Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

Information Processing in Agriculture - Tập 10 - Trang 289-300 - 2023
Inna Novianty1, Ringga Gilang Baskoro2, Muhammad Iqbal Nurulhaq3, Muhammad Achirul Nanda4
1Computer Engineering Study Program, College of Vocational Studies, IPB University, Bogor 16680, Indonesia
2Informatics Management Study Program, College of Vocational Studies, IPB University, Bogor 16680, Indonesia
3Technology and Plantation Production Management Study Program, College of Vocational Studies, IPB University, Bogor 16680, Indonesia
4Department of Agricultural and Biosystem Engineering, Faculty of Agro-Industrial Technology, Universitas Padjadjaran, Jatinangor 45363, Indonesia

Tài liệu tham khảo

Sinambela, 2020, Application of an inductive sensor system for identifying ripeness and forecasting harvest time of oil palm, Sci Hortic, 265, 109231, 10.1016/j.scienta.2020.109231 Makky, 2014, In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach, J Food Eng, 120, 248, 10.1016/j.jfoodeng.2013.08.011 Misron, 2017, Relative estimation of water content for flat-type inductive-based oil palm fruit maturity sensor, Sensors, 17, 52 Kasemsumran, 2012, A feasibility study on non-destructive determination of oil content in palm fruits by visible–near infrared spectroscopy, J Near Infrared Spectrosc, 20, 687, 10.1255/jnirs.1025 Sudarno, 2017, Rapid determination of oil content in dried-ground oil palm mesocarp and kernel using near infrared spectroscopy, J Near Infrared Spectrosc, 25, 338, 10.1177/0967033517732679 Sinambela, 2020, A Ripeness study of oil palm fresh fruit at the bunch different positions, Jurnal Keteknikan Pertanian, 8, 9, 10.19028/jtep.08.1.9-14 Khalid, 1992, A microstrip sensor for determination of harvesting time for oil palm fruits (Tenera: Elaeis Guineensis), J Microw Power Electromagn Energy, 27, 3, 10.1080/08327823.1992.11688165 Novianty, 2020, Improving the accuracy of near-infrared (NIR) spectroscopy method to predict the oil content of oil palm fresh fruits, IOP Conf. Ser.: Earth Environ. Sci., 460, 012004, 10.1088/1755-1315/460/1/012004 Kaufmann, 2019, Portable NIR spectrometer for prediction of palm oil acidity, J Food Sci, 84, 406, 10.1111/1750-3841.14467 Aliteh, 2018, Triple flat-type inductive-based oil palm fruit maturity sensor, Sensors, 18, 2496, 10.3390/s18082496 Mohd Ali, 2020, Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity, Comput Electron Agric, 169, 105235, 10.1016/j.compag.2020.105235 Makky, 2014, Automatic non-destructive quality inspection system for oil palm fruits, Int Agrophys, 28, 319, 10.2478/intag-2014-0022 Brosnan, 2004, Improving quality inspection of food products by computer vision––a review, J Food Eng, 61, 3, 10.1016/S0260-8774(03)00183-3 Sosa-Morales, 2010, Dielectric properties of foods: reported data in the 21st century and their potential applications, LWT-Food Science and Technology, 43, 1169, 10.1016/j.lwt.2010.03.017 Blanco, 2002, NIR spectroscopy: a rapid-response analytical tool, TrAC, Trends Anal Chem, 21, 240, 10.1016/S0165-9936(02)00404-1 Makky, 2013, Towards sustainable green production: exploring automated grading for oil palm fresh fruit bunches (FFB) using machine vision and spectral analysis, International Journal on Advanced Science, Engineering and Information Technology, 3, 1, 10.18517/ijaseit.3.1.267 Yang, 2002, Rapid determination of vitamin C by NIR, MIR and FT-Raman techniques, J Pharm Pharmacol, 54, 1247, 10.1211/002235702320402099 Silalahi, 2020, Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data, Sensors, 20, 5001, 10.3390/s20175001 Iqbal Z, Herodian S, Widodo S. Pendugaan kadar air dan total karoten tandan buah segar (TBS) kelapa sawit menggunakan NIR spektroskopi. Jurnal Keteknikan Pertanian, 2015, 2(2). (In Indonesian). Iqbal, 2019, Development of Partial Least Square (PLS) Prediction Model to Measure the Ripeness of Oil Palm Fresh Fruit Bunch (FFB) by Using NIR Spectroscopy, IOP Conf. Ser.: Earth Environ. Sci., 347, 012079, 10.1088/1755-1315/347/1/012079 Silalahi, 2016, Near infrared spectroscopy: a rapid and non-destructive technique to assess the ripeness of oil palm (Elaeis guineensis Jacq.) fresh fruit, J Near Infrared Spectrosc, 24, 179, 10.1255/jnirs.1205 Silalahi, 2016, Using genetic algorithm neural network on near infrared spectral data for ripeness grading of oil palm (Elaeis guineensis Jacq.) fresh fruit. Information Processing, Agriculture, 3, 252 Huang, 1971, The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis, Proceedings of the Royal Society A, 1998, 903 Kim, 2012, Extending