Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms.

Nitrogen plays an important role in rice growth, and determination of nitrogen content in rice plants is of great significance in assessing plant nutritional status and allowing precision cultivation. Traditional chemical methods for determining nitrogen content have the disadvantages of destructive sampling and lengthy analysis times. Here, the feasibility of rapid nitrogen content analysis by near-infrared (NIR) spectroscopy of rice plants was studied. Spectral data from 447 rice samples at several growth stages were used to establish a predictive model. Different spectral preprocessing methods and characteristic selection methods were compared, such as interval partial least-squares (iPLS), synergy interval partial least-squares (SiPLS), and moving-window partial least-squares (mwPLS). The SiPLS method exhibited better performance than mwPLS or iPLS. Specifically, the combination of four subintervals (7, 26, 27, and 28), with characteristic bands at 5299-4451 cm-1 and 10445-10423 cm-1, resulted in the best model. The optimal SiPLS model had a correlation coefficient of 0.9533 and a root mean square error of prediction (RMSEP) of 0.1952 on the prediction set. Compared to using the full spectra, using SiPLS reduced the number of characteristics by 87 % in the model, and RMSEP was reduced from 0.2284 to 0.1952. The results demonstrate that NIR spectroscopy combined with the SiPLS algorithm can be applied to quickly determine nitrogen content in rice plants. This study provides a technical framework to guide future precision agriculture efforts with respect to nitrogen application.

NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples.

Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of near-infrared spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. A total of 825 rice samples were collected and scanned by NIRS. The Kennard-Stone method was applied to divide the samples into calibration and validation sets. Before modeling, the spectrum was preprocessed using first derivation to reduce the baseline shift. Different chemometric tools such as interval partial least squares, moving window partial least squares, synergy interval partial least squares, and backward interval partial least squares were proposed to extract and optimize spectral interval from full-spectrum data. The performance of the calibration models generated on the basis of different regression algorithms was compared and evaluated. Results showed that the PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model. Among the tools, biPLS performed better with the optimal subinterval selection. The root-mean-square error of prediction and correlation coefficient (R) of the biPLS model were 0.2133 and 0.9020, respectively. In addition, the low root-mean-square error of cross-validation was obtained in biPLS, which was 0.1756. NIRS technology combined with biPLS could be considered as an effective and convenient tool for primary screening and measuring of cadmium content in rice. In comparison with classical methodologies, this new technology was beneficial because of its eco-friendliness, fast analysis, and virtually no sample preparation required.

Blockade of ankyrin repeat-rich membrane spanning protein modulates extracellular signal-regulated kinase expression and inhibits allergic inflammation in ovalbumin-sensitized mice.

Ankyrin repeat-rich membrane spanning protein (ARMS), also known as kinase D-interacting substrate of 220 kDa (Kidins220), is a transmembrane protein that has been reported to be involved in the pathogenesis of asthma through the nerve growth factor (NGF)/tyrosine kinase A (TrkA) receptor signaling pathway. To investigate whether NGF/TrkA-Kidins220/ARMS-extracellular signal-regulated kinase (ERK) signaling is activated in airway inflammation of asthma, BALB/c mice were sensitized and challenged with ovalbumin (OVA). The effects of Kidins220/ARMS on ERK, interleukin (IL)-1ß, IL-4 and tumor necrosis factor (TNF)-α in lung tissues following the allergic airway challenge in mice were assessed by administering anti-ARMS antibody to the mice. Pathological changes in the bronchi and lung tissues were examined via hematoxylin and eosin staining. The phosphorylated ERK, IL-1ß, IL-4 and TNF-α levels were determined using western blot analysis and ELISA and were found to be overexpressed in lung tissues following the allergen challenge. Moreover, after the mice were treated with anti-NGF, anti-TrkA or anti-ARMS, the levels of Kidins220/ARMS, phosphorylated ERK, IL-1ß, IL-4, TNF-α and allergen-induced airway inflammation were downregulated. These results suggested that NGF/TrkA-Kidins220/ARMS-ERK signaling was activated in airway inflammation induced by the allergic airway challenge, possibly representing a new mechanism in asthma.