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Imaging Techniques for Evaluation of Ripening and Maturity of Fruits and Vegetables

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Nondestructive Quality Assessment Techniques for Fresh Fruits and Vegetables
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Abstract

Optimal harvesting time of fruits and vegetables is an important factor, which is directly associated with the postharvest quality of the produce and shelf life. Depending on the variety of horticultural products, maturity can be assessed using internal properties like moisture, sugar, starch, oil content, soluble solid content (SSC), titratable acidity (TA), SSC/TA, pH, and firmness, or using external properties like surface or peel color (chlorophyll, carotenoids, lycopene, etc.), size, volume, shape, and peel/flesh ratio that are taken into consideration. The level of maturity for these products is determined by the limits based on the internal and external properties of that specific product. Conventional maturity evaluation methods generally employ destructive analysis; however, an increasing number of studies in the last decade have shown that nondestructive methods have been successfully applied to determine the maturity of produce. Nondestructive methods allow analyzing the raw data extracted from the original image and reconstructing a 3D model of dissected sample for visualization of internal structure. Surface color or the structure of samples is also analyzed with several imaging and image processing techniques in order to determine the maturity levels. Whether the internal or external structure is scrutinized, the compliance of extracted data with destructive maturity or ripening parameters must be clearly verified. Statistical models like artificial neural network, principal component analysis, or machine learning approaches are applied because of reducing the amount of extracted data from imaging analysis and its complexity. In this chapter, the imaging techniques used for determining the maturity or ripening levels of fruits and vegetables are discussed.

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References

  • Abasi, S., Minaei, S., Jamshidi, B., & Fathi, D. (2018). Dedicated non-destructive devices for food quality measurement: A review. Trends in Food Science & Technology, 78, 197–205.

    Article  CAS  Google Scholar 

  • Adebayo, S. E., Hashim, N., Hass, R., Reich, O., Regen, C., Münzberg, M., et al. (2017). Using absorption and reduced scattering coefficients for non-destructive analyses of fruit flesh firmness and soluble solids content in pear (Pyrus communis ‘conference’)—An update when using diffusion theory. Postharvest Biology and Technology, 130, 56–63.

    Article  Google Scholar 

  • Agati, G., D’Onofrio, C., Ducci, E., Cuzzola, A., Remorini, D., Tuccio, L., et al. (2013). Potential of a multiparametric optical sensor for determining in situ the maturity components of red and white Vitis vinifera wine grapes. Journal of Agricultural and Food Chemistry, 61(50), 12211–12218.

    Article  CAS  PubMed  Google Scholar 

  • Ali, M. M., Hashim, N., Bejo, S. K., & Shamsudin, R. (2017). Quality evaluation of watermelon using laser-induced backscattering imaging during storage. Postharvest Biology and Technology, 123, 51–59.

    Article  Google Scholar 

  • Ali, M. M., Hashim, N., & Hamid, A. S. A. (2020). Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity. Computers and Electronics in Agriculture, 169, 105235.

    Article  Google Scholar 

  • Aredo, V., Velásquez, L., Carranza-Cabrera, J., & Siche, R. (2019). Predicting of the quality attributes of orange fruit using hyperspectral images. Journal of Food Quality and Hazards Control, 6, 82–92.

    CAS  Google Scholar 

  • Arendse, E., Fawole, O. A., Magwaza, L. S., & Opara, U. L. (2018). Non-destructive prediction of internal and external quality attributes of fruit with thick rind: A review. Journal of Food Engineering, 217, 11–23.

    Article  Google Scholar 

  • Ariana, D. P., & Lu, R. (2008). Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—Part II. Performance of a prototype. Sensing and Instrumentation for Food Quality and Safety, 2(3), 152–160.

    Article  Google Scholar 

  • Ariana, D. P., & Lu, R. (2010). Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. Journal of Food Engineering, 96(4), 583–590.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Barrett, D. M., Somogyi, L., & Ramaswamy, H. S. (Eds.). (2005). Processing fruits: Science and technology (2nd ed.). CRC Press.

    Google Scholar 

  • Beghi, R., Giovenzana, V., Tugnolo, A., & Guidetti, R. (2018). Application of visible/near infrared spectroscopy to quality control of fresh fruits and vegetables in large-scale mass distribution channels: A preliminary test on carrots and tomatoes. Journal of the Science of Food and Agriculture, 98(7), 2729–2734.

    Article  CAS  PubMed  Google Scholar 

  • Benelli, A., Cevoli, C., & Fabbri, A. (2020). In-field Vis/NIR hyperspectral imaging to measure soluble solids content of wine grape berries during ripening. In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) (pp. 99–103). IEEE.

    Google Scholar 

  • Betemps, D. L., Fachinello, J. C., Galarça, S. P., Portela, N. M., Remorini, D., Massai, R., & Agati, G. (2012). Non-destructive evaluation of ripening and quality traits in apples using a multiparametric fluorescence sensor. Journal of the Science of Food and Agriculture, 92(9), 1855–1864.

