Hyperspectral imaging/reflectance as a tool for assessment of nutritional and  quality-related parameters in tomato (Solanum lycopersicum L.) fruits

Rajeev Kumar; Vijay Paul; Rakesh Pandey

doi:10.48165/

Authors

Rajeev Kumar Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
Vijay Paul Principal Scientists, Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
Rakesh Pandey Principal Scientists, Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India

DOI:

https://doi.org/10.48165/

Keywords:

Carotenoids, Firmness, Lycopene, Maturity, Non-destructive methods, Nutritional quality, uality assessment, Reflectance based indices, Ripeness

Abstract

Tomato (Solanum lycopersicum L.) is most important vegetable crop for human health. The postharvest handling and management of tomato is prime concern in India because the annual postharvest losses for tomato can reach up to 25 - 40 %. Non-destructive approaches for quantification and monitoring of nutritional and quality aspects of horticultural commodities have come up in a big way in the recent past that can also serves towards better postharvest management. Out of various non-destructive approaches, optical method based on visible-near infrared (Vis-NIR) spectroscopy (hyperspectral imaging and reflectance) is the most important analytical tool that provides spatial and spectral information simultaneously for a commodity towards non destructive assessment of food quality-related parameters. Therefore, an overview with latest developments and applications of hyperspectral imaging and reflectance techniques for the assessment of nutritional and quality parameters of tomato fruits has been discussed. The advantages and disadvantages of this tool along with the future perspectives are also highlighted.

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References

Alenazi M M, Shafiq M, Alsadon A A, Alhelal I M, Alhamdan A M, Solieman T H, Ibrahim A A, Shady M R and Saad M A. 2020. Non-destructive assessment of flesh firmness and dietary antioxidants of greenhouse-grown tomato (Solanum lycopersicum L.) at different fruit maturity stages. Saudi Journal of Biological Sciences 27: 2839-

Ali M Y, Sina A A I, Khandker S S, Neesa L, Tanvir E M, Kabir A, Khalil, M I and Gan S H. 2021. Nutritional composition and bioactive compounds in tomatoes and their impact on human health and disease: A review. Foods 10: 45.

Alsina I, Dubova L, Duma M, Erdberga I, Avotiņs A and Rakutko S. 2019. Comparison of lycopene and β-carotene content in tomatoes determined with chemical and non-destructive methods. Agronomy Research 17: 343–348.

Berra W G. 2012. Visible/near infrared spectroscopy method for the prediction of lycopene in tomato (Lycopearsicon esculentum Mill) fruits. Science, Technology and Arts Research Journal 1: 17-23.

Chen L. 2008. Non-destructive measurement of tomato quality using visible and near-infrared reflectance spectroscopy. M. Sc. Thesis, McGill University, Canada.

Cho B K, Kim M S, Back I S, Kim D Y, Lee W H, Kim J, Bae H and Kim Y S. 2013. Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imaging. Postharvest Biology and Technology 76: 40-49.

Clement A, Bacon R, Sirois S and Dorais M. 2015. Mature ripe tomato spectral classification according to lycopene content and fruit type by visible, NIR reflectance and intrinsic fluorescence. Quality Assurance and Safety of Crops and Foods 7: 747-756.

Collins E J, Bowyer C, Tsouza A and Chopra M. 2022. Tomatoes: an extensive review of the associated health impacts of tomatoes and factors that can affect their cultivation. Biology, 11: 239.

Dale L M, Theois A, Boudry C, Rotar I, Dardenne P, Baeten V and Pierna J A F. 2013. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review. Applied Spectroscopy Reviews 48: 142-159.

Dale L, Rotar I, Bogdan A D, Pacurar F, Thewis A, Fernandez J A and Baeten V. 2011. Pure species of grass discrimination with the help of hyperspectral imaging NIR. Agronomy Series of Scientific Research 2: 23-27.

Deak K, Szigedi T, Pek Z, Baranowski P and Helyes L. 2015. Carotenoid determination in tomato juice using near infrared spectroscopy. International Agrophysics 29: 275–282.

