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In order to purify Medicago sativa leaf protein effectively, foam fractionation which could increase both enrichment ratio and recovery of Medicago sativa leaf protein, was developed based on the research of the influences of initial pH value, the air flow rate, the loading liquid volume and the ratio of material to solvent. Then the best single factor was defined which was the loading liquid volume of 550 mL, pH value of 6.5, the ratio of material to solvent of 87.5 mg/L, the air flow rate of 250 mL/min. Based on the single factor experiment, Box-Behnken design was adopted to investigate the influence of four factors: pH value, the loading liquid volume, concentration and the air flow rate on recovery and enrichment ratio of leaf protein from Medicago sativa. Response surface and contour plots were finally graphed with the recovery and enrichment ratio as the response value. The foam fractionation of leaf protein from Medicago sativa was optimized by response surface methodology. The model P-value were both smaller than 0.0001 and the model were goodness-of-fit and the adequacy of the regression model. The optimum conditions for the foam fractionation of leaf protein from Medicago sativa were found to pH value of 6.92 , the ratio of material to solvent of 87.5 mg/L, the loading liquid volume of 600 mL, the air flow rate of 200 mL/min. Under the conditions, the recovery of Medicago sativa leaf protein was 92.2% and the enrichment ratio was 8.03. Considering actual process, The optimization processes were pH value of 7.0 , the ratio of material to solvent of 87.5 mg/L, the loading liquid volume of 600 mL, the air flow rate of 200 mL/min. The recovery of Medicago sativa leaf protein was 90.2% and the enrichment ratio was 7.64. respectively. The experimental results showed that foam fractionation was an effective method to separate leaf protein from Medicago sativa. [ABSTRACT FROM AUTHOR]

Laser speckle is a new technology for non-destructive detection in agriculture. When laser light with good coherence irradiates on the optically rough surface or the surface with some kind of activity, the scattering light will interfere with each other and form a mottled pattern called laser speckle. The laser speckle contains various information of the reflector, such as the roughness, the particle's motion and the temperature information. Huangguan pears were used as the object in this research to investigate the possibility of identifying the defects from the stem/calyx and the sound area of pears based on laser speckle technology. A laser speckle imaging system was established which contained a semiconductor laser (635 nm, 50 mw) applied as the light source and a digital signal generator applied as the trigger source of the CCD (charge coupled device) camera. Two hundred pears including one hundred sound pears and one hundred pears with the defect (rot) were tested. The speckle images of defect (rot) parts and good parts (calyx/stem, sound area) of the pears were collected under the same condition. First of all, the speckle images were converted into the grayscale images using Matlab 2011b. The method of Fujii and the weighted generalized differences method (WGD) were used to analyze the grayscale speckle images to get the images of Fujii and WGD. Then gray level co-occurrence matrix (GLCM) was used to extract the mean and the standard deviation values of the angular second moment (ASM), entropy (ENT), moment of inertia (INE) and correlation (COR) from the images of Fujii and WGD, respectively. Therefore, in total, 16 features were extracted. The performance of each feature was evaluated by the receiver operator characteristic (ROC) curve. According to the ROC curves, the features whose values of the area under the curves (AUC) were higher than 0.5 were chosen for further analysis. The best threshold value of each selected feature was calculated by Youden's index. The classification analysis based on single feature was tested using the best threshold value. Besides, the classification analysis based on multiple features was carried out by the binary logistic regression. The combination of every two features was tested to get better classification accuracy. The results showed that there were seven features whose AUC values were bigger than 0.5. In classification of single feature, the ASM extracted from WGD image had the best performance whose overall accuracies of calibration and validation sets were 96.4% and 96.7% respectively. In classification of multiple features, the combination of the ASM and standard deviation of correlation extracted from WGD image had the best performance whose overall accuracies of calibration and validation sets were both 97.5%. In order to find out the causes of the error, the original RGB (red, green and blue) images of the misjudged samples were studied. It turned out that the defect area which was misjudged to be normal was not obvious. Besides, the defect area was larger than the light spot whose diameter was 20 mm which could not cover the defect area completely. Therefore, the texture features of laser speckle images of misjudged samples were closer to the sound area than the defect area, which led to the miscalculation. This research shows that the method of laser speckle imaging is feasible in the detection of pear defect (rot) and stem/calyx. [ABSTRACT FROM AUTHOR]