the scope of empirical mode decomposition by smoothing, EURASIP Journal on Advances in Signal Processing, 2012, 168, 10.1186/1687-6180-2012-168 Xu, 2020, An improved method for pipeline leakage localization with a single sensor based on modal acoustic emission and empirical mode decomposition with hilbert transform, IEEE Sens J, 20, 5480, 10.1109/JSEN.2020.2971854 Mostafiz, 2020, Gastrointestinal polyp classification through empirical mode decomposition and neural features, SN Applied Sciences, 2, 1, 10.1007/s42452-020-2944-4 Du, 2020, Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method, J Cleaner Prod, 270, 122248, 10.1016/j.jclepro.2020.122248 Dai, 2020, Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition, Appl Econ, 52, 2309, 10.1080/00036846.2019.1688244 BUCHI. NIRFlex N-500 (the modular FT-NIR spectrometer). Link: https://www.buchi.com/en/products/nirsolutions/nirflex-n-500. 2021. Yang, 2005, Discriminant analysis of edible oils and fats by FTIR, FT-NIR and FT-Raman spectroscopy. Food Chemistry, 93, 25 Faricha, 2018, Design of electronic nose system using gas chromatography principle and surface acoustic wave sensor, TELKOMNIKA (Telecommunication Computing Electronics and Control), 16, 1457, 10.12928/telkomnika.v16i4.7127 Nanda, 2018, A Comparison study of kernel functions in the support vector machine and its application for termite detection, Information, 9, 5, 10.3390/info9010005 Wang, 2014, Forecasting wind speed using empirical mode decomposition and Elman neural network, Appl Soft Comput, 23, 452, 10.1016/j.asoc.2014.06.027 Peng, 2005, An improved Hilbert-Huang transform and its application in vibration signal analysis, J Sound Vib, 286, 187, 10.1016/j.jsv.2004.10.005 Kim, 2009, EMD: a package for empirical mode decomposition and Hilbert spectrum, The R Journal, 1, 40, 10.32614/RJ-2009-002 Gabriëls, 2020, Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis, Postharvest Biol Technol, 166, 111206, 10.1016/j.postharvbio.2020.111206 Pourdarbani, 2020, Non-destructive estimation of total chlorophyll content of apple fruit based on color feature, spectral data and the most effective wavelengths using hybrid artificial neural network—imperialist competitive algorithm, Plants, 9, 1547, 10.3390/plants9111547 Hecht-Nielsen, 1987, Kolmogorov’s mapping neural network existence theorem, Proceedings of the international conference on Neural Networks, 3, 11 Nanda, 2018, Discriminant analysis as a tool for detecting the acoustic signals of termites Coptotermes curvignathus (Isoptera: Rhinotermitidae), International Journal of Technology, 9, 840, 10.14716/ijtech.v9i4.455 Achirul Nanda, 2019, Development of termite detection system based on acoustic and temperature signals, Measurement, 147, 106902, 10.1016/j.measurement.2019.106902 Fourie, 1991, Sugar content of fresh apples and pears in South Africa, J Agric Food Chem, 39, 1938, 10.1021/jf00011a008 Cayuela, 2018, Nondestructive measurement of squalene in olive oil by near infrared spectroscopy, LWT, 88, 103, 10.1016/j.lwt.2017.09.047 Bian, 2017, Rapid identification of milk samples by high and low frequency unfolded partial least squares discriminant analysis combined with near-infrared spectroscopy, Chemometrics and Intelligent Laboratory Systems, 170, 96, 10.1016/j.chemolab.2017.09.004 Mandal, 2020, Hilbert-Huang transform analysis of surface wavefield under tropical cyclone Hudhud, Appl Ocean Res, 101, 102269, 10.1016/j.apor.2020.102269 Motulsky, 2006, Detecting outliers when fitting data with nonlinear regression–a new method based on robust nonlinear regression and the false discovery rate, BMC Bioinf, 7, 1, 10.1186/1471-2105-7-123 Cheng Z, Zou C, Dong J. Outlier detection using isolation forest and local outlier factor. In: Proc. International Conference on Research in Adaptive and Convergent Systems. Chongqing, China; 2019. p.161–8. Achirul Na, 2018, Population survey of subterranean termite Coptotermes curvignathus (Isoptera: Rhinotermitidae) on infested pine boards, Journal of Entomology, 15, 93, 10.3923/je.2018.93.100 Jian­hua, 2014, Near-infrared spectrum detection of fish oil DHA content based on empirical mode decomposition and independent component analysis, Journal of Food and Nutrition Research, 2, 62, 10.12691/jfnr-2-2-1 Gu, 2016, Empirical mode decomposition-based motion artifact correction method for functional near-infrared spectroscopy, J Biomed Opt, 21, 015002, 10.1117/1.JBO.21.1.015002 Molinari, 2015, Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients, Comput Biol Med, 59, 1, 10.1016/j.compbiomed.2015.01.011