Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden
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Dec 28, 2020
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Abstract
Kadar air merupakan salah satu atribut kualitas yang penting pada komoditas hortikultura. Penetapan kadar air buah melon dengan metode konvensional memakan waktu yang lama dan perlu merusak sampel buah. Penelitian ini bertujuan untuk memprediksi kadar air buah melon golden menggunakan teknologi visible-near infrared spectroscopy (Vis-NIRS). Metode koreksi spektra orthogonal signal correction (OSC) diterapkan pada spektra original untuk meningkatkan kehandalan model kalibrasi. Partial least squares regression (PLSR) digunakan sebagai metode pendekatan regresi untuk mengekstraksi data spektra Vis-NIRS. Hasil penelitian membuktikan bahwa Vis-NIRS dapat diandalkan untuk memprediksi kadar air buah melon golden. Metode koreksi spektra OSC mampu memperkecil jumlah principal component (PC) pada spektra original. Linieritas pada model kalibrasi menggunakan spektra OSC tercatat memperoleh nilai tertinggi sebesar 0,92. Ratio of performance to deviation (RPD) pada spektra OSC menampilkan nilai tertinggi pula yaitu 3,63. Model kalibrasi yang diperoleh pada penelitian ini dapat ditransfer ke dalam spektrometer Vis-NIRS untuk prediksi kadar air melon golden secara cepat dan simultan.
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How to Cite
Hadiwijaya, Y., Kusumiyati, K., & Munawar, A. A. (2020). Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden. Agroteknika, 3(2), 67-74. https://doi.org/10.32530/agroteknika.v3i2.83
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Ribera, A., Noferini, M., & Rombolà, A. (2016). Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible/Near Infrared (vis/NIR) Spectroscopy Device: A Preliminary Approach. Journal of Soil Science and Plant Nutrition, 16(1), 174–186. https://doi.org/10.4067/S0718-95162016005000014
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Shao, Y., Xuan, G., Hu, Z., Gao, Z., & Liu, L. (2019). Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis. PLoS ONE, 14(9), 1–13. https://doi.org/10.1371/journal.pone.0222633
Shen, F., Zhang, B., Cao, C., & Jiang, X. (2018). On-line discrimination of storage shelf-life and prediction of post-harvest quality for strawberry fruit by visible and near infrared spectroscopy. Journal of Food Process Engineering, 41(7). https://doi.org/10.1111/jfpe.12866
Zhang, Q., Li, Q., & Zhang, G. (2012). Rapid Determination of Leaf Water Content Using VIS/NIR Spectroscopy Analysis with Wavelength Selection. Spectroscopy: An International Journal, 27(2), 93–105. https://doi.org/10.1155/2012/276795
Boháč, M., Loeprecht, B., Damborský, J., & Schüürmann, G. (2002). Impact of Orthogonal Signal Correction (OSC) on the Predictive Ability of CoMFA Models for the Ciliate Toxicity of Nitrobenzenes. Quantitative Structure-Activity Relationships, 21(1), 3–11. https://doi.org/10.1002/1521-3838(200205)21:1<3::AID-QSAR3>3.0.CO;2-D
Brogna, N., Palmonari, A., Canestrari, G., Mammi, L., Dal Prà, A., & Formigoni, A. (2018). Technical note: Near infrared reflectance spectroscopy to predict fecal indigestible neutral detergent fiber for dairy cows. Journal of Dairy Science, 101(2), 1234–1239. https://doi.org/10.3168/jds.2017-13319
Fan, Y., Liao, Y., & Cheng, F. (2018). Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis. International Journal of Food Properties, 21(1), 1180–1189. https://doi.org/10.1080/10942912.2018.1460606
Hadiwijaya, Y., Kusumiyati, K., & Munawar, A. A. (2020). PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT. Jurnal Penelitian Saintek, 25(2), 103–114. https://doi.org/10.21831/jps.v25i2.34487
Jankovská, R., & Šustová, K. (2003). Analysis of cow milk by near-infrared spectroscopy. Czech Journal of Food Sciences, 21(4), 123–128. https://doi.org/10.17221/3488-cjfs
Jin, X., Shi, C., Yu, C. Y., Yamada, T., & Sacks, E. J. (2017). Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus. Frontiers in Plant Science, 8, 721. https://doi.org/10.3389/fpls.2017.00721
Khodabakhshian, R., Emadi, B., Khojastehpour, M., Golzarian, M. R., & Sazgarnia, A. (2017). Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy. International Journal of Food Properties, 20(1), 41–52. https://doi.org/10.1080/10942912.2015.1126725
Kusumiyati., Hadiwijaya, Y., & Putri, I. E. (2018). Determination of water content of intact sapodilla using near infrared spectroscopy. IOP Conference Series: Earth and Environmental Science (Vol. 207). 20-21 Oktober 2018. https://doi.org/10.1088/1755-1315/207/1/012047
Kusumiyati., Hadiwijaya, Y., & Putri, I. E. (2019). Non-Destructive Classification of Fruits Based on Vis-nir Spectroscopy and Principal Component Analysis. Jurnal Biodjati, 4(1), 89–95. https://doi.org/10.15575/biodjati.v4i1.4389
Kusumiyati., Hadiwijaya, Y., Putri, I. E., & Mubarok, S. (2019). Water content prediction of “crystal” guava using visible-near infrared spectroscopy and chemometrics approach. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/393/1/012099
Maniwara, P., Nakano, K., Boonyakiat, D., Ohashi, S., Hiroi, M., & Tohyama, T. (2014). The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality. Journal of Food Engineering, 143, 33–43. https://doi.org/10.1016/j.jfoodeng.2014.06.028
Martelo-Vidal, M., & Vázquez, M. (2014). Evaluation of Ultraviolet, Visible, and Near Infrared Spectroscopy for the Analysis of Wine Compounds. Czech Journal of Food Sciences, 32(1), 37–47. https://doi.org/10.17221/167/2013-CJFS
Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2), 99–118. https://doi.org/10.1016/j.postharvbio.2007.06.024
Ribera, A., Noferini, M., & Rombolà, A. (2016). Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible/Near Infrared (vis/NIR) Spectroscopy Device: A Preliminary Approach. Journal of Soil Science and Plant Nutrition, 16(1), 174–186. https://doi.org/10.4067/S0718-95162016005000014
Saad, A. G., Jaiswal, P., & Jha, S. N. (2014). Non-destructive quality evaluation of intact tomato using VIS-NIR spectroscopy. International Journal of Advanced Research, 2(12), 632–639.
Shao, Y., Xuan, G., Hu, Z., Gao, Z., & Liu, L. (2019). Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis. PLoS ONE, 14(9), 1–13. https://doi.org/10.1371/journal.pone.0222633
Shen, F., Zhang, B., Cao, C., & Jiang, X. (2018). On-line discrimination of storage shelf-life and prediction of post-harvest quality for strawberry fruit by visible and near infrared spectroscopy. Journal of Food Process Engineering, 41(7). https://doi.org/10.1111/jfpe.12866
Zhang, Q., Li, Q., & Zhang, G. (2012). Rapid Determination of Leaf Water Content Using VIS/NIR Spectroscopy Analysis with Wavelength Selection. Spectroscopy: An International Journal, 27(2), 93–105. https://doi.org/10.1155/2012/276795