Deteksi Varietas Padi Menggunakan Gelombang Near Infrared dan Model Jaringan Saraf Tiruan

jonni firdaus

Abstract


Rice seeds is one of the important components in increasing rice production. Nowdays, there are many varieties that had been created. Each variety has the advantage based on its  assembly destination. A large number of varieties can cause errors in seed processing, storage and distribution, because the physical shape and size are almost similar and the appearance is very difficult to distinguish. The alternative to detect rice seed varieties are using near infrared (NIR) as sensors and artificial neural network (ANN) as a data processor. This research aims to study the NIR spectroscopy and ANN for detecting varieties of rice seed. NIR reflectance (1000-2500 nm) of 12 varieties were given pretreatment data such as first derivative, second derivative, normalization and standard normal variate. The pretreatment data ware used as input in ANN models. Each variety was consist of 12 samples and the weight of each sample was 40 grams. ANN model used was backpropagation multilayer perceptron with three layers as input, hiden, and output. Network weights ware estimated using gradient descent algorithm. The study showed that the form of NIR spectra was similar among varieties but had different absorption in intensity, so it could be used for determining the rice seed varieties. The best model was an ANN with standard normal variate pretreatment as input data.  The accuracy varieties prediction was 100% for traning, 99.1% for testing and 98.1% for validation. This research showed that the  NIR spectra and ANN model can be used for detection methods in rice varieties.

Keywords


Near infrared (NIR), artificial neural network (ANN), varieties, paddy, seed

Full Text:

PDF (Indonesian)

References


Adnan, M. L. Widiastuti, dan S. Wahyuni, 2015. Identifikasi varietas padi menggunakan pengolahan citra digital dan analisis diskriminan, Jurnal Penelitian Pertanian Tanaman Pangan 34(2): 89-96.

Balabin R.M. and Ravilya Z. Safieva, 2011. Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data. Analytica Chimica Acta 689: 190–197.

Cao, F., Di Wu, and Yong He, 2010. Soluble solids content and pH prediction and varieties discrimination of grapes based on visible–near infrared spectroscopy. Computers and Electronics in Agriculture 71S: 15–18.

Chen, L., Jiahua Wang, Zhihua Ye, Jing Zhao, Xiaofeng Xue, Yvan Vander Heyden, and Qian Sun, 2012. Analytical Methods: Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Food Chemistry 135: 338–342.

Cocchi, M., Corbellini, M., Foca, G., Lucisano, M., Pagani, M. A., and Tassi L. 2005. Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra. Analytica Chimica Acta, 544(1–2): 100–107.

Cozzolino, D., Kwiatkowski, M. J., Parker, M., Cynkar, W. U., Dambergs, R. G., and Gishen M., 2004. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. In Xie L, Xingqian Ye, Donghong Liu, Yibin Ying, 2009. Analytical Methods: Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS. Food Chemistry 114: 1135–1140.

Firdaus, J., R. Hasbullah, U. Ahmad dan M.R. Suhartanto, 2014. Deteksi cepat viabilitas benih padi menggunakan gelombang near infrared dan model jaringan saraf tiruan, Jurnal Penelitian Tanaman Pangan, 33(2):77-86.

Guine, RFP, Maria Joao Barroca, Fernando J. Goncalvesa, Mariana Alves, Solange Oliveira,dan Mateus Mendes. 2015. Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 168: 454-459.

Hartati, S., Y. Marsono, Suparmo, dan U. Santoso, 2015. Komposisi kimia serta aktivitas antioksidan ekstrak hidrofilik bekatul beberapa varietas padi, Jurnal Agritech, 35(1), 36-42.

He, Y., Xiaoli Li, and Xunfei Deng, 2007. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. Journal of Food Engineering 79:1238–1242.

Indrasari, S.D., E.Y. Purwani, P. Wibowo, dan Jumali, 2008. Nilai Indeks Glikemik Beras Beberapa Varietas Padi. Jurnal Penelitian Pertanian Tanaman Pangan, 27(3): 127-134

Li, X., He Y., and Fang H., 2007. Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy. J Food Engineering 81;357–363.

Liu, Y., Sun X., dan Ouyang A. 2010. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT - Food Science and Technology. 43: 602–607.

Luo, W., Shuangyan Huan, Haiyan Fu, Guoli Wen, Hanwen Cheng, Jingliang Zhou, Hailong Wu, Guoli Shen, and Ruqin Yu, 2011. Analytical Methods: Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apple samples. Food Chemistry 128: 555–561.

Nicolai, BM, Katrien Beullens, Els Bobelyn, Ann Peirs, Wouter Saeys, Karen I. Theron, and Jeroen Lammertyn. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A riview. Postharvest Biology dan Technology. 46:99-118.

Osborne, B.G., Fearn T., and Hindle P.H. 1993. Practical NIR spectroscopy with aplication in food and baverage analysis. Ed ke-2. Longman Group UK Limited. p.227.

Purwani, E.Y., S. Yuliani, S.D. Indrasari, S. Nugraha dan R. Thahir. 2007. Sifat fisiko-kimia beras dan indeks glikemiknya. Jurnal Teknologi Industri Pangan, XVIII(1): 59-66.

Rinnan, A., Lars N., Frans V.D.B., Jonas T., Rasmus B. and Soren B.E. 2009. Infrared Spectroscopy for Food Quality Analysis and Control: Data Pre-processing. Elsevier Inc. 29-50.

Shao, Y., He Y., and Feng S. 2007. Measurement of yogurt internal quality through using Vis/NIR spectroscopy. J Food Research International, 40:835-841.

Sun, S.Q., Tang J.M., and Yuan Z.M. (2003). Discrimination of trueborn tuber discourse by fingerprint infrared spectra and principle component analysis. Spectroscopy and Spectral Analysis, 23(2), 25–29.

Suphamitmongkol, W., Guangli Nie, Rong Liu, Sumaporn Kasemsumran, and Yong Shi, 2013. An alternative approach for the classification of orange varieties based on near infrared spectroscopy. Computers and Electronics in Agriculture, 91: 87–93.




DOI: http://dx.doi.org/10.21082/jpptp.v1n1.2017.p29-36

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 Jurnal Penelitian Pertanian Tanaman Pangan

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


P-ISSN: 2541-5166
E-ISSN: 2541-5174
Terakreditasi No.646/AU3/P2MI-LIPI/2015 oleh Lembaga Ilmu Pengetahuan Indonesia


Jurnal Penelitian Pertanian Tanaman Pangan telah terindeksasi oleh:

       


Editorial Office

Jurnal Penelitian Pertanian Tanaman Pangan

Pusat Penelitian dan Pengembangan Tanaman Pangan
Jln Merdeka no. 147, Bogor 16111, Indonesia
Phone/Fax.: +62-251-8312755 
E-mail: publikasi_puslitbangtan@litbang.pertanian.go.id
Website: http://pangan.litbang.pertanian.go.id

View My Stats