Modelling and nondestructive quality prediction of Guanxi Honey Red Pomelo (Citrus grandis (L.) Osbeck) based on shape characteristics and bitterness
Main Article Content
Keywords
bitterness, canonical correlation analysis, grey correlation analysis, Guanxi honey red pomelo, inner quality indexes, shape characteristics
Abstract
Currently, it is difficult to distinguish the inner quality of pachycarpous fruits using nondestructive instruments, and trace nutrients and flavor compounds are rarely considered in fruit sorting. In this study, the external features and internal quality indices of an extensively farmed citrus fruit, the Guanxi honey red pomelo, were measured. The correlation between the inner and external quality was analyzed using Pearson correlation, canonical correlation, and grey correlation analyses. This research innovatively introduced bitter substances and revealed that the height-diameter ratio could serve as a predictor for ultra-size fruits, which partially reflects inner quality indices. Several grey formulas were developed to express these correlations and were fitted to a matrix, which demonstrated accuracies at all level 1 (C < 0.35, P > 0.95). Based on the matrix, fruit quality analysis of white and red pomelo showed high prediction accuracy in TSS and bitter substance indices (R2 = 0.6 – 0.8) and indicated that mature and symmetrical pomelo (height-diameter ratio of 0.9 – 1.1) exhibit a similar quality pattern. This study achieved rapid quality assessment using easy measurable shape characteristics and explored the application of characteristic bitter substances in the quality evaluation model, providing theoretical references and potential application guidance for pachycarpous fruit sorting, as well as introducing a new idea to incorporating characteristic quality into modeling index.
References
Akter, T., Bhattacharya, T., Kim, J.-H., Kim, M. S., Baek, I., Chan, D.E., & Cho, B.-K. (2024). A comprehensive review of external quality measurements of fruits and vegetables using nondestructive sensing technologies. Journal of Agriculture and Food Research, 15, 101068. https://doi.org/10.1016/j.jafr.2024.101068
Beckles, D.M. (2012). Factors affecting the postharvest soluble solids and sugar content of tomato (Solanum lycopersicum (L.)) fruit. Postharvest Biology and Technology, 63(1), 129–140. https://doi.org/10.1016/j.postharvbio.2011.05.016
Bhargava, A., & Bansal, A. (2021). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University - Computer and Information Sciences, 33(3), 243–257. https://doi.org/10.1016/j.jksuci.2018.06.002
Chen, Y.H., An, X.S., Gao, S.M., Li, S.J., & Kang, H.W., (2021). A deep learning-based vision system combining detection and tracking for fast on-line citrus sorting. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.622062
Cheng, M.H., Wang, H.D., Fan, J.L., Xiang, Y.Z., Tang, Z.J., Pei, S.Z., Zeng, H.L. et al. (2021). Effects of nitrogen supply on tomato yield, water use efficiency and fruit quality: a global meta-analysis. Scientia Horticulturae, 290. https://doi.org/10.1016/j.scienta.2021.110553
Cheng, Y.H., Tseng, C.Y., Nguyen, D.M., & Lin, Y.D. (2022). YOLOv4-driven appearance grading filing mechanism: toward a high-accuracy Tomato Grading Model through a deep-learning framework. Mathematics, 10(18). https://doi.org/10.3390/math10183398
Dea, S., Plotto, A., Manthey, J.A., Raithore, S., Irey, M., & Baldwin, E. (2013). Interactions and thresholds of limonin and nomilin in bitterness perception in orange juice and other matrices. Journal of Sensory Studies, 28(4), 311–323. https://doi.org/10.1111/joss.12046
Deng, J.L. (1982). Control-problems of grey systems. Systems & Control Letters, 1(5), 288-294. https://doi.org/10.1016/S0167-6911(82)80025-X
Desai, P.S., Sunka, S., Shukla, A., Narula, H., & Tak, K. (2023). Fruit Grading System. Interantional Journal of Scientific Research in Engineering and Management, 07(04). https://doi.org/10.55041/ijsrem18896