    Article  CAS  PubMed  Google Scholar 

  • Brandt, S., Pék, Z., Barna, É., Lugasi, A., & Helyes, L. (2006). Lycopene content and colour of ripening tomatoes as affected by environmental conditions. Journal of the Science of Food and Agriculture, 86(4), 568–572.

    Article  CAS  Google Scholar 

  • Brummell, D., Atkinson, R., Burdon, J. N., Patterson, K. J., & Schaffer, R. (2016). Fruit growth, ripening and post-harvest physiology. In B. J. Atwell, P. E. Kriedemann, & C. G. N. Turnbull (Eds.), Plants in action (pp. 350–380). Macmillan.

    Google Scholar 

  • Cáez-Ramírez, G., Alamilla-Beltrán, L., & Gutiérrez-López, G. F. (2018). Morphometric analysis and tissue structural continuity evaluation of senescence progression in fresh cut papaya (Carica papaya L.). Journal of Food Engineering, 216, 107–119.

    Article  Google Scholar 

  • Cakmak, H. (2019). Assessment of fresh fruit and vegetable quality with non-destructive methods. In C. M. Galanakis (Ed.), Food quality and shelf life (pp. 303–331). Academic Press.

    Chapter  Google Scholar 

  • Cantre, D., Herremans, E., Verboven, P., Ampofo-Asiama, J., & Nicolaï, B. (2014). Characterization of the 3-D microstructure of mango (Mangifera indica L. cv. Carabao) during ripening using X-ray computed microtomography. Innovative Food Science & Emerging Technologies, 24, 28–39.

    Article  Google Scholar 

  • Cen, H., Lu, R., Mendoza, F., & Beaudry, R. M. (2013). Relationship of the optical absorption and scattering properties with mechanical and structural properties of apple tissue. Postharvest Biology and Technology, 85, 30–38.

    Article  Google Scholar 

  • Chen, Y., Grimplet, J., David, K., Castellarin, S. D., Terol, J., Wong, D. C., et al. (2018). Ethylene receptors and related proteins in climacteric and non-climacteric fruits. Plant Science, 276, 63–72.

    Article  CAS  PubMed  Google Scholar 

  • Cho, J. S., Lim, J. H., Park, K. J., Choi, J. H., & Ok, G. S. (2021). Prediction of pelargonidin-3-glucoside in strawberries according to the postharvest distribution period of two ripening stages using VIS-NIR and SWIR hyperspectral imaging technology. LWT, 141, 110875.

    Article  CAS  Google Scholar 

  • Clark, C. J., & MacFall, J. S. (2003). Quantitative magnetic resonance imaging of ‘Fuyu’persimmon fruit during development and ripening. Magnetic Resonance Imaging, 21(6), 679–685.

    Article  PubMed  Google Scholar 

  • Cruz, J. O. (2020). Terahertz time-domain spectroscopy (THz-TDS) for classification of blueberries according to their maturity. In 2020 IEEE Engineering International Research Conference (EIRCON) (pp. 1–4). IEEE.

    Google Scholar 

  • Dadwal, M., & Banga, V. K. (2012). Estimate ripeness level of fruits using RGB color space and fuzzy logic technique. International Journal of Engineering and Advanced Technology, 2(1), 225–229.

    Google Scholar 

  • de Azevedo, C. H., & Rodriguez-Amaya, D. B. (2005). Carotenoid composition of kale as influenced by maturity, season and minimal processing. Journal of the Science of Food and Agriculture, 85(4), 591–597.

    Article  Google Scholar 

  • de Azevedo-Meleiro, C. H., & Rodriguez-Amaya, D. B. (2005). Carotenoids of endive and New Zealand spinach as affected by maturity, season and minimal processing. Journal of Food Composition and Analysis, 18(8), 845–855.

    Article  Google Scholar 

  • DeEll, J. R., & Toivonen, P. M. (Eds.). (2003). Practical applications of chlorophyll fluorescence in plant biology. Springer Science & Business Media. https://doi.org/10.1007/978-1-4615-0415-3

    Book  Google Scholar 

  • Diels, E., van Dael, M., Keresztes, J., Vanmaercke, S., Verboven, P., Nicolai, B., et al. (2017). Assessment of bruise volumes in apples using X-ray computed tomography. Postharvest Biology and Technology, 128, 24–32.

    Article  Google Scholar 

  • do Nascimento Nunes, M. C. (2008). Color atlas of postharvest quality of fruits and vegetables. John Blackwell Publishing.

    Book  Google Scholar 

  • Du, C. J., & Sun, D. W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology, 15(5), 230–249.

    Article  CAS  Google Scholar 

  • El-Bendary, N., El Hariri, E., Hassanien, A. E., & Badr, A. (2015). Using machine learning techniques for evaluating tomato ripeness. Expert Systems with Applications, 42(4), 1892–1905.

    Article  Google Scholar 

  • Fan, S., Huang, W., Guo, Z., Zhang, B., & Zhao, C. (2015). Prediction of soluble solids content and firmness of pears using hyperspectral reflectance imaging. Food Analytical Methods, 8(8), 1936–1946.

    Article  Google Scholar 

  • Fashi, M., Naderloo, L., & Javadikia, H. (2020). Pomegranate grading based on pH using image processing and artificial intelligence. Journal of Food Measurement and Characterization, 14(6), 3112–3121.

