Search

A DA-meter, a hand-held instrument developed from Vis/NIR spectroscopy, and a portable chroma meter based on CIELab coordinates were used to classify the maturity stage of six early-to-late apricot varieties at the wholesale market, using the linear discriminant analysis (LDA) and the k Nearest Neighbours (k-NN), and to predict their quality and nutraceutical attributes by a stepwise regression analysis. Results pointed out a significant variation among cultivars analysed, regardless of the ripening season. LDA highlighted the key role of fresh weight and flesh firmness (FF), and to a lesser extent of sugar content (TSS) and dry matter (DM), in discriminating apricot cultivars. Based on LDA results and DA-meter index (IAD), it was possible to distinguish samples into four robust ripening classes (0.00-0.10; 0.11-0.20; 0.21-0.30; >0.30). The colorimetric indices allowed to define three classes (a* 70; a* from 5 to 20; a*>20) and to assess the relative ranges of TSS and DM values. Finally, good predictive multi-cultivar models were carried out for TSS, DM, FF, and total carotenoid content (R2: 0.60–0.65). These techniques are promising tools to assess fruit quality, to identify fruit uniform ripening classes and to predict the quality attributes of early-to-late fresh apricots.

In the present study, eight early-ripening apricot cultivars of the Italian and international germplasm were evaluated during three years of investigation (2013–2015), according to their quality traits, biochemical composition, antiradical capacity and hydroxycinnamic acid (HCA) profile, using a chemometric approach. Among cultivars analysed, the highest fresh weight was detected in ‘Maia’ (106 ± 3 g), whereas the lightest fruits were produced by ‘Ottavianese’ cv (51 ± 4 g). ‘Orange Rubis’ and ‘Maia’ were characterized by the highest levels of dry matter, which represent a key processing parameter for the production of dried apricots. The highest content of bioactive compounds such as total phenols and flavans were found in ‘Spring Blush’, Orange Rubis’ and ‘Monaco Bello’, indicating a greater susceptibility to browning during processing. Chlorogenic acid was the most abundant hydroxycinnamic acid found in all tested genotypes, being ‘Portici’ the richest one (338 ± 21 μg g −1 on dry basis). High significant correlations were found among parameters analysed, especially between colour coordinates and bioactive compounds. Finally, application of stepwise discriminant analysis to apricot samples showed that total phenols, total flavans and antiradical capacity were the most important variables to differentiate the cultivars analysed. Besides, principal component analysis made it possible to establish similarities among the cultivars depending on their quality and biochemical characteristics. It is concluded that the combination of quality, biochemical traits analysis and chemometric techniques can be used as a consistent procedure to provide breeders with useful information about the identification and characterization of the most promising early ripening genotypes, both for fresh consumption and processing.

Sour cherry (Prunus cerasus L.) contains substantial quantities of phenolic compounds, specifically anthocyanins, and is mainly processed into different products including juice, whose by-product, namely sour cherry pomace (SCP), represents a potential source of high added bioactive compounds currently underutilized. Oven drying and freeze drying (FD) are simple methods, often used to stabilize food matrices, and can be also successfully employed for the stabilization of sour cherry pomace. In the present study, the influence of genotype (Bianchi d’Offagna and Montmorency cvs), drying method and their interaction on the extractability of phenolic and anthocyanin profiles of sour cherry pomace were evaluated. Both genotype and drying method significantly influenced (p ≤ 0.001) the overall phytochemical content (total monomeric anthocyanin, total flavan-3-ol, total phenolic, and vitamin C) of the analyzed pomaces. The interrelationships between the parameters analyzed, the genotype, and the different drying conditions, as well as the relationships among variables, were investigated by principal component analysis (PCA). PCA results pointed out that the phytochemical content of sour cherry pomace was firstly influenced by the cultivar (which accounted for about 70% of the total variance), followed by the drying process (about 18% of the total variance), and their interaction (about 12% of the total variance), with the exception of total flavan-3-ols, where a similar contribution (about 50%) of the two factors was observed. Besides, the antidiabetic potential of SCPs was also investigated, showing Bianchi D’Offagna FD pomace to be the most active. The obtained results point out that the SCP stabilized by means of the freeze-drying process is a valuable second-generation biorefinery for food supplements and nutraceutical and pharmaceutical markets.

The use of natural compounds to preserve fruit quality and develop high value functional products deserves attention especially in the growing industry of processing and packaging ready-to-eat fresh-cut fruit. In this work, potential mechanisms underlying the effects of postharvest biofumigation with brassica meal-derived allyl-isothiocyanate on the physiological responses and quality of ‘Hayward’ kiwifruits were studied. Fruits were treated with 0.15 mg L−1 of allyl-isothiocyanate vapours for 5 h and then stored in controlled atmosphere (2% O2, 4.5% CO2) at 0 °C and 95% relative humidity, maintaining an ethylene concentration

In the present study, combining near-infrared spectroscopy (NIRs) and chemometric analysis was tested for the characterization and commercial classification of green-fleshed kiwifruit (A. deliciosa, Hayward and Bo-Erica® cvs). Two approaches were studied: 1) development of PLS models using both spectral and reference data to assess the fruit quality and nutraceutical levels and 2) supervised classification methods (LDA and SIMCA) using only spectral data in order to sort fruit on the basis of their ripening time and consumer’s acceptability. Several traditional quality indexes (i.e. soluble solids, firmness, titratable acidity, dry matter), as well as nutritional ones such as total phenols, carotenoids and antioxidant potential were evaluated. Very good prediction of the internal quality attributes, using partial least squares regressions (PLS), was observed, especially for both soluble solid content and dry matter (R2: 0.993, RMSEP: 0.40 and R2: 0.983, RMSEP: 0.33, respectively). An accurate predictive performance of PLS model was also obtained for the total titratable acidity (R2: 0.933, RMSEP: 6.65). Good model performances were obtained for the prediction of the main nutraceutical traits of the samples, developed herein for the first time on kiwifruit. However, fairly good prediction performances were obtained for both total flavans and vitamin C (R2: 0.813, RMSEP: 0.07 and R2: 0.870, RMSEP: 6.04, respectively). Linear Discriminant Analysis (LDA) and Soft Indipendent Modelling by Class Analogy (SIMCA) models provided good results with rates of 92% and 99% of correctly classified samples. This study highlights that FT-NIRs can be used successfully to improve the quality control of kiwifruit at harvest and during storage as well as to identify and segregate samples prior to distribution in order to reduce fruit loss.