Random Articles
Reseach Article
Mango Fruit Quality Prediction using Associative Classification Rules
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 16 |
| Year of Publication: 2012 |
| Authors: Rattapol Pornprasit, Juggapong Natwichai, Bowonsak Srisungsittisunti |
10.5120/9198-3711
|
Rattapol Pornprasit, Juggapong Natwichai, Bowonsak Srisungsittisunti . Mango Fruit Quality Prediction using Associative Classification Rules. International Journal of Computer Applications. 57, 16 ( November 2012), 20-25. DOI=10.5120/9198-3711
Abstract
Near-infrared (NIR) spectroscopy is a non-destructive technique which can provide the quality measurement for agriculture products. In this paper, we propose an approach to utilize the NIR spectrum for mango fruits quality prediction. The prediction model is based on one of the most prominent machine learning approaches, associative classification. The associative classifiers are trained from the spectrum data of each mango fruit, and the chemical property represented fruit quality as the class label. When a classifier is to be applied to predict the quality, the spectrum of the mango fruits is measured, and the class label is determined by the classification rules subsequently. Series of experiments were conducted under various parameter settings to evaluate the accuracy of the prediction. The results showed that the highest accuracy, the optimal performance, can be obtained when the number of boxes, the number of partitions of each spectrum for rule generation, was set at 10, and the minimum support threshold and the minimum confidence threshold were set at 1% and 50%, respectively. Based on the thorough experiments, a guideline for optimal parameter determination is also proposed for the practitioners.
References
- R. Agrawal, T. Imieliski, and A. Swami. Mining association rules between sets of items in large databases. SIGMOD Rec., 22(2):207–216, June 1993.
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. pages 487–499, 1994.
- C.H. Choi. Development of apple sorter by soluble solid content using photodiodes. In Proceeding of Winter Conference of KSAM, 1, pages 362–367, 1998.
- A. M. C. Davies and A. Grant. Review: Near infra-red analysis of food. International Journal of Food Science and Technology, 22(3):191–207, 1987.
- C.V. Greensil and D.S. Newman. An experimental comparison of simple nir spectrometers for fruit grading applications. 17:63–76, 2001.
- S. Gunasekaran and J. Irudayaraj. Nondestructive food evaluation: techniques to analyze properties and quality, chapter Optical methods: visible NIR and FTIR spectroscopy. Marcel Dekker Inc., New York, USA, 2001.
- J. Guthrie and K.B. Walsh. Non-invasive assessment of pineapple and mango fruit quality using near infrared spectroscopy. 37:253–263, 1997.
- S. Kawano, T. Fujiwara, and M. Iwamoto. Nondestructive determination of sugar content in satsuma mandarin using near infrared (nir) transmittance. Journal of the Japanese Society for Horticultural Science, 62(2):465–470, 1993.
- S. Kawano, H. Watanabe, and M. Iwamoto. Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode. Journal of the Japanese Society for Horticultural Science, 61(2):445– 451, 1992.
- J. Li, H. Shen, and R. Topor. Mining optimal class association rule set. In David Cheung, Graham Williams, and Qing Li, editors, Advances in Knowledge Discovery and Data Mining, volume 2035 of Lecture Notes in Computer Science, pages 364–375. Springer Berlin / Heidelberg, 2001.
- H. Martens, K. H. T. Naes, Norris, and P. C. Williams. Near-Infrared Technology in the Agricultural and Food Industries (second edition), chapter Multivariate Calibration by Data Compression. American Association of Cereal Chemists, Inc., St. Pual, Minnesota, USA, 2001.
- A. Mizrach and U. Flitsanov. Nondestructive ultrasonic determination of avocado softening process. Journal of Food Engineering, 40(3):139 – 144, 1999.
- K. H. Norris. Design and development of a new moisture meter. Agricultural Engineering, 45(7):370–372, 1964.
- B. G. Osborne, T. Fearn, and P. H. Hindle. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis. Longman Scientific and Technical ; Wiley, Harlow, Essex, England; New York, 2nd edition, 1993.
- S. Saranwong, J. Sornsrivichai, and S. Kawano. Improvement of pls calibration for bixavalue and dry matter of mango using information from mlr calibration. Journal of Near Infrared Spectroscopy, 9:287–265, 2001.
- S. Saranwong, J. Sornsrivichai, and S. Kawano. Performance of a portable near infrared instrument for brix value determination of intact mango fruit. Journal of Near Infrared Spectroscopy, 11:175–181, 2003.
- S. Saranwong, J. Sornsrivichai, and S. Kawano. Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biology and Technology, 31(2):137 – 145, 2004.
- P.P. Subedi, K.B. Walsh, and G. Owens. Prediction of mango eating quality at harvest using short-wave near infrared spectrometry. Postharvest Biology and Technology, 43(3):326 – 334, 2007.
- Z. Tang and Q. Liao. A new class based associative classification algorithm. In IMECS’07.
Index Terms
Keywords