Exploring the chemical diversity of Capsicum chinense cultivars using NMR-based metabolomics and machine learning methods.

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.

Experimental races of Capsicum annuum cv. jalapeño: Chemical characterization and classification by 1H NMR/machine learning.

This work reports on the metabolic fingerprinting of ten new races of Capsicum annuum cv. jalapeño using 1H NMR based metabolomics coupled to machine learning projections. Ten races were classified and evaluated according to their differential metabolites, variables of commercial interest and by multivariate data analysis/machine learning algorithm. According to our results, experimental races of jalapeño peppers exhibited differences in carbohydrate, amino acid, nucleotide and organic acid contents. Forty-eight metabolites were identified by 1D and 2D NMR and the differential metabolites were quantified by qNMR. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the studied races into two groups. The group A included the races Colosus, Emperador, Fundador and Rayo whereas the group B included the races Don Benito, SMJ 1416, SMJ 1417, SMJ 1423, SMJ 145 and STAM J0904. OPLS-DA revealed that levels of citric acid in group A were higher than in group B, while the levels of asparagine, fumaric acid, GABA, glucose, malic acid, pyruvic, quinic acid, sucrose and tryptophan were higher in the group B. Remarkably, ascorbic acid was exclusively found in the race Colosus. Random forest model revealed the diversity of the experimental races and the similarity rate with the well-established races. The most relevant variables used to generate a model were length, weight, yield, width, xylose content and organic acids content.

1H NMR-based fingerprinting of eleven Mexican Capsicum annuum cultivars.

Approximately 90% of the chili peppers consumed in the world are harvested in Mexico. The present article describes the untargeted 1H NMR-based metabolomic profiling of 11 cultivars of Capsicum annuum species which are routinely consumed worldwide. The metabolomic fingerprinting detected via 1H NMR contained 44 metabolites including sugars, amino acids, organic acids, polyphenolic acids and alcohols which were identified by comparison with the literature data, with Chenomx database and by 2D NMR. Statistical approaches based on principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the Capsicum annuum cultivars according to their metabolite profile. LDA revealed metabolomic differences and similarities among Capsicum annuum cultivars, whereas hierarchical cluster analysis (HCA) significantly separated the cultivars according to the phylogenetic trees obtained. Substantial endogenous levels of free amino acids and carbohydrates were detected in all the studied cultivars but interestingly, Capsicum annuum cv. mirasol and C. annuum cv. chilaca contained almost three-fold more endogenous levels of vitamin C than the other cultivars. Considering that this antioxidant was found in crude aqueous extracts, its abundance could be directly proportional to its bioavailability for human nutrition. The results suggest that 1H NMR is an effective method to determine differences among cultivars of the Capsicum annuum species.

1H NMR-based metabolomics profiling of ten new races from Capsicum annuum cv. serrano produced in Mexico.

Herein we report on the 1H NMR-based metabolomics profiling of ten new races of Capsicum annuum cv. serrano, cultivated in Mexico. Forty eight metabolites (including sugars, amino acids, organic acids, polyphenolic acids and alcohols) were identified and quantified by 2D NMR and qNMR, respectively. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) separated the ten races into two clusters, from which citric acid, formic acid, fumaric acid, malic acid, glucose, fructose, sucrose and galactose were found as differential metabolites. This is the first study describing the chemical profiling of ten new races of Capsicum annuum cv. serrano and the spectrometric method used presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of different races of Capsicum annuum cv. serrano.