The results showed that table grape sensory quality could be increased by delaying harvest up to a certain time of the season, while excessive delay could reduce final grape quality.
Measurement of certain grape quality parameters (sugars, acidity, and pH-value) is essential for the determination of the optimum harvest time. Non-destructive analytical techniques, including near infrared (NIR) spectroscopy, can be valid alternatives to traditional analytical methods for the determination of maturity indexes, enabling the possibility of on-field applications. This work aims to study the reliability to monitor spectra changes related with ripening of table grapes and to select optimal wavelengths for the discrimination of bunches from different harvests, in addition to the prediction of total soluble solids, pH, titratable acidity, phenols and antioxidant activity of table grapes. Grapes were harvested four times from the same plants at day 0 (I HT), and after 11 (II HT), 27 (III HT) and 48 (IV HT) days. Spectra were acquired from the images obtained using a spectral scanner Vis-NIR (ver 1.4.; DV Srl, Padova, Italy), with a detector in the region between 400-1000 nm principal component analysis was used to remove outliers followed by spectra pre-treatment. The best prediction model was achieved for soluble solids with the regression coefficient values of 0.91 for calibration and 0.88 for validation followed by titratable acidity (0.71 and 0.78) and antioxidant activity (0.68 and 0.62). In addition an excellent correlation was observed between spectra and days before harvest (R 2 of 0.98 for calibration and prediction models) indicating that is possible to relate spectra changes with ripening, leading also to the effective discrimination of the fruits from the different harvest times. The results showed that this technique may be a valid support to select the optimal harvest time also based on the prediction of the maturity related constituents.
Traditional analytical methods applied to the measurement of grape maturity and quality index in order to assess optimal harvest time have been proved to be slow and destructive. Therefore, non-destructive analytical techniques, including spectroscopy, can be a valid support for the choice of the best time to harvest. This study evaluated the feasibility of using a visible and near infrared spectral scanner (v. 1.4; DV Srl, Padova, Italy) with a detector in the region between 400-1000 nm to discriminate between grapes harvested at different times. Twelve clusters were harvested at 5 different times between October and December 2011. Spectra were acquired with a Spectral scanner on 3 intact berries from each bunch. These were randomly selected from top, medium and bottom zones, for a total of 180 spectra. Classification models were construed comparing 2 methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The SIMCA model was developed building individual principal component analysis (PCA) models for the spectra of each harvest time. Different pre-treatment methods were tested in order to enhance the power of the model, thus enhancing the score differences among samples from different harvest times. The transformation that allowed the best statistical separation among scores of grapes from different harvest times was the second derivate of Norris. Therefore, the PCA model obtained from the spectra subjected to this pre-treatment was used for SIMCA classification. The PLS-DA model were developed applying the PLS2 algorithm. In order to construct discriminant models to classify bunch spectra according to the 5 harvest times, spectral variations were correlated with the 5 categories established. No pretreatments were previously applied in this last case since they did not improve the final result. The SIMCA method was unable to correctly classify grapes from harvest time 2 (59% of correct classification) and was less efficient compared to the PLS-DA model. Using the PLS-DA model, all the grapes were correctly classified (100%) with the exception of those from harvest time 5 (94%). The overall results demonstrate that this method has excellent potential for discriminating grape quality
Traditional analytical methods applied to the measurement of grape maturity and quality index in order to assess optimal harvest time have been proved to be slow and destructive. Therefore, non-destructive analytical techniques, including spectroscopy, can be a valid support for the choice of the best time to harvest. This study evaluated the feasibility of using a visible and near infrared spectral scanner (v. 1.4; DV Srl, Padova, Italy) with a detector in the region between 400-1000 nm to discriminate between grapes harvested at different times. Twelve clusters were harvested at 5 different times between October and December 2011. Spectra were acquired with a Spectral scanner on 3 intact berries from each bunch. These were randomly selected from top, medium and bottom zones, for a total of 180 spectra. Classification models were construed comparing 2 methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The SIMCA model was developed building individual principal component analysis (PCA) models for the spectra of each harvest time. Different pre-treatment methods were tested in order to enhance the power of the model, thus enhancing the score differences among samples from different harvest times. The transformation that allowed the best statistical separation among scores of grapes from different harvest times was the second derivate of Norris. Therefore, the PCA model obtained from the spectra subjected to this pre-treatment was used for SIMCA classification. The PLS-DA model were developed applying the PLS2 algorithm. In order to construct discriminant models to classify bunch spectra according to the 5 harvest times, spectral variations were correlated with the 5 categories established. No pretreatments were previously applied in this last case since they did not improve the final result. The SIMCA method was unable to correctly classify grapes from harvest time 2 (59% of correct classification) and was less efficient compared to the PLS-DA model. Using the PLS-DA model, all the grapes were correctly classified (100%) with the exception of those from harvest time 5 (94%).The overall results demonstrate that this method has excellent potential for discriminating grape quality.
The aim of this work was to evaluate the effect of the type of fertilization (mineral and combined fertilization with compost in pre-transplant plus mineral addition during cultivation) and stage of maturity at harvest (mature-green and full-colored) on post-cutting quality of red and yellow 'Cazzone' peppers. Peppers were cut into strips, and air-stored for 8 days at 5°C. During storage, color, appearance score, firmness, respiration rate, soluble solids, acidity, pH, vitamin C, total phenols, and antioxidant activity were measured. The maturity stage influenced color parameters and soluble solids, acidity and pH for both yellow and red types. Full-colored peppers showed a lower respiration rate, and higher SSC than mature-green peppers; for the yellow type, a lower firmness value was observed for full-colored fruits compared to the mature-green ones. A lower antioxidant activity was also observed in the yellow type fertilized with the combined treatment, while phenol content in full-colored peppers was higher than in mature-green ones. Fresh-cut yellow peppers showed higher susceptibility to decay compared to red types: after 8 days of storage, the appearance score in mineral fertilized full-colored yellow peppers dramatically decreased below the limit of marketability. The results of this experiment show that the type of fertilization and maturity stage can have varying impact on the quality of yellow and red peppers.
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