Search Results (376)

Cooked-rice aroma strongly affects consumer choice, yet the chemical traits distinguishing glutinous rice from normal-amylose japonica rice remain underexplored because earlier studies targeted only a few dozen volatiles using one-dimensional gas chromatography–mass spectrometry (GC-MS). In this study, four glutinous and seven normal Japanese cultivars were cooked under identical conditions, their headspace volatiles trapped with MonoTrap and qualitatively profiled by comprehensive GC × GC-TOFMS. The two-dimensional platform resolved 1924 peaks—about ten-fold previous coverage—and, together with hierarchical clustering, PCA, heatmap visualization and volcano plots, cleanly separated the starch classes (78.3% cumulative PCA variance; Euclidean distance >140). Volcano plots highlighted 277 compounds enriched in the glutinous cultivars and 295 in Koshihikari, including 270 compounds that were not previously documented in rice. Normal cultivars were dominated by ethers, aldehydes, amines and other nitrogenous volatiles associated with grainy, grassy and toasty notes. Glutinous cultivars showed abundant ketones, furans, carboxylic acids, thiols, steroids, nitro compounds, pyrroles and diverse hydrocarbons and aromatics, yielding sweeter, fruitier and floral accents. These results expand the volatile library for japonica rice, provide molecular markers for flavor-oriented breeding and demonstrate the power of GC × GC-TOFMS coupled with chemometrics for grain aroma research. Full article

Petit Manseng is a variety of Vitis vinifera gaining popularity in Virginia, USA because it consistently produces high quality grapes under variable growing conditions. However, its high sugar and acid levels complicate dry wine production. The goal of this study was to characterize Petit Manseng ripening kinetics from veraison to harvest to identify optimal harvest timing for producing dry white wines, using Chardonnay as a comparator because of its popularity in Virginia, well-known ripening kinetics, and ability to produce high quality dry white wines. A total of 74 samples of Petit Manseng and Chardonnay grapes were collected from five commercial sites over 2 years and evaluated for berry weight, pH, titratable acidity (TA), malic acid, total soluble solids (TSS), glucose, and fructose, with ripening kinetics modeled using segmented regressions. Results indicated that harvest timing and grape variety were the primary factors influencing ripening kinetics. In contrast, growing location and vintage had limited impact. In Chardonnay grapes, TA declined from 21 to 7.1 g/L and TSS increased from 6.1 to 19.5 g/L. In Petit Manseng, TA declined from 25 to 10.8 g/L and TSS increased from 8.0 to 23.6 g/L. Acid depletion plateaued ~2 weeks after sugar accumulation plateaued in Petit Manseng grapes, though the plateaus were similar in Chardonnay grapes. Linear discriminant analysis (LDA) completely separated grapes based on pH or TA vs. sugars, but not malic acid vs. sugars, suggesting that tartaric acid is driving acidity differences between cultivars. These data indicate that regardless of when grapes are harvested, winemakers may need to employ targeted acid management strategies with Petit Manseng because of its ripening kinetics. Full article

In this paper, an in-depth characterization of the composition of extra-virgin olive oil (EVOO) from different cultivars was performed, with the aim of obtaining the fingerprint profile of bioactive constituents and studying the oxidative stability of the samples, both by an accelerated stability test and after four months of storage at room temperature. Among the investigated cultivars, some were typical of Umbria (Central Italy), namely Moraiolo, Frantoio, and Dolce Agogia, others of Apulia (Southern Italy), Coratina, Peranzana, and Bella di Cerignola, and others were typical Spanish cultivars cultivated in Umbria (Arbequina and Arbosana). The comparison of the chemical parameters among oils from the different cultivars allowed for their discrimination by multivariate statistical analysis. Some phenolic compounds were mainly responsible for the sample group’s differentiation, with the oils from the Spanish cultivars clearly distinguished from the Umbrian and Apulian sample groups. The processing of the results by chemometric analysis during oil storage and stability tests again allowed the discrimination of the samples analyzed at different storage times. This study contributes to increasing knowledge on olive oils—chemical and nutraceutical properties from specific cultivars, particularly some less studied so far, such as the Bella di Cerignola cultivar, and their changes in their nutraceutical properties during storage. Full article

