TY - JOUR
T1 - Experimental races of Capsicum annuum cv. jalapeño
T2 - Chemical characterization and classification by 1H NMR/machine learning
AU - Ramírez-Meraz, Moisés
AU - Méndez-Aguilar, Reinaldo
AU - Hidalgo-Martínez, Diego
AU - Villa-Ruano, Nemesio
AU - Zepeda-Vallejo, L. Gerardo
AU - Vallejo-Contreras, Fernando
AU - Hernández-Guerrero, Claudia J.
AU - Becerra-Martínez, Elvia
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Jalapeno pepper
KW - Machine learning
KW - Multivariate statistical analysis
KW - NMR
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85092212093&partnerID=8YFLogxK
U2 - 10.1016/j.foodres.2020.109763
DO - 10.1016/j.foodres.2020.109763
M3 - Artículo
C2 - 33292944
AN - SCOPUS:85092212093
SN - 0963-9969
VL - 138
JO - Food Research International
JF - Food Research International
M1 - 109763
ER -