Rezazad Bari Laya, Ghanbari Alireza, Darvishzadeh Reza, Giglou Mousa Torabi, Baneh Hamed Doulati
Department of Horticultural Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Department of Horticultural Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Food Chem. 2021 Dec 15;365:130408. doi: 10.1016/j.foodchem.2021.130408. Epub 2021 Jun 19.
In the present study, first, the 19 parameters of 21 grapevine rootstocks under salinity were measured. Then chemometrics methods including principal component analysis (PCA) and quadratic discriminant analysis (QDA) were used to select the most significant and responsible characteristics for discrimination of grapevine rootstocks. For QDA, the 19 parameters were arranged in 4 sets. The first set includes total phenolic content, total flavonoid content, total anthocyanin content, and free radicals scavenging activity showed 88.10% correct classification. The second set (phenylalanine ammonia-lyase, superoxide dismutase, ascorbate peroxidase, and catalase activity) had 94.64% correct classification. Na, K, K/Na, electrolyte leakage, and malondialdehyde content parameters were in the third set and had 89.29% correct discrimination. The best discrimination was obtained by the fourth set, including total carbohydrate content, total protein content, proline, glycine-betaine, chlorophyll a, and chlorophyll b characteristics with 100% correct discrimination.
在本研究中,首先测量了21种葡萄砧木在盐胁迫下的19个参数。然后使用包括主成分分析(PCA)和二次判别分析(QDA)在内的化学计量学方法,来选择用于区分葡萄砧木的最显著且关键的特征。对于QDA,将19个参数分为4组。第一组包括总酚含量、总黄酮含量、总花青素含量和自由基清除活性,其正确分类率为88.10%。第二组(苯丙氨酸解氨酶、超氧化物歧化酶、抗坏血酸过氧化物酶和过氧化氢酶活性)的正确分类率为94.64%。钠、钾、钾/钠、电解质渗漏和丙二醛含量参数在第三组,其正确判别率为89.29%。第四组包括总碳水化合物含量、总蛋白质含量、脯氨酸、甘氨酸 - 甜菜碱、叶绿素a和叶绿素b特征,获得了最佳判别效果,正确判别率为100%。