Metabonomic and transcriptomic profiling reveals amino acid metabolism affects the quality of premium japonica rice varieties in Northeast China.

Rice consumption and demand for premium rice are increasing worldwide. However, the characterizations and how to identify the premium rice are still unclear. Small molecular metabolites have a great advantage in distinguishing subtle differences among similar agricultural products.

Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms.

The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (CI) were extracted from hyperspectral and RGB sensors. Combining Pearson-competitive adaptive reweighted sampling (CARs)-variance inflation factor (VIF) with four machine learning (ML) algorithms were used to model accuracy of PC and WGC.

Genetic dissection of QTLs for oil content in four maize DH populations.

Oil is one of the main components in maize kernels. Increasing the total oil content (TOC) is favorable to optimize feeding requirement by improving maize quality. To better understand the genetic basis of TOC, quantitative trait loci (QTL) in four double haploid (DH) populations were explored.

Genetic dissection of QTLs for starch content in four maize DH populations.

Starch is the principal carbohydrate source in maize kernels. Understanding the genetic basis of starch content (SC) benefits greatly in improving maize yield and optimizing end-use quality. Here, four double haploid (DH) populations were generated and were used to identify quantitative trait loci (QTLs) associated with SC.

MiR-340 regulates the growth and metabolism of renal cell carcinoma cells by targeting frizzled class receptor 3.

MicroRNA(miR)-340 is known as a multifunctional miRNA related to various types of cancer while its role in renal cell carcinoma (RCC) remains to be further investigated. In the present study, an apparent increase in miR-340 expression was observed in both clear cell RCC tissues and RCC cell line 786-O and Caki-1. Functionally, the overexpression of miR-340 promoted cell proliferation, migration, invasion, extracellular alanine (Ala) level, and glycolysis level in 786-O cells.

Physicochemical measurements of Japonica rice cultivars in Heilongjiang Province.

We here characterized 27 japonica rice cultivars grown in Heilongjiang province and evaluated the relationship among their iodine absorption curve, physical properties, and ratio of 13 kDa prolamin. We developed the novel estimation formulae for ratio of 13 kDa prolamin and overall hardness (H2) with the use of A and λmax.

Application of Visible/Near-Infrared Spectroscopy in the Prediction of Azodicarbonamide in Wheat Flour.

Unlabelled: Azodicarbonamide is wildly used in flour industry as a flour gluten fortifier in many countries, but it was proved by some researches to be dangerous or unhealthy for people and not suitable to be added in flour. Applying a rapid, convenient, and noninvasive technique in food analytical procedure for the safety inspection has become an urgent need. This paper used Vis/NIR reflectance spectroscopy analysis technology, which is based on the physical property analysis to predict the concentration of azodicarbonamide in flour.

[Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy].

Grain hardness is an important quality parameter of wheat which has great influence on the classification, usage and composition research of wheat. To achieve rapid and accurate detection of wheat hardness, radial basis function (RBF) neural network model was built to predict the hardness of unknown samples on the basis of analyzing the absorptive characteristics of the composition of wheat grain in infrared, besides, the effects of different spectral pretreatment methods on the predictive accuracy of models were emphatically analyzed. 111 wheat samples were collected from major wheat-producing areas in China; then, spectral data were obtained by scanning samples.