    Article  Google Scholar 

  • Fatchurrahman, D., Amodio, M. L., de Chiara, M. L. V., Chaudhry, M. M. A., & Colelli, G. (2020). Early discrimination of mature-and immature-green tomatoes (Solanum lycopersicum L.) using fluorescence imaging method. Postharvest Biology and Technology, 169, 111287.

    Article  CAS  Google Scholar 

  • Fathizadeh, Z., Aboonajmi, M., & Beygi, S. R. H. (2020). Nondestructive firmness prediction of apple fruit using acoustic vibration response. Scientia Horticulturae, 262, 109073.

    Article  Google Scholar 

  • Fernández-Espinosa, A. J. (2016). Combining PLS regression with portable NIR spectroscopy to on-line monitor quality parameters in intact olives for determining optimal harvesting time. Talanta, 148, 216–228.

    Article  PubMed  Google Scholar 

  • Ferrer, A., Remón, S., Negueruela, A. I., & Oria, R. (2005). Changes during the ripening of the very late season Spanish peach cultivar Calanda: Feasibility of using CIELAB coordinates as maturity indices. Scientia Horticulturae, 105(4), 435–446.

    Article  CAS  Google Scholar 

  • Garillos-Manliguez, C. A., & Chiang, J. Y. (2021). Multimodal deep learning and visible-light and hyperspectral imaging for fruit maturity estimation. Sensors, 21(4), 1288.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gonçalves, B. J., de Oliveira Giarola, T. M., Pereira, D. F., Boas, E. V. D. B. V., & de Resende, J. V. (2016). Using infrared thermography to evaluate the injuries of cold-stored guava. Journal of Food Science and Technology, 53(2), 1063–1070.

    Article  PubMed  Google Scholar 

  • Gupta, A. K., Medhi, M., Chakraborty, S., Yumnam, M., & Mishra, P. (2021). Development of rapid and non-destructive technique for the determination of maturity indices of pomelo fruit (Citrus grandis). Journal of Food Measurement and Characterization, 15(2), 1463–1474.

    Article  Google Scholar 

  • Gupta, A. K., Pathak, U., Tongbram, T., Medhi, M., Terdwongworakul, A., Magwaza, L. S., et al. (2022). Emerging approaches to determine maturity of citrus fruit. Critical Reviews in Food Science and Nutrition, 62, 5245–5266.

    Article  PubMed  Google Scholar 

  • Harel, B., Parmet, Y., & Edan, Y. (2020). Maturity classification of sweet peppers using image datasets acquired in different times. Computers in Industry, 121, 103274.

    Article  Google Scholar 

  • Hashim, N., Adebayo, S. E., Abdan, K., & Hanafi, M. (2018). Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system. Postharvest Biology and Technology, 135, 38–50.

    Article  Google Scholar 

  • Herrero-Langreo, A., Fernández-Ahumada, E., Roger, J. M., Palagós, B., & Lleó, L. (2012). Combination of optical and non-destructive mechanical techniques for the measurement of maturity in peach. Journal of Food Engineering, 108(1), 150–157.

    Article  Google Scholar 

  • Hitzmann, B., & Ahmad, M. H. (Eds.). (2017). Measurement, modeling and automation in advanced food processing (Vol. 161). Springer.

    Google Scholar 

  • Hoffmann, A. M., Noga, G., & Hunsche, M. (2015). Fluorescence indices for monitoring the ripening of tomatoes in pre-and postharvest phases. Scientia Horticulturae, 191, 74–81.

    Article  CAS  Google Scholar 

  • Hsiao, W. T., Kuo, W. C., Lin, H. H., & Lai, L. H. (2021). Assessment and feasibility study of lemon ripening using x-ray image of information visualization. Applied Sciences, 11(7), 3261.

    Article  CAS  Google Scholar 

  • Huang, Y., Dong, W., Chen, Y., Wang, X., Luo, W., Zhan, B., et al. (2021). Online detection of soluble solids content and maturity of tomatoes using Vis/NIR full transmittance spectra. Chemometrics and Intelligent Laboratory Systems, 210, 104243.

    Article  CAS  Google Scholar 

  • Hui, Y. H. (Ed.). (2006). Handbook of fruits and fruit processing. Blackwell Publishing Ltd.

    Google Scholar 

  • Hui, Y. H., Ghazala, S., Graham, D. M., Murrell, K. D., & Nip, W.-K. (Eds.). (2004). Handbook of vegetable preservation and processing (1st ed.). Marcel Dekker.

    Google Scholar 

  • Hussain, A., Pu, H., & Sun, D. W. (2018). Innovative nondestructive imaging techniques for ripening and maturity of fruits–a review of recent applications. Trends in Food Science & Technology, 72, 144–152.

    Article  CAS  Google Scholar 

  • Irudayaraj, J., & Reh, C. (Eds.). (2008). Nondestructive testing of food quality (Vol. 18). Wiley.

    Google Scholar 

  • Jongen, W. (Ed.). (2002). Fruit and vegetable processing: Improving quality. Elsevier.

