Multi-omics integration to explore the molecular insight into the volatile organic compounds in watermelon

Comprehensive review of sweetpotato flavor compounds: Opportunities for developing consumer-preferred varieties

Flavor contributes significantly to consumer preferences of cooked sweetpotato. Sugars largely drive the sweet taste, while the volatile organic compounds (VOCs), mainly classified as alcohols, aldehydes, ketones, and terpenes, provide characteristic aromas and influence the overall perception of flavor. In this paper, we review sweetpotato VOCs identified in the literature from 1980 to 2024 and discuss the efforts to understand how these compounds influence sensory perception and consumer preferences. Over 400 VOCs have been identified in cooked sweetpotato with 76 known to be aroma-active. Most of these aroma-active compounds are generated from Maillard reactions, Strecker, lipid and carotenoid degradation, or thermal release of terpenes from glycosidic bonds during cooking. Suggested mechanisms of formation of these aroma-active compounds are described. However, specific VOCs that are responsible for different aromas and flavors in cooked sweetpotatoes are yet to be fully characterized. There are significant opportunities to further identify the key predictors of aroma and flavor attributes in sweetpotato, which can be used to enhance the quality of existing varieties and develop new ones using a wide range of genetic tools. This review summarizes 44 years of research aimed at identifying key aroma compounds in cooked sweetpotato and provides a roadmap for future studies to guide breeders in developing high-quality, consumer-preferred varieties.

Bacterial volatiles from aphid honeydew mediate ladybird beetles oviposition site choice

BACKGROUND

The cotton-melon aphid Aphis gossypii Glover is a destructive pest worldwide that causes substantial damage to diverse crops. The ladybird beetle Propylea japonica Thunberg is the dominant predatory natural enemy of A. gossypii. To date, the chemical cues of P. japonica associated with the selection of oviposition sites remain unclear.

RESULTS

Our results revealed that crude honeydew, but not sterilized honeydew, was strongly attractive to mated P. japonica. A total of eight bacterial strains were isolated from crude honeydew, with two (Acinetobacter sp. and Pseudomonas sp.) showing significant attractiveness. Volatiles from these bacteria were identified, and three compounds-DL-lactic acid, 4, 6-dimethyl-2-heptanone, and didodecyl phthalate-were found to significantly attract mated P. japonica in olfactometer assays. Further cage experiments confirmed that P. japonica preferred oviposition sites near these volatile substances.

CONCLUSION

The oviposition site selection by the ladybird beetle P. japonica was found to be influenced by volatiles produced by bacteria associated with cotton-melon aphid honeydew. These findings contribute to biologically based, environmentally friendly pest management strategies in agriculture. © 2025 Society of Chemical Industry.

Comprehensive investigation on the dynamic changes of volatile organic compounds in three peach cultivars during fruit development

To deepen the understanding of peach aroma, we investigated the changes of volatile organic compound (VOC) profiles during fruit development in three fresh peach cultivars: 'Jiucui (JC)', 'Zhongyoupan No. 9 (ZYP9)' and 'Zhongyou No. 8 (ZY8)'. The classes and contents of VOCs changed significantly during fruit ripening. The representative VOC changes were categorized into three developmental stages, with the lowest volatile content observed during the middle stage. At maturity, ester-related volatiles increased significantly, dominating the VOC profiles and accounting for 78 %, 82 % and 88 % in JC, ZYP9 and ZY8, respectively, while alcohols decreased dramatically. Multivariate analysis identified 23, 21 and 20 potential volatile markers in JC, ZYP9 and ZY8, respectively, with 9 shared markers distinguishing the different developmental stages. Odor activity values (OAVs) and olfactometry analysis highlighted 36, 29, and 26 aroma-active compounds (OAV > 1) across the cultivars, with key contributors to the mature peach aroma including hexyl acetate (OAV 860-2896), (E)-2-hexenyl acetate, (Z)-3-hexenyl acetate, (E)-3-hexenyl acetate, linalool, and γ-decalactone. The fruity aroma intensified as ripening progressed, with ZY8, a cultivar with a longer maturation cycle, exhibiting the highest odor intensity. This study offers insights into the dynamic changes in VOC profiles during peach development, highlighting key aroma-active compounds and their stage-specific variations.

Enhanced multivariate data fusion and optimized algorithm for comprehensive quality profiling and origin traceability of Chinese jujube

Chinese jujube (CJ) is a nutritious food. Its authenticity has received increasing attention. This research utilized computer vision, ultrafast gas-phase electronic nose, and GC-MS technologies to collect jujube samples from various regions in China, including Xinjiang, Gansu, Shaanxi, Henan, Shandong, and Hebei. Multidimensional trait data, encompassing spectra, texture, and odour, were gathered. By employing multivariate statistical methods, 46 trait characteristic factors (VIP > 1, P < 0.05) were identified and utilized to rapidly differentiate jujube samples originating from different regions. The multivariate statistical analysis and support vector machine (SVM) classification were also combined to develop a novel artificial intelligence algorithm. The accuracy of this innovative method was significantly higher than that of conventional discriminant analysis methods, achieving a perfect 100.0 % accuracy. As a consequence of this research, more intelligent algorithms can be developed that trace the origin of food based on multidimensional data.

