关键词: Artificial selection Cultivar differentiation Cultural emblem Metabolic profiling Nutraceutical properties Random forest

Mesh : Capsicum / chemistry Reproducibility of Results Capsaicin Magnetic Resonance Spectroscopy Fruit / chemistry Amino Acids / analysis Sugars / analysis

来  源:   DOI:10.1016/j.foodres.2023.113796

Abstract:
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.
摘要:
哈瓦那胡椒(Capsicumchinense)是历史上不可或缺的重要辛辣水果,社会,文化,以及墨西哥尤卡坦半岛的经济结构。这项研究利用1HNMR光谱的力量与机器学习算法相结合,剖析了11个C.chinense品种的代谢组学概况,包括INIFAP(Habanero-Jaguar,Antillano-HRA1-1,Antillano-HRA7-1,Habanero-HAm-18A,Habanero-HC-23C,和Jolokia-NJolokia-22)和商业混合动力车(Habanero-ReyVotán,Habanero-Kabal,Balam,USAPR10117和ReyPakal)。总共50种代谢物,包括糖,氨基酸,短链有机酸,和核苷,从1HNMR光谱中鉴定。优化的机器学习模型熟练地预测了INIFAP种植品种和商业杂种之间的相似性百分比,从而促进全面比较。描绘了每个品种独特的生物标志物,揭示Habanero-ReyVotán品种的特征是糖的最高浓度。相比之下,巴拉姆品种富含氨基酸和短链有机酸,与Jolokia-NJolokia-22品种具有相似的代谢组学特征。这项研究的结果强调了基于NMR的代谢组学作为根据其复杂的化学特征区分C.chinense品种的强大工具的有效性和可靠性。这种方法不仅有助于对哈瓦那辣椒代谢组学多样性的科学理解,而且对食品科学具有潜在的意义。农业,和烹饪艺术。
公众号