In - laser light backscattering image to monitor physical and chemical properties of ginger during drying.

1. In-line laser light backscattering image
analysis to monitor physico-chemical
properties of ginger during drying
M.Sc. Thesis
Aditya Parmar
Supervised by:
Dr. Giuseppe Romano
Dr. Marcus Nagle
MSc. Dimitri Argyropoulos
Prof. Dr. Joachim Müller
1

2. Why Lasers?
 Low cost and uncomplicated (vs. NIR and hyperspectral
imaging)
 Continous monitoring possible (In-line)
 Avoids product contamination (Non-contact)
 Easy handling (More flexible in terms of portability)
 Simultaneous prediction of physico-chemical properties
2

3. Ginger
Ginger is the rhizome of the plant Zingiber officinale consumed as a
delicacy, medicine and spice. Other notable members of this plant family
are turmeric, cardamom and galangal.
FAOSTAT 2010
3

4. Properties
Fresh Dry
No cell Oil
fracture in the
tissue
studied.
In addition to moisturecell
Ruptured
content and colour
changes, oil content will be investigated as
releases oil
and starch
well. grains in Starch
surrounding
tissue.
Source: M. Noor Azian el al ( 2003) 4

5. Lorentzian Distribution Function
• Analysis of laser backscattering images with image
processing software to obtain LD parameters.
• R-value (mean light intensity of each circular band) will be
calculated by MLD function .
Peng el al. (2006)
5

6. Pre-Test:Ginger
MCwb
92%
Fresh Sliced (5mm) Ginger
Ginger Fresh_535nm
Ginger Dry_535nm
Ginger Fresh_650nm
6

7. Pre-Test:Ginger
MCwb
3.4%
Over drying Senario
Ginger Dry_535nm
Ginger Dry_650nm
7

8. Failed Tests
MaizeWet_535nm
RiceWet_650nm
8

9. Objectives
 Establish laser backscattering image analysis as a valid
method to quantify the major quality parameters of ginger
during drying.
 To find the most sensitive wavelengths to predict physical
and chemical properties.
 Develop a prototype (In-line laser appratus) for monitoring of
product properties in the dryer.
Properties Method
Moisture Content Gravimetric oven method
Colour L*a*b ( Colorimeter)
Ginger Oil Hydrodistillation of essential oils
9

10. Experimental Procedures
• Mono-wavelength laser light
diodes ( 532, 635 & 785 nm)
• Camera and PaxCam software for
obtaining scattering images.
• A high-precision convective over-
flow dryer with adjustable
temperature, humidity and air
velocity.
• The dryer is modified by fitting a
transparent glass plate over the
airstream and product which can
be removed for measurement and
sampling 10

11. Experimental Procedure
• Electronic balance to measure
continuous mass reduction.
• A laboratory oven is used to
calculate the final moisture content.
• Hyrodistillation in Cleavanger
appratus.
• Preprocessing tools
(peeling/sclicing/washing etc)
• Colorimeter Minolta Chroma-meter
• Softwares for image analysis and
statistical evaluation.
11

12. Preparation and Data Collection
Step 1. Pre-drying Treatment
•Washing and peeling
•Slicing* (whole ginger rhizome for oil extraction)
•Pretreatment (optional)  Soaking in 0.5% citric acid to
prevent enzymatic browning reactions (S.Phoungchandang et al ,2009)
Drying Conditions
Program No. Tem. °C RH (%) Air Velocity (m/
s)
59 40°C 29% 0.25 & 0.75
60 60°C 11% 0.25 & 0.75
* Slice size ( TBA) 12

13. Preparation and Data Collection
Step 2. Obtaining scattering images and measurement of
physico-chemical properties
•Moisture content will be calculated (wet basis) at a frequency
of 15 minutes until constant mass is reached.
• Oil content every 30 minutes
• Surafce colour every 30 minutes
•Laser scattering images at each wavelength will be obtained
corresponding to the timing of the measurement of selected
physico-chemical properties
13

14. Data Analysis and Interpretation
Step 1. Relationship between LD parameters function (R)
and MCa.
• Moisture content actual (MCa) of the product will be
projected on a scatter diagram against the corresponding R-
values.
• A regression function for predicted moisture content (MCp)
will be developed with simple regression tools (MCp=f(R))
EXAMPLE 
14

15. Data Analysis and Interpretation
Step 2. Correlation analysis (r), coefficient of determination
(r2), and mean squared error (MSE)
After predicting the values of MCp from the calibration model
which were developed in the previous step, we can perform a
number of statistical analysis and validation techniques to
evaluate the accuracy and performance.
EXAMPLE 
Peng el al. (2006)
15

16. Data Analysis and Interpretation
Step 3. Model development and statistical analysis for
colour and oil content.
A similar approach as it was performed for moisture content
would be followed to develop calibration models (Xp* = f(R))
for predicting colour and ginger oil concentration and then
performing the statistical tests to validate the model.
* Xp  Predicted product properties
16

17. Expected Result and Conclusion
• We expect high coefficient of determination (r2 >0.70) for
the calibration models.
• The SEP is the standard error of prediction in order to
evaluate the effectiveness of each wavelength to predict a
certain quality parameter.
• If the model is suitably validated by the different statistical
tests and techniques, we can conclude that the laser
backscattering image analysis could be an applicable
technology in the field of in-line monitoring of drying process
for the future.
17

18. Working Plan
Time Period ( April to September Tasks and Mile Stones
2012)
April Conducting experiments
End of April First set of data analysis
May Conducting experiments
June Conducting experiments
July Data evaluation
August Presenting reuslt and analysis
Thesis preparation
September Thesis presentation
18

19. Thank you very much
for your kind attention
19