Effects of Siraitia grosvenorii seed flour on the properties and quality of steamed bread

Siraitia grosvenorii seeds are rich in abundant active compounds beneficial to human health. To clarify the digestion characteristics of Siraitia grosvenorii seed flour (SSF) and promote the use of SSF in the processing of functional staple foods, SSF was prepared, its composition and physicochemical properties were studied, and the processing characteristics of SSF-wheat flour were systematically investigated. The results showed that the torque curve and other parameters of the dough were significantly affected by the amount of SSF added. With the increase of SSF proportion, the water absorption showed an increasing trend, while the degree of protein weakening first weakened and then enhanced. At 20% SSF, the dough was more resistant to kneading. In response to an increase in SSF, the L* value decreased significantly, and the a* and b* values increased gradually, while the specific volume decreased gradually. Additionally, the hardness, adhesiveness, and chewiness of the bread enhanced gradually, while its elasticity, cohesiveness, and resilience decreased gradually. After the addition of 30% SSF, the inner tissue of steamed bread was more delicate. With an increase in SSF proportion, the predicted glycemic index (pGI) of steamed bread weakened markedly. Overall, these results showed that SSF, as a kind of food ingredient with hypoglycemic activity, can be used in the production of new functional steamed bread products. This study provides basic research data for the development of products containing S. grosvenorii seed

Spatio-temporal changes in biomass carbon sinks in China's forests from 1977 to 2008

Forests play a leading role in regional and global carbon (C) cycles. Detailed assessment of the temporal and spatial changes in C sinks/sources of China's forests is critical to the estimation of the national C budget and can help to constitute sustainable forest management policies for climate change. In this study, we explored the spatio-temporal changes in forest biomass C stocks in China between 1977 and 2008, using six periods of the national forest inventory data. According to the definition of the forest inventory, China's forest was categorized into three groups: forest stand, economic forest, and bamboo forest. We estimated forest biomass C stocks for each inventory period by using continuous biomass expansion factor (BEF) method for forest stands, and the mean biomass density method for economic and bamboo forests. As a result, China's forests have accumulated biomass C (i.e., biomass C sink) of 1896 Tg (1 Tg=10(12) g) during the study period, with 1710, 108 and 78 Tg C in forest stands, and economic and bamboo forests, respectively. Annual forest biomass C sink was 70.2 Tg C a(-1), offsetting 7.8% of the contemporary fossil CO2 emissions in the country. The results also showed that planted forests have functioned as a persistent C sink, sequestrating 818 Tg C and accounting for 47.8% of total C sink in forest stands, and that the old-, mid- and young-aged forests have sequestrated 930, 391 and 388 Tg C from 1977 to 2008. Our results suggest that China's forests have a big potential as biomass C sink in the future because of its large area of planted forests with young-aged growth and low C density.BiologySCI(E)PubMed11ARTICLE7661-6715

The stage-classified matrix models project a significant increase in biomass carbon stocks in China's forests between 2005 and 2050

China's forests are characterized by young age, low carbon (C) density and a large plantation area, implying a high potential for increasing C sinks in the future. Using data of provincial forest area and biomass C density from China's forest inventories between 1994 and 2008 and the planned forest coverage of the country by 2050, we developed a stage-classified matrix model to predict biomass C stocks of China's forests from 2005 to 2050. The results showed that total forest biomass C stock would increase from 6.43 Pg C (1 Pg = 10(15) g) in 2005 to 9.97 Pg C (95% confidence interval: 8.98 - 11.07 Pg C) in 2050, with an overall net C gain of 78.8 Tg C yr(-1) (56.7 - 103.3 Tg C yr(-1); 1 Tg = 10(12) g). Our findings suggest that China's forests will be a large and persistent biomass C sink through 2050

The stage-classified matrix models project a significant increase in biomass carbon stocks in China’s forests between 2005 and 2050

China's forests are characterized by young age, low carbon (C) density and a large plantation area, implying a high potential for increasing C sinks in the future. Using data of provincial forest area and biomass C density from China's forest inventories between 1994 and 2008 and the planned forest coverage of the country by 2050, we developed a stage-classified matrix model to predict biomass C stocks of China's forests from 2005 to 2050. The results showed that total forest biomass C stock would increase from 6.43 Pg C (1 Pg = 10(15) g) in 2005 to 9.97 Pg C (95% confidence interval: 8.98 - 11.07 Pg C) in 2050, with an overall net C gain of 78.8 Tg C yr(-1) (56.7 - 103.3 Tg C yr(-1); 1 Tg = 10(12) g). Our findings suggest that China's forests will be a large and persistent biomass C sink through 2050.National Natural Science Foundation of China [31321061, 31330012]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA05050701]; State Forestry Administration of ChinaSCI(E)[email protected]

Estimation of biomass in wheat using random forest regression algorithm and remote sensing data

Wheat biomass can be estimated using appropriate spectral vegetation indices. However, the accuracy of estimation should be further improved for on-farm crop management. Previous studies focused on developing vegetation indices, however limited research exists on modeling algorithms. The emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling. The objectives of this study were to (1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass, (2) test the performance of the RF regression model, and (3) compare the performance of the RF algorithm with support vector regression (SVR) and artificial neural network (ANN) machine-learning algorithms for wheat biomass estimation. Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing, booting, and anthesis stages of growth. Fifteen vegetation indices were calculated based on these images. In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition. The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage, and its robustness is as good as SVR but better than ANN. The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China

Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth

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