Sirisomboon, Panmanas

Preferred name

Alternative Name

Sirisomboon, P.

Main Affiliation

School of Engineering

panmanas.si@kmitl.ac.th

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2D/3D vision-based mango's feature extraction and sorting
(2006-01-01)
Chalidabhongse, Thanarat
;
Yimyam, Panitnat
;
Sirisomboon, Panmanas
This paper describes a vision system that can extract 2D and 3D visual properties of mango such as size (length, width, and thickness), projected area, volume, and surface area from images and use them in sorting. The 2D/3D visual properties are extracted from multiple view images of mango. The images are first segmented to extract the silhouette regions of mango. The 2D visual properties are then measured from the top view silhouette as explained in [7]. The 3D mango volume reconstruction is done using volumetric caving on multiple silhouette images. First the cameras are calibrated to obtain the intrinsic and extrinsic camera parameters. Then the 3D volume voxels are crafted based on silhouette images of the fruit in multiple views. After craving all silhouettes, we obtain the coarse 3D shape of the fruit and then we can compute the volume and surface area. We then use these features in automatic mango sorting which we employ a typical backpropagation neural networks. In this research, we employed the system to evaluate visual properties of a mango cultivar called "Nam Dokmai". There were two sets total of 182 mangoes in three various sizes sorted by weights according to a standard sorting metric for mango export. Two experiments were performed. One is for showing the accuracy of our vision-based feature extraction and measurement by comparing results with the measurements using various instruments. The second experiment is to show the sorting accuracy by comparing to human sorting. The results show the technique could be a good alternative and more feasible method for sorting mango comparing to human's manual sorting. © 2006 IEEE.
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Physical properties of green soybean: Criteria for sorting
(2007-03-01)
Sirisomboon, Panmanas
;
Pornchaloempong, Pimpen
;
Romphophak, Teeranud
The objective of this research was to explore the possibility in developing a criteria for green soybean (Glycine max variety AGS 292) sorter. Green soybean could be classified into two main categories by its usage; the perfect pods (3-seed pod and 2-big seed pod) contained 47%, the rest was imperfect pods including 2-small seed pod, 1-seed pod, atrophied pod, twisted pod, and defected pod. Some important physical characteristics were investigated including width, length, thickness, pod weight, pod projected area, apparent density, bulk density, and seed firmness. The results illustrated that the perfect pods had length, pod weight, projected area and seed firmness significantly larger than the imperfect pods. All pod groups had apparent density equal or lower than water. Among all pod groups, the 2-big seed pods had the highest bulk density, thickest pod, and the firmest seed. The application of these physical properties for green soybean sorting was also proposed. © 2006 Elsevier Ltd. All rights reserved.
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Physical and mechanical properties of Jatropha curcas L. fruits, nuts and kernels
(2007-06-01)
Sirisomboon, P.
;
Kitchaiya, P.
;
Pholpho, T.
;
Mahuttanyavanitch, W.
The post-harvest physical and mechanical properties of Jatropha curcas L. fruits, nuts and kernels were investigated and reported, and their application was also discussed. The physical properties studied include moisture content, 1000-unit mass, fruit part fraction, dimensions, geometric mean diameter, sphericity, bulk density, solid density, porosity, surface area, specific surface area, static friction coefficient on various surfaces and angle of repose. The mechanical properties were rupture force, deformation at rupture point, deformation ratio at rupture point, hardness and energy used for rupture (toughness). The hull of the fruit had very high moisture content compared to nut shell and kernel. The whole fruit contained 77.03% w.b. moisture content. The sphericity values indicated that fruit shape (0.95) is close to a sphere compared to nut (0.64) and kernel (0.68), both of which are close to an ellipsoid. Bulk densities of fruits, nuts and kernels were 0.47, 0.45 and 0.42 g/cm3, the corresponding solid densities were 0.95, 1.04 and 1.02 g/cm3, and the corresponding porosities were 50.53%, 56.73% and 58.82%, respectively. The surface area of fruit was larger than those of nut and kernel, by 5.88% and 10.24%, respectively. The static coefficient of friction and angle of repose of kernels on all surfaces studied (plywood, steel, and stainless steel) were the highest as the surface is viscous and hardness is less. Rupture force, hardness and toughness of fruit, nut and kernel were 135.39, 146.63 and 67.72 N; 30.58, 69.98 and 38.52 N/mm and 300.88, 124.44 and 51.61 N mm, respectively. © 2007 IAgrE.
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Plant compounds and fruit texture: The case of pear
(2004-04-14)
Kojima, T.
;
Fujita, S.
;
Tanaka, M.
;
Sirisomboon, P.
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A preliminary study on classification of mango maturity by compression test
(2008-01-01)
Sirisomboon, Panmanas
;
Boonmung, Suwanee
;
Pornchaloempong, Pimpen
;
Pithuncharurnlap, Manat
