Near-infrared reflectance spectroscopy was used to address the genetic potential for 990 rice lines for 26 quality traits. The predicted genotypic values for quality traits were calculated using the Mahalanobis distance method and used to measure the genetic similarities among rice varieties. To make the core collection, Manhalanobis distance was employed to calculate the genetic distance among the accessions, and the hierarchical clustering method was used to group the accessions, accompanied by sampling method under the pre-concerted sample proportion (i.e., the ratio of accessions in the core collection to those in the initial population). In this experiment, 24 core collections were developed by using eight hierarchical clustering methods, combined with random, preferred and deviation sampling at a sample proportion of 15% (i.e., 149 for the 15% core collection). These core collections were compared with others constructed at sample proportions of 10% and 20% (i.e., 99 for the 10% core and 198 for the 20% core, respectively). In addition, a trend at increasing sample proportion from 5% to 60% showed that core collection development could be achieved at a sample proportion range of 10–25%. Further results revealed that deviation sampling strategy in combination with the single linkage method retained the greatest degree of genetic diversity of the initial collection. The core collection developed using a sample size of 15% retained the highest degree of diversity, and was stable for all the cluster methods and, hence, the best in developing a core collection of rice quality traits.

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid method to estimate the cooking characteristics of rice. A total of 586 samples from rice breeding lines from 1999 to 2002, which had high variation for agronomy, location and year, were scanned by NIRS for calibration optimization by chemometric methods. Two subsets of 212 samples from one year and 400 samples from three years were employed to find suitable sample status for extending the NIRS utilities. There were three algorithms of PCA, PL1 and PL2, in which the first one was only based on the sample spectra variant and the remaining two based on the variant of both spectra and chemical characteristics to describe the relationship between any two neighboring samples. According to the results of calibration and validation by the three algorithms used, the suitable calibration samples could be chosen by the cutoff of neighborhood distance (NH) of 0.35, 0.4 and 0.45, respectively. For the cooking characteristics, the combination of SNV+D/4,4,1 was the best pretreatment and the accuracy models were obtained with low SECV and high 1 VRof amylose content (1.42% and 0.95%), gel consistency (9.49 and 0.76 mm) and alkali spread value (0.86 and 0.79 grade) . The models developed using brown rice and milled rice were superior to those using intact rice grains, but slightly poorer to those using their corresponding flour samples. Therefore, on-line monitoring of rice quality could be conducted in rice processing at milling stages. Due to the fewer sample mass destroyed, the brown rice (3 g) and brown rice flour (3g or 0.5g), by which the NIRS models were successfully developed for cooking characteristic analyses, could be introduced into quality evaluation of germplasm and intermediate lines selection in breeding projects.

The genetic effects including genetic main and genotype

on heterosis and cytoplasm interaction heterosis were more important for protein content，but endosperm heterosis was only important for lysine content in GE interaction heterosis. It was shown that some indiea hybrid crosses had significant positive heterosis for protein content. Negative heterosis for lysine content was observed in most hybrid crosses.

The relationships among potassium (K), calcium (Ca), sodium (Na), magnesium (Mg), iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn) contents in milled rice (Oryza stavia L.) of 274 genotypes and the relationships between these mineral element contents and other rice quality traits including 3 cooking quality traits, 17 amino acid contents, and protein content were investigated. The results showed that there were significant correlations among most of mineral element contents. Mg, Fe, and Mn contents were significantly correlated with most of the other mineral element contents, while Cu content had significantly negative associations with the K and Mg contents of rice. The relationships between mineral element contents and cooking quality traits showed that gel consistency (GC) was significantly correlated with K, Cu, and Mn contents of rice. Amylose content (AC) was significantly associated with K, Na, Mg, Cu, and Mn contents. The alkali spreading value (ASV) had closely positive relationships with Ca, Mg, and Mn contents. In addition, 8 mineral element contents had obvious correlations with different amino acid contents. Mg, Ca, and Zn contents were significantly correlated with most of the 17 amino acid contents, but Na content did not correlate with amino acid contents except aspartic acid of rice. Furthermore, significant associations were found between protein content and Na, Mg, Zn, Cu, or Mn content. Six principal components were extracted to explain 84.50% of the total variances and contained the information provided by the original 29 variables according to the principal component analysis.