Effects of spectral transformations in support vector machine on predicting 'Arumanis' mango ripeness using near-infrared spectroscopy


Ali Khumaidi(1*); Y. Aris Purwanto(2); Heru Sukoco(3); Sony Hartono Wijaya(4);

(1) Institut Pertanian Bogor
(2) Institut Pertanian Bogor
(3) Institut Pertanian Bogor
(4) Institut Pertanian Bogor
(*) Corresponding Author

  

Abstract


One of the challenges of exporting Arumanis mangoes is their accurate grading ability because the mangoes do not change color during ripening. Near-Infrared (NIR) spectroscopy is a non-destructive method for detecting the internal ripeness of fruit which is quite reliable. However, NIR absorbance bands are often nonspecific, extensive, and overlapping. Although SVM modeling is quite good in performance, it can still be improved by spectral transformation. In this study, 11 spectral transformation operations were compared with their combinations to find the best input model. Spectral transformation operations include SAVGOL, RNV, BASELINE, MSC, EMSC, NORML, CLIP, RESAMPLE, DETREND, SNV, and LSNV. In the 2 class classification model, the highest accuracy is obtained using RNV and SAVGOL. The prediction model for SSC content with the best MSE value uses 3 combinations of spectral transformation operations, namely DETREND, LSNV, and SAVGOL with parameter values: 'deriv_order': 0, 'filter_win': 31, 'poly_order': 6. As for the prediction model of mango hardness with The best MSE value uses 2 combinations of spectral transformation operations, namely LSNV and SAVGOL with parameter values: deriv_order ': 0,' filter_win ': 15,' poly_order ': 6.

Keywords


Maturity Prediction; Arumanis Mango; Near Infrared; Support Vector Machines; Spectral Transformation

  
  

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doi  https://doi.org/10.33096/ilkom.v13i3.856.206-215 		  

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S. Plazzotta, L. Manzocco, and M. C. Nicoli, Fruit and vegetable waste management and the challenge of fresh-cut salad, Trends Food Sci. Technol., vol. 63, pp. 5159, May 2017, doi: 10.1016/j.tifs.2017.02.013.

V. Corts, C. Ortiz, N. Aleixos, J. Blasco, S. Cubero, and P. Talens, A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy, Postharvest Biol. Technol., vol. 118, pp. 148158, Aug. 2016, doi: 10.1016/j.postharvbio.2016.04.011.

L. S. Magwaza and U. L. Opara, Analytical methods for determination of sugars and sweetness of horticultural productsA review, Sci. Hortic. (Amsterdam)., vol. 184, pp. 179192, Mar. 2015, doi: 10.1016/j.scienta.2015.01.001.

S. N. Jha, S. Chopra, and A. R. P. Kingsly, Modeling of color values for nondestructive evaluation of maturity of mango, J. Food Eng., vol. 78, no. 1, Jan. 2007, doi: 10.1016/j.jfoodeng.2005.08.048.

Paull and Duarte, Tropical Fruits, in CAB International, 2011, pp. 110.

T. Thanaraj, L. A. Terry, and C. Bessant, Chemometric profiling of pre-climacteric Sri Lankan mango fruit (Mangifera indica L.), Food Chem., vol. 112, no. 4, pp. 786794, Feb. 2009, doi: 10.1016/j.foodchem.2008.06.040.

E. M. Yahia, Healing Ears : The Efficacy of a Web-based Listening Service A Dissertation Presented to the Faculty of the School of Psychology & Counseling Regent University In Partial Fulfillment Of the Requirements for the Degree , Doctor of Psychology By Treg A . Th, no. April. Woodhead Publishing Limited, 2016.

H. Harianto, D. Anggraini, A. Astuti, and H. Adinegoro, Uji Metode Pengkelasan Tingkat Kematangan Buah Mangga Berdasar Posisi Buah di dalam Air, J. Agro-based Ind., vol. 27, no. 1, pp. 4147, 2020.

P. P. Subedi, K. B. Walsh, and G. Owens, Prediction of mango eating quality at harvest using short-wave near infrared spectrometry, Postharvest Biol. Technol., vol. 43, no. 3, pp. 326334, 2007, doi: https://doi.org/10.1016/j.postharvbio.2006.09.012.

S. Agustina, Y. A. Purwanto, and I. W. Budiastra, Arumanis Mango Chemical Contents Prediction during Storage using NIR Spectroscopy, J. Keteknikan Pertan., vol. 03, no. 1, pp. 5763, Apr. 2015, doi: 10.19028/jtep.03.1.57-63.

J. P. dos Santos Neto, M. W. D. de Assis, I. P. Casagrande, L. C. Cunha Jnior, and G. H. de Almeida Teixeira, Determination of Palmer mango maturity indices using portable near infrared (VIS-NIR) spectrometer, Postharvest Biol. Technol., vol. 130, pp. 7580, Aug. 2017, doi: 10.1016/j.postharvbio.2017.03.009.

