Ahmadi, K., Abadzadeh, H., Abdshah, H., Hatami, F., & Hosseinpour, R. (2017). Agricultural Statistics of 1396: Horticultural Products (Vol. 3). Tehran: Information Technology and Communications of the Ministry of Agriculture Jihad.
Association of Official Analytical Chemists. (2000). Official Methods of Analysis (17th ed.). Gaithersburg, MD, USA: The Association of Official Analytical Chemists.
Campbell, C., & Ying, Y. (2022). Learning with support vector machines: Springer Nature.
Carvalho, L. C., Morais, C. L. M., Lima, K. M. G., Leite, G. W. P., Oliveira, G. S., Casagrande, I. P., Teixeira, G. H. A. (2017). Using Intact Nuts and Near Infrared Spectroscopy to Classify Macadamia Cultivars. Food Analytical Methods, 11(7): 1857-1866. doi:https://doi.org/10.1007/s12161-017-1078-9
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408: 189-215.
Fattahi Moghadam, J., & Faqih Nasiri, M. (2015). Solutions for picking, storing, grading and packing citrus fruits. 1-22. Retrieved from https://agrilib.areeo.ac.ir/book_4479.pdf
Food and Agriculture Organization. (2018). World Food and Agriculture-Statistical Pocketbook 2018. Rome: FAO.
Henderi, H. (2021). Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. IJIIS: International Journal of Informatics and Information Systems, 4(1): 13-20. doi:https://doi.org/10.47738/ijiis.v4i1.73
Jamshidi, B., Minaei, S., Mohajerani, E., & Ghassemian, H. (2014). Effect of Spectral Pre-Processing Methods on Non-Destructive Quality Assessment of Oranges Using NIRS. Journal of Agricultural Engineering Research, 15(2): 27-44. doi:https://doi.org/10.22092/jaer.2014.100188
Jia, N., Liu, J., Sun, Y., Tan, P., Cao, H., Xie, Y., Wen, B., Gu, T., Liu, J., Li, M., & Huang, Y. (2018). Citrus sinensis MYB transcription factors CsMYB330 and CsMYB308 regulate fruit juice sac lignification through fine-tuning expression of the Cs4CL1 gene. Plant science, 277: 334-43. https://doi.org/10.1016/j.plantsci.2018.10.006
Jie, D., Wu, S., Wang, P., Li, Y., Ye, D., & Wei, X. (2020). Research on Citrus grandis Granulation Determination Based on Hyperspectral Imaging through Deep Learning. Food Analytical Methods, 14(2): 280-289. doi:https://doi.org/10.1007/s12161-020-01873-6
Jiménez-Jiménez, F., Castro-García, S., Blanco-Roldán, G. L., Agüera-Vega, J., & Gil-Ribes, J. A. (2012). Non-destructive determination of impact bruising on table olives using Vis–NIR spectroscopy. biosystems engineering, 113(4): 371-378. doi:https://doi.org/10.1016/j.biosystemseng.2012.09.007
Kumari, N., Bhatt, A., Dwivedi, R. K., & Belwal, R. (2019). Performance analysis of support vector machine in defective and non defective mangoes classification. International Journal of Engineering and Advanced Technology (IJEAT), 8(4): 1563-1572. Retrieved from https://www.ijeat.org/wp-content/uploads/papers/v8i4/D6669048419.pdf
Law, S. E. (2006). Scatter of near‐Infrared Radiation by Cherries as a Means of Pit Detection. Journal of Food Science, 38(1): 102-107. doi:https://doi.org/10.1111/j.1365-2621.1973.tb02788.x
Magwaza, L. S. (2013). Non-destructive prediction and monitoring of postharvest quality of citrus fruit. (Doctor of Philosophy). Stellenbosch Retrieved from https://scholar.sun.ac.za/bitstreams/245500e7-09a3-4606-9132-f90227a6b317/download
Moghaddam, S., Goudarzi, A. R., Oskooi, B., & Azad, A. (2022). GPR Random noise attenuation using Savitzky-Golay filter in the dual-tree complex wavelet domain. Journal Of Research on Applied Geophysics, 7(4): 361-379.
Mogollon, M., Jara, A., Contreras, C., & Zoffoli, J. P. (2020). Quantitative and qualitative VIS-NIR models for early determination of internal browning in 'Cripps Pink'apples during cold storage. Postharvest Biology and Technology, 161: 111060.
