The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO₂) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (2²) conditions considering two CO₂ levels and two soil-water availabilities. The ¹H NMR mixture design-fingerprinting effects of CO₂ and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO₂ factor had a significant effect on changing the ¹H NMR profile of fingerprints. The soil-water factor and interaction (CO₂ × soil-water) were not significant. ¹H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO₂, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO₂. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO₂ levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

由于各种（生物）化学反馈，面对大气中二氧化碳（CO ₂）浓度升高和土壤水可利用量减少的情况，阿拉伯咖啡树的代谢反应非常复杂。现代分析工具和农学科学的实验性进步倾向于对植物代谢复杂性的理解。在这项工作中，考虑到两个CO ₂水平和两个土壤水的利用率，在析因设计（2 ²）条件下，在自由空气二氧化碳富集沉积物中种植阿拉伯咖啡树。的¹ CO的1 H NMR混合物设计指纹效果₂使用主成分分析（PCA），方差分析（ANOVA）-同时成分分析（ASCA）和偏最小二乘判别分析（PLS-DA）从战略上调查了豆类的土壤水分和水位。从ASCA中，CO ₂因子对改变指纹的¹ H NMR谱具有显着影响。土壤水因子和相互作用（CO ₂  ×土壤-水）不显着。使用PCA，ASCA和PLS-DA分析的¹ H NMR指纹图谱确定了当前CO ₂中豆类中脂肪酸，咖啡因，松果碱和葡萄糖的光谱特征，而咖啡豆中奎宁酸/绿原酸，苹果酸和卡哇尔醇/卡夫甾醇的光谱特征来自CO升高₂。PLS-DA结果显示，在大气CO ₂的显着影响之间具有良好的分类性能不论土壤中的水分利用率如何，指纹图谱上的水平。最后，PLS-DA模型显示出良好的预测能力，成功地对植物垂直剖面上收集到的咖啡豆的验证数据集进行了分类，并包括不同提取溶剂的多个指纹。这项调查的结果表明，将实验设计，混合物设计，PCA，ASCA和PLS-DA结合起来，除了可以最大程度地减少额外工作之外，还可以提供有关气候变化引起的一系列代谢变化的准确信息，而这些代谢变化是具有商业意义的产品。是经典分析方法中必不可少的，这鼓励了类似策略的发展。