    Google Scholar 

  • Kader, A. A. (2011). Postharvest technology of horticultural crops (Vol. 3529, 3rd ed.). University of California Agriculture and Natural Resources.

    Google Scholar 

  • Kasampalis, D. S., Tsouvaltzis, P., & Siomos, A. S. (2020). Chlorophyll fluorescence, non-photochemical quenching and light harvesting complex as alternatives to color measurement, in classifying tomato fruit according to their maturity stage at harvest and in monitoring postharvest ripening during storage. Postharvest Biology and Technology, 161, 111036.

    Article  CAS  Google Scholar 

  • Khodabakhshian, R., & Emadi, B. (2017). Application of Vis/SNIR hyperspectral imaging in ripeness classification of pear. International Journal of Food Properties, 20(Suppl. 3), S3149–S3163.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Khojastehnazhand, M., Mohammadi, V., & Minaei, S. (2019). Maturity detection and volume estimation of apricot using image processing technique. Scientia Horticulturae, 251, 247–251.

    Article  Google Scholar 

  • Kotwaliwale, N., Kalne, A., & Singh, K. (2012). Monitoring of mango (Mangifera indica L.) (cv.: Chousa) ripening using X-ray computed tomography. In 2012 Sixth International Conference on Sensing Technology (ICST) (pp. 326–330). IEEE.

    Google Scholar 

  • Kotwaliwale, N., Singh, K., Kalne, A., Jha, S. N., Seth, N., & Kar, A. (2014). X-ray imaging methods for internal quality evaluation of agricultural produce. Journal of Food Science and Technology, 51(1), 1–15.

    Article  PubMed  Google Scholar 

  • Kusumiyati, K., Sutari, W., Hamdani, J. S., Mubarok, S., Sitepu, R. B., & Oktavia, A. R. (2018). Non-destructive measurement of green bitter gourd quality component using near infrared spectroscopy (NIRS). Science and Technology Indonesia, 3(2), 59–65.

    Article  Google Scholar 

  • Lai, A., Santangelo, E., Soressi, G. P., & Fantoni, R. (2007). Analysis of the main secondary metabolites produced in tomato (Lycopersicon esculentum, mill.) epicarp tissue during fruit ripening using fluorescence techniques. Postharvest Biology and Technology, 43(3), 335–342.

    Article  CAS  Google Scholar 

  • Lakshmi, S., Pandey, A. K., Ravi, N., Chauhan, O. P., Gopalan, N., & Sharma, R. K. (2017). Non-destructive quality monitoring of fresh fruits and vegetables. Defence Life Science Journal, 2(2), 103–110.

    Article  Google Scholar 

  • Lamikanra, O. (Ed.). (2002). Fresh-cut fruits and vegetables: Science, technology, and market. CRC press.

    Google Scholar 

  • Leon, K., Mery, D., Pedreschi, F., & Leon, J. (2006). Color measurement in L∗ a∗ b∗ units from RGB digital images. Food Research International, 39(10), 1084–1091.

    Article  Google Scholar 

  • Li, H., Lee, W. S., & Wang, K. (2014). Identifying blueberry fruit of different growth stages using natural outdoor color images. Computers and Electronics in Agriculture, 106, 91–101.

    Article  Google Scholar 

  • Liu, Y., Pu, H., & Sun, D. W. (2017). Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications. Trends in Food Science & Technology, 69, 25–35.

    Article  CAS  Google Scholar 

  • Li, B., Lecourt, J., & Bishop, G. (2018a). Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction—A review. Plants, 7(1), 3.

    Article  PubMed Central  Google Scholar 

  • Li, X., Wei, Y., Xu, J., Feng, X., Wu, F., Zhou, R., et al. (2018b). SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology. Postharvest Biology and Technology, 143, 112–118.

    Article  CAS  Google Scholar 

  • Lleó, L., Roger, J. M., Herrero-Langreo, A., Diezma-Iglesias, B., & Barreiro, P. (2011). Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening. Journal of Food Engineering, 104(4), 612–620.

    Article  Google Scholar 

  • Lockman, N. A., Hashim, N., & Onwude, D. I. (2019). Laser-based imaging for cocoa pods maturity detection. Food and Bioprocess Technology, 12(11), 1928–1937.

    Article  Google Scholar 

  • Lu, J., Qi, S., Liu, R., Zhou, E., Li, W., Song, S., & Han, D. (2015). Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy. Journal of Innovative Optical Health Sciences, 8(06), 1550032.

    Article  CAS  Google Scholar 

  • Lu, R. (2007). Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing and Instrumentation for Food Quality and Safety, 1(1), 19–27.

    Article  Google Scholar 

  • Lu, R., & Peng, Y. (2006). Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering, 93(2), 161–171.

    Article  Google Scholar 

  • Lu, Y., Saeys, W., Kim, M., Peng, Y., & Lu, R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biology and Technology, 170, 111318.

    Article  CAS  Google Scholar 

  • Mahesh, S., Jayas, D. S., Paliwal, J., & White, N. D. G. (2015). Hyperspectral imaging to classify and monitor quality of agricultural materials. Journal of Stored Products Research, 61, 17–26.