Gao, Y., Wang, Q., Rao, X., Xie, L., & Ying, Y. (2024). OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction. Computers and Electronics in Agriculture, 217, 108626. https://doi.org/10.1016/j.compag.2024.108626
Han, Y.Y., Xu, S., Zhang, Q., Lu, H.Z., Liang, X., & Fan, C.X. (2024). Non-destructive detection method and experiment of pomelo volume and flesh content based on image fusion. Postharvest Biology and Technology, 213. https://doi.org/10.1016/j.postharvbio.2024.112953
Jie, D., Wu, S., Wang, P., Li, Y., Ye, D., & Wei, X. (2021). Research on citrus grandis granulation determination based on hyperspectral imaging through deep learning. Food Analytical Methods, 14(2), 280-289. https://doi.org/10.1007/s12161-020-01873-6
Jie, D.F., & Wei, X. (2018). Review on the recent progress of non-destructive detection technology for internal quality of watermelon. Computers and Electronics in Agriculture, 151, 156–164. https://doi.org/10.1016/j.compag.2018.05.031
Khodabakhshian, R., Emadi, B., Khojastehpour, M., & Golzarian, M.R. (2021). Developmental changes in ripeness indexes and physico-chemical properties of pomegranate fruit during maturity on tree. Erwerbs-Obstbau, 63(2), 215–225. https://doi.org/10.1007/s10341-021-00562-w
Kim, D.S., Lee, S., Park, S.M., Yun, S.H., Gab, H.S., Kim, S.S. & Kim, H.J. (2021). Comparative metabolomics analysis of citrus varieties. Foods, 10(11). https://doi.org/10.3390/foods10112826
Liu, S.F., Forrest, J., & Vallee, R. (2009). Emergence and development of grey systems theory. Kybernetes, 38(7–8), 1246–1256. https://doi.org/10.1108/03684920910976943
Ma, Y., Wang, Z., Li, Y., Feng, X., Song, L., Gao, H., & Cao, B. (2023). Fruit morphological and nutritional quality features of goji berry (Lycium barbarum L.) during fruit development. Scientia Horticulturae, 308, 111555. https://doi.org/10.1016/j.scienta.2022.111555
Masoudi, H., & Rohani, A. (2021). Citrus fruit grading based on the volume and mass estimation from their projected areas using ANFIS and GPR models. Fruits, 76(4), 169–180. https://doi.org/10.17660/1112021/76.4.21
Miranda, J.C., Gené-Mola, J., Zude-Sasse, M., Tsoulias, N., Escolà, A., Arnó, J., Rosell-Polo, J.R. et al. (2023). Fruit sizing using AI: A review of methods and challenges. Postharvest Biology and Technology, 206. https://doi.org/10.1016/j.postharvbio.2023.112587
Moon, S.H., Assefa, A.D., Ko, E.Y., & Park, S.W. (2015). Comparison of flavonoid contents and antioxidant activity of Yuzu (Citrus junos Sieb. ex Tanaka) based on harvest time. Korean Journal of Horticultural Science & Technology, 33(2), 283–291. https://doi.org/10.7235/hort.2015.14180
Nagy, M.M., Wang, S.P., & Farag, M.A. (2022). Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: a comprehensive review of novel trends and applications. Trends in Food Science & Technology, 123, 290–309. https://doi.org/10.1016/j.tifs.2022.03.005
Nayak, S.L., Sethi, S., Sharma, R.R., Dubey, A.K., & Bhowmik, A. (2020). Variation in fruit quality traits and health promoting compounds of citrus fruits grown in semi-arid region. Indian Journal of Horticulture, 77(4), 627–632. https://doi.org/10.5958/0974-0112.2020.00090.0
Nhi, T.T.Y., Phat, D.T., Quyen, N.N., Cang, M.H., Truc, T.T., Bach, L.G., & Muoi, N.V. (2020). Effects of vacuum concentration on color, polyphenol and flavonoid contents and antioxidant activity of pomelo citrus maxima (Burm. f.) Merr. juice. 5th International Conference of Chemical Engineering and Industrial Biotechnology (Icceib 2020), 991. https://doi.org/10.1088/1757-899x/991/1/012060
Nordey, T., Joas, J., Davrieux, F., Chillet, M., & Léchaudel, M. (2017). Robust NIRS models for non-destructive prediction of mango internal quality. Scientia Horticulturae, 216, 51–57. https://doi.org/10.1016/j.scienta.2016.12.023
Nuruzzaman, M., Hossain, M.S., Rahman, M.M., Shoumik, A.S.H.C., Khan, M.A.A., & Habib, M.T. (2021). Machine Vision Based Potato Species Recognition. Paper presented at the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS).
