HI
Maria Ricci
Created on May 8, 2022
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Transcript
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!