Computerized recognition of pineapple grades using physicochemical properties and flicking sounds
Keywords:
Ananas comosus L., translucency, total soluble solid, pH, physiochemical properties, flicking sounds, computerized pineapple grading, non-destructive gradingAbstract
Fruit is one of the essential sources of human nutrition. Consumers around the world need to be able to purchase fruit of reliable flavor and nutritional quality. Physical appearance and physicochemical properties play a key role in determining desirable quality and flavor. However, for some fruits such as watermelon, durian, pineapple, it is very hard to determine quality and flavor by external appearance. Therefore, a practical method to predict physical and physicochemical properties of fruit needs to be developed. In this study, a computerized technique is investigated to determine pineapple grades and their physical and physicochemical properties, including ripeness, total soluble solids, pH value and water content. The results reveal that by grading using pulp characteristics it is possible to classify pineapples into three distinct groups, which are significantly different in TSS, pH value and water content. In addition, predicting pineapple grades using flicking sounds and signal processing demonstrates that pineapples classified as grade 1 and grade 3 are significantly different in TSS, pH value and water content. This suggests that the estimation of the texture of pineapple pulp and its physicochemical properties can be performed prior to cutting. Therefore, it is feasible to develop an automated grading technique that can be used to determine pineapple quality as accurately as destructive grading to predict pineapple grades, texture and physicochemical properties. Keywords: Ananas comosus L., total soluble solid, pH, physiochemical properties, flicking sounds, computerized pineapple grading, non-destructive grading DOI: 10.3965/j.ijabe.20140703.011 Citation: Phoophuangpairoj R, Srikun N. Computerized recognition of pineapple grades using physicochemical properties and flicking sounds. Int J Agric & Biol Eng, 2014; 7(3): 93-101.References
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[10] Asnor J I, Rosnah S, Wan Z W H, Badrul H A B. Pineapple maturity recognition using RGB extraction. World Academy of Science, Engineering and Technology, 2013; 78: 147-150.
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[12] Kaewapichai W, Kaewtrakulpong P, Prateepasen A, Khongkraphan K. Fitting a pineapple model for automatic maturity grading. Institute of Electrical and Electronics Engineers, 2007; I-257-260.
[13] Zhu L, Seburg R A, Tsai E, Puech S, Mifsud J. Flavor analysis in a pharmaceutical oral solution formulation using an electronic-nose. Journal of Pharmaceutical and Biomedical Analysis, 2004; 34: 453-461.
[14] Anonymous. The Hidden Markov Model Toolkit (HTK). 2013, http://htk.eng.cam.ac.uk/. Accessed on [2013-07-12].
[15] Tangwongsan S, Po-Aramsri P, Phoophuangpairoj R. Highly efficient and effective techniques for Thai syllable speech recognition. Lecture Notes in Computer Sciences, 2004; 3321: 259-270.
[16] Phoophuangpairoj R. Determining guava freshness by flicking signal recognition using HMM Acoustic Models. International Journal of Computer Theory and Engineering, 2013; 5(6): 877-884.
[17] Phoophuangpairoj R. Automated classification of watermelon quality using non-flicking reduction and HMM sequences derived from flicking sound characteristics. Journal of Information Science and Engineering, 2014; 30(4): 1015-1033.
[18] Phoophuangpairoj R. Computerized unripe and ripe durian striking sound recognition using syllable-based HMMs. Applied Mechanics and Materials, 2014; 446-447: 927-935.
[19] Joomwong A. Impact of cropping season in northern Thailand on the quality of smooth cayenne pineapple. II. Influence on physico-chemical attributes. International Journal of Agriculture and Biology, 2006; 8(6): 330-336.
[2] Paull R E, Chen C-C. Postharvest physiology, handling and storage of pineapple. In: The Pineapple: Botany, Production and Uses. Bartholomew D P, Paull R E, Rohrbach K G (Eds.), CABI, New York, 2003; 253-278.
[3] Pongjanta J, Nualbunruang A, Panchai L. Effect of location and storage time on physicochemical properties of pineapple fruit. Asian Journal Food and Agro-Industry, 2011; 4(3): 153-160.
[4] Smith L G. Indices of physiological maturity and eating quality in smooth cayenne pineapples 1: Indices of physiological maturity, Queensland. Journal of Agriculture Animal Science, 1988; 45: 213-218.
[5] Kader A A. Pineapple: recommendation for maintaining postharvest quality. Agriculture and natural resources, 2013. http://postharvest.ucdavis.edu/PFfruits/Pineapple/. Accessed on [2013-07-20].
[6] Haff R P, Slaughter D C, Sarig Y, Kader A. X-Ray assessment of translucency in pineapple. Journal of Food Processing and Preservation, 2006; 30: 527-533.
[7] Bowden R P. Further studies on ripeness in pineapple. Food Technology Australia, 1969; 21: 160-163.
[8] Chen C C. Effect of fruit temperature, calcium, crown and sugar metabolizing enzymes on the occurrence of pineapple fruit translucency, PhD dissertation. Hawaii: University of Hawaii, 1999.
[9] Chen C C, Paull R E. Sugar metabolism and pineapple flesh translucency. Journal of the American Society for Horticultural Science, 2000; 125(5): 558-562.
[10] Asnor J I, Rosnah S, Wan Z W H, Badrul H A B. Pineapple maturity recognition using RGB extraction. World Academy of Science, Engineering and Technology, 2013; 78: 147-150.
[11] Dole Fruit Hawii. How to select a fresh pineapple, 2013, www.dolefruithawaii.com/Articles.asp?ID=143. Accessed on [2013-07-12].
[12] Kaewapichai W, Kaewtrakulpong P, Prateepasen A, Khongkraphan K. Fitting a pineapple model for automatic maturity grading. Institute of Electrical and Electronics Engineers, 2007; I-257-260.
[13] Zhu L, Seburg R A, Tsai E, Puech S, Mifsud J. Flavor analysis in a pharmaceutical oral solution formulation using an electronic-nose. Journal of Pharmaceutical and Biomedical Analysis, 2004; 34: 453-461.
[14] Anonymous. The Hidden Markov Model Toolkit (HTK). 2013, http://htk.eng.cam.ac.uk/. Accessed on [2013-07-12].
[15] Tangwongsan S, Po-Aramsri P, Phoophuangpairoj R. Highly efficient and effective techniques for Thai syllable speech recognition. Lecture Notes in Computer Sciences, 2004; 3321: 259-270.
[16] Phoophuangpairoj R. Determining guava freshness by flicking signal recognition using HMM Acoustic Models. International Journal of Computer Theory and Engineering, 2013; 5(6): 877-884.
[17] Phoophuangpairoj R. Automated classification of watermelon quality using non-flicking reduction and HMM sequences derived from flicking sound characteristics. Journal of Information Science and Engineering, 2014; 30(4): 1015-1033.
[18] Phoophuangpairoj R. Computerized unripe and ripe durian striking sound recognition using syllable-based HMMs. Applied Mechanics and Materials, 2014; 446-447: 927-935.
[19] Joomwong A. Impact of cropping season in northern Thailand on the quality of smooth cayenne pineapple. II. Influence on physico-chemical attributes. International Journal of Agriculture and Biology, 2006; 8(6): 330-336.
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Published
2014-06-25
How to Cite
Phoophuangpairoj, R., & Srikun, N. (2014). Computerized recognition of pineapple grades using physicochemical properties and flicking sounds. International Journal of Agricultural and Biological Engineering, 7(3), 93–101. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1063
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Agro-product and Food Processing Systems
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