Paudyal, K.P., & Haq, N. (2008). Variation of pomelo (Citrus grandis (L.) Osbeck) in Nepal and participatory selection of strains for further improvement. Agroforestry Systems, 72(3), 195–204. https://doi.org/10.1007/s10457-007-9088-z
Pawelczyk, A., Zwawiak, J., & Zaprutko, L. (2023). Kumquat Fruits as an Important Source of Food Ingredients and Utility Compounds. Food Reviews International, 39(2), 875–895. https://doi.org/10.1080/87559129.2021.1928179
Phuangsombut, K., Phuangsombut, A., & Terdwongworakul, A. (2021). Combination of visible reflectance and acoustic response to improve non-destructive assessment of maturity and indirect prediction of internal quality of red-fleshed pomelo. International Journal of Food Science & Technology, 56(2), 936–944. https://doi.org/10.1111/ijfs.14746
Rehman, S.U., Abbasi, K.S., Qayyum, A., Jahangir, M., Sohail, A., Nisa, S., Tareen, M.N., Tareen, M.J., & Sopade, P. (2020). Comparative analysis of citrus fruits for nutraceutical properties. Food Science and Technology (Brazil), 40, 153–157. https://doi.org/10.1590/fst.07519
Rungpichayapichet, P., Nagle, M., Yuwanbun, P., Khuwijitjaru, P., Mahayothee, B., & Müller, J. (2017). Prediction mapping of physicochemical properties in mango by hyperspectral imaging. Biosystems Engineering, 159, 109–120. https://doi.org/10.1016/j.biosystemseng.2017.04.006
Saikumar, A., Nickhil, C., & Badwaik, L.S. (2023). Physicochemical characterization of elephant apple (Dillenia indica L.) fruit and its mass and volume modeling using computer vision. Scientia Horticulturae, 314, 111947. https://doi.org/10.1016/j.scienta.2023.111947
Salihah, B.N., Rosnah, S., & Norashikin, A.A. (2015). Mass modeling of Malaysian varieties pomelo fruit (Citrus Grandis (L.) Osbeck) with some physical characteristics. International Food Research Journal, 22(2), 488–493.
Sanganamoni, S., Kancherla, S., Pedapati, A., Akki, S., & Patel, J. (2024). Physical characterization and development of mathematical models for predicting mass and area of oil palm fruits. Journal of Food Process Engineering, 47(1). https://doi.org/10.1111/jfpe.14512
Sasikumar, R., Vivek, K., Chakkaravarthi, S., & Deka, S.C. (2021). Physicochemical characterization and mass modeling of blood fruit (Haematocarpus Validus) - an underutilized fruit of northeastern India. International Journal of Fruit Science, 21(1), 12–25. https://doi.org/10.1080/15538362.2020.1848752
Shicheng, Q., Youwen, T., Qinghu, W., Shiyuan, S., & Ping, S. (2021). Nondestructive detection of decayed blueberry based on information fusion of hyperspectral imaging (HSI) and low-Field nuclear magnetic resonance (LF-NMR). Computers and Electronics in Agriculture, 184, 106100. https://doi.org/10.1016/j.compag.2021.106100
Sun, C.J., Van Beers, R., Aernouts, B., & Saeys, W. (2020). Bulk optical properties of citrus tissues and the relationship with quality properties. Postharvest Biology and Technology, 163. https://doi.org/10.1016/j.postharvbio.2020.111127
Tian, W., Li, Y., Guzman, C., Ibba, M.I., Tilley, M., Wang, D., & He, Z. (2023). Quantification of food bioactives by NIR spectroscopy: Current insights, long-lasting challenges, and future trends. Journal of Food Composition and Analysis, 124, 105708. https://doi:https://doi.org/10.1016/j.jfca.2023.105708
Tsai, M.S., & Hsu, F.Y. (2010). Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE Transactions on Power Systems, 25(2), 1126–1133. https://doi.org/10.1109/Tpwrs.2009.2032325
Volschenk, T. (2021). Effect of water deficits on pomegranate tree performance and fruit quality - A review. Agricultural Water Management, 246. https://doi.org/10.1016/j.agwat.2020.106499
Wang, C.Y., Wang, Y.T., Wu, S.J., & Shyu, Y.T. (2018). Quality changes in high hydrostatic pressure and thermal pasteurized grapefruit juice during cold storage. Journal of Food Science and Technology-Mysore, 55(12), 5115–5122. https://doi.org/10.1007/s13197-018-3452-z
