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Study: Reflectance based non-destructive determination of lycopene content in tomato fruits
The findings will prove helpful in the development of non-destructive, cost-effective, and simple tools for rapid monitoring, sorting, grading, and phenotyping of tomato fruits based on their lycopene content. This, in turn, will be of immense use for processing, value-addition, pharmaceutical, and marketing of tomato fruits.
Lycopene is a pigment present in tomato fruits with multiple health benefits. Thereby, non-destructive and simple methods of lycopene estimation are needed. In the present investigation, hyperspectral technique was used for the development of models for the prediction of lycopene content in tomato fruits. Tomato fruits of four varieties at six different ripening stages were either harvested directly from the plants or obtained during the period of postharvest storage.
Reflectance of individual tomato fruit was recorded at each wavelength in a spectrum of 350-2500 nm. Subsequently, an actual estimation of lycopene content was done. After that, reflectance values and actual lycopene content data were subjected to chemometric analysis. The best model was y [lycopene content, µg g-1 fresh weight (FW)] = 0.1713x-1.789, where x is reflectance at 546 nm (R546). This model can accurately predict the lycopene content for a difference of > 5.04 with biasness of 0.10.
The second-best model was y = 0.0726x² + 0.3272x + 0.5482, where x is the inverse of reflectance at 550 nm (1/R550). This model had a predictability of >5.06 with biasness of 0.67. The developed models were valid across the varieties, ripening stages, and ripening conditions, i.e., plant harvested (fresh fruits) and stored (aged fruits).
The second-best model was y = 0.0726x² + 0.3272x + 0.5482, where x is the inverse of reflectance at 550 nm (1/R550). This model had a predictability of >5.06 with biasness of 0.67. The developed models were valid across the varieties, ripening stages, and ripening conditions, i.e., plant harvested (fresh fruits) and stored (aged fruits).
Conclusions
Nowadays, non-destructive ways of prediction of nutritional and quality aspect of fruits and vegetables are emerging avenues in the field of postharvest physiology and food science. This can assist in rapid monitoring and better postharvest management. The present study was taken up to develop reflectance-based models to predict the lycopene content in tomato fruits. In the present study, reflectance-based indices R546 and 1/R550 yielded the best prediction models for non-destructive estimation of lycopene content in tomato fruits. The models were found to be valid across the varieties, ripening stages, and ripening conditions (on the plant ripening and off the plant ripening). Further, as the models are based on reflectance at single wavelengths and that too in the visible range so they are simple and rapid. In future, the identified indices and the developed models can be translated into cost-effective technique/instrument for rapid phenotyping/screening of tomato fruits for lycopene content. Further, various steps like monitoring, sorting, and grading of tomato fruits (based on the lycopene content) which are also pre-requisite for automation will get facilitated in postharvest management, processing, and pharmaceutical aspects of tomato. Additionally, besides the prediction of lycopene content, the identified indices (R546 and 1/R550) will also provide opportunity in the direction of simultaneous prediction of other quality-related parameters in tomato fruits such as ripeness and firmness.
Some complementary data
Read the complete research at www.researchgate.net.
Kumar, Rajeev & Paul, Vijay & Pandey, Rakesh & Rabi, • & Sahoo, Narayan & Gupta, Vinod. (2022). Reflectance based non-destructive determination of lycopene content in tomato fruits. Proceedings of the National Academy of Sciences, India – Section B: Biological Sciences.
Sources: hortidaily.com, researchgate.net