    Article  Google Scholar 

  • Makino, Y., & Kousaka, Y. (2020). Prediction of degreening velocity of broccoli buds using hyperspectral camera combined with artificial neural networks. Food, 9(5), 558.

    Article  CAS  Google Scholar 

  • Makky, M., & Soni, P. (2014). In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach. Journal of Food Engineering, 120, 248–259.

    Article  Google Scholar 

  • Marques, E. J. N., de Freitas, S. T., Pimentel, M. F., & Pasquini, C. (2016). Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer. Food Chemistry, 197, 1207–1214.

    Article  CAS  PubMed  Google Scholar 

  • Mendoza, F., Lu, R., Ariana, D., Cen, H., & Bailey, B. (2011). Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 62(2), 149–160.

    Google Scholar 

  • Mendoza, F., Lu, R., & Cen, H. (2012). Comparison and fusion of four nondestructive sensors for predicting apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 73, 89–98.

    Article  CAS  Google Scholar 

  • Mendoza, F., Lu, R., & Cen, H. (2014). Grading of apples based on firmness and soluble solids content using Vis/SWNIR spectroscopy and spectral scattering techniques. Journal of Food Engineering, 125, 59–68.

    Article  CAS  Google Scholar 

  • Mercado-Silva, E., Benito-Bautista, P., & de los Angeles Garcı́a-Velasco, M. (1998). Fruit development, harvest index and ripening changes of guavas produced in Central Mexico. Postharvest Biology and Technology, 13(2), 143–150.

    Article  Google Scholar 

  • Minas, I. S., Blanco-Cipollone, F., & Sterle, D. (2021). Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy. Food Chemistry, 335, 127626.

    Article  CAS  PubMed  Google Scholar 

  • Mo, C., Kim, M. S., Kim, G., Lim, J., Delwiche, S. R., Chao, K., et al. (2017). Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging. Biosystems Engineering, 159, 10–21.

    Article  Google Scholar 

  • Mohammadi, V., Kheiralipour, K., & Ghasemi-Varnamkhasti, M. (2015). Detecting maturity of persimmon fruit based on image processing technique. Scientia Horticulturae, 184, 123–128.

    Article  Google Scholar 

  • Mohapatra, A., Shanmugasundaram, S., & Malmathanraj, R. (2017). Grading of ripening stages of red banana using dielectric properties changes and image processing approach. Computers and Electronics in Agriculture, 143, 100–110.

    Article  Google Scholar 

  • Mollazade, K., Omid, M., Akhlaghian Tab, F., Rezaei Kalaj, Y., & Mohtasebi, S. S. (2015). Data mining-based wavelength selection for monitoring quality of tomato fruit by backscattering and multispectral imaging. International Journal of Food Properties, 18(4), 880–896.

    Article  CAS  Google Scholar 

  • Munera, S., Besada, C., Aleixos, N., Talens, P., Salvador, A., Sun, D. W., Cubero, S., & Blasco, J. (2017). Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT-Food Science and Technology, 77, 241–248.

    Article  CAS  Google Scholar 

  • Musse, M., Quellec, S., Cambert, M., Devaux, M. F., Lahaye, M., & Mariette, F. (2009). Monitoring the postharvest ripening of tomato fruit using quantitative MRI and NMR relaxometry. Postharvest Biology and Technology, 53(1–2), 22–35.

    Article  CAS  Google Scholar 

  • Muthulakshmi, A., Renjith, P. N. (2020). Classification of durian fruits based on ripening with machine learning techniques. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (pp. 542–547). IEEE.

    Google Scholar 

  • Muziri, T., Theron, K. I., Cantre, D., Wang, Z., Verboven, P., Nicolai, B. M., & Crouch, E. M. (2016). Microstructure analysis and detection of mealiness in ‘Forelle’pear (Pyrus communis L.) by means of X-ray computed tomography. Postharvest Biology and Technology, 120, 145–156.

    Article  Google Scholar 

  • Nagata, M., Tallada, J. G., Kobayashi, T., Cui, Y., & Gejima, Y. (2004). Predicting maturity quality parameters of strawberries using hyperspectral imaging. In 2004 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

    Google Scholar 

  • Nassif, R., Pellen, F., Magné, C., Le Jeune, B., Le Brun, G., & Abboud, M. (2012). Scattering through fruits during ripening: Laser speckle technique correlated to biochemical and fluorescence measurements. Optics Express, 20(21), 23887–23897.

    Article  PubMed  Google Scholar 

  • Ncama, K., Opara, U. L., Tesfay, S. Z., Fawole, O. A., & Magwaza, L. S. (2017). Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of ‘Valencia’ orange (Citrus sinensis) and ‘star Ruby’ grapefruit (citrus x paradisi Macfad). Journal of Food Engineering, 193, 86–94.

    Article  CAS  Google Scholar 

  • Nogales-Bueno, J., Hernández-Hierro, J. M., Rodríguez-Pulido, F. J., & Heredia, F. J. (2014). Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach. Food Chemistry, 152, 586–591.