Wang, F.S., Yu, X.H., Liu, X.N., Shen, W.X., Zhu, S.P., & Zhao, X.C. (2016). Temporal and spatial variations on accumulation of nomilin and limonin in the pummelos. Plant Physiology and Biochemistry, 106, 23–29. https://doi.org/10.1016/j.plaphy.2016.04.039
Wang, H.G., Mei, M.W., & Li, J.B. (2023). Research progress on non-destructive detection of internal quality of fruits with large size and thick peel: a review. Agriculture-Basel, 13(9). https://doi.org/10.3390/agriculture13091838
Wang, J., Lin, Y., Li, Q., Lu, Z., Qian, J., Dai, H., Pi, F., Liu, X., & He, Y. (2024). Non-destructive detection and grading of chilling injury-induced lignification of kiwifruit using X-ray computer tomography and machine learning. Computers and Electronics in Agriculture, 218, 108658. https://doi:https://doi.org/10.1016/j.compag.2024.108658
Wang, L., Luo, J., Pei, J., Wang, Q., Wu, Y., & Jiao, M. (2017). Grey relational analysis of relationship between limonin compounds accumulation and functional genes expression of Citri Reticulatae Semen. Chinese Traditional and Herbal Drugs, 48(2), 345–350. https://doi:10.7501/j.issn.0253-2670.2017.02.021
Wang, S.W., Wang, P., & Zhang, Y.F. (2020). A prediction method for urban heat supply based on grey system theory. Sustainable Cities and Society, 52. https://doi.org/10.1016/j.scs.2019.101819
Wei, H.C., He, C.B., Zhang, S., Xiong, H.J., Ni, H., & Li, Q.B. (2021). Effects of four storage conditions on the sugar content, acidity, and flavor of "Guanxi" honey pomelo. Journal of Food Processing and Preservation, 45(1). https://doi.org/10.1111/jfpp.15088
Wu, L.F., Liu, S.F., & Yang, Y.J. (2018). Using the fractional order method to generalize strengthening buffer operator and weakening buffer operator. IEEE-CAA Journal of Automatica Sinica, 5(6), 1074–1078. https://doi:10.1109/Jas.2016.7510214
Xu, S., Lu, H., Liang, X., Ference, C., Qiu, G., & Fan, C. (2023). Modeling and de-noising for nondestructive detection of total soluble solid content of pomelo by using visible/near infrared spectroscopy. Foods, 12(15), 2966. https://doi.org/10.3390/foods12152966
Xu, S., Lu, H., Wang, X., Ference, C.M., Liang, X., & Qiu, G. (2021). Nondestructive detection of internal flavor in ‘shatian’ pomelo fruit based on visible/near infrared spectroscopy. Hortscience, 56(11), 1325–1330. https://doi.org/10.21273/HORTSCI16136-21
Yang, H., Li, M., Kui, X., Sun, Y., Li, J.-a., & Qiu, D. (2020). Comparative studies on the quality and lycopene content of pomelo (Citrus grandis Osbeck) cultivars. Natural Product Communications, 15(9), 1934578X20953290. https://doi.org/10.1177/1934578x20953290
Yin, J., Hu, X., Hou, Y., Liu, S., Jia, S., Gan, C., Ou, Y., & Zhang, X. (2023). Comparative analysis of chemical compositions and antioxidant activities of different pomelo varieties from China. Food Chemistry Advances, 2, 100180. https://doi.org/10.1016/j.focha.2022.100180
Yu, Y., & Yao, M.B. (2022). A portable NIR system for nondestructive assessment of SSC and firmness of Nanguo pears. Lwt-Food Science and Technology, 167. https://doi.org/10.1016/j.lwt.2022.113809
Yuan, L., Mao, F., Chen, X., Li, L., & Huang, G. (2020). Non-invasive measurements of ‘Yunhe’ pears by vis-NIRS technology coupled with deviation fusion modeling approach. Postharvest Biology and Technology, 160, 111067. https://doi.org/10.1016/j.postharvbio.2019.111067
Zhong, Y., Liu, Z., Zhong, Y., Ma, L., Liu, J., Liu, H., Wang, Q., Wu, H., Lai, F., & Xiao, G. (2021). Source of the bitterness of golden pomelo from meizhou after a long-term storage. Modern Food Science & Technology, 37(5), 230–236. https://doi.org/10.13982/j.mfst.1673-9078.2021.5.0063