Munera, S., Besada, C., Aleixos, N., Talens, P., Salvador, A., Sun, D. W., & Blasco, J. (2017). Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT, 77: 241-248. doi:https://doi.org/10.1016/j.lwt.2016.11.063
Ritenour, M. A., Albrigo, L. G., Burns, J. K., & Miller, W. M. (2004). Granulation in Florida citrus. Proceedings of the Florida State Horticultural Society, 117, 358-361.
Sharma, R. R., Singh, R., & Saxena, S. K. (2006). Characteristics of citrus fruits in relation to granulation. Scientia horticulturae, 111(1): 91-96. doi:https://doi.org/10.1016/j.scienta.2006.09.007
Sharma, R., & Saxena, S. (2004). Rootstocks influence granulation in Kinnow mandarin (Citrusnobilis× C. deliciosa). Scientia horticulturae, 101(3): 235-242.
Shi, X., Yao, L., & Pan, T. (2021). Visible and Near-Infrared Spectroscopy with Multi-Parameters Optimization of Savitzky-Golay Smoothing Applied to Rapid Analysis of Soil Cr Content of Pearl River Delta. Journal of Geoscience and Environment Protection, 9(3): 75-83. doi:https://doi.org/10.4236/gep.2021.93006
Sonego, L., Ben-Arie, R., Raynal, J., & Pech, J. C. (1995). Biochemical and physical evaluation of textural characteristics of nectarines exhibiting woolly breakdown: NMR imaging, X-ray computed tomography and pectin composition. Postharvest Biology and Technology, 5(3): 187-198. doi:https://doi.org/10.1016/0925-5214(94)00026-O
Sun, X., Xu, S., & Lu, H. (2020). Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. Applied Sciences, 10(16): 5399. doi:https://doi.org/10.3390/app10165399
Suphamitmongkol, W., Nie, G., Liu, R., Kasemsumran, S., & Shi, Y. (2013). An alternative approach for the classification of orange varieties based on near infrared spectroscopy. Computers and electronics in agriculture, 91: 87-93. doi:https://doi.org/10.1016/j.compag.2012.11.014
Theanjumpol, P., Wongzeewasakun, K., Muenmanee, N., Wongsaipun, S., Krongchai, C., Changrue, V., & Kittiwachana, S. (2019). Non-destructive identification and estimation of granulation in 'Sai Num Pung' tangerine fruit using near infrared spectroscopy and chemometrics. Postharvest Biology and Technology, 153: 13-20. doi:https://doi.org/10.1016/j.postharvbio.2019.03.009
Van Dael, M., Lebotsa, S., Herremans, E., Verboven, P., Sijbers, J., Opara, U., & Nicolaï, B. (2016). A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs. Postharvest Biology and Technology, 112: 205-214.
Wang, S. Y., Wang, P. C., & Faust, M. (1988). Non-destructive detection of watercore in apple with nuclear magnetic resonance imaging. Scientia horticulturae, 35(3-4): 227-234. doi:https://doi.org/10.1016/0304-4238(88)90116-1
Wang, X.-Y., Wang, P., Qi, Y.-P., Zhou, C. P., Yang, L.-T., Liao, X.-Y., & Chen, L. S. (2014). Effects of granulation on organic acid metabolism and its relation to mineral elements in Citrus grandis juice sacs. Food chemistry, 145: 984-990.
Wang, Z., Künnemeyer, R., McGlone, A., & Burdon, J. (2020). Potential of Vis-NIR spectroscopy for detection of chilling injury in kiwifruit. Postharvest Biology and Technology, 164: 111160. doi:https://doi.org/10.1016/j.postharvbio.2020.111160
Xiong, B.,
Ye, Sh.,
Xia Q., &
Liao L. (2017).
Exogenous spermidine alleviates fruit granulation in a Citrus cultivar (Huangguogan) through the antioxidant pathway. Acta Physiologiae Plantarum, 39(4): 1-8. DOI:
10.1007/s11738-017-2397-6
Zhang, J., & Mouazen, A. M. (2023). Fractional-order Savitzky–Golay filter for pre-treatment of on-line vis–NIR spectra to predict phosphorus in soil. Infrared Physics & Technology, 131: 104720. doi:https://doi.org/10.1016/j.infrared.2023.104720
