Ahmadi, K., Ebadzadeh H. R., Hatami, F., Hosseinpour, R., & Abdeshah, H. (2020). Agricultural statistics (vol. 3). Communication and Information Technology Center, Planning and Economic Deputy, Minstry of Hihad-e-Agriculture. Retrieved from https:// www. maj.ir / Dorsapax/ user files/ Sub65/ Amarnamehj1-97-98-site.pdf on date (Sept. 2021). (in Farsi)
Arendse, E., Fawole, O. A., Magwaza, L. S., & Opara, U. L. (2016). Non-destructive characterization and volume estimation of pomegranate fruit external and internal morphological fractions using X-ray computed tomography. Journal of Food Engineering, 186, 42-49.
Arendse, E., Fawole, O. A., Magwaza, L. S., Nieuwoudt, H. H., & Opara, U. L. (2017). Development of calibration models for the evaluation of pomegranate aril quality by Fourier-transform near infrared spectroscopy combined with chemometrics. Biosystems Engineering, 159, 22-32.
Arendse, E., Fawole, O. A., Magwaza, L. S., Nieuwoudt, H., & Opara, U. L. (2018). Fourier transform near infrared diffuse reflectance spectroscopy and two spectral acquisition modes for evaluation of external and internal quality of intact pomegranate fruit. Postharvest Biology and Technology, 138, 91-98.
Baek, S., Lim, J., Lee, J. G., McCarthy, M. J., & Kim, S. M. (2020). Investigation of the Maturity Changes of Cherry Tomato Using Magnetic Resonance Imaging. Applied Sciences, 10(15), 5188.
Bashghareh, A. (2019). The effect of Pre-harvest chitosan application on quantitative and qualitative characteristics of pomegranate fruit. M.Sc. Thesis on Horticultural Sciences Engineering, Gorgan University. Gorgan, Iran. (in Farsi)
Blakey, R. J., Bower, J. P., & Bertling, I. (2009). Influence of water and ABA supply on the ripening pattern of avocado (Persea americana Mill.) fruit and the prediction of water content using Near Infrared Spectroscopy. Postharvest Biology and Technology, 53(1-2), 72-76.
Bulut, E., & Alma, Ö. G. (2010). Dimensionality Reduction Methods: PCR, PLSR, RRR and health application. Physical Sciences, 6(2), 36-47.
Fazayeli, A., Kamgar, S., Nassiri, S. M., Fazayeli, H., & De La Guardia, M. (2019). Dielectric spectroscopy as a potential technique for prediction of kiwifruit quality indices during storage. Information Processing in Agriculture, 6(4), 479-486.
Hagen, C. L., & Sanders, S. T. (2007). Investigation of multi-species (H2O2 and H2O) sensing and thermometry in an HCCI engine by wavelength-agile absorption spectroscopy. Measurement Science and Technology, 18(7), 1992.
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.
Khodabakhshian, R., Emadi, B., Khojastehpour, M., & Golzarian, M. R. (2019). A comparative study of reflectance and transmittance modes of Vis/NIR spectroscopy used in determining internal quality attributes in pomegranate fruits. Journal of Food Measurement and Characterization, 13(4), 3130-3139.
Mohsenin, N. N. (1996). Physical properties of plant and animal materials (vol. 1). Gordon Publication. Canada.
Munera, S., Hernández, F., Aleixos, N., Cubero, S., & Blasco, J. (2019). Maturity monitoring of intact fruit and arils of pomegranate cv. ‘Mollar de Elche’using machine vision and chemometrics. Postharvest Biology and Technology, 156, 110936.
Neto, A. J. S., Lopes, D. C., Pinto, F. A., & Zolnier, S. (2017). Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves. Biosystems Engineering, 155, 124-133.
Pelliccia, D. (2018). Partial Least Square Regression in Python. Retrieved from https://nirpyresearch.com/partial-least-squares-regression-python.
Pourdarbani, R., Sabzi, S., Kalantari, D., & Arribas, J. I. (2020). Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stages. Chemometrics and Intelligent Laboratory Systems, 206, 104147.
Saleem, S., Aslam, M., & Shaukat, M. R. (2021). A review and empirical comparison of univariate outlier detection methods. Pakistan Journal of Statistics, 37(4), 447-462.
Salmanizadeh, F., Nassiri, S. M., Jafari, A., & Bagheri, M. H. (2015). Volume estimation of two local pomegranate fruit (Punica granatum L.) cultivars and their components using non-destructive X-ray computed tomography technique. International Journal of Food Properties, 18(2), 439-455.
Shirzadifar, A., Bajwa, S., Mireei, S. A., Howatt, K., & Nowatzki, J. (2018). Weed species discrimination based on SIMCA analysis of plant canopy spectral data. Biosystems Engineering, 171, 143-154.
Shirzadifar, A., Bajwa, S., Nowatzki, J., & Shojaeiarani, J. (2020). Development of spectral indices for identifying glyphosate-resistant weeds. Computers and Electronics in Agriculture, 170, 105276.
Xiao, H., Feng, L., Song, D., Tu, K., Peng, J., & Pan, L. (2019). Grading and sorting of grape berries using visible-near infrared spectroscopy on the basis of multiple inner quality parameters. Sensors, 19(11), 2600.
Xu, D., Wang, Y., Meng, Y., & Zhang, Z. (2017, December). An improved data anomaly detection method based on isolation forest. 10th International Symposium on Computational Intelligence and Design (ISCID) (Vol. 2, pp. 287-291). IEEE.
Yang, L., Gao, H., Meng, L., Fu, X., Du, X., Wu, D., & Huang, L. (2020). Nondestructive measurement of pectin polysaccharides using hyperspectral imaging in mulberry fruit. Food Chemistry, 334, 127614.
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