[1] Food and Agriculture Organization (FAO), 2017., Available at: www.fao.org/publications [Accessed: 2020-10-20]
[2] Liu, Y., Heying, E., & Tanumihardjo, S.A. (2012). History, Global Distribution, and Nutritional Importance of Citrus Fruits. Journal of Food Science and Food Safety, 11(6), 530–545. Doi.org/10.1111/ j.1541-4337.2012.00201.x
[3] Codex Alimentarius Commission (CAC), (2020)., Available at: http://www.fao.org/faowho- codexalimentarius/codex-texts/list-standards/En/[Accessed: 2020-12-07]
[4] Ladaniya, M.S. (2008). Citrus fruit: Biology, Technology and Evaluation. 1st Edition. Academic Press (Elsevier); San Diego. 576 p
[5] Food and Agriculture Organization (FAO), 2010., Available at: www.fao.org/statistics.htm
[6] Rozbahani, A., Movahhed, S., & Ahmadi Chenarbon, H. (2019). Mathematical modeling of hydrodynamic properties of lime (Mexican lime). Journal of Food Process Engineering. 42(4), e13054. doi.org/10.1111/jfpe.13054
[7] Rivera Cabrera, F., Artes Hernandez, F., & Kader, A.A. (2006). Quality retention and potential shelf-life of fresh-cut lemons as affected by cut type and temperature. Journal of Postharvest Biology and Technology, 43 (2): 245-254. Doi.org/10.1016/j.postharvbio.2006.09.009
[8] Conrad, Z., Niles, M.T., Neher, D.A, Roy, E.D, Tichenor, N.E, & Jahns, L. (2018). Relationship between food waste, diet quality, and environmental sustainability. Journal of PLoS ONE, 13(4), e0195405. Doi.org/10.1371/journal. pone.0195405
[9] Yahaya, S.M., & Mardiyya, A.Y. (2019). Review of Post-Harvest Losses of Fruits and Vegetables. Biomedical Journal of Scientific and Technical Research, 13(4), 10192–10200. Doi.org/ 10.26717/BJSTR.2019.13.002448
[10] Food and Agriculture Organization (FAO), 2015., Available at: www.fao.org/3/a-i4951e.pdf [Accessed: 2020-12-10]
[11] Steensland, A., & Zeigler, M. (2021). Productivity in Agriculture for a Sustainable Future. In: Campos H, Editor. The Innovation Revolution in Agriculture. Springer, 33–69. Doi.org/443.webvpn.fjmu.edu. cn/10.1007/978-3-030-50991-0_2
[12] Moghimi, A., Aghkhani, M. H., Sazgarnia, A., & Sarmad, M. (2010). Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Journal of Biosystems Engineering, 106, 295-302. Doi.org/10.1016/j.biosystemseng.2010.04.002
[13] Liu, Y.d., Ying, Y.b., Fu, X., & Lu, H. (2006). Experiments on predicting sugar content in apples by FT-NIR technique. Journal of Food Engineering, 80: 986-989. Doi.org/ 10.1016/j.jfoodeng.2006.06.035
[14] Flores, K., Sánchez, M.T., Pérez-Marín, D., Guerrero, J.E., & Garrido-Varo, A. (2008). Feasibility in NIRS instruments for predicting internal quality in intact tomato. Journal of Food Engineering, 91: 311-318. Doi.org/10.1016/j.jfoodeng.2008.09.013
[15] Liu, Y., Sun, X., & Ouyang, A. (2009). Nondestructive measurementsof soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLS and PCABPNN. Journal of LWT Food Science and Technology, 43: 602–607. Doi.org/10.1016/j.lwt.2009.10.008
[16] Paz, P., Sánchez, M.T., Pérez-Marín, D., Guerrero, J.E., & Garrido-Varo, A. (2009). Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy. Journal of Computers and electronics in agriculture, 69: 24-32. Doi.org/ 10.1016/j.compag.2009.06.008
[17] Jamshidi, B., Minaei, S., Mohajerani, E., & Ghassemian, H. (2012). Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges. Journal of Computers and Electronics in Agriculture, 85: 64-69. Doi.org/10.1016/j.compag.2012.03.008
[18] Walsh, K.B., Blasco, J., Zude-Sasse, M., & Sun, X. (2020). Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Journal of Postharvest Biology and Technology, 168, 111246. Doi.org/10.1016/j. postharvbio.2020.111246
[19] Walsh, K.B., McGlone, V.A., & Han, D.H. (2020). The uses of near infra-red spectroscopy in postharvest decision support: A review. Journal of Postharvest Biology and Technology, 163, 111139. Doi.org/ 10.1016/j.postharvbio.2020.111139
[20] Alhamdan, A. M., Fickak, A., & Atia, A. R. (2019). Evaluation of sensory and texture profile analysis properties of stored Khalal Barhi dates nondestructively using Vis/NIR spectroscopy. Journal of Food Process Engineering, 42(6). Doi.org/10.1111/jfpe.13215
[21] 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. Journal of Postharvest Biology and Technology, 46, 99–118. Doi.org/10.1146/annurev-food-030713-092410
[22] Jha, S.N. (2010). Nondestructive Evaluation of Food Quality. Springer Heidelberg Dordrecht. London. New York
[23] Brezmes, J., Llobet, E., Vilanova, X., Orts, J., Saiz, G., & Correig, X., (2001). Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples. Journal of Sensors and Actuators B: Chemical, 80(1), 41–50. Doi.org/10.1016/S0925-4005(01)00867-X
[24] Saevels, S., Lammertyn, J., Berna, A.Z., Veraverbeke, E., Natale, C. D., & Nicolai, B. (2003). Electronic nose as a non-destructive tool to evaluate the optimal harvest date of apples. Journal of Postharvest Biology and Technology, 30(1), 3–14.