Telomere-to-telomere Citrullus super-pangenome provides direction for watermelon breeding

To decipher the genetic diversity within the cucurbit genus Citrullus, we generated telomere-to-telomere (T2T) assemblies of 27 distinct genotypes, encompassing all seven Citrullus species. This T2T super-pangenome has expanded the previously published reference genome, T2T-G42, by adding 399.2 Mb and 11,225 genes. Comparative analysis has unveiled gene variants and structural variations (SVs), shedding light on watermelon evolution and domestication processes that enhanced attributes such as bitterness and sugar content while compromising disease resistance. Multidisease-resistant loci from Citrullus amarus and Citrullus mucosospermus were successfully introduced into cultivated Citrullus lanatus. The SVs identified in C. lanatus have not only been inherited from cordophanus but also from C. mucosospermus, suggesting additional ancestors beyond cordophanus in the lineage of cultivated watermelon. Our investigation substantially improves the comprehension of watermelon genome diversity, furnishing comprehensive reference genomes for all Citrullus species. This advancement aids in the exploration and genetic enhancement of watermelon using its wild relatives.

A newly-discovered tea population variety processed Bai Mu Dan white tea: Flavor characteristics and chemical basis

The quality of white tea (WT) is impacted by selected tea cultivars. To explore the organoleptic quality of a recently-discovered WT ("Caicha", CC), HS-SPME/GC-MS and UPLC were employed to identify volatile and non-volatile compounds in tea samples. Multiple statistical methods demonstrated the distinctions between CC and four mainstream WT varieties from main producing areas. CC exhibited abundant volatile alcohol, terpenoids, ketone, aldehyde and ester, as well as non-volatile lignans and coumarins, phenolic acids and low-molecular carbohydrates. These substances combinedly contributed to the flavor attributes of CC, characterized by an intense herbal/citrus-like cleanness and flower/fruit-like sweetness, scarce in existing commercial WT varieties. Sensory evaluation corroborated these findings. In conclusion, we have processed a new tea variety (CC) with WT manufacturing technology, and discovered the unique cleanness and sweetness of it. This study enriches the raw material database for WT production and blending, and boosts the development of more premium WT varieties.

Mechanisms and quality variations of non-volatile and volatile metabolites in black tea from various ages of tea trees: Insights from metabolomics analysis

The sensory quality of black tea (BT) influenced by various factors, among which tree age is particularly significant. People prefer BT produced by fresh leaves from old tea trees, yet the correlation between tree age and tea quality has not been thoroughly investigated. In this study, we analyzed the quality of BT from young trees (H-JYH) and old trees (H-OJYH) using e-tongue technology and sensory evaluation. Our findings revealed that H-OJYH had stronger sweetness and sourness, richer flavor, and diminished bitter-astringency compared to H-JYH. 1231 non-volatile metabolites and 504 volatile metabolites were discovered by ultra-performance liquid chromatography (UPLC) and gas chromatography-mass spectrometry (GC-MS). L-tartaric acid and trans-citridic acid were found to contribute to increase acidity, and 7,8-dihydroxy-6-methoxycoumarin and d-fructose 6-phosphate were associated with enhanced sweetness in H-OJYH. Additionally, lower levels of octyl gallate and vanillic acid in H-OJYH contributed to the diminished bitter-astringency. β-ionone, 2-phenylethanol and phenylacetaldehyde merged as characteristic compounds of older tree BT with stronger floral and sweet aroma. Our study serves as a guideline to explore the relationship between tree age and tea quality.

Ester-related volatile compounds reveal the diversity and commonalities of different types of late-ripening peaches

To recognize the key ester-related volatile compounds, 5 types of peaches including 54 late-ripening peach materials were examined by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and E-nose. Here, a large number of esters were identified to be released by ripe peach fruits and were mainly characterized by fruity, green, and fatty notes. The variety and content of esters had greatly changed within or between cultivars, indicating that the fruit volatiles were highly differentiated depending on the specific genotypes and cultivation conditions. The ester types showed that fatty acid-derived C6 alcohols and methyl-/ethyl- short-chain alcohol were the main ester precursors, which were more likely to be utilized and well selected by alcohol acyltransferases, whereas the preference of acyl donors was not observed. The common peach type, which exhibited a unique volatile profile, displayed broader diversity and more abundant characteristics in ester-related volatiles than the other four types. A total of 19 key esters were identified as the main components and the content of most esters showed no significant difference among different peach types. Some key esters had even been enriched in nectarines. Moreover, the multiple discriminant analysis revealed a possible relationship between peach types and the domestication of the peach evolution. This study investigated ester-related volatiles released by different types of peach fruits and can be further used to evaluate the peach qualities, providing an important reference for peach breeding and processing.

Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on cytidine diphosphate-diacylglycerol synthase-mediated lipid metabolism

Deep learning is evolving in nutritional epidemiology to address challenges including precise nutrition and data-driven disease modeling. Fermented dairy products consumption as the implementation of specific dietary priority contributes to a lower risk of all-cause mortality, cardiovascular disease, and obesity. Various lipid types play different roles in cardiometabolic health and fermentation process changes the lipid profile in dairy products. Leveraging the power of multiple biological datasets can provide mechanistic insights into how proteins impact lipid pathways, and establish connections among fermentation-lipid biomarkers-protein. The recent leap of deep learning has been performed in food category recognition, agro-food freshness detection, and food flavor prediction and regulation. The proposed multimodal deep learning method includes four steps: (i) Forming data matrices based on data generated from different omics layers. (ii) Decomposing high-dimensional omics data according to self-attention mechanism. (iii) Constructing View Correlation Discovery Network to learn the cross-omics correlations and integrate different omics datasets. (iv) Depicting a biological network for lipid metabolism-centered quantitative multi-omics data analysis. Relying on the cytidine diphosphate-diacylglycerol synthase-mediated lipid metabolism regulates the glycerophospholipid composition of fermented dairy effectively. Innovative processing strategies including ohmic heating and pulsed electric field improve the sensory qualities and nutritional characteristics of the products.