Beckles, D.M. (2012). Factors affecting the postharvest soluble solids and sugar content of tomato (Solanum lycopersicum (L.)) fruit. Postharvest Biology and Technology, 63(1), 129–140. https://doi.org/10.1016/j.postharvbio.2011.05.016
Bhargava, A., & Bansal, A. (2021). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University - Computer and Information Sciences, 33(3), 243–257. https://doi.org/10.1016/j.jksuci.2018.06.002
Chen, Y.H., An, X.S., Gao, S.M., Li, S.J., & Kang, H.W., (2021). A deep learning-based vision system combining detection and tracking for fast on-line citrus sorting. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.622062
Cheng, M.H., Wang, H.D., Fan, J.L., Xiang, Y.Z., Tang, Z.J., Pei, S.Z., Zeng, H.L. et al. (2021). Effects of nitrogen supply on tomato yield, water use efficiency and fruit quality: a global meta-analysis. Scientia Horticulturae, 290. https://doi.org/10.1016/j.scienta.2021.110553
Cheng, Y.H., Tseng, C.Y., Nguyen, D.M., & Lin, Y.D. (2022). YOLOv4-driven appearance grading filing mechanism: toward a high-accuracy Tomato Grading Model through a deep-learning framework. Mathematics, 10(18). https://doi.org/10.3390/math10183398
Dea, S., Plotto, A., Manthey, J.A., Raithore, S., Irey, M., & Baldwin, E. (2013). Interactions and thresholds of limonin and nomilin in bitterness perception in orange juice and other matrices. Journal of Sensory Studies, 28(4), 311–323. https://doi.org/10.1111/joss.12046
Deng, J.L. (1982). Control-problems of grey systems. Systems & Control Letters, 1(5), 288-294. https://doi.org/10.1016/S0167-6911(82)80025-X
Desai, P.S., Sunka, S., Shukla, A., Narula, H., & Tak, K. (2023). Fruit Grading System. Interantional Journal of Scientific Research in Engineering and Management, 07(04). https://doi.org/10.55041/ijsrem18896
Gao, Y., Wang, Q., Rao, X., Xie, L., & Ying, Y. (2024). OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction. Computers and Electronics in Agriculture, 217, 108626. https://doi.org/10.1016/j.compag.2024.108626
Han, Y.Y., Xu, S., Zhang, Q., Lu, H.Z., Liang, X., & Fan, C.X. (2024). Non-destructive detection method and experiment of pomelo volume and flesh content based on image fusion. Postharvest Biology and Technology, 213. https://doi.org/10.1016/j.postharvbio.2024.112953
Jie, D., Wu, S., Wang, P., Li, Y., Ye, D., & Wei, X. (2021). Research on citrus grandis granulation determination based on hyperspectral imaging through deep learning. Food Analytical Methods, 14(2), 280-289. https://doi.org/10.1007/s12161-020-01873-6
Jie, D.F., & Wei, X. (2018). Review on the recent progress of non-destructive detection technology for internal quality of watermelon. Computers and Electronics in Agriculture, 151, 156–164. https://doi.org/10.1016/j.compag.2018.05.031
Khodabakhshian, R., Emadi, B., Khojastehpour, M., & Golzarian, M.R. (2021). Developmental changes in ripeness indexes and physico-chemical properties of pomegranate fruit during maturity on tree. Erwerbs-Obstbau, 63(2), 215–225. https://doi.org/10.1007/s10341-021-00562-w
Kim, D.S., Lee, S., Park, S.M., Yun, S.H., Gab, H.S., Kim, S.S. & Kim, H.J. (2021). Comparative metabolomics analysis of citrus varieties. Foods, 10(11). https://doi.org/10.3390/foods10112826
Liu, S.F., Forrest, J., & Vallee, R. (2009). Emergence and development of grey systems theory. Kybernetes, 38(7–8), 1246–1256. https://doi.org/10.1108/03684920910976943
Ma, Y., Wang, Z., Li, Y., Feng, X., Song, L., Gao, H., & Cao, B. (2023). Fruit morphological and nutritional quality features of goji berry (Lycium barbarum L.) during fruit development. Scientia Horticulturae, 308, 111555. https://doi.org/10.1016/j.scienta.2022.111555
Masoudi, H., & Rohani, A. (2021). Citrus fruit grading based on the volume and mass estimation from their projected areas using ANFIS and GPR models. Fruits, 76(4), 169–180. https://doi.org/10.17660/1112021/76.4.21