    Article  CAS  PubMed  Google Scholar 

  • Noh, H. K., & Lu, R. (2007). Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality. Postharvest Biology and Technology, 43(2), 193–201.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Paniagua, A. C., East, A. R., Hindmarsh, J. P., & Heyes, J. (2013). Moisture loss is the major cause of firmness change during postharvest storage of blueberry. Postharvest Biology and Technology, 79, 13–19.

    Article  Google Scholar 

  • Park, B., & Lu, R. (Eds.). (2015). Hyperspectral imaging technology in food and agriculture. Springer.

    Google Scholar 

  • Patel, K. K., Khan, M. A., & Kar, A. (2015). Recent developments in applications of MRI techniques for foods and agricultural produce—An overview. Journal of Food Science and Technology, 52(1), 1–26.

    Article  CAS  Google Scholar 

  • Pathmanaban, P., Gnanavel, B. K., & Anandan, S. S. (2019). Recent application of imaging techniques for fruit quality assessment. Trends in Food Science & Technology, 94, 32–42.

    Article  CAS  Google Scholar 

  • Pereira, L. F. S., Barbon, S., Jr., Valous, N. A., & Barbin, D. F. (2018). Predicting the ripening of papaya fruit with digital imaging and random forests. Computers and Electronics in Agriculture, 145, 76–82.

    Article  Google Scholar 

  • Pieczywek, P. M., Nowacka, M., Dadan, M., Wiktor, A., Rybak, K., Witrowa-Rajchert, D., & Zdunek, A. (2018). Postharvest monitoring of tomato ripening using the dynamic laser speckle. Sensors, 18(4), 1093.

    Article  PubMed Central  Google Scholar 

  • Polder, G., Van Der Heijden, G. W. A. M., Van der Voet, H., & Young, I. T. (2004). Measuring surface distribution of carotenes and chlorophyll in ripening tomatoes using imaging spectrometry. Postharvest Biology and Technology, 34(2), 117–129.

    Article  CAS  Google Scholar 

  • Pourdarbani, R., Sabzi, S., Kalantari, D., Paliwal, J., Benmouna, B., García-Mateos, G., & Molina-Martínez, J. M. (2020). Estimation of different ripening stages of Fuji apples using image processing and spectroscopy based on the majority voting method. Computers and Electronics in Agriculture, 176, 105643.

    Article  Google Scholar 

  • Pu, H., Liu, D., Wang, L., & Sun, D. W. (2016). Soluble solids content and pH prediction and maturity discrimination of lychee fruits using visible and near infrared hyperspectral imaging. Food Analytical Methods, 9(1), 235–244.

    Article  Google Scholar 

  • Pu, Y. Y., Sun, D. W., Buccheri, M., Grassi, M., Cattaneo, T. M., & Gowen, A. (2019). Ripeness classification of bananito fruit (Musa acuminata, AA): A comparison study of visible spectroscopy and hyperspectral imaging. Food Analytical Methods, 12(8), 1693–1704.

    Article  Google Scholar 

  • Pullanagari, R. R., & Li, M. (2021). Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. Journal of Food Engineering, 289, 110177.

    Article  CAS  Google Scholar 

  • Qin, J., Chao, K., & Kim, M. S. (2011). Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. Journal of Food Engineering, 107(3–4), 277–288.

    Article  CAS  Google Scholar 

  • Qin, J., Chao, K., & Kim, M. S. (2012). Nondestructive evaluation of internal maturity of tomatoes using spatially offset Raman spectroscopy. Postharvest Biology and Technology, 71, 21–31.

    Article  CAS  Google Scholar 

  • Rahman, A., Park, E., Bae, H., & Cho, B. K. (2018). Hyperspectral imaging technique to evaluate the firmness and the sweetness index of tomatoes. Korean Journal of Agricultural Science, 45(4), 823–837.

    CAS  Google Scholar 

  • Rajkumar, P., Wang, N., EImasry, G., Raghavan, G. S. V., & Gariepy, Y. (2012). Studies on banana fruit quality and maturity stages using hyperspectral imaging. Journal of Food Engineering, 108(1), 194–200.

    Article  Google Scholar 

  • Reid, M. S. (2002). Maturation and maturity indices. In A. A. Kader (Ed.), Postharvest technology of horticultural crops (3rd ed., pp. 55–62). University of California Agriculture and Natural Resources.

    Google Scholar 

  • Rodríguez-Pulido, F. J., Gil-Vicente, M., Gordillo, B., Heredia, F. J., & González-Miret, M. L. (2017). Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques. Journal of Food Science and Technology, 54(9), 2797–2803.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ropodi, A. I., Panagou, E. Z., & Nychas, G. J. (2016). Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends in Food Science & Technology, 50, 11–25.

    Article  CAS  Google Scholar 

  • Rungpichayapichet, P., Mahayothee, B., Nagle, M., Khuwijitjaru, P., & Müller, J. (2016). Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biology and Technology, 111, 31–40.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Russ, J. C. (2005). Image analysis of food microstructure. CRC press.

    Google Scholar 

  • Saltveit, M. E. (2002). Mature fruit vegetables. In J. K. Brecht & J. A. Bartz (Eds.), Postharvest physiology and pathology of vegetables (pp. 731–756). CRC Press.