A preliminary study toward the design of a firmness tester suitable for mango maturity classification was conducted through an experiment consisting of two parts: (1) probe selection, followed by (2) evaluation of the selected probe in mango maturity classification using a texture analyzer. This resulted in a technique based on the firmness of the fruit measured by a compression test with a maximum force of 3N using a 5mm diameter spherical stainless steel probe. This technique demonstrated the possibility of classifying the maturity of mangoes into two different stages, i.e., 60% and 80% of full ripeness. However, it could not detect the difference between 60% and 70% of full ripeness or between 70% and 80% of full ripeness.
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Physical properties of Jatropha curcas L. kernels after heat treatments
(2009-01-01)
Sirisomboon, P.
;
Kitchaiya, P.
The drying characteristic and physical properties of kernels of Jatropha curcas L. after heat treatments were investigated. The treatments included drying at three different temperatures (40, 60, and 80 °C) and steaming. The drying characteristics studied included the relationship of moisture content, moisture ratio, and drying rate to drying time. The best fit for all parameters was the logarithmic model. Important physical properties of the kernels were measured. The kernels contained moisture 3.78, 4.01 and 2.82% wet basis at 40, 60 and 80 °C, respectively. The sphericity of dried kernels was 0.65-0.66 and 0.53 for steamed kernels. The bulk densities of dried kernels and steamed kernels were 403-513, and 509 kg m-3, solid densities were 951-971 and 1082 kg m-3, porosities were 46.00-59.31 and 52.86%, and specific surface areas were 177-241 and 154 m2 m-3, respectively. The static coefficient of friction and angle of repose of the steamed kernels were the highest because of their half section shape, lower sphericity, more viscous surface and soft texture. Drying at 80 °C gave the highest oil yield at 47.06% and the highest acid value. Drying at 40 °C gave a lower oil yield at 36.83% but the lowest acid value. Oil yield of steamed kernels was very low (18.13%). The temperature of the drying process had a minor effect on viscosity and ash content but had a significant effect on free fatty acid content and acid value. The viscosity of the kernel oil was 33.91-34.53 cSt at 40 °C. © 2008 IAgrE.
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Study on non-destructive evaluation methods for defect pods for green soybean processing by near-infrared spectroscopy
(2009-08-01)
Sirisomboon, Panmanas
;
Hashimoto, Yuki
;
Tanaka, Munehiro
Reflectance spectroscopy ranging from visible light to the near infrared region (600-1100 nm) was investigated for the detection of outer and inner defects of fresh green soybean pods. The outer defect parameters, defined by the processing factory which exports frozen green soybean, were hangnail, thinness, brown spot, insect-eaten, rotten, and worm-eaten, and the inner parameters were those caused by disease, i.e. downy mildew and anthracnose, or by worm inside the pod. Using 802 samples, classification models for each group were identified, based on principle component analysis (PCA). Models were developed using primary spectra and second derivative spectra. The PCA score plots could classify clearly the group affected by downy mildew, those with worm inside, and the worm-eaten, rotten, and thin groups from the good pod group. The primary spectra with or without some kind of pretreatment showed a higher classification performance than the second derivative spectra. The good pod model created by primary spectra correctly classified 77.2% of samples as good pods or defective pods. SIMCA showed obviously better performance than PLS-DA in classification of green soybean pods. The good pod model by SIMCA could 100% self-prediction, though it showed low performance in predicting the other groups. This study provided the information by using NIR spectroscopy in the green soybean grading process in order for the appropriate sorting instruments to be developed. © 2009 Elsevier Ltd. All rights reserved.
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Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy
(2007-01-01)
Sirisomboon, Panmanas
;
Tanaka, Munehiro
;
Fujita, Shuji
;
Kojima, Takayuki
Japanese pears (Pyrus serotina Rehder var. culta 'Housui') collected in 1997 and 1998 were measured for their pectin constituents including alcohol insoluble solids, water soluble pectin, oxalate soluble pectin, non-soluble pectin and total pectin. Near infrared (NIR) spectra (1100-2500 nm) were measured within the range of at 2 nm intervals. The NIR spectra of intact Japanese pear were measured by fiber optics in interactance mode and the spectra of juice were measured by diffuse trans-reflectance. The spectral data used were raw spectra and their second derivative. By using multiple linear regression, calibration equations developed from the intact fruit spectra and juice spectra, the alcohol insoluble solids in the fresh weight (AIS in the FW) and the oxalate soluble pectin content in the alcohol insoluble solids (OSP in the AIS) were accurately predicted (For intact fruit spectra: R = 0.93, SEP = 0.62 for AIS in the FW, and R = 0.95, SEP = 8.48 for OSP in the AIS; For juice spectra: R = 0.93, SEP = 0.63 for AIS in the FW and R = 0.91, SEP = 7.93 for OSP in the FW). In addition, the equations from the juice spectra could be used to predict the water soluble pectin in the alcohol insoluble solids (WSP in the AIS), and the total pectin in the alcohol insoluble solids (TP in the AIS) (R = 0.91, SEP = 1.41 for WSP in the AIS and R = 0.94, SEP = 11.52 for TP in the AIS). The NIR models developed with the data collected in 1998 were not able to predict the 1997 data. This study showed that near infrared spectroscopy has potential to measure the pectin constituents of the Japanese pear. © 2005 Elsevier Ltd. All rights reserved.

Sirisomboon, Panmanas

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