N. T. Anderson, K. B. Walsh, P. P. Subedi, and C. H. Hayes, Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content, Postharvest Biol. Technol., vol. 168, p. 111202, Oct. 2020, doi: 10.1016/j.postharvbio.2020.111202.

A. Khumaidi, Teknologi Non Destruktif Dan Machine Learning Untuk Prediksi Kualitas Buah: Tinjauan Literatur 2015-2020, Agrointek J. Teknol. Ind. Pertan., vol. 15, no. 1, pp. 310325, 2021.

C. Pasquini, Near infrared spectroscopy: A mature analytical technique with new perspectives A review, Anal. Chim. Acta, vol. 1026, pp. 836, Oct. 2018, doi: 10.1016/j.aca.2018.04.004.

S. Fan, C. Li, W. Huang, and L. Chen, Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths, Postharvest Biol. Technol., vol. 134, pp. 5566, Dec. 2017, doi: 10.1016/j.postharvbio.2017.08.012.

D. Zhang, L. Xu, Q. Wang, X. Tian, and J. Li, The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy, Food Anal. Methods, vol. 12, no. 1, pp. 136147, Jan. 2019, doi: 10.1007/s12161-018-1346-3.

J. Li, H. Zhang, B. Zhan, Y. Zhang, R. Li, and J. Li, Nondestructive firmness measurement of the multiple cultivars of pears by Vis-NIR spectroscopy coupled with multivariate calibration analysis and MC-UVE-SPA method, Infrared Phys. Technol., vol. 104, p. 103154, Jan. 2020, doi: 10.1016/j.infrared.2019.103154.

C. Liu, S. X. Yang, X. Li, L. Xu, and L. Deng, Noise level penalizing robust Gaussian process regression for NIR spectroscopy quantitative analysis, Chemom. Intell. Lab. Syst., vol. 201, p. 104014, Jun. 2020, doi: 10.1016/j.chemolab.2020.104014.

J. Gerretzen et al., Boosting model performance and interpretation by entangling preprocessing selection and variable selection, Anal. Chim. Acta, vol. 938, pp. 4452, Sep. 2016, doi: 10.1016/j.aca.2016.08.022.

J. Torniainen, I. O. Afara, M. Prakash, J. K. Sarin, L. Stenroth, and J. Tyrs, Open-source python module for automated preprocessing of near infrared spectroscopic data, Anal. Chim. Acta, vol. 1108, pp. 19, Apr. 2020, doi: 10.1016/j.aca.2020.02.030.

D. Suhandy, R. Hartanto, S. Prabawati, Y. Yulianingsih, and M. Yamin, Penggunaan Near Infrared Spectroscopy pada Penentuan Kandungan Padatan Terlarut Buah Mangga Indramayu secara Tidak Merusak, J. Keteknikan Pertan., vol. 22, no. 2, pp. 129134, 2008.

D. Suhandy, S. Prabawati, N. Yulianingsih, and N. Yatmin, Penentuan Bahan Kering Buah Mangga secara Intact Menggunakan Near Infrared Spectroscopy, J. Penelit. Pascapanen Pertan., vol. 5, no. 2, pp. 1017, 2008, doi: 10.21082/jpasca.v5n2.2008.10-17.

H. P. Sari, Y. A. Purwanto, and I. W. Budiastra, Pendugaan Kandungan Kimia Mangga Gedong Gincu Menggunakan Spektroskopi Inframerah Dekat (Prediction of Chemical Contents in Gedong Gincu Mango using Near Infrared Spectroscopy), J. Agritech, vol. 36, no. 03, p. 294, Dec. 2016, doi: 10.22146/agritech.16599.

Ikhram, Muhammad, Zulfahrizal, and A. A. Munawar, Development of Fourier Transform Near InfraRed Spectroscopy (FT-NIR) Through Wavelet Transformation For Sugar Content Evaluation Mango Gadung (Mangifera Indica, J. Ilm. Mhs. Pertan. Unsyiah, vol. 2, no. 3, pp. 276293, 2017.

P. Mishra and D. Passos, A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit, Chemom. Intell. Lab. Syst., vol. 212, p. 104287, May 2021, doi: 10.1016/j.chemolab.2021.104287.

R. Hayati, A. A. Munawar, and F. Fachruddin, Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango, Data Br., vol. 30, p. 105571, Jun. 2020, doi: 10.1016/j.dib.2020.105571.

P. Mishra, E. Woltering, and N. El Harchioui, Improved prediction of Kent mango firmness during ripening by near-infrared spectroscopy supported by interval partial least square regression, Infrared Phys. Technol., vol. 110, p. 103459, Nov. 2020, doi: 10.1016/j.infrared.2020.103459.

X. Tian, J. Li, S. Yi, G. Jin, X. Qiu, and Y. Li, Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm, Artif. Intell. Agric., vol. 4, pp. 4857, 2020, doi: 10.1016/j.aiia.2020.05.001


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