Reflectance Based Models for Non-Destructive Prediction of Lycopene Content in Tomato Fruits

Abstract

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 towards the development of models for 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, actual estimation of lycopene content was done. Thereafter, 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 inverse of reflectance at 550 nm (1/R₅₅₀). This model had 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 findings will prove useful in 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.