Characterization of optical parameters in seeded and seedless watermelon using laser-induced backscattering imaging
Monitoring of watermelon fruit quality is essential in order to regulate proper postharvest handling and yield production. Problems arise in forecasting the quality parameters of watermelon during storage as the shelf-life of the fruit is only lasts for three weeks. The non-climacteric nature of...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/71135/1/FK%202017%2031%20-%20IR.pdf http://psasir.upm.edu.my/id/eprint/71135/ |
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| Summary: | Monitoring of watermelon fruit quality is essential in order to regulate proper
postharvest handling and yield production. Problems arise in forecasting the quality
parameters of watermelon during storage as the shelf-life of the fruit is only lasts for
three weeks. The non-climacteric nature of the fruit is also related to high perishability
which does not undergo a continuous process to ripen after being harvested. In the
present study, laser-induced backscattering imaging was used to determine the
firmness, soluble solids content (SSC), pH, moisture content (MC), and colour changes
of watermelons in seven interval days starting from storage day 0 after harvesting (day
0, day 4, day 8, day 12, day 15, day 18, and day 21). Two types of watermelon
cultivars were used in this study; seeded watermelon (Black Beauty) and seedless
watermelon (Red Seedless). The backscattered images of the fruit surface were
obtained at six different locations using laser diodes emitting at 658 nm wavelength.
The backscattered images were analysed and the feature information was extracted
based on the backscattering image parameters which are minor length, major length,
perimeter, maximum intensity, minimum intensity, and mean intensity. The standard
reference methods were carried out after the image acquisition process in order to
determine the quality parameter measurements.
The multivariate analysis was used to optimise the classification between seeded and
seedless watermelons, as well as the regression models between backscattering data
and quality parameters, were also discussed. Principal component analysis (PCA) was
used to classify the seeded and seedless watermelons into two different classes
according to the physicochemical changes. Partial least squares (PLS) regression with
full cross-validation method was used to establish the regression models between the
backscattering data and quality parameters. The firmness values obtained were 5.07 to
3.24 kg/cm2 and 4.78 to 3.03 kg/cm2, whereas the SSC values achieved 9.06 to 5.66
Brix and 8.52 to 6.41 Brix for the seeded and seedless watermelons, respectively. The
pH values were 5.27 to 7.10 pH and 5.60 to 6.42 pH while the moisture content values revealed 95.46 to 82.79 % w.b. and 94.53 to 86.43 % w.b. for the seeded and seedless
watermelons, respectively. For colour parameters, the L* values ranged from 24.08 to
51.06, whereas the b* values ranged from 11.52 to 36.84. The chroma and hue values
were also increased from 12.41 to 38.95 and 104.55º to 111.60º, respectively. The a*
value reduced significantly (P<0.05) from -4.32 to -13.06 for both seeded and seedless
watermelons. For seedless watermelon, the colour prediction (L*, a*, b*, chroma, and
hue) gave the highest coefficient of determination (R2) with all of them above 0.90.
Meanwhile, the firmness prediction gave the highest R2 of 0.92 for seeded watermelon.
On the whole, it is concluded that the application of laser-induced backscattering
imaging is a promising technique for assessing quality parameters of watermelons
during storage. The proposed approach has significant potential as a well-controlled
system for developing automated sorting and grading system in the future. |
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