4.15 Avocados - Biological

4.15.1 Background

This case study was performed for a group of the CSIR’s food technologists. The study was based on a similar case that was performed in the USA with nuts. The aim of this study was to separate the fat rich areas from the water rich areas within the avocado. The separation is normally done by destroying the avocado in order to determine the stage of ripeness. The food technologists hoped to develop a non-destructive evaluation method to determine the ripeness of the avocado. This same process can then be used to analyze other fruit such as mangos.

The avocados were marked and placed in fixture. Avocados at different stages of ripeness were selected.

Pretoria East Hospital was again approached to perform the CAT scanning. . A series of axial sections were produced. The data was retrieved and converted. The next step was to separate the fat-rich areas from the water-rich areas. The water rich areas were the regions of interest.

Every material has certain properties. Digital images are produced as a result of the CAT scanning. These digital images consist of pixels. Each pixel represents a part of the component that is scanned. The pixels are also made up of different Grey scale values, better known as the Hounsfield unit. This phenomenon was used to separate the water rich areas from the fat rich areas. Pure water’s Hounsfield unit is 1000 while that of air is 0. The Hounsfield theory is described in the Basic Principals of CAT. Separation was achieved by setting the bandwidth of the threshold to approximately 1000 Hounsfield units. A 10 units tolerance band was used.

4.15.2 Conclusion

This unique method was successfully applied to supply the food technologists with results. Again, this method proved to be the only non-destructive method to perform the analysis.

The volumes and areas of dispersion were determined for every avocado of the water rich areas as well as the complete avocado. The food technologists further manipulated the data that showed a clear trend. This process proved to be helpful towards the predicament of the avocado’s ripeness. Avocados could be scanned on a routine basis where the ideal ripeness stage could be predicted. Environmental influences had to be taken into account during the predicament. This case study opened a brand new field of application for this wonderful unique combination of technologies. Biological applications can now be added to the medical, industrial and fossil related fields of applications. The following table will list the mass and volumetric data.

Table 4.15.2 – Avocado Mass & Volumetric Data
Avocado Reference Original Mass


Total Volume


Water Rich Volume


2 (green)r2c1 299.13 285196.44 32736.81
3 (green)r3c1 303.65 283934.00 22351.92
4 (green)r1c2 277.85 280365.84 17893.51
5 (green)r2c2 290.17 278140.41 14342.11
6 (green)r3c2 311.41 293161.38 27504.15
8 (green)r1c1 336.5 355902.69 12883.40
10 (green)r1c3 256.02 282911.09 21972.28
12 (green)r2c3 272.72 257097.56 14684.34
13 (green)r3c3 217.72 207626.73 8024.77
14 (ripe)r1c4 265.02 230853.88 26589.66
15 (ripe)r2c4 272.72 230994.16 21502.88
16 (ripe)r3c4 217.72 278660.28 72146.28 Images

Isometric view of several avocado’s water rich areas.


Front view of several avocado’s water rich areas.


4.15.3 Avocado Data Sheet:
  Description Options (Default) Data


1 CT Image Names   Avos.00
2 Patient/Project Name   Avos.pat
3 Number of First Input Image   212
4 Number of Last Input Image   -254
5 Number of First Output Image   000
7 Horisontal Nr. Of Image Pixels 0 to 65535 (265,512,1024) 512
8 Vertical Nr. Of Image Pixels 0 to 65535 (265,512,1024) 512
9 Number of Images per File (1) 1
10 File Swap Format (0,3) 0,3 0
11 Pixel Type B,UB,S,US,L,UL,F Toshiba
12 Header Size *see formula below -
13 Inter Image Header Size 0 -
14 Add Value 0 to 4095 -
15 Scale Value 0 to 4095 -
16 Table Position (mm) -254
17 Distance Between Slices (mm) 3
18 Slice thickness (mm) 3
19 Pixel Size SQ. F.O.R./Nr. Hor. Pixels (mm) 0.7
20 Gantry Tilt Angle Degrees 0
21 Field of Reconstruction/View (mm) 380
22 Number of Images   150
23 File Size of CAT Image kb 521
24 File Size of Converted Image kb 10-40
25 .3dd file size Mb 2.5 ea.
26 .STL file size Mb -
27 RP Method (SLA,FDM,OTHER) -
28 .IGS file size Mb -
29 RP Slice file size Mb -
30 RP Download File size Mb -
31 Grow Time Hour -
32 Tip size (T12, T25) -
33 Slice Thickness (0.01", 0.014") -
34 Finishing Time Hour -
35 Processing Time Hour 5
36 Data Retrieval Time Hour 4
37 Total Cost Rand =9*100+300=1200


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