    Google Scholar 

  • Sánchez, M. T., De la Haba, M. J., Benítez-López, M., Fernández-Novales, J., Garrido-Varo, A., & Pérez-Marín, D. (2012). Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. Journal of Food Engineering, 110(1), 102–108.

    Article  Google Scholar 

  • Sanchez, P. D. C., Hashim, N., Shamsudin, R., & Nor, M. Z. M. (2020). Applications of imaging and spectroscopy techniques for non-destructive quality evaluation of potatoes and sweet potatoes: A review. Trends in Food Science & Technology, 96, 208–221.

    Article  CAS  Google Scholar 

  • Schoeman, L., Williams, P., du Plessis, A., & Manley, M. (2016). X-ray micro-computed tomography (μCT) for non-destructive characterisation of food microstructure. Trends in Food Science & Technology, 47, 10–24.

    Article  CAS  Google Scholar 

  • Singh, B., Singh, S., & Koley, T. K. (Eds.). (2018). Advances in postharvest technologies of vegetable crops. Apple Academic Press.

    Google Scholar 

  • Sirisomboon, P., Tanaka, M., Kojima, T., & Williams, P. (2012). Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy. Journal of Food Engineering, 112(3), 218–226.

    Article  Google Scholar 

  • Sripaurya, T., Sengchuai, K., Booranawong, A., & Chetpattananondh, K. (2021). Gros Michel banana soluble solids content evaluation and maturity classification using a developed portable 6 channel NIR device measurement. Measurement, 173, 108615.

    Article  Google Scholar 

  • Srivastava, S., & Sadistap, S. (2018). Non-destructive sensing methods for quality assessment of on-tree fruits: A review. Journal of Food Measurement and Characterization, 12(1), 497–526.

    Article  Google Scholar 

  • Su, W. H., & Sun, D. W. (2018). Multispectral imaging for plant food quality analysis and visualization. Comprehensive Reviews in Food Science and Food Safety, 17(1), 220–239.

    Article  PubMed  Google Scholar 

  • Suchanek, M., Kordulska, M., Olejniczak, Z., Figiel, H., & Turek, K. (2017). Application of low-field MRI for quality assessment of ‘Conference’pears stored under controlled atmosphere conditions. Postharvest Biology and Technology, 124, 100–106.

    Article  Google Scholar 

  • Sumriddetchkajorn, S., & Intaravanne, Y. (2013, June). Two-dimensional fruit ripeness estimation using thermal imaging. In ICPS 2013: International Conference on Photonics Solutions (Vol. 8883, p. 88831C). International Society for Optics and Photonics.

    Google Scholar 

  • Sun, D. W. (Ed.). (2010). Hyperspectral imaging for food quality analysis and control. Elsevier.

    Google Scholar 

  • Sun, R., Zhou, J. Y., & Yu, D. (2021). Nondestructive prediction model of internal hardness attribute of fig fruit using NIR spectroscopy and RF. Multimedia Tools and Applications, 80, 21579–21594.

    Article  Google Scholar 

  • Taghadomi-Saberi, S., Masoumi, A. A., Sadeghi, M., & Zekri, M. (2019). Integration of wavelet network and image processing for determination of total pigments in bitter orange (Citrus aurantium L.) peel during ripening. Journal of Food Process Engineering, 42(5), e13120.

    Article  Google Scholar 

  • Taghadomi-Saberi, S., Omid, M., Emam-Djomeh, Z., & Faraji-Mahyari, K. H. (2015). Determination of cherry color parameters during ripening by artificial neural network assisted image processing technique. Journal of Agricultural Science and Technology, 17, 589–600.

    Google Scholar 

  • Taghizadeh, M., Gowen, A. A., & O’Donnell, C. P. (2011). Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Biosystems Engineering, 108(2), 191–194.

    Article  Google Scholar 

  • Takahashi, N., Maki, H., Nishina, H., & Takayama, K. (2013). Evaluation of tomato fruit color change with different maturity stages and storage temperatures using image analysis. IFAC Proceedings Volumes, 46(4), 147–149.

    Article  Google Scholar 

  • Tallada, J. G., Nagata, M., & Kobayashi, T. (2006). Non-destructive estimation of firmness of strawberries (Fragaria× ananassa Duch.) using NIR hyperspectral imaging. Environmental Control in Biology, 44(4), 245–255.

    Article  Google Scholar 

  • Teerachaichayut, S., & Ho, H. T. (2017). Non-destructive prediction of total soluble solids, titratable acidity and maturity index of limes by near infrared hyperspectral imaging. Postharvest Biology and Technology, 133, 20–25.

    Article  CAS  Google Scholar 

  • Thompson, A. K. (2003). Fruit and vegetables: Harvesting, handling and storage. Blackwell Publishing Ltd.

    Book  Google Scholar 

  • Tripathi, A., Baran, C., Jaiswal, A., Awasthi, A., Uttam, R., Sharma, S., et al. (2020). Investigating the carotenogenesis process in papaya fruits during maturity and ripening by non-destructive spectroscopic probes. Analytical Letters, 53(18), 2903–2920.