Ecarnot M, Baogonekczyk P, Tessarotto L and Chervin C. 2013. Rapid phenotyping of the tomato fruit model, Micro-Tom, with a portable VIS-NIR spectrometer. Plant Physiology and Biochemistry, 70: 159-163.

Egei M, Takacs S, Palotas G, Palotas G, Szuvandzsiev P, Daood H G, Helyes L and Pek Z. 2022. Prediction of Soluble Solids and Lycopene Content of Processing Tomato Cultivars by Vis-NIR. Frontiers in Nutrition 9: 845317.

ElMasry G and Sun D W. 2010. Principles of hyperspectral imaging technology. In: Hyperspectral Imaging for Food Quality Analysis and Control. Sun D W (Ed.). Elsevier, Academic Press, London, UK, pp 3-42.

Fernandez Pierna J A, Vermeulen P, Amand O, Tossens A, Dardenne P and Baeten V. 2012. NIR Hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemometrics and Intelligent Laboratory System 117: 233-239.

Gomez-Sanchis J, Martin-Guerrero J D, Soria-Olivas E, Martinez-Sober M, Magdalena-Benedito R and Blasco J. 2012. Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Systems with Applications 39: 780-785.

Hahn F. 2002. Multi-spectral prediction of unripe tomatoes. AE Automation and Emerging Technologies. Biosystems Engineering 81:147-155.

Hampton K A, Wutzke J L, Cavinato A G, Mayes D M, Lin M and Rasco B A. 2003. Characterization of optical probe light penetration depth for noninvasive analysis. Eastern Oregon Science Journal 28: 14-18.

Hasanzadeh B, Abbaspour-Gilandeh Y, Soltani-Nazarloo A, Hernandez-Hernandez M, Gallardo-Bernal I and Hernandez-Hernáadez J L. 2022. Non-destructive detection of fruit quality parameters using hyperspectral imaging, multiple regression analysis and artificial intelligence. Horticulturae 8: 598.

Huang Y, Lu R, Hu D, and Chen K. 2018. Quality assessment of tomato fruit by optical absorption and scattering properties. Postharvest Biology and Technology 143: 78-85.

Hussain A, Pu H and Sun D W. 2018. Innovative non-destructive imaging techniques for ripening and maturity of fruits - A review of recent applications. Trends in Food Science and Technology 72: 144-152.

Hussain N, Sun DW and Pu H. 2019. Classical and emerging non destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends in Food Science and Technology 91: 598-608.

Ibrahim A O, Abdul-Hammed M, Adegboyega S A, Olajide M and Aliyu A A. (2019). Influence of the techniques and degrees of ripeness on the nutritional qualities and carotenoid profiles of tomatoes (Solanum lycopersicum). Annal of Science and Technology 4: 48-55.

Jha S N and Garg R. 2010. Non-destructive prediction of quality of intact apples using infrared spectroscopy. Journal of Food Science and Technology 47: 207-213.

Jiang L, Shen Z, Zheng H, He W, Deng G and Lu H. 2013. Non-invasive evaluation of fructose, glucose, and sucrose contents in fig fruits during development using chlorophyll fluorescence and chemometrics. Journal of Agriculture Science and Technology, 15: 333-342.

Kumar R, Paul V, Pandey R, Sahoo R N and Gupta V K. 2022a. Reflectance based non-destructive determination of colour and ripeness of tomato fruits. Physiology and Molecular Biology of Plants 28: 275-288.

Kumar R, Paul V, Pandey R, Sahoo R N and Gupta V K. 2022b. Reflectance based non-destructive determination of lycopene content in tomato fruits. Proceedings of National Academy of Science (India), Section B, Biological Sciences 92: 759-769.

Kumar R, Paul V, Pandey R, Sahoo R N and Gupta V K. 2022d. Reflectance-based non-destructive determination of total carotenoids in tomato fruits. Plant Physiology Reports 28: 152-160.

Kumar R, Paul V, Pandey R, Sahoo R N, Gupta V K, Asrey R and Jha S K. 2022c. Reflectance based non-destructive assessment of tomato fruit firmness. Plant Physiology Reports 27: 374-382.

