[Influence of different slope position and profile in Disporopsis pernyi forest land on soil microbial biomass and enzyme activity in southwest Karst mountain of China ].

Soil microbial biomass and enzyme activity are important parameters to evaluate the quality of the soil environment. The goal of this study was to determine the influence of different slope position and section in Disporopsis pernyi forest land on the soil microbial biomass and enzyme activity in southwest Karst Mountain. In this study, we chose the Dip forest land at Yunfo village Chengdong town Liangping country Chongqing Province as the study object, to analyze the influence of three different slope positions [Up Slope(US), Middle Slope(MS), Below Slope(BS)] and two different sections-upper layer(0-15 cm) and bottom layer(15-30 cm) on the soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), microbial carbon entropy (qMBC), microbial nitrogen entropy (qMBN) , catalase(CAT), alkaline phosphatase (ALK), urease(URE), and invertase(INV). The results showed that the same trend (BS > MS > US) was found for SMBC, SMBN, qMBC, qMBN, CAT and INV of upper soil layer, while a different trend (BS > US > MS) was observed for ALK. In addition, another trend (MS > US > BS) was observed for URE. The same trend (BS > MS >US) was observed for SMBN, qMBN, CAT, ALK, URE and INV in bottom layer, but a different trend (MS > BS > US) was observed for SMBC and qMBC. The SMBC, SMBN, CAT, ALK, URE and INV manifested as upper > bottom with reduction of the section, while qMBC and qMBN showed the opposite trend. Correlation analysis indicated that there were significant (P <0.05) or highly significant (P < 0.01) positive correlations among SMBC in different slope position and section, soil enzyme activity and moisture. According to the two equations of regression analysis, SMBC tended to increase with the increasing CAT and ALK, while decreased with the increasing pH. Then SMBN tended to increase with the increasing URE and INV.

[Study on the multicomponent quantitative analysis using near infrared spectroscopy based on building elman model].

The present paper introduces an application of near infrared spectroscopy (NIRS) multi-component quantitative analysis by building a kind of recurrent network (Elman) model. Elman prediction model for phenylalanine (Phe), lysine (Lys), tyrosine (Tyr) and cystine (Cys) in 45 feedstuff samples was established with good veracity. Twelve peak value data from 3 principal components straight forward compressed from the original data by PLS were taken as inputs of Elman, while 4 predictive targets as outputs. Forty seven nerve cells were taken as hidden nodes with the lowest error compared with taking 43 and 45 nerve cells. Its training iteration times was supposed to be 1000. Predictive correlation coefficients by the model are 0.960, 0.981, 0.979 and 0.952. The results show that Elman using in NIRS is a rapid, effective means for measuring Phe, Lys, Tyr and Cys in feedstuff powder, and can also be used in quantitative analysis of other samples.

[Determination of four contents of feedstuff powder using near infrared spectroscopy by PLS-BP model].

Partial least squares (PLS) and artificial neural networks (ANN) prediction model for four components of feedstuff has been established with good veracity and recurrence. The spectra put into the model should be processed by second derivative and standard normal variate (SNV). Ten principal components compressed from original data by PLS and two peak values were taken as the inputs of Back-Propagation Network (BP), while four predictive targets as outputs, according to Kolmogorov theorem and experiment, and twenty three nerve cells were taken as hidden nodes. Its training iteration times was supposed to be 10,000. Prediction deciding coefficient of four components by the model are 0.9950, 0.9980, 0.9990 and 0.9670, while the standard deviation of an unknown sample scanned parallelly are 0.02774, 0.04853, 0.03292 and 0.02204.

[Multicomponent quantitative analysis using near infrared spectroscopy by building PLS-GRNN model].

The present paper introduces an application of near infrared spectroscopy(NIRS) multi-component quantitative analysis by building partial least squares (PLS)-generalized regression neural networks (GRNN) model. The PLS-GRNN prediction model for chlorine, fibre and fat in 45 feedstuff samples was established with good veracity and recurrence. Eight peak values in principal components compressed from original data by PLS and four in original spectra were taken as inputs of GRNN while 4 predictive targets as outputs. 0.1 was chosen as smoothing factor for its good approximation and prediction with the lowest error compared with 0.2, 0.3, 0.4 and 0.5. Predictive correlation coefficient and Standard error of the estimate of three components by the model are 0.984 0, 0.987 0 and 0.983 0, and 0.015 89, 0.154 1 and 0.115 1, while the Standard deviations of an unknown sample scanned 8 times are 0.003 26, 0.065 5 and 0.031 4. The results show that PLS-GRNN used in NIRS is a rapid, effective means for measuring chlorine, fibre in the fat in feedstuff powder, and can also be used in quantitative analysis of other samples. A settlement in the high error of prediction of other samples with lower contents was also shown.