Olive tree leaf is a phenol-rich, high-potential-value biomass that can be used to formulate food additives and supplements. Leaf phenolic content varies depending on numerous factors, like cultivar, geographical origin, year, and season of harvest. The aim of this research was to evaluate the variations in phenolic profile of four major Tuscan cultivars (Frantoio, Leccio del Corno, Leccino, and Moraiolo) over four different phenological phases and across two years. All 96 olive leaf samples were harvested from trees grown in the same orchard located in Florence. After drying, their phenolic profile was characterized using HPLC-DAD-MS, and the obtained data were processed by ANOVA, GA-LDA, and RF methods. A total of 25 phenolic derivatives were analyzed, with total contents ranging 16,674.0–50,594.3 mg/kg and oleuropein (4570.0–27,547.7 mg/kg) being the predominant compound regardless of cultivar, year, and season of harvest. Oleuropein and hydroxytyrosol glucoside showed inverse proportionality and similar behavior across years in all cultivars, and therefore were highlighted as main phenolic compounds correlated with the seasonal variability in studied cultivars. Interesting behavior was also pointed out for apigenin rutinoside. Application of GA-LDA and RF methods allowed pointing out the excellent performance of leaf phenols in discriminating samples based on cultivar, harvest year, and harvesting season. Full article

Unconventional food plants (UFPs) are increasingly valued for their nutritional composition and bioactive potential. This study proposes a comprehensive characterization of the chemical and bioactive properties of Pereskia aculeata Miller (Cactaceae) (PA); Xanthosoma sagittifolium (L.) Schott (Araceae) (XS); Stachys byzantina K. Koch (Lamiaceae) (SB); and inflorescences from three cultivars of Musa acuminata (Musaceae) var. Dwarf Cavendish, var. BRS Platina, and var. BRS Conquista (MAD, MAP, and MAC), including the assessment of physical, nutritional, phytochemical, and biological parameters. Notably, detailed phenolic profiles were established for these species, many of which are poorly documented in the literature. XS was characterized by a unique abundance of C-glycosylated flavones, especially apigenin and luteolin derivatives, rarely described for this species. SB exhibited high levels of phenylethanoid glycosides, particularly verbascoside and its isomers (up to 21.32 mg/g extract), while PA was rich in O-glycosylated flavonols such as quercetin, kaempferol, and isorhamnetin derivatives. Nutritionally, XS had the highest protein content (16.3 g/100 g dw), while SB showed remarkable dietary fiber content (59.8 g/100 g). Banana inflorescences presented high fiber (up to 66.5 g/100 g) and lipid levels (up to 7.35 g/100 g). Regarding bioactivity, PA showed the highest DPPH radical scavenging activity (95.21%) and SB the highest reducing power in the FRAP assay (4085.90 µM TE/g). Cellular antioxidant activity exceeded 2000% in most samples, except for SB. Cytotoxic and anti-inflammatory activities were generally low, with only SB showing moderate effects against Caco-2 and AGS cell lines. SB and PA demonstrated the strongest antimicrobial activity, particularly against Yersinia enterocolitica, methicillin-resistant Staphylococcus aureus (MRSA), and Enterococcus faecalis, with minimum inhibitory concentrations ranging from 0.156 to 0.625 mg/mL. Linear discriminant analysis revealed distinctive chemical patterns among the species, with organic acids (e.g., oxalic up to 7.53 g/100 g) and fatty acids (e.g., linolenic acid up to 52.38%) as key discriminant variables. Overall, the study underscores the nutritional and functional relevance of these underutilized plants and contributes rare quantitative data to the scientific literature regarding their phenolic signatures. Full article

Stropharia rugosoannulata, an ecologically valuable and economically important edible mushroom, faces challenges in strain-level identification and breeding due to limited genomic resources and the lack of high-resolution molecular markers. In this study, we generated high-quality genomic data for 105 S. rugosoannulata strains and identified over 2.7 million SNPs, unveiling substantial genetic diversity within the species. Using core gene-associated multiple nucleotide polymorphism (cgMNP) markers, we developed an efficient and transferable framework for strain discrimination. The analysis revealed pronounced genetic differentiation among cultivars, clustering them into two distinct phylogenetic groups. Nucleotide diversity (π) across 83 core genes varied significantly, highlighting both highly conserved loci under purifying selection and highly variable loci potentially associated with adaptive evolution. Phylogenetic analysis of the most variable gene, Phosphatidate cytidylyltransferase mitochondrial, identified 865 SNPs, enabling precise differentiation of all 85 cultivars. Our findings underscore the utility of cgMNP markers in addressing challenges posed by horizontal gene transfer and phylogenetic noise, demonstrating their robustness in cross-species applications. By providing insights into genetic diversity, evolutionary dynamics, and marker utility, this study establishes a foundation for advancing breeding programs, conservation strategies, and functional genomics in S. rugosoannulata. Furthermore, the adaptability of cgMNP markers offers a universal tool for high-resolution strain identification across diverse fungal taxa, contributing to broader fungal phylogenomics and applied mycology. Full article

