imaging in food industry

A possible solution to the necessary revolution in the food industry

START

Dr Maria Ricci, riccimaria@gmail.com

Index

Context

Technique

Information

Examples

Challenges

SECTION 01

Context

What do we want from our food?

Quality

Safety

Authenticity and Compliance

Minimal food waste

Which aspects are relevant for each category?

Quality

Safety

Authenticity and Compliance

Minimal food waste

How do we normally inspect food?

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.

SECTION 02

Technique

Spectroscopy

Imaging

Concept of hyperspectral 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.

Reference

Acquisition of hyperspectral images

The hardware of an HSI system is fundamental in the acquisition of hyperspectral image data. A typical HSI system consits on: 1. light sources 2. wavelength dispersion devices 3. detectors

Reference

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

Reference

trasmittance

interactance

reflectance

SECTION 03

Information

Reference

Reference

Hyperspectral image processing methods

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.

Reference

Reference

Reference

SECTION 04

Examples

+ info

SECTION 05

Challenges

Where does the devil hide?

• 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

Thanks for your attention!

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