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. 2023 Feb 24;12(5):962.
doi: 10.3390/foods12050962.

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling

Daniel Schorn-García  1 , Barbara Giussani  2 , María Jesús García-Casas  3 , Daniel Rico  3 , Ana Belén Martin-Diana  3 , Laura Aceña  1 , Olga Busto  1 , Ricard Boqué  4 , Montserrat Mestres  1
Affiliations

Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling

Daniel Schorn-García et al. Foods. .

Abstract

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.

Keywords: ASCA; portable MIR; precision viticulture; process monitoring; variability.

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Conflict of interest statement

The authors declare that they have no known competing financial or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Spectra of 90 grapes using different IR analysis configurations (a) DRIFT for intact berries; (b) ATR-FTIR for crushed berries.
Figure 2
Figure 2
Predicted vs. measured values for the best prediction models of (a) TSS and (b) pH (blue circles for calibration samples, red squares for validation samples).
Figure 3
Figure 3
Score (a) and loading (b) plots of the first factor of the maturity factor submodel. Different colors mean that samples belong to different times (Black—Time 1; Blue—Time 2; Green—Time 3; Orange—Time 4; Red—Time 5).
Figure 4
Figure 4
Q residual and Hotelling T2 for Time 4 (red circles) and the projection in the model of (a) Time 3 (blue squares) and (b) Time 5 (green squares).
See this image and copyright information in PMC

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