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Iron (Fe) is an essential element for plant growth, development and metabolism. Due to its lack of solubility and low bioavailability in soil, Fe levels are usually far below the optimum amount for most plants' growth and development. In apple production, excessive use of nitrogen fertilizer may cause iron chlorosis symptoms in the newly growing leaves, but the regulatory mechanisms underlying this phenomenon are unclear. In this study, low nitrate (NO3−, LN) application alleviated the symptoms of Fe deficiency and promoted lower rhizosphere pH, which was beneficial for root Fe acquisition. At the same time, LN treatment increased citrate and abscisic acid accumulation in roots, which promoted Fe transport from root to shoot and maintained Fe homeostasis. Moreover, qRT‐PCR analysis showed that nitrate application caused differential expression of genes related to Fe uptake and transport, as well as transcriptional regulators. In summary, our data reveal that low nitrate alleviated Fe deficiency through multiple pathways, demonstrating a new option for minimizing Fe deficiency by regulating the balance between nutrients. In this manuscript, we describe an extensive study of the effects of low nitrate (LN) on iron‐deficiency responses. LN treatment aids rhizosphere acidification and increases citrate and abscisic acid accumulation in the roots, thus promoting Fe acquisition and transport from root to shoot, ultimately alleviating Fe deficiency. [ABSTRACT FROM AUTHOR]

BACKGROUND: Non‐volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non‐volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non‐volatile compounds. RESULTS: Seventy‐two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high‐performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%–99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)‐catechin, (−)‐epigallocatechin gallate, theaflavin‐3‐gallate, theaflavin‐3,3′‐di‐gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross‐validation rate (88.9%) for KBT samples. CONCLUSION: The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head‐space solid phase microextraction approach and gas chromatography–mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS: Thirty‐one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k‐nearest neighbor (k‐NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross‐validation prediction abilities (84.6%) was obtained for the PCA–k‐NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION: The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]