The integration of HS-SPME-GC/MS and UPLC-MS/MS techniques enabled the profiling of volatile organic compounds (VOCs) and amino acids (AAs) in 18 crabapple flower cultivars, facilitating the development of a novel VOC–AA model. Among the 51 identified VOCs, benzyl alcohol, benzaldehyde, and ethyl benzoate were predominant, categorizing cultivars into fruit-almond, fruit-sweet, and mixed types. The amino acids, namely glutamic acid (Glu), asparagine (Asn), aspartic acid (Asp), serine (Ser), and alanine (Ala) constituted 83.6% of the total AAs identified. Notably, specific amino acids showed positive correlations with key VOCs, suggesting a metabolic regulatory mechanism. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, when combined with volatile organic compounds (VOCs) and amino acid profiles, enabled more effective aroma type classification, providing a robust foundation for further studies on aroma mechanisms and targeted breeding. Full article

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and RGB images for 740 Gannan navel oranges of five cultivars are collected. Based on preprocessed spectra, optimally selected hyperspectral images, and registered RGB images, a dual-branch multi-modal feature fusion convolutional neural network (CNN) model is established. In this model, a spectral branch is designed to extract spectral features reflecting internal compositional variations, while the image branch is utilized to extract external color and texture features from the integration of hyperspectral and RGB images. Finally, growth stages are determined via the fusion of features. To validate the availability of the proposed method, various machine-learning and deep-learning models are compared for single-modal and multi-modal data. The results demonstrate that multi-modal feature fusion of HSI and MV combined with the constructed dual-branch CNN deep-learning model yields excellent growth stage discrimination in navel oranges, achieving an accuracy, recall rate, precision, F1 score, and kappa coefficient on the testing set are 95.95%, 96.66%, 96.76%, 96.69%, and 0.9481, respectively, providing a prominent way to precisely monitor the growth stages of fruits. Full article

The sensory quality of black tea is intrinsically linked to cultivar genetics, yet comprehensive characterization of flavor compounds in emerging northern Guangdong black tea (NGBT) remains limited. This study employed high-performance liquid chromatography-ultraviolet (HPLC-UV) and headspace solid-phase microextraction coupled with GC-MS (HS-SPME-GC-MS) to analyze non-volatile and volatile compounds in five NGBT cultivars—Jinshahong (JSH), Danxia No.1 (DXY), Danxia No.2 (DXE), Yingde Black Tea (QTZ), and Yinghong No.9 (YHJ)—alongside sensory evaluation. Orthogonal partial least squares-discriminant analysis (OPLS-DA) identified key non-volatile discriminants (VIP > 1) ranked by contribution: total catechins > simple catechins > CG > EGCG > ester catechins > EGC. HS-SPME-GC-MS detected 97 volatiles, with eight aroma-active compounds exhibiting OAV > 1 and VIP > 1: Geraniol > Methyl salicylate > Linalool > β-Myrcene > Benzyl alcohol > (Z)-Linalool Oxide > Phenethyl alcohol > (Z)-Jasmone. These compounds drive cultivar-specific aromas in NGBTs. Findings establish a theoretical framework for evaluating cultivar-driven flavor quality and provide novel insights for targeted breeding and processing optimization of NGBTs. Full article

Two germplasm collections, comprising 1026 peach accessions located in Italy, were analyzed with 12 simple sequence repeat (SSR) markers. SSR reactions were performed using the multiplex-ready PCR protocol, and 147 alleles were amplified with an average of 12 alleles per locus. BPPCT001 was the most informative marker displaying the highest discrimination power (0.734). The observed heterozygosity showed an average of 0.45 alleles per locus, lower than expected (0.61). The fixation index (F) values were positive in all loci, with an average of 0.27 alleles per locus, suggesting the presence of endogamy. The DNA fingerprinting data allowed the discrimination of 80.95% of the analyzed accessions. If we exclude known sport mutations, known synonymies, and cultivars with the same pedigree, 161 accessions are mislabeled, with an error rate of 16.56% within or between collections. Population structure analysis revealed three subpopulations: modern peach cultivars, modern nectarine cultivars, and a third group mainly comprising traditional peach cultivars. The results obtained in this work will be useful to efficiently manage Genebank, reducing unwanted redundancy, synonyms and homonyms, mislabeling, and spelling errors, as well as identifying parents in controlled crosses. Full article