    Article  CAS  Google Scholar 

  • Ukirade, N. S. (2014). Color grading system for evaluating tomato maturity. International Journal of Research in Management, Science & Technology, 2(1), 41–45.

    Google Scholar 

  • Urraca, R., Sanz-Garcia, A., Tardaguila, J., & Diago, M. P. (2016). Estimation of total soluble solids in grape berries using a hand-held NIR spectrometer under field conditions. Journal of the Science of Food and Agriculture, 96(9), 3007–3016.

    Article  CAS  PubMed  Google Scholar 

  • van Roy, J., Keresztes, J. C., Wouters, N., De Ketelaere, B., & Saeys, W. (2017). Measuring colour of vine tomatoes using hyperspectral imaging. Postharvest Biology and Technology, 129, 79–89.

    Article  Google Scholar 

  • Vanoli, M., Van Beers, R., Sadar, N., Rizzolo, A., Buccheri, M., Grassi, M., et al. (2020). Time-and spatially-resolved spectroscopy to determine the bulk optical properties of ‘Braeburn’apples after ripening in shelf life. Postharvest Biology and Technology, 168, 111233.

    Article  CAS  Google Scholar 

  • Vega Díaz, J. J., Sandoval Aldana, A. P., & Reina Zuluaga, D. V. (2021). Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging. Journal of the Science of Food and Agriculture, 101(3), 897–906.

    Article  PubMed  Google Scholar 

  • Villaseñor-Aguilar, M. J., Sánchez-Bravo, M. G., Padilla-Medina, J. A., Vázquez-Vera, J. L., Guevara-González, R. G., García-Rodríguez, F. J., & Barranco-Gutiérrez, A. I. (2020). A maturity estimation of bell pepper (Capsicum annuum L.) by artificial vision system for quality control. Applied Sciences, 10(15), 5097.

    Article  Google Scholar 

  • Waldron, K. W., Parker, M. L., & Smith, A. C. (2003). Plant cell walls and food quality. Comprehensive Reviews in Food Science and Food Safety, 2(4), 128–146.

    Article  CAS  PubMed  Google Scholar 

  • 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. Postharvest Biology and Technology, 168, 111246.

    Article  CAS  Google Scholar 

  • Wang, Z., Van Beers, R., Aernouts, B., Watte, R., Verboven, P., Nicolai, B., & Saeys, W. (2020). Microstructure affects light scattering in apples. Postharvest Biology and Technology, 159, 110996.

    Article  CAS  Google Scholar 

  • Williams, P., & Norris, K. H. (2001). Near-infrared technology in the agricultural and food industries (2nd ed.). American Association of Cereal Chemists.

    Google Scholar 

  • Wu, D., & Sun, D. W. (2013). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review—Part I: Fundamentals. Innovative Food Science & Emerging Technologies, 19, 1–14.

    Article  Google Scholar 

  • Xiao, H., Li, A., Li, M., Sun, Y., Tu, K., Wang, S., & Pan, L. (2018). Quality assessment and discrimination of intact white and red grapes from Vitis vinifera L. at five ripening stages by visible and near-infrared spectroscopy. Scientia Horticulturae, 233, 99–107.

    Article  CAS  Google Scholar 

  • Xiao, Z., Stait-Gardner, T., Willis, S. A., Price, W. S., Moroni, F. J., Pagay, V., et al. (2021). 3D visualisation of voids in grapevine flowers and berries using X-ray micro computed tomography. Australian Journal of Grape and Wine Research, 27(2), 141–148.

    Article  CAS  Google Scholar 

  • Xie, C., Chu, B., & He, Y. (2018). Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging. Food Chemistry, 245, 132–140.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, L., & McCarthy, M. J. (2012). Measurement and evaluation of tomato maturity using magnetic resonance imaging. Postharvest Biology and Technology, 67, 37–43.

    Article  Google Scholar 

  • Zhang, L., & McCarthy, M. J. (2013). Assessment of pomegranate postharvest quality using nuclear magnetic resonance. Postharvest Biology and Technology, 77, 59–66.

    Article  Google Scholar 

  • Zhu, H., Chu, B., Fan, Y., Tao, X., Yin, W., & He, Y. (2017). Hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models. Scientific Reports, 7(1), 1–13.

    Google Scholar 

  • Zhuang, J., Hou, C., Tang, Y., He, Y., Guo, Q., Miao, A., et al. (2019). Assessment of external properties for identifying banana fruit maturity stages using optical imaging techniques. Sensors, 19(13), 2910.

    Article  PubMed Central  Google Scholar 

  • Zulkifli, N., Hashim, N., Abdan, K., & Hanafi, M. (2019). Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas. Computers and Electronics in Agriculture, 160, 100–107.

    Article  Google Scholar 

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Cakmak, H., Sogut, E. (2022). Imaging Techniques for Evaluation of Ripening and Maturity of Fruits and Vegetables. In: Pathare, P.B., Rahman, M.S. (eds) Nondestructive Quality Assessment Techniques for Fresh Fruits and Vegetables . Springer, Singapore. https://doi.org/10.1007/978-981-19-5422-1_3

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