基于可见光光谱分析的黄瓜白粉病识别研究

白粉病是黄瓜常见病害之一,传播速度极快,严重时可造成黄瓜大量减产,对其进行快速准确识别,对黄瓜白粉病诊断和防治具有重要意义,应用可见光谱技术,结合主成分分析和支持向量机算法,实现对黄瓜白粉病的快速识别。配制白粉病菌孢子悬浮液,并人工接种于科研温室内的黄瓜叶片上,以诱发黄瓜白粉病,待白粉病有一定面积暴发后,利用海洋光学USB2000+型便携式光谱仪对黄瓜叶片光谱信息进行采集,利用五点取样法采集样本,在5个检查点,每点选取2株黄瓜进行调查,每株选取4枚感病叶片,每枚叶片随机选取5个感病区域进行光谱采集,共计采集200个感病叶片光谱样本,同样采集200个健康叶片样本作为对照。通过Ocean Optics Spectra-Suite软件采集漫反射标准白板信息和光谱仪暗电流实现光谱仪校正,调节积分时间、扫描次数以及平滑度等参数来实现光谱曲线平滑处理,以有效抑制光谱噪声,对光谱特征进行分类识别,去掉首尾噪声较大的波段,保留光谱的可见光波段进行研究,最终选取450~780 nm波段范围作为研究对象。利用主成分分析对所研究波段范围内的高维光谱数据（947维）进行降维处理,根据主成分的累计贡献率,选取前5个主成分作为分类模型的输入,以白粉病和健康叶片的判别结果作为输出,利用支持向量机算法,通过对样本的分类学习训练构建黄瓜白粉病和健康叶片的分类识别模型,随机选取120个样本作为训练集用于分类模型构建,其余80个样本作为测试集用于模型检验,并通过选取不同的核函数来获得最优模型。利用混淆矩阵对分类识别模型的准确率进行评价,当选取径向基核函数时,分类识别模型对黄瓜健康叶片和白粉病叶片的识别准确率最高,分别为100%和96.25%,总准确率为98.125%,具有较高的准确率。结果表明,利用可见光光谱信息并结合主成分分析和支持向量机算法,可以实现对黄瓜白粉病的快速准确识别,为黄瓜病害诊断提供了方法和参考依据。     

Direct screening of herbal blends for new synthetic cannabinoids by MALDI-TOF MS

Since 2004, a number of herbal blends containing different synthetic compounds mimicking the pharmacological activity of cannabinoids and displaying a high toxicological potential have appeared in the market. Their availability is mainly based on the so-called "e-commerce", being sold as legal alternatives to cannabis and cannabis derivatives. Although highly selective, sensitive, accurate, and quantitative methods based on GC-MS and LC-MS are available, they lack simplicity, rapidity, versatility and throughput, which are required for product monitoring. In this context, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) offers a simple and rapid operation with high throughput. Thus, the aim of the present work was to develop a MALDI-TOF MS method for the rapid qualitative direct analysis of herbal blend preparations for synthetic cannabinoids to be used as front screening of confiscated clandestine preparations. The sample preparation was limited to herbal blend leaves finely grinding in a mortar and loading onto the MALDI plate followed by addition of 2 μl of the matrix/surfactant mixture [α-cyano-4-hydroxy-cinnamic acid/cetyltrimethylammonium bromide (CTAB)]. After drying, the sample plate was introduced into the ion source for analysis. MALDI-TOF conditions were as follows: mass spectra were analyzed in the range m/z 150-550 by averaging the data from 50 laser shots and using an accelerating voltage of 20?kV. The described method was successfully applied to the screening of 31 commercial herbal blends, previously analyzed by GC-MS. Among the samples analyzed, 21 contained synthetic cannabinoids (