[25] Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti,M., Ahmadi, H., & Lozano, J. (2014). Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM). Czech Journal of Food Sciences, 32 (6), 538–548. Doi.org/doi.org/10.17221/113/2014-CJFS
[26] Sinelli, N., Spinardi, A., Egidio, V.D., Mignani, I., & Casiraghi, E. (2008). Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. Journal of Postharvest Biology and Technology, 50(1), 31–36. Doi.org/ 10.1016/j.postharvbio.2008.03.013
[27] Tian, X., Wang, Q., Li, J., Peng, F., & Huang, W. (2018). Non-destructive prediction of soluble solids content of pear based on fruit surface feature classification andmultivariate regression analysis. Journal of Infrared Physics and Technology, 92, 336–344. Doi.org/10.1016/j.infrared.2018.06.019
[28] Li, Y., Jin, G., Jiang, X., Yi, S., & Tian, X. (2020). Non-destructive determination of soluble solids content using a multi-region combination model in hybrid citrus. Journal of Infrared Physics and Technology, 104, 103138. Doi.org/10.1016/j.aiia.2020.05.001
[29] Kim, K. B., Lee, S., Kim, M. S., & Cho, B.K., (2008). Determination of apple firmness by nondestructive ultrasonic measurement. Journal of Postharvest Biology and Technology, 52(1), 44–48. Doi.org/10.1016/j.postharvbio.2008.04.006
[30] Aboudaoud, I., Faiz, B., Aassif, E., Izbaim, D., Abassi, D.E., & Malainine, M. (2012). Thematurity characterization of orange fruit by using high frequency ultrasonic echo pulse method. Iop Conference, 42, 012038. Doi.org/1088/1757-899X/42/1/012038
[31] Blasco, J., Aleixos, N., Gomez, J., & Molto, E. (2007). Citrus sorting by identification of themost common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393. Doi.org/10.1016/j.jfoodeng.2007.03.027
[32] Elmasry, G.,Wang, N., & Vigneault, C. (2008). Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks. Journal of Postharvest Biology and Technology, 52 (1), 1–8. Doi.org/10.1016/j.postharvbio.2008.11.008
[33] Lopezgarcia, F., Andreugarcia, G., Blasco, J., Aleixos, N., & Valiente, J. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Journal of Computers and Electronics in Agriculture, 71(2), 189–197. Doi.org/10.1016/j.compag.2010.02.001
[34] Hong, X., & Wang, J. (2013). Detection of adulteration in cherry tomato juices based on electronic nose and tongue: comparison of different data fusion approaches. Journal of Food Engineering, 126, 89–97. Doi.org/10.1016/j.jfoodeng.2013.11.008
[35] Mendoza, F., Lu, R., & Cen, H. (2012). Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Journal of Postharvest Biology and Technology, 73, 89–98. Doi.org/10.1016/j.postharvbio.2012.05.012
[36] Alhamdan, A. M., & Atia, A. (2017). Non-destructive method to predict Barhi dates quality at different stages of maturity utilising near-infrared (NIR) spectroscopy. International Journal of Food Properties, 20(sup3). Doi.prg/10.1080/10942912.2017.1387794
[37] Wigati, L. P., Sutrisno, & Darmawati, E. (2019). Losses and waste of tomato and red chilli along the supply chain. IOP Conference Series: Earth and Environmental Science, 230, 012001. Doi.org/ 10.1088/1755-1315/230/1/012001
[38] Sánchez, J. C. (2012). Using near-infrared spectroscopy to predict postharvest quality. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 7(021). Doi.org/10.1079/pavsnnr20127021
[39] Kader, K.K. (2000). Quality assurance of harvested horticultural perishables. Acta Horticulturae, 553, 8-51.
