Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data

Abstract

Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive-physical and colorimetric aspects (predictive variables)-and destructive determinations-bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in 'Nanicão' bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R², which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors.

Keywords: Musa sp.; linear regression; non-destructive food analyses; predictive model.

Figure 1

Image of the RAW file captured directly by the camera, demonstrating how the images were obtained on the bench (a); and image file after digital development and framing adjustment (b).

Figure 2

Final image and indication of the points to obtain L*, a* and b* values on the fruit peel surface.

Figure 3

Diagram of the CIELAB color space, where L* indicates luminosity, a* and b* are chromaticity coordinates, chroma (C*) represents color saturation and the hue angle represents hue.

Figure 4

Two-dimensional projection of non-destructive (predictive) and destructive (predicted) parameters analyzed in fruits at stage 2, stage 4, stage 6 and stage 7, respectively. Non-destructive (predictive—in blue) and destructive (predicted—in red).

Figure 5

Two-dimensional projection of non-destructive (predictive) and destructive (predicted) parameters selected as most in fruits at stage 2, stage 4, stage 6 and stage 7, respectively. Non-destructive (predictive—in blue) and destructive (predicted—in red).