Kusumiyati, Akinaga T, Tanaka M and Kawasaki S. 2008. On tree and after harvesting evaluation of firmness, color and lycopene content of tomato fruit using portable NIR spectroscopy. Journal of Food Agriculture and Environment 6: 327-332.

Lammertyn J, Peirs A, De Baerdemaeker J and Nicolai B. 2000. Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Post Harvest Biology and Technology, 18: 121-132.

Magwaza L S, Landahl S, Cronje P J R, Nieuwoudt H M, Mouzaen A M, Nicolai B M, Terry L A and Opara U L. 2014. The use of Vis/NIRS and chemometic analysis to predict fruit defects and post-harvest behaviour of ‘Nules Clementine’ mandarin fruit. Food Chemistry 163: 267-274.

Menesatti P, Zanella A, D’Andrea S, Costa C, Paglia G and Pallottino F. 2008. Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food and Bioprocess Technology 2: 308-314.

Paul V and Pandey R. 2016. Internal atmosphere of fruits: role and significance in ripening and storability. In: Postharvest Ripening Physiology of Crops. Series: Innovation in Postharvest Technology. Pareek S (Ed.). CRC Press, Taylor & Francis Group. Boca Raton FL. 359-412. ISBN: 13:978-1-4987-0381-9.

Paul V and Pandey R. 2018. 1-Methylcyclopropene (1-MCP) treatments. In: Novel Postharvest Treatments of Fresh Produce. Series: Innovation in Postharvest Technology. Pareek S (Ed.). CRC Press, Taylor & Francis Group. Boca Raton FL. 149-215. ISBN: 9781498729918.

Paul V, Pandey R and Singh A. 2014. 1-Methylcyclopropene (1-MCP) mediated delay in tomato fruit ripening: Potential and perspectives for postharvest management. Processed Food Industry 17: 16-22

Paul V, Pandey R Srivastava G C. 2011. Tomato fruit ripening: Regulation of ethylene production and its response. Indian Journal of Plant Physiology 16: 117-131.

Paul V, Pandey R, Kumar R and Sahoo R N. 2018. High throughput phenotyping for postharvest quality and shelf life in horticultural crops. Training Manual of ICAR-Short Course on “Phenomics, The Next Generation Phenotyping (NGP), for Trait Dissection and Crop Improvement” (22-31 October, 2018). Division of Plant Physiology, IARI, New Delhi, India. Pp: 115-123.

Paul V, Pandey R, Ramesh K V and Singh A. 2012. Role of mineral nutrients in physiology, ripening and storability of fruits. In: Advances in Plant Physiology, Vol. 13, An International Treatise Series – Nutriophysiological and Molecular Interventions for Crop Improvement Under Changing Climate. Hemantaranjan (Editor). Scientific Publishers (India), Jodhpur, 56-96. ISBN: 978-81-7233-

-8

Pedro A M and Ferreira M M. 2005. Non-destructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration. Analytical Chemistry 77: 2505–2511.

Polder G and van der Heijden G. 2010. Measuring ripening of tomatoes using imaging spectrometry. In: Hyperspectral Imaging for Food Quality Analysis and Control. Sun D W (Ed.). Elsevier, Academic Press, London, UK, pp 369-402.

Polder G, van der Heijden G W and Yong I T. 2002. Spectral image analysis for measuring ripeness of tomatoes. Transactions of the American Society of Agricultural Engineers 45: 1155-1161.

Punalekar S, Verhoef A, Tatarenko I V, Van der Tol C, Macdonald D M J, Marchant B, Gerard F, White K and Gowing D. 2016 characterization of a highly biodiverse floodplain meadow using hyperspectral remote sensing within a plant functional trait framework. Remote Sensing 8: 112.

Rahman A, Kandpal L M, Lohumi S, Kim M S, Lee H, Mo C, and Cho B K. 2017. Non-destructive estimation of moisture content, pH and soluble solid contents in intact tomatoes using hyperspectral imaging. Applied Sciences 7: 109.

Rahman A, Park E, Bae H, and Cho B K. 2018. Hyperspectral imaging technique to evaluate the firmness and the sweetness index of tomatoes. Korean Journal of Agricultural Science 45: 823-837.

