In this article, a combination of non-destructive NIR spectroscopy and machine learning techniques was applied to predict the texture parameters and the total soluble solids content (TSS) in intact berries. The multivariate models obtained by building artificial neural networks (ANNs) and applying partial least squares (PLS) regressions showed a better prediction ability after the elimination of uninformative spectral ranges. A very good prediction was obtained for TSS and springiness (R² 0.82 and 0.72). Qualitative models were obtained for hardness and chewiness (R² 0.50 and 0.53). No satisfactory calibration model could be established between the NIR spectra and cohesiveness. Textural parameters of grape are strictly related to the berry size. Before any grape textural measurement, a time-consuming berry-sorting step is compulsory. This is the first time a complete textural analysis of intact grape berries has been performed by NIR spectroscopy without any a priori knowledge of the berry density class.

Postharvest spoilage fungi, such as Botrytis cinerea, are considered the main cause of losses of fresh fruit quality and vegetables during storage, distribution, and consumption. The current control strategy is the use of SO₂ generator pads whose application is now largely under observation. A high quantity of SO₂ can be deleterious for fresh fruits and vegetables and it is not allowed in organic agriculture. For this reason, great attention has been recently focused on identifying Biological Control Agents (BCA) to implement biological approaches devoid of chemicals. In this direction, we carried out our study in isolating five different non-Saccharomyces yeast strains from local vineyards in the South of Italy as possible BCA. We performed both in vitro and in vivo assays in semi-commercial conditions on detached grape berries stored at 0 °C, simulating the temperature normally used during cold storage, and obtained relevant results. We isolated three M. pulcherrima strains and one L. thermotolerans strain able to largely antagonize the development of the B. cinerea, at both in vitro and in vivo conditions. In particular, we detected the ability of the three isolates of M. pulcherrima strains Ale4, N20/006, and Pr7 and the L. thermotolerans strain N10 to completely inhibit (100% in reduction) the mycelial growth of B. cinerea by producing fungistatic compounds. We found, using an extracellular lytic enzymes activity assay, that such activity could be related to lipid hydrolyzation, β-1,3-glucanase and pectinase activity, and pectinase and protease activity, depending on the yeasts used. Results from our in vitro assays allowed us to hypothesize for M. pulcherrima strains Ale4 and N20/006 a possible combination of both the production of soluble metabolites and volatile organic compounds to antagonize against B. cinerea growth. Moreover, in semi-commercial conditions, the M. pulcherrima strain N20/006 and L. thermotolerans strain N10 showed relevant antagonistic effect also at low concentrations (with a significantly reduction of ‘slip skin’ incidence of 86.4% and 72.7%, respectively), thus highlighting a peculiar property to use in commercial development for organic agriculture and the handling process.

Texture characteristics are valuable parameters in the perceived quality and overall acceptability of fresh fruit. The characterization of grape texture attributes, such as firmness and crunchiness, is usually performed by sensory analysis or instrumental texture analysis. Both methodologies are destructive. Hence, it is not possible to test multiple times or perform any other analysis on the same sample. In this article, near-infrared (NIR) spectroscopy was applied to intact berries of table grape cv. Regal Seedless. NIR spectra were employed to predict both the physical parameter “hardness”, which is correlated with the crunchiness of berry flesh and the sweetness, which is correlated with the total soluble solids content (TSS, as °Brix). The chemometric analysis was carried out exclusively based on an open-source software environment, producing results readily usable for any operator, besides the specific level of experience with NIR spectroscopy.

Seedless inheritance has been considered a quasi-monogenic trait based on the VvAGL11 gene. An intragenic simple sequence repeat (SSR) marker, p3_VvAGL11, is currently used to opportunely discard seeded progeny, which represents up to 50% of seedlings to be established in the field. However, the rate of false positives remains significant, and this lack of accuracy might be due to a more complex genetic architecture, some intrinsic flaws of p3_VvAGL11, or potential recombination events between p3_VvAGL11 and the causal SNP located in the coding region. The purpose of this study was to update the genetic architecture of this trait in order to better understand its implications in breeding strategies. A total of 573 F1 individuals that segregate for seedlessness were genotyped with a 20K SNP chip and characterized phenotypically during four seasons for a fine QTL mapping analysis. Based on the molecular diversity of p3_VvAGL11 alleles, we redesigned this marker, and based on the causal SNP, we developed a qPCR-HRM marker for high-throughput and a Tetra-ARMS-PCR for simple predictive analyses. Up to 10 new QTLs were identified that describe the complex nature of seedlessness, corresponding to small but stable effects. The positive predictive value, based on VvAGL11 alone (0.647), was improved up to 0.814 when adding three small-effect QTLs in a multi-QTL additive model as a proof of concept. The new SSR, 5U_VviAGL11, is more informative and robust, and easier to analyze. However, we demonstrated that the association can be lost by intragenic recombination and that the e7_VviAGL11 SNP-based marker is thus more reliable and decreases the occurrence of false positives. This study highlights the bases of prediction failure based solely on a major gene and a reduced set of candidate genes, in addition to opportunities for molecular breeding following further and larger validation studies.