Effect of konjac glucomannan on physical and sensory properties of noodles made from low-protein wheat flour

Effect of heat-moisture treatment of germinated brown rice (GBR) on the texture and cooking quality of the noodles containing mixtures of wheat and GBR flours was investigated. With the increase in GBR content, hardness and tensile strength of the composite noodles decreased and cooking loss and water absorption increased. Pasting viscosity of the flour mixtures was significantly decreased by increasing the amount of GBR. Heat-moisture treatment of GBR (17 or 20 g/100 g moisture at 100 °C for 4 h), however, apparently increased the pasting viscosity and improved the texture and cooking quality of the composite noodles. The mixture of wheat and a treated GBR (1:1 weight ratio) showed a pasting viscosity similar to pure wheat flour. The noodle containing the heat-moisture treated GBR flours showed the lower cooking loss, and higher hardness and tensile strength than the noodle containing untreated counterparts. Among the treated GBR flours tested, the GBR treated at 100 °C for 4 h at a moisture level of 17 g/100 g was the most acceptable in terms of cooking quality and textural property, close to those of the noodle of pure wheat flour. Substitution with the heat-moisture treated GBR, however, made the noodle darker because of thermal discoloration of GBR.

Effect of heat-moisture treatment for utilization of germinated brown rice in wheat noodle

The impact of protein cross-linking induced by alkali on the quality of buckwheat noodles.

Effects of multi-frequency ultrasound on physicochemical properties, structural characteristics of gluten protein and the quality of noodle.

Physiochemical properties of four types of sweet potato starch (SPS) and mung bean starch (MBS), and their blends for noodle production were accessed. The results indicated that MBS was significantly higher in amylose content (40.69%), gel hardness, hot paste stability and cold paste viscosity; but substantially lower in protein, lipid, ash content, and gel stickiness than those of all sweet potato starches. Among all sweet potato varieties, the white skin and yellow-red flesh color sweet potato variety (SP1_W_YR) was the most suitable for noodle production due to its the highest starch yield (17%) and starch purity as well as the best starch color. The MBS noodle quality was superior to SPS noodle quality, and blending SPS with MBS for noodle production resulted in markedly reducing quality. However, the quality of noodles made from mixture of 20% SPS and 80% MBS was not significant difference to that of MBS noodles. The increase of solid content of starch slurry resulted in considerable increasing in cooking time, cooking loss, rehydration and tensile of noodles while aging time only markedly affected cooking loss and tensile. For noodles prepared from mixture of 20% SPS and 80% MBS, the most suitable initial solid content of starch slurry and aging time at 4 o C were 35% and 10-20 hrs respectively.

Physiochemical Properties of Sweet Potato and Mung Bean Starch and Their Blends for Noodle Production

Quantitative assessment of protein fractions of Chinese wheat flours and their contribution to white salted noodle quality

To investigate the effect of aerated irrigation on the soil environment and yield in the root zone of maize, and to provide a basis for the extension of aerated irrigation, a 2-year experiment (2020–2021) was conducted at the Zhanjiang National Soil Quality Observation Experiment Station, with two experimental observations per year (spring-summer and fall-winter) to investigate the effects of aerated irrigation (AI) and non-aerated irrigation (CK) on soil respiration rate, soil temperature, water content, oxygen content, soil bacterial biomass and root biomass. We used partial least square regression analysis (PLSR) to establish the regression equations of soil respiration rate, soil temperature, water content, oxygen content, soil bacterial biomass and root biomass under the two treatments, and the screening of the main soil environmental factors affecting changes in soil respiration rate under aerated irrigation technology. The results showed that, compared with CK, the AI treatment significantly increased the soil respiration rate and soil oxygen content (15.38~17.87% and 18.94~25.17%, respectively), as well as the root biomass and soil bacterial biomass (14.99~19.09% and 35.10~45.59%, respectively), and reduced the soil water content by 5.33~12.71% (p < 0.05). The effects of different treatments on soil temperature were not significant. The mean fruit yield with AI treatment was also 7.16~20.51% higher (p < 0.05) than that with CK, and the stem thickness and leaf area of maize plants were significantly increased (9.31~17.06% and 8.68~15.20%, respectively (p < 0.05)). The regression fitting results showed that the soil respiration rate is quadratic polynomial negatively correlated with soil temperature, water content and soil oxygen, and the power function is positively correlated with root biomass and bacterial biomass under the two treatments. The variable importance for projection (VIP) values of the PLSR model showed a soil temperature VIP = 1.51, soil oxygen content VIP = 1.42 and root biomass VIP = 1.40, demonstrating that aerated irrigation technology can drive soil respiration rate by changing soil oxygen content and root biomass. Furthermore, the improvements in soil aeration conditions and respiration with AI appeared to facilitate the improvement in fruit yields, which also suggests the economic benefits of AI.