Miranda, J.C., Gené-Mola, J., Zude-Sasse, M., Tsoulias, N., Escolà, A., Arnó, J., Rosell-Polo, J.R. et al. (2023). Fruit sizing using AI: A review of methods and challenges. Postharvest Biology and Technology, 206. https://doi.org/10.1016/j.postharvbio.2023.112587
Moon, S.H., Assefa, A.D., Ko, E.Y., & Park, S.W. (2015). Comparison of flavonoid contents and antioxidant activity of Yuzu (Citrus junos Sieb. ex Tanaka) based on harvest time. Korean Journal of Horticultural Science & Technology, 33(2), 283–291. https://doi.org/10.7235/hort.2015.14180
Nagy, M.M., Wang, S.P., & Farag, M.A. (2022). Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: a comprehensive review of novel trends and applications. Trends in Food Science & Technology, 123, 290–309. https://doi.org/10.1016/j.tifs.2022.03.005
Nayak, S.L., Sethi, S., Sharma, R.R., Dubey, A.K., & Bhowmik, A. (2020). Variation in fruit quality traits and health promoting compounds of citrus fruits grown in semi-arid region. Indian Journal of Horticulture, 77(4), 627–632. https://doi.org/10.5958/0974-0112.2020.00090.0
Nhi, T.T.Y., Phat, D.T., Quyen, N.N., Cang, M.H., Truc, T.T., Bach, L.G., & Muoi, N.V. (2020). Effects of vacuum concentration on color, polyphenol and flavonoid contents and antioxidant activity of pomelo citrus maxima (Burm. f.) Merr. juice. 5th International Conference of Chemical Engineering and Industrial Biotechnology (Icceib 2020), 991. https://doi.org/10.1088/1757-899x/991/1/012060
Nordey, T., Joas, J., Davrieux, F., Chillet, M., & Léchaudel, M. (2017). Robust NIRS models for non-destructive prediction of mango internal quality. Scientia Horticulturae, 216, 51–57. https://doi.org/10.1016/j.scienta.2016.12.023
Nuruzzaman, M., Hossain, M.S., Rahman, M.M., Shoumik, A.S.H.C., Khan, M.A.A., & Habib, M.T. (2021). Machine Vision Based Potato Species Recognition. Paper presented at the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS).
Paudyal, K.P., & Haq, N. (2008). Variation of pomelo (Citrus grandis (L.) Osbeck) in Nepal and participatory selection of strains for further improvement. Agroforestry Systems, 72(3), 195–204. https://doi.org/10.1007/s10457-007-9088-z
Pawelczyk, A., Zwawiak, J., & Zaprutko, L. (2023). Kumquat Fruits as an Important Source of Food Ingredients and Utility Compounds. Food Reviews International, 39(2), 875–895. https://doi.org/10.1080/87559129.2021.1928179
Phuangsombut, K., Phuangsombut, A., & Terdwongworakul, A. (2021). Combination of visible reflectance and acoustic response to improve non-destructive assessment of maturity and indirect prediction of internal quality of red-fleshed pomelo. International Journal of Food Science & Technology, 56(2), 936–944. https://doi.org/10.1111/ijfs.14746
Rehman, S.U., Abbasi, K.S., Qayyum, A., Jahangir, M., Sohail, A., Nisa, S., Tareen, M.N., Tareen, M.J., & Sopade, P. (2020). Comparative analysis of citrus fruits for nutraceutical properties. Food Science and Technology (Brazil), 40, 153–157. https://doi.org/10.1590/fst.07519
Rungpichayapichet, P., Nagle, M., Yuwanbun, P., Khuwijitjaru, P., Mahayothee, B., & Müller, J. (2017). Prediction mapping of physicochemical properties in mango by hyperspectral imaging. Biosystems Engineering, 159, 109–120. https://doi.org/10.1016/j.biosystemseng.2017.04.006
Saikumar, A., Nickhil, C., & Badwaik, L.S. (2023). Physicochemical characterization of elephant apple (Dillenia indica L.) fruit and its mass and volume modeling using computer vision. Scientia Horticulturae, 314, 111947. https://doi.org/10.1016/j.scienta.2023.111947
Salihah, B.N., Rosnah, S., & Norashikin, A.A. (2015). Mass modeling of Malaysian varieties pomelo fruit (Citrus Grandis (L.) Osbeck) with some physical characteristics. International Food Research Journal, 22(2), 488–493.
