The habanero pepper (Capsicum chinense) is a prominent spicy fruit integral to the historical, social, cultural, and economic fabric of the Yucatan peninsula in Mexico. This study leverages the power of 1H NMR spectroscopy coupled with machine learning algorithms to dissect the metabolomic profile of eleven C. chinense cultivars, including those grown by INIFAP (Habanero-Jaguar, Antillano-HRA 1-1, Antillano-HRA 7-1, Habanero-HAm-18A, Habanero-HC-23C, and Jolokia-NJolokia-22) and commercial hybrids (Habanero-Rey Votán, Habanero-Kabal, Balam, USAPR10117, and Rey Pakal). A total of fifty metabolites, encompassing sugars, amino acids, short-chain organic acids, and nucleosides, were identified from the 1H NMR spectra. The optimized machine learning model proficiently predicted the similarity percentage between the INIFAP-grown cultivars and commercial hybrids, thereby facilitating a comprehensive comparison. Biomarkers unique to each cultivar were delineated, revealing that the Habanero-Rey Votán cultivar is characterized by the highest concentration of sugars. In contrast, the Balam cultivar is rich in amino acids and short-chain organic acids, sharing a similar metabolomic profile with the Jolokia-NJolokia-22 cultivar. The findings of this study underscore the efficacy and reliability of NMR-based metabolomics as a robust tool for differentiating C. chinense cultivars based on their intricate chemical profiles. This approach not only contributes to the scientific understanding of the metabolomic diversity among habanero peppers but also holds potential implications for food science, agriculture, and the culinary arts.

One of the ultimate goals of plant breeding is the development of new crop cultivars capable of withstanding increasing environmental stresses, to sustain the constantly growing population and economic demands. Investigating the chemical composition of the above and underground tissues of cultivars is crucial for the understanding of common and specific traits thereof. Using an untargeted metabolomics approach together with appropriate chemometrics tools, the differential metabolite profiles of leaf and root extracts from five cultivars of barley (\'Erica\', \'Elim\', \'Hessekwa\', \'S16\' and \'Agulhas\') were explored and potential signatory biomarkers were revealed. The study was conducted on seedlings grown for 21 days under identical controlled conditions. An ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) was employed to analyse hydromethanolic leaf and root extracts of barley cultivars. Furthermore, unsupervised and supervised learning algorithms were applied to mine the generated data and to pinpoint cultivar-specific metabolites. Among all the classes of metabolites annotated, phenolic acids and derivatives formed the largest group and also represented the most discriminatory metabolites. In roots, saponarin, an important allelochemical differentially distributed across cultivars, was the only flavonoid annotated. The application of an untargeted metabolomics approach in phenotyping grain crops such as barley was demonstrated, and the metabolites responsible for differentiating between the selected cultivars were revealed. The study provides insights into the chemical architecture of barley, an agro-economically relevant cereal crop; and reiterates the importance of metabolomics tools in plant breeding practices for crop improvement.

In the present study, volatile compound analysis of olive oil samples belonging to ten Greek cultivars was carried out. A total of 167 olive oil samples collected from two consecutive harvest years were analyzed by Head Space-Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Volatile compound data were combined with chemometric methods (Multivariate Analysis of Variance (MANOVA) and Linear Discriminant Analysis (LDA)) with the aim not only to differentiate olive oils but also to identify characteristic volatile compounds that would enable differentiation of botanical origin (marker compounds). The application of Stepwise LDA (SLDA) effectively reduced the large number of statistically significant volatile compounds involved in the differentiation process, and thus, led to a set of parameters, the majority of which belong to compounds that are highly dependent on variety. In addition, the use of these marker compounds resulted in an increased correct classification rate (85.6%) using the cross-validation method indicating the validity of the model developed despite the use of a large number of dependent variables (cultivars).

The detailed phenolic composition of different berry parts from two novel V. vinifera L. red grape genotypes (Moribel and Tinto Fragoso), together the well-known Tempranillo, was established using high performance liquid chromatography-diode array detection-electrospray ionization tandem mass spectrometry (HPLC-DAD-ESI-MS/MS) over two consecutive vintages (2016 and 2017). More than 50 phenolic compounds were identified and quantified: 25 anthocyanins, 17 flavonols, 7 hydroxycinnamic acid derivatives, 2 stilbenes, and several flavan-3-ols. As far as we know, some anthocyanin and flavonol dihexosides were reported for the first time in V. vinifera L. grapes. Application of Principal Component Analysis (PCA) to experimental data showed a good separation of the novel grape genotypes and Tempranillo according to the phenolic profile of skins and seeds, mainly based on the proportion of trisubstituted anthocyanin derivatives, flavonols and flavan-3-ols, being a useful tool to differentiate these grape varieties.

Domestication and cultivar differentiation are requisite processes for establishing cultivated crops. These processes inherently involve substantial changes in population structure, including those from artificial selection of key genes. In this study, accessions of peach (Prunus persica) and its wild relatives were analysed genome-wide to identify changes in genetic structures and gene selections associated with their differentiation. Analysis of genome-wide informative single-nucleotide polymorphism loci revealed distinct changes in genetic structures and delineations among domesticated peach and its wild relatives and among peach landraces and modern fruit (F) and modern ornamental (O-A) cultivars. Indications of distinct changes in linkage disequilibrium extension/decay and of strong population bottlenecks or inbreeding were identified. Site frequency spectrum- and extended haplotype homozygosity-based evaluation of genome-wide genetic diversities supported selective sweeps distinguishing the domesticated peach from its wild relatives and each F/O-A cluster from the landrace clusters. The regions with strong selective sweeps harboured promising candidates for genes subjected to selection. Further sequence-based evaluation further defined the candidates and revealed their characteristics. All results suggest opportunities for identifying critical genes associated with each differentiation by analysing genome-wide genetic diversity in currently established populations. This approach obviates the special development of genetic populations, which is particularly difficult for long-lived tree crops.

Compositions of fatty acid, amino acids, and volatile compound were investigated in green coffee beans of seven cultivars of Coffea robusta grown in Hainan Province, China. The chlorogenic acids, trigonelline, caffeine, total lipid, and total protein contents as well as color parameters were measured. Chemometric techniques, principal component analysis (PCA), hierarchical cluster analysis (HCA), and analysis of one-way variance (ANOVA) were performed on the complete data set to reveal chemical differences among all cultivars and identify markers characteristic of a particular botanical origin of the coffee. The major fatty acids of coffee were linoleic acid, palmitic acid, oleic acid, and arachic acid. Leucine (0.84 g/100 g DW), lysine (0.63 g/100 g DW), and arginine (0.61 g/100 g DW) were the predominant essential amino acids (EAAs) in the coffee samples. Seventy-nine volatile compounds were identified and semi-quantified by HS-SPME/GC-MS. PCA of the complete data matrix demonstrated that there were significant differences among all cultivars, HCA supported the results of PCA and achieved a satisfactory classification performance.