Jing Li

To explore the characteristics of and variations in aroma components across different plum varieties and maturity stages, this study employed headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC-MS). This method was used to systematically analyze the aroma components of 12 early-maturing, 15 medium-maturing, and 11 late-maturing plum varieties. The variations in volatile components among these three germplasm types were then compared using multivariate statistical methods. The examination revealed that 138 aromatic components were meticulously identified and quantified, such as 26 aldehydes, 63 esters, 13 ketones, 30 alcohols, and six other compounds. Thirteen main aroma compounds including acetic acid hexyl ester, (Z)-3-hexen-1-ol acetate, hexanal, 1-hexanol, 3-hexenal, butanoic acid butyl ester, (E)-2-hexen-1-ol, hexanoic acid butyl ester, propanoic acid butyl ester, (E)-2-hexenal, L-.alpha.-terpineol, (Z)-2-hexen-1-ol acetate, and 1-butanol were considered dominant. The orthogonal partial least squares discriminant analysis (OPLS-DA) combined with variable importance projection (VIP) results showed that 24 differential aroma compounds were screened out from 38 varieties of plum fruits based on their differences in aroma components, which can be used to distinguish plum fruits at different ripening times. Twenty-four aroma-contributing compounds were identified based on their odor activity values (OAVs). Among these, 14 key aroma components with OAVs ≥ 10 were highlighted. In summary, the aroma compounds of early- and late-maturing plum germplasm exhibited rich diversity, with significant differences in aroma components between plums of varying maturity and germplasm. These differences can serve as indicators for identifying different plum germplasm.

Pears are popular among consumers for their juicy and delicious taste. In this study, the sugar and organic acid compositions of pear fruits with different texture types during development were determined by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), and fruit texture traits were determined by a texture analyzer. The results showed that the dominant sugar in soft and crispy types of pear fruits was fructose. The main difference between pears was the second-highest sugar component; glucose content was higher in crispy-flesh pear fruits while sucrose content was higher in soft-flesh pear fruits. The composition of organic acid components in both texture types of pear fruits was similar. The turning points of changes in the content of sucrose, sorbitol, glucose and quinic acid were different between different-textured pear varieties. A Pearson correlation analysis showed that sugar and organic acid components were significantly correlated with single fruit weight and soluble solid contents (SSCs), respectively. There was a high correlation among texture traits, individual sugars and organic acids. A partial least squares discriminant analysis (PLS-DA) VIP score plot showed that the differential traits with scores greater than 1 were total soluble sugars/total organic acids (TSSs/TAs), fracture and malic acid/citric acid (MA/CA), which could distinguish pear fruits of different texture types better and reflect the uneven quality differences among pear fruits adapted to different origins. The comprehensive analysis results of the flesh texture parameters and sugar and organic acid components are in line with objective reality and will provide a reference for quantitative indicators of the sensory evaluation of pear varieties as well as for molecular mechanism research on trait differences.

(1) Background: Vaccinium vitis-idaea is a nutritionally and economically valuable natural wild plant species that produces berries useful for treating various diseases. There is growing interest in lingonberry, but there is limited information regarding lingonberry reference genes suitable for gene expression analyses of different tissues under various abiotic stress conditions. The objective of this study was to identify stable reference genes suitable for different lingonberry tissues in response to abiotic stress. (2) Methods: The delta Ct method and the GeNorm v3.5 and NormFinder v20 programs were used to comprehensively analyze gene expression stability. (3) Results: Actin Unigene23839 was the best reference gene for analyzing different cultivars, whereas Actin CL5740.Contig2 was the most suitable reference gene for analyzing different tissues and alkali stress. In contrast, 18S rRNA CL5051.Contig1 was the most stable reference gene under drought conditions. (4) Conclusions: These suitable reference genes may be used in future qRT-PCR analyses of different lingonberry tissues and the effects of abiotic stresses. Furthermore, the study data may be useful for functional genomics studies and the molecular breeding of lingonberry. In summary, internal reference genes or internal reference gene combinations should be carefully selected according to the experimental conditions to ensure that the generated gene expression data are accurate.

As a highly economic small fruit crop, blueberry is enjoyed by most people in terms of color, taste, and rich nutrition. To better understand its coloring mechanism on the process of ripening, an integrative analysis of the metabolome and transcriptome profiles was performed in three blueberry varieties at three developmental stages. In this study, 41 flavonoid metabolites closely related to the coloring in blueberry samples were analyzed. It turned out that the most differential metabolites in the ripening processes were delphinidin-3-O-arabinoside (dpara), peonidin-3-O-glucoside (pnglu), and delphinidin-3-O-galactoside (dpgal), while the most differential metabolites among different varieties were flavonols. Furthermore, to obtain more accurate and comprehensive transcripts of blueberry during the developmental stages, PacBio and Illumina sequencing technology were combined to obtain the transcriptome of the blueberry variety Misty, for the very first time. Finally, by applying the gene coexpression network analysis, the darkviolet and bisque4 modules related to flavonoid synthesis were determined, and the key genes related to two flavonoid 3′, 5′-hydroxylase (F3′5′H) genes in the darkviolet module and one bHLH transcription factor in the bisque4 module were predicted. It is believed that our findings could provide valuable information for the future study on the molecular mechanism of flavonoid metabolites and flavonoid synthesis pathways in blueberries.