Sanganamoni, S., Kancherla, S., Pedapati, A., Akki, S., & Patel, J. (2024). Physical characterization and development of mathematical models for predicting mass and area of oil palm fruits. Journal of Food Process Engineering, 47(1). https://doi.org/10.1111/jfpe.14512
Sasikumar, R., Vivek, K., Chakkaravarthi, S., & Deka, S.C. (2021). Physicochemical characterization and mass modeling of blood fruit (Haematocarpus Validus) - an underutilized fruit of northeastern India. International Journal of Fruit Science, 21(1), 12–25. https://doi.org/10.1080/15538362.2020.1848752
Shicheng, Q., Youwen, T., Qinghu, W., Shiyuan, S., & Ping, S. (2021). Nondestructive detection of decayed blueberry based on information fusion of hyperspectral imaging (HSI) and low-Field nuclear magnetic resonance (LF-NMR). Computers and Electronics in Agriculture, 184, 106100. https://doi.org/10.1016/j.compag.2021.106100
Sun, C.J., Van Beers, R., Aernouts, B., & Saeys, W. (2020). Bulk optical properties of citrus tissues and the relationship with quality properties. Postharvest Biology and Technology, 163. https://doi.org/10.1016/j.postharvbio.2020.111127
Tian, W., Li, Y., Guzman, C., Ibba, M.I., Tilley, M., Wang, D., & He, Z. (2023). Quantification of food bioactives by NIR spectroscopy: Current insights, long-lasting challenges, and future trends. Journal of Food Composition and Analysis, 124, 105708. https://doi:https://doi.org/10.1016/j.jfca.2023.105708
Tsai, M.S., & Hsu, F.Y. (2010). Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE Transactions on Power Systems, 25(2), 1126–1133. https://doi.org/10.1109/Tpwrs.2009.2032325
Volschenk, T. (2021). Effect of water deficits on pomegranate tree performance and fruit quality - A review. Agricultural Water Management, 246. https://doi.org/10.1016/j.agwat.2020.106499
Wang, C.Y., Wang, Y.T., Wu, S.J., & Shyu, Y.T. (2018). Quality changes in high hydrostatic pressure and thermal pasteurized grapefruit juice during cold storage. Journal of Food Science and Technology-Mysore, 55(12), 5115–5122. https://doi.org/10.1007/s13197-018-3452-z
Wang, F.S., Yu, X.H., Liu, X.N., Shen, W.X., Zhu, S.P., & Zhao, X.C. (2016). Temporal and spatial variations on accumulation of nomilin and limonin in the pummelos. Plant Physiology and Biochemistry, 106, 23–29. https://doi.org/10.1016/j.plaphy.2016.04.039
Wang, H.G., Mei, M.W., & Li, J.B. (2023). Research progress on non-destructive detection of internal quality of fruits with large size and thick peel: a review. Agriculture-Basel, 13(9). https://doi.org/10.3390/agriculture13091838
Wang, J., Lin, Y., Li, Q., Lu, Z., Qian, J., Dai, H., Pi, F., Liu, X., & He, Y. (2024). Non-destructive detection and grading of chilling injury-induced lignification of kiwifruit using X-ray computer tomography and machine learning. Computers and Electronics in Agriculture, 218, 108658. https://doi:https://doi.org/10.1016/j.compag.2024.108658
Wang, L., Luo, J., Pei, J., Wang, Q., Wu, Y., & Jiao, M. (2017). Grey relational analysis of relationship between limonin compounds accumulation and functional genes expression of Citri Reticulatae Semen. Chinese Traditional and Herbal Drugs, 48(2), 345–350. https://doi:10.7501/j.issn.0253-2670.2017.02.021
