[Detection of Adulteration in Milk Powder with Starch Near Infrared].

Three China trademarks of milk powder called Mengniu, Yili, Wandashan were taken as testing samples. Each of them mixed varied amount of starch in different gradient, which were consisted of 32 adulterated milk powder samples mixed with starch, was taken as standard samples for constructing predicted model. To those 32 samples, the reflecting spectrum characteristics in middle wave of near infrared spectrum with Near Infrared Spectrum Analyzer (Micro NIR 1700) produced by JDSU Ltd. USA were collected for five repeats in five different days. The time span was nearly two months. Firstly, we build the model used the reflecting spectrum characteristics of those samples with biomimetic pattern recognition (BPR) arithmetic to do the qualitative analysis. The analysis included the reliability of testing result and stability of the model. When we took ninety percent as the evaluation threshold of testing result of CAR (Correct Acceptance Rate) and CRR (Correct Rejection Rate), the lowest starch content of adulterate milk powder in all tested samples which the tested result were bigger than that abovementioned threshold was designated CAR threshold (CAR-T) and CRR threshold (CRR-T). CAR means the correct rate of accepting a sample which is belong to itself, CRR means correct rate of refusing to accept a sample which is not belong to itself. The results were shown that, when we constructed a model based on the near infrared spectrum data from each of three China trademark milk powders, respectively, if we constructed a model with infrared spectrum data tested in a same day, both the CAR-T and CRR-T of adulterate starch content of a sample can reach 0.1% in predicting the remainder infrared spectrum data tested within a same day. The three China trademarks of milk powder had the same result. In addition, when we ignored the trademarks, put the spectrum data of adulterate milk powder samples mixed with the same content of starch of three China trademarks milk powder together to construct a model, the CAR-T of mixed starch content of a sample may reach 0.1%, the CRR-T can reach 1%, if the model construction and predicting were performed with near infrared spectrum data tested in a same day. However, the CAR-T can just stably reach up to 5% and the CRR-T have the same result, if the model construction and predicting were crossly performed with mixed near infrared spectrum data tested in different days. Furthermore, the correct recognizing threshold mixed starch of a sample can stably reach up to 1% and the CAR-T can reach 5%, if the model construction was based on near infrared spectrum data combined the previous four days to predict the output of the another day. On the other hand, we also engaged quantitative analysis to the starch content in milk power with two kinds of arithmetic (PLSR, LS-SVR). In contrast with the testing outputs, the reliability of both the CAR-T and CRR-T in qualitative analysis was further validated.

Performance of hybrid progeny formed between genetically modified herbicide-tolerant soybean and its wild ancestor.

Gene flow from genetically modified (GM) crops to wild relatives might affect the evolutionary dynamics of weedy populations and result in the persistence of escaped genes. To examine the effects of this gene flow, the growth of F1 hybrids that were formed by pollinating wild soybean (Glycine soja) with glyphosate-tolerant GM soybean (G. max) or its non-GM counterpart was examined in a greenhouse. The wild soybean was collected from two geographical populations in China. The performance of the wild soybean and the F2 hybrids was further explored in a field trial. Performance was measured by several vegetative and reproductive growth parameters, including the vegetative growth period, pod number, seed number, above-ground biomass and 100-seed weight. The pod setting percentage was very low in the hybrid plants. Genetically modified hybrid F1 plants had a significantly longer period of vegetative growth, higher biomass and lower 100-seed weight than the non-GM ones. The 100-seed weight of both F1 and F2 hybrids was significantly higher than that of wild soybean in both the greenhouse and the field trial. No difference in plant growth was found between GM and non-GM F2 hybrids in the field trial. The herbicide-resistant gene appeared not to adversely affect the growth of introgressed wild soybeans, suggesting that the escaped transgene could persist in nature in the absence of herbicide use.

Seed coat microsculpturing is related to genomic components in wild Brassica juncea and Sinapis arvensis.

It has been reported that wild Brassica and related species are widely distributed across Xinjiang, China, and there has been an argument for species identification. Seed coat microsculpturing (SCM) is known to be an excellent character for taxonomic and evolutionary studies. By identifying collections from Xinjiang, China, and combining SCM pattern, flow cytometry, and genome-specific DNA markers as well as sexual compatibility with known species, this study aimed to detect potential relationships between SCM and genomic types in wild Brassica and related species. Three wild collections were found to be tetraploid with a SCM reticulate pattern similar to B. juncea, and containing A and B genome-specific loci, indicating relatively high sexual compatibility with B. juncea. The others were diploid, carrying S-genome-specific DNA markers, and having relatively high sexual compatibility with Sinapis arvensis. Moreover, their SCM was in a rugose pattern similar to that of S. arvensis. It was suggested that SCM, as a morphological characteristic, can reflect genomic type, and be used to distinguish B-genome species such as B. juncea from the related S. arvensis. The relationship between SCM and genomic type can support taxonomic studies of the wild Brassica species and related species.

[Identification and analysis of rice drought tolerance using near infrared diffuse reflection spectra of leaves].

In the present study, different drought tolerance rice from different countries and areas were selected and grown in water field and drought field respectively, including 4 traditional varieties of drought rice, 18 varieties of modified drought rice, 2 varieties of drought traits rice, 2 varieties of drought tolerance rice, and a total of 30 different varieties of drought tolerance rice were involved. Using near infrared diffuse reflection spectra of leaves from water field and drought field, we studied the rice drought tolerance identification analysis. Results showed that: using the average spectra of several leaves' spectra, selecting 4,500-7,500 cm(-1) as effective analysis spectra zone, choosing the first derivative and multiple scattering correction (MSC) as spectra preprocessing method, we can set up the calibration models between the spectra of leaves from drought field and the yield of rice. Simultaneously, we concluded that the performance of calibration model for rice yield and drought tolerance identification indexes in the upper booting stage was better than in the previous booting stage whose correlation coefficient of cross validation could reach 0.8. But there was no obvious relation between the spectra from water field and the yield, the drought tolerance identification indexes. We explained the difference in these two series models' performance from the relationship between some parameter of the leaves' biochemistry (chlorophyll, moisture, etc) and yield, the drought tolerance identification indexes.