Apricot is one of the most important Mediterranean fruits with high diversity and fruit quality properties, being an excellent raw material for polyphenol compounds. This study aimed to determine the anthocyanin, quercetin glycoside and phenolic acid contents in new apricot genotypes from the breeding program at the Instituto Valenciano de Investigaciones Agrarias, confirming the potential of the ‘Goldrich’ cultivar to be a parental donor for increasing the antioxidant content, which would, in turn, enhance fruit quality. Phenolic composition of the apricot accessions is strongly genotype-dependent, with the concentrations of overall total phenolic compounds ranging from 770 to 260 mg 100 g⁻¹ DW, reflecting significant genetic diversity. ‘Goldrich’ contributed to the polyphenol content; however, its influence varied across derived varieties, with ‘GG9310’ and ‘GG979’ enhancing the shikimic acid pathway and accumulating high levels of total phenolics. In contrast, ‘Mitger’ and ‘HG9850’ stood out for high anthocyanin synthesis, despite their lower levels of flavonols and phenolic acids. The predominant anthocyanin was cyanidin-3-O-rutinoside, followed by cyanidin-3-O-glucoside and peonidin-3-O-rutinoside in smaller amounts. Other phenolics were rutin and quercetin-3-O-glucuronide, as well as neochlorogenic and chlorogenic acids. The PCA model was applied to all data to identify the most attractive cultivars, and chromatographic analysis was performed in a short time using Ultra-High-Performance Liquid Chromatography (UHPLC) with diode array and mass spectrometric detection. Apricot peel is an excellent source of nutraceutical compounds with a chemical composition strongly determined by the cultivar. Results can help establish authenticity markers for apricot cultivars. Full article

Wine organic acids influence their overall quality. This study examines the variability of major acids (tartaric, malic, lactic, citric, acetic, and gluconic) and minor phenolic acids (gallic, protocatechuic, syringic, caftaric, caffeic, cutaric, coumaric, and 2-S-glutathionylcaftaric) in varietal red wines produced from predominantly autochthonous grape cultivars of the Canary Islands. Two hundred and five monovarietal red wine samples were analyzed to assess how the organic acid profiles differed depending on the grape cultivars employed in their production, island of provenance, denomination of origin, and aging, supporting relationships between both genetic and environmental factors. High-performance liquid chromatography and enzymatic methods were used for determining minor and major organic acids, respectively. Many significant differences between grape cultivars, geographical origin, and aging were observed, reflecting the complexity of the composition of the organic acids in red wine and its relationship with viticultural factors. Linear discriminant analysis achieved classification accuracies up to 88.3% by cultivar and 83.4% by island. Correlation and multivariate analyses helped identify compositional patterns and key discriminant acids. This study advances the comprehension of major and minor acid composition and equilibria in red wines and may support future research on quality parameters and regional typicity. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Olive trees are generally considered a species well-adapted to drought, but the impact of water shortage is of critical importance on olive production. For this reason, developing tolerant cultivars could be an effective strategy to mitigate the impact of drought in the future. Characterizing drought stress tolerance in olive is a complex task due to the numerous traits involved in this response. In this study, plant growth, pressure–volume curves, gas-exchange and chlorophyll fluorescence traits, and stomata characteristics were monitored in nine cultivars to assess the effects of mild and severe drought stress conditions induced by withholding water for 7 and 21 days, respectively, and were compared to a well-watered control treatment. The plant materials evaluated included traditional cultivars, as well as new developed cultivars suited for high-density hedgerow olive orchards or resistant to verticillium wilt. Significant differences between cultivars were observed for most evaluated traits, with more pronounced differences under severe drought conditions. A multivariate analysis of the complete dataset recorded throughout the evaluation period allowed for the identification of promising cultivars under stress conditions (‘Sikitita’, ‘Sikitita-2’, and ‘Martina’) as well as highly discriminative traits that could serve as key selection parameters in future breeding programs. Full article

Efficient nitrogen management is key to maximizing production and minimizing the environmental impact of horticultural crops. This study analyses the effect of different doses of nitrogen on the development of broccoli (Brassica oleracea var. italica) (cultivar Parthenon), processing tomato (Solanum lycopersicum) (cultivar H1015), and processing pepper (Capsicum annuum) (cultivar Ramonete Lamuyo) in open fields in Extremadura and evaluates rapid and efficient methods for diagnosing their nutritional status. Trials were carried out at the La Orden Experimental Farm (CICYTEX) with different nitrogen fertilization rates. The N doses were 0–60–120–180 kg N/ha for peppers in 2020 and 2021 and 0–200–300 kg N/ha for 2022. For broccoli, the N doses were 0–100–200–300 kg N/ha in 2020 and 0–200–300 kg N/ha for 2022. For tomatoes, the N doses were 0–100–200–300 kg N/ha in 2021 and 0–200–350 kg N/ha for 2022. The following three indicators were compared: chlorophyll content measured with optical sensors, petiole sap nitrate concentration, and the nitrogen nutrition index (NNI). The results indicate that chlorophyll measurement is not suitable for broccoli due to the characteristics of its leaves, but is useful for tomatoes and peppers, providing a quick and non-destructive diagnosis. Nitrate concentration in sap, although more laborious and destructive, was found to be effective in discriminating nutritional status in the three species. However, the NNI did not prove to be a good reference method in open field conditions. These results highlight the importance of adapting nutrient monitoring strategies to the crop and management conditions, contributing to a more efficient use of nitrogen and a reduction in the environmental impact of nitrate leaching. Full article