[40] Quilitzsch, R., Baranska, M., Schulz, H., & Hoberg, E. (2005). Fast determination of carrot quality by spectroscopy methods in the UV-VIS, NIRS and IR range. Journal of Applied Botany and Food Quality, 79:163-7.
[41] Dull, G.G., Birth, G.S., & Leffler, R.G. (1989). Use of near infrared analysis for the nondestructive measurement of dry matter in potatoes. American Potato Journal, 66:215-25.
[42] McGoverin, C.M., Weeranantanaphan, J., Downey, G., & Manley, M. (2010). The application of near infrared spectroscopy to the measurement of bioactive compounds in food commodities. Journal of Near Infrared Spectroscopy,18:87-11.
[43] Bureau, S., Ruiz, D., Reich, M., Gouble, B., Bertrand, D., & Renard, C.M.G.C. (2009). Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Journal of Food Chemistry, 113: 1323–1328. Doi.org/10.1016/j.foodchem.2008.08.066
[44] Esteki, M., Farajmand, B., Kolahderazi, Y., & Simal-Gandara, J. (2017). Chromatographic Fingerprinting with Multivariate Data Analysis for Detection and Quantification of Apricot Kernel in Almond Powder. Journal of Food Analytical Methods, 10, 3312-3320. Doi.org/10.1007/s12161-017-0903-5
[45] Cen, H. & He, Y. (2006). Theory and application of near infrared reflectance spectroscopy in determination of food quality. Journal of Trends in Food Science and Technology, 18, 72-83. Doi.org/10.1016/j.tifs.2006.09.003
[46] Jamshidi, B., Minaei, S., Mohajerani, E. & Ghassemian, H. (2013). Linear multivariate model based on NIR spectroscopy for non-destructive internal quality prediction of orange. Proceeding of the 19th Iranian Conference on Optics and Photonics, and 5th Iranian Conference on Photonics Engineering. Jan. 22-24. Zahedan. Iran. (in Farsi)
[47] Gomez, H., He, Y., & Pereira, A.G. (2005). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR spectroscopy techniques. Journal of Food Engineering, 77:313-319. Doi.org/10.1016/j.jfoodeng.2005.06.036
[48] Shao, Y., Gomez, H., Pereir, G., Qiu, Z., & Zhag, Y. (2006). Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’ (Lycopersicum esculentum) quality characteristics. Journal of Food Engineering, 85:672-678. Doi.org/10.1016/j.jfoodeng.2006.12.026
[49] Hai-qing, T., Yi-bin, Y., Hui-shan, L., Xia-ping, F., & Hai-yan, Y. (2007). Measurement of soluble solids content in watermelon by Vis/NIR diffuse transmittance technique. Journal of Zhejiang University SCIENCE B, 8: 105-110. Doi.org/10.1631/jzus.2007.B0105
[50] Bagherpour, H., Minaei, S., Abdollahian, N. M., & Khorasani Fardvani, M. E. (2017). Non-Destructive Determination of Sugar Content in Root Beet by Near Infrared Spectroscopy (NIRS). determination of dry matter in onions. Journal of the American Society for Horticultural Science, 110(2),297–303. Doi.org/10.22059/ijbse.2017.208413.664785
[51] Schmilovitch, Z., Hoffman, A., Egozi, H., Ben-Zvi, R., Bernstein, Z., & Alchanatis, V. (1999). Maturity determination of fresh dates by near infrared spectrometry. Journal of the Science of Food and Agriculture. 79(1), 86-90. doi.org/10.1002/(SICI)1097-0010(199901)79
[52] Tohidi, M., Ghasemi-Varnamkhasti, M., Ghafarinia, V., Bonyadian, M., & Mohtasebi, S. S. (2017). Development of a metal oxide semiconductor-based artificial nose as a fast, reliable and non-expensive analytical technique for aroma profiling of milk adulteration. International Dairy Journal, 77, 38-46. Doi.org/10.1016/j.idairyj.2017.09.003
[53] Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2014). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18(7), 1391-1401. Doi.org/ 10.1080/10942912.2014.915850
[54] Izenman, A. J. (2013). Linear discriminant analysis. PP. 237-280. In. Modern multivariate statistical techniques, Springer
[55] Ghasemi-Varnamkhasti, M., Tohidi, M., Mishra, P., & Izadi, Z. (2018). Temperature modulation of electronic nose combined with multi-class support vector machine classification for identifying export caraway cultivars. Journal of Postharvest Biology and Technology, 138, 134-139. Doi.org/10.1016/j.postharvbio.2018.01.011
[56] Mohammad-Razdari, A., Ghasemi-Varnamkhasti, M., Yoosefian, S.H., Siadat, M., Izadi, Z., & Rostami, S. (2018). Detection of pumpkin puree adulteration in tomato paste using a gas sensor array. Journal of New Food Technologies, 6(1), 137-148. Doi.org/10.22104/jift.2018.2982.1726