Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
Department of Statistics, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
PLoS One. 2021 Jan 12;16(1):e0245298. doi: 10.1371/journal.pone.0245298. eCollection 2021.
Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing.
多种因素，如基因型、环境和采后处理，会影响咖啡生产链中重要性状的反应。确定这些因素的影响非常重要，因为它们可以作为所生产咖啡特征的指标。应选择最有效的模型，以考虑到各种信息和每种生物材料的特殊性。本研究旨在评估统计和机器学习模型，通过咖啡基因型的多性状更好地区分环境，并确定导致环境变化的主要农艺和饮料质量性状。为此，评估了 31 个形态农艺和采后性状，这些性状来自巴西米纳斯吉拉斯州马塔斯·德·米纳斯地区的三个城市的田间试验。评估了两种采后处理方法：自然处理和浆处理。估计了每种方法的明显错误率。与其他方法相比，多层感知器和径向基函数网络能够更有效地对多环境中的咖啡样品进行区分，根据生产地点和采后处理类型识别多性状反应的差异。地方因素没有表现出有利于疾病严重程度和区分营养生长的特定性状。感官性状酸度和香气/香味评分对区分过程的贡献也很小，表明酸度和香气/香味是所生产咖啡的特征，评价的所有咖啡样品均属于米纳斯吉拉斯州马塔斯地区的特殊类型。负责区分生产地点的主要性状是株高、果实大小和豆产量。感官性状“酒体”是区分采后处理形式的主要性状。