Ramesh KV, Paul Vand Pandey R. 2021. Dynamics of mineral nutrients in tomato (Solanum lycopersicum L.) fruits during ripening: part I - on the plant. Plant Physiology Reports 26: 23-37.

Ramesh KV, Paul Vand Pandey R. 2021. Dynamics of mineral nutrients in tomato (Solanum lycopersicum L.) fruits during ripening: part II - off the plant. Plant Physiology Reports 26: 284-300.

Saad A G, Jaiswal P and Jha S N. 2014. Non-destructive quality evaluation of intact tomato using VIS-NIR spectroscopy. International Journal of Advanced Research 2: 632-639.

Saad A G, Pek Z, Szuvandzsiev P, Gehad D H and Helyes L. 2021. Determination of carotenoids in tomato products using Vis/NIR spectroscopy. Journal of Microbiology, Biotechnology and Food Sciences 7: 27-31.

Sharma L, Kumar R, Paul V, Pandey R and Lal M K. 2022. Purple tomato - Importance and scope: A Review. Agricultural Reviews Online First: DOI: 10.18805/ag. R-2408.

Sharma L, Ramesh K V, Paul V and Pandey R. 2020. Ripening index: A better parameter for colour-based assessment of ripening behaviour of tomato fruits. Plant Physiology Reports 25: 171–177.

Sirisomboon P, Tanaka M, Kojima T and Willians P. 2012. Non destructive estimation of maturity and textural properties on tomato “Momotaro” by near infrared spectroscopy. Journal of Food Engineering 112: 218-226.

Sun D W. 2010. Principles of hyperspectral imaging technology. In: Hyperspectral Imaging for Food Quality Analysis and Control. Academic Press/Elsevier; San Diego. CA, USA.

Szuvandzsiev P, Helyes L, Lugasi A, Szanto C, Baranowski P and Pek Z. 2014. Estimation of antioxidant components of tomato using VIS-NIR reflectance data by handheld portable spectrometer. International Agrophysics 28: 521-527.

Tilahun S, Seo M H, Hwang I G, Kim S H, Choi H R and Jeong C S. 2018. Prediction of lycopene and β-carotene in tomatoes by portable chroma-meter and VIS/NIR spectra. Postharvest Biology and Technology 136:

-56.

Tiwari G, Slaughter D C and Cantwell M. 2013. Non-destructive maturity determination in green tomatoes using a handheld

visible and near infrared instrument. Postharvest Biology and Technology 86: 221-229.

Van de Poel B, Bulens I, Hertog M L A T M, Van Gastel L, De Proft M P, Nicolai B M and Geeraerda A H. 2012. Model-based classification of tomato fruit development and ripening related to physiological maturity. Postharvest Biology and Technology 67: 59–67.

Van Roy J, Keresztes J C, Wouters N, De Ketelaere B, and Saeys W. 2017. Measuring colour of vine tomatoes using hyperspectral imaging. Postharvest Biology and Technology 129: 79-89.

Wang H, Hu R, Zhang M, Zhai Z and Zhang R. 2021. Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and image spectrum merging technique. Journal of Food Process Engineering 44: e13654.

Wu D and Sun D W. 2013. Colour measurements by computer vision for food quality control -A review. Trends in Food Science and Technology 29: 5-20.

Xiang Y, Chen Q, Su Z, Zhang L, Chen Z, Zhou G, Yao Z, Xuan Q and Cheng Y. 2022. Deep learning and hyperspectral images-based tomato soluble solids content and firmness estimation. Frontiers in Plant Science 13 (Online First).

Xie L, Ying Y, Ying T, Yu H and Fu X. 2007. Discrimination of transgenic tomatoes based on visible near-infrared spectra. Analytica Chimica Acta 584: 379-384.

Zhao J, Kechasov D, Rewald B, Bodner G, Verheul M, Clarke N and Clarke J L. 2020. Deep learning in hyperspectral image reconstruction from single RGB images-A case study on tomato quality parameters. Remote Sensing

: 3258.