2023
DOI: 10.3390/foods12050962
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Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling

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) … Show more

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Cited by 7 publications
(4 citation statements)
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“…Thus, as shown by the values in Table 1, the evolution of grapes from both varieties over the studied period was as expected, which is a crucial factor for grape composition [35]. Furthermore, considering that different sunlight exposure significantly affects the main grape parameters, the 26 samples collected (comprising two varieties, six or seven harvest days, and two light orientations) exhibit variations that were also reflected in subsequent analyses, mainly in those related to their antioxidant capabilities.…”
Section: Determination Of Indicators Of Ripening: Total Soluble Solid...supporting
confidence: 73%
See 1 more Smart Citation
“…Thus, as shown by the values in Table 1, the evolution of grapes from both varieties over the studied period was as expected, which is a crucial factor for grape composition [35]. Furthermore, considering that different sunlight exposure significantly affects the main grape parameters, the 26 samples collected (comprising two varieties, six or seven harvest days, and two light orientations) exhibit variations that were also reflected in subsequent analyses, mainly in those related to their antioxidant capabilities.…”
Section: Determination Of Indicators Of Ripening: Total Soluble Solid...supporting
confidence: 73%
“…The ASCA results emphasise the similarities between the two grape varieties, with the maturation process proving to be the most influential factor [ 35 ]. Moreover, it can be observed that despite variations in the number of sampling points, both grape varieties encapsulate the variability inherent in an evolving sample, even belonging to the same vineyard.…”
Section: Resultsmentioning
confidence: 99%
“…However, in terms of large-scale origin testing of wine samples, the aforementioned methods encounter challenges such as labor-intensive sample preparation, expensive laboratory equipment, and specific experimental environment requirements. Spectral technology has been widely applied in wine origin identification, wine quality evaluation 7 , and various food research 8 , 9 due to its simplicity, high sensitivity, no need for sample pretreatment, and no need for experimental reagents. Lu et al identified corresponding biomarkers by searching for Raman spectra of red wine, analyzed Raman spectra using PCA, and established a red wine origin recognition model by combining dimensionality reduction data with deep learning, achieving a more accurate classification of red wine origins 10 .…”
Section: Introductionmentioning
confidence: 99%
“…Regarding grape maturity estimation, there are two main methods used in viticulture to estimate the maturity level of grapes. The first one is the traditional method, where we measure the sugar level using a specific instrument that is usually calibrated in Brix or pH, called a refractometer [26]. However, this specialized sensor is costly and requires farmers with technological knowledge to handle it.…”
Section: Introductionmentioning
confidence: 99%