Wang, S.W., Wang, P., & Zhang, Y.F. (2020). A prediction method for urban heat supply based on grey system theory. Sustainable Cities and Society, 52. https://doi.org/10.1016/j.scs.2019.101819
Wei, H.C., He, C.B., Zhang, S., Xiong, H.J., Ni, H., & Li, Q.B. (2021). Effects of four storage conditions on the sugar content, acidity, and flavor of "Guanxi" honey pomelo. Journal of Food Processing and Preservation, 45(1). https://doi.org/10.1111/jfpp.15088
Wu, L.F., Liu, S.F., & Yang, Y.J. (2018). Using the fractional order method to generalize strengthening buffer operator and weakening buffer operator. IEEE-CAA Journal of Automatica Sinica, 5(6), 1074–1078. https://doi:10.1109/Jas.2016.7510214
Xu, S., Lu, H., Liang, X., Ference, C., Qiu, G., & Fan, C. (2023). Modeling and de-noising for nondestructive detection of total soluble solid content of pomelo by using visible/near infrared spectroscopy. Foods, 12(15), 2966. https://doi.org/10.3390/foods12152966
Xu, S., Lu, H., Wang, X., Ference, C.M., Liang, X., & Qiu, G. (2021). Nondestructive detection of internal flavor in ‘shatian’ pomelo fruit based on visible/near infrared spectroscopy. Hortscience, 56(11), 1325–1330. https://doi.org/10.21273/HORTSCI16136-21
Yang, H., Li, M., Kui, X., Sun, Y., Li, J.-a., & Qiu, D. (2020). Comparative studies on the quality and lycopene content of pomelo (Citrus grandis Osbeck) cultivars. Natural Product Communications, 15(9), 1934578X20953290. https://doi.org/10.1177/1934578x20953290
Yin, J., Hu, X., Hou, Y., Liu, S., Jia, S., Gan, C., Ou, Y., & Zhang, X. (2023). Comparative analysis of chemical compositions and antioxidant activities of different pomelo varieties from China. Food Chemistry Advances, 2, 100180. https://doi.org/10.1016/j.focha.2022.100180
Yu, Y., & Yao, M.B. (2022). A portable NIR system for nondestructive assessment of SSC and firmness of Nanguo pears. Lwt-Food Science and Technology, 167. https://doi.org/10.1016/j.lwt.2022.113809
Yuan, L., Mao, F., Chen, X., Li, L., & Huang, G. (2020). Non-invasive measurements of ‘Yunhe’ pears by vis-NIRS technology coupled with deviation fusion modeling approach. Postharvest Biology and Technology, 160, 111067. https://doi.org/10.1016/j.postharvbio.2019.111067
Zhong, Y., Liu, Z., Zhong, Y., Ma, L., Liu, J., Liu, H., Wang, Q., Wu, H., Lai, F., & Xiao, G. (2021). Source of the bitterness of golden pomelo from meizhou after a long-term storage. Modern Food Science & Technology, 37(5), 230–236. https://doi.org/10.13982/j.mfst.1673-9078.2021.5.0063
Article Sidebar
Published
Sep 24, 2024
Article Details
How to Cite
Yang, Y. ., Pan, H. ., Xiao, Y., Li, L. ., Hu, Y. ., Chen, L. ., Wang, Y. ., Ni, H. ., Chen, F. ., Tan, J. ., & He, F. (2024). Modelling and nondestructive quality prediction of Guanxi Honey Red Pomelo (Citrus grandis (L.) Osbeck) based on shape characteristics and bitterness. Quality Assurance and Safety of Crops & Foods, 16(3), 127-138. https://doi.org/10.15586/qas.v16i3.1480
Section
Research Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
Yang, Y. ., Pan, H. ., Xiao, Y., Li, L. ., Hu, Y. ., Chen, L. ., Wang, Y. ., Ni, H. ., Chen, F. ., Tan, J. ., & He, F. (2024). Modelling and nondestructive quality prediction of Guanxi Honey Red Pomelo (Citrus grandis (L.) Osbeck) based on shape characteristics and bitterness. Quality Assurance and Safety of Crops & Foods, 16(3), 127-138. https://doi.org/10.15586/qas.v16i3.1480