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The Effects of Aerated Irrigation on Soil Respiration and the Yield of the Maize Root Zone

The agriculture field is one of the most important fields where computational techniques play an imperative role for decision-making whether it is the automation of watering of plants, controlling of humidity levels, and detection of plant diseases and growth of plants. There are problems in the conventional methods where newer computational techniques and image processing methods are not used to keep track of growth of plants. The traditional image capturing and processing models have problems of large image segmentation error, excessive feature extraction time, and poor recognition output. In order to overcome the problems in the traditional plant growth methods based on image processing automations, computer image processing with computational method has been proposed to analyze the plant growth by utilizing state recognition method for corn and rice crops. An image acquisition platform is established on the basis of CMOS image sensor for crop image acquisition. The binary processing is performed, and then the images are segmented to reduce error of segmentation results in the traditional methods. To extract image features of corn and rice crops, convolution neural network (CNN) with newer architecture is used. According to contour information of images, the block wavelet transform method is used for feature adaptive matching. The binary tree structure is used to divide the growth period of corn and rice crops. The fuzzy mathematical model is also devised to identify the characteristics of crops in different growth periods and to complete the identification of growth state. Experimental results show that the proposed method effectively improves problems of traditional methods with better image recognition effect and reduces the time of feature recognition. The time to extract features by the proposed method is 1.4 seconds, whereas comparative methods such as random forest (RF) take 3.8 s and other traditional techniques take 4.9 s. Segmentation result error of the recognition method is also reduced significantly.

Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology

,e agriculture field is one of the most important fields where computational techniques play an imperative role for decisionmaking whether it is the automation of watering of plants, controlling of humidity levels, and detection of plant diseases and growth of plants. ,ere are problems in the conventional methods where newer computational techniques and image processing methods are not used to keep track of growth of plants. ,e traditional image capturing and processing models have problems of large image segmentation error, excessive feature extraction time, and poor recognition output. In order to overcome the problems in the traditional plant growth methods based on image processing automations, computer image processing with computational method has been proposed to analyze the plant growth by utilizing state recognition method for corn and rice crops. An image acquisition platform is established on the basis of CMOS image sensor for crop image acquisition. ,e binary processing is performed, and then the images are segmented to reduce error of segmentation results in the traditional methods. To extract image features of corn and rice crops, convolution neural network (CNN) with newer architecture is used. According to contour information of images, the block wavelet transform method is used for feature adaptive matching. ,e binary tree structure is used to divide the growth period of corn and rice crops. ,e fuzzy mathematical model is also devised to identify the characteristics of crops in different growth periods and to complete the identification of growth state. Experimental results show that the proposed method effectively improves problems of traditional methods with better image recognition effect and reduces the time of feature recognition.,e time to extract features by the proposed method is 1.4 seconds, whereas comparative methods such as random forest (RF) take 3.8 s and other traditional techniques take 4.9 s. Segmentation result error of the recognition method is also reduced significantly.

RecognitionMethodofCorn andRiceCropGrowth StateBased on Computer Image Processing Technology

Rhizoctonia solani AG1-IA (Thandfephorus cucumeris teleomorph) is the causative agent of rice sheath blight in Chinese paddy fields. Due to the importance of the disease and the lack of comprehensive information on the genetic structure of the fungus populations, 25 isolates collected from Hubei, Sichuan, Anhui, and Jiangsu provinces, and southern China's Yangtze River basin were examined for morphological characteristics, growth rate, and genetic diversity of this pathogen. The anastomosis group determination test results showed that all isolates belong to the AG1-IA anastomosis group. To quickly diagnose and confirm the anastomosis group of isolates, ten isolates along with AG1-IA and AGA standard isolates were examined by specific primers AG1-IA. A 256bp band was amplified in all of them. The results of the growth velocity study divided the isolates into two groups' fast growth (68% of isolates) and slow growth (32% of isolates). The genetic diversity of 25 isolates was assessed using the RAPD marker. Among the 20 primers, bands from seven primers ranging from 250 to 5,000bp were performed using the Jaccard similarity coefficient and UPGMA method by data cluster analysis of NTSYS-pc software. The cluster analysis results divided the isolates into two groups with a similarity level of 36%, which corresponded to the grouping of isolates into two groups of fast growth and slow growth. At the level of 80% similarity, the isolates were divided into 23 groups, which indicate the high genetic diversity of these isolates. The results of the molecular analysis showed that isolates belonging to a geographical area do not necessarily have a genetic affinity. This study performed rapid detection of R. solani AG1-IA using specific primers AG1-IA, and evaluation of genetic diversity of rice sheath blight isolates was performed by RAPD marker in this study.

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Chun Wang

Papers

Quantitative assessment of protein fractions of Chinese wheat flours and their contribution to white salted noodle quality

The Effects of Aerated Irrigation on Soil Respiration and the Yield of the Maize Root Zone

Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology

RecognitionMethodofCorn andRiceCropGrowth StateBased on Computer Image Processing Technology

Genetic diversity of rice sheath blight in different geographical regions of China.