酶法提高草莓出汁率的工艺优化

采用单一果胶酶处理草莓,通过正交试验优化酶解工艺,当加酶量0.04%、pH 4.0、酶解温度40℃、酶解时间60min时,草莓出汁率最高,为91.21%,比对照组(直接挤压榨汁)提高29.97%。采用复合酶(果胶酶、纤维素酶、木聚糖酶、β-葡聚糖酶)处理,草莓出汁率比对照组(直接挤压榨汁)提高30.94%,比单一果胶酶处理提高0.97%,复合酶解最佳条件为:果胶酶添加量0.04%、纤维素酶添加量0.03%、木聚糖酶添加量0.02%、β-葡聚糖酶添加量0.04%、酶解温度40℃p、H 4.0、酶解时间60 min。     

草莓灰霉病抗性遗传分析和分子标记初定位

灰霉病是草莓生产中普遍发生的病害之一,造成其严重减产.为了加快草莓灰霉病抗病分子标记辅助育种进程,本文以草莓灰霉病高感亲本达赛莱克特(89)与高抗亲本甜查理(103)杂交得到34株F1个体,经F1代自交得到的248株F2代群体为试验材料,进行草莓灰霉病抗性鉴定和遗传分析,探讨草莓灰霉病抗性的遗传规律,结合集群分离分析法...     

草莓根腐病病原鉴定及生物学特性研究

对吉林省几个草莓种植基地草莓根腐病菌进行研究,明确了引起吉林省草莓根腐病的病原物主要是尖孢镰刀菌(F.oxysporum)和立枯丝核菌(Rhizoctonia solani Kuhn).对不同培养条件下两种菌的生长量及产孢量进行测定,结果表明,尖孢镰刀菌在以蔗糖为碳源时生长最好,而立枯丝核菌生长的最适培养基碳源为淀粉尖...     

吉林地区草莓根腐病病原及生物学特性研究

通过对吉林地区几个草莓种植基地草莓根腐病菌的研究,明确吉林地区引起草莓根腐病的病原物主要有尖孢镰刀菌（F.oxysporum）、立枯丝核菌（Rhizoctonia solaniKuhn）,尖孢镰刀菌在以蔗糖为碳源的培养基上生长最好,而立枯丝核菌则在以淀粉为碳源的培养基上生长最好。并明确了该病原物对温度、湿度、光照及pH值的适应性。     

自然休眠期低温处理时间对生长期草莓叶片光合特性的影响

以草莓为料材,研究自然休眠期不同低温处理时间对其生长期叶片光合特性的影响.结果表明:在自然休眠过程中,随着低温处理时间延长,草莓新生叶叶面积、叶柄长度不断增加,而初生叶柄角度不断变小,完全通过休眠后,不再有显著变化.新生叶片的光合速率、气孔导度、叶绿素质量比和光合作用关键酶活性也随着低温处理时间延长而不断升高,当低温处理时间大于765 h后,不再有显著变化.自然休眠过程中,低温处理时间可通过调节气孔因素、叶绿素质量比和光合作用关键酶活性来调控生长期草莓叶片的光合作用,当完全通过休眠后,低温处理时间对草莓的生长发育和叶片的光合特性不再有显著影响.     

AdaBoost classifiers for pecan defect classification

One of the constraints in the adoption of machine vision inspection systems for food products is low classification accuracy. This study attempts to improve pecan defect classification accuracy by using machine learning classifiers: AdaBoost and support vector machine (SVM). X-ray images of good and defective pecans, 100 each, were segmented and features were extracted. Twenty classification runs were made to adjust parameters and 300 classification runs to compare classifiers. The Real AdaBoost classifier gave average classification accuracy of 92.2% for the Reverse water flow segmentation method and 92.3% for the Twice Otsu segmentation method. The Linear SVM classifier gave average classification accuracy of 90.1% for the Reverse water flow method and 92.7% for the Twice Otsu method. Computational time for the classifiers varied by two orders of magnitude: Bayesian (10⁻⁴ s), SVM (10⁻⁵ s), and AdaBoost (10⁻⁶ s). AdaBoost classifiers improved classification accuracy by 7% when Bayesian accuracy was poor (less than 89%). The AdaBoost classifiers also adapted well to data variability and segmentation methods. A minimalist AdaBoost classifier, more suitable for real time applications, using fewer features can be built. Overall, the selected AdaBoost classifiers improved classification accuracy, reduced classification time, and performed consistently better for pecan defect classification.     

塑料大棚内麻地膜覆盖草莓效应的初步研究

为明确麻地膜在大棚内覆盖栽培对作物的影响特点,在大棚和露地同时进行不同地膜覆盖草莓的比较试验。结果表明：麻地膜在大棚内覆盖的增温、保水效应比在露地大,能够提早草莓开花结果,显著提高产量。大棚和露地不同种植环境与不同覆盖处理间的互作效应极显著。麻地膜在大棚内的增产效果更好,比无覆盖增产12.8%,比塑料地膜增产1.73%。麻地膜比无覆盖在大棚内的增产幅度要比在露地多增产4.3%。     

Feature extraction and recognition technique for human pulse signals

In Chinese traditional medicine, the pulse signal plays an important role for diagnosis. To investigate the effective technique for identifying heroin druggers through the pulse signal, the feature extraction algorithm based on the biocepstrum and the third-order cepstrum entropy of the pulse signal is studied. On the basis of concise and rigorous discussion for the algorithm, the biocepstrum-based diagonal slice components are estimated for human pulse signals of 20 heroin druggers and 20 healthy normal subjects. The magnitude of the sample value of diagonal slice at m=n=1 and the third-order cepstrum entropy of magnitudes of sample values of the diagonal slice within a particular region are used as two feature parameters for every human pulse signal to form a feature vector. A classifier based on the criterion of squared Mahalanobis distance is successfully designed. Applying the designed classifier to 40 feature vectors, the correct identification rate reaches 87.5%. The research result shows that the method of the feature extraction and classifier design presented in is valuable for identifying the human pulse signals of heroin druggers.     

基于改进Faster R-CNN的苹果叶部病害识别方法

针对苹果叶片图像中小尺度病斑和复杂背景带来的病斑目标难以精确定位和识别的问题,以苹果的斑点落叶病、黑星病、灰斑病、雪松锈病和花叶病为研究对象,提出一种基于改进Faster R-CNN的苹果叶片病害识别方法.先通过数据增广操作对训练集数据进行扩充以增强模型鲁棒性,再通过对增广训练集图像进行训练来得到一个可靠的病害识别模型...     

Comparative study on the identification of structural modal parameter using different methods

Chongqing Chaotianmen Yangtze River Bridge is the world's longest-span arch bridge. The node connections on its main truss’s lower chords are to bear millions of large-amplitude stress cycles caused by automobile and light-rail train loads. However, there exists no referential designing and testing experience in such detailing on long-span steel bridges at home and abroad as yet. High-cycle fatigue behavior testing on a detailing model has been conducted to study the fatigue reliability of the bridge’s critical node connections. The testing cycling load on the model is determined according to conventional specifications and the expected traffic flow on the Bridge. The 1/2-scale detailing model is carefully fabricated to simulate the node prototype and undergo the 2 million-cycle testing with the design load. Through testing, fatigue reliability of Chaotianmen Yangtze River Bridge’s main-truss lower-chord node connections during the design service life is verified. In addition, static test data are compared with those calculated by finite element analysis to prove the validity of the node model. In the end, fatigue destruction test of the model was carried out to get the fatigue failure law of the node connection and provide necessary parameters and reference for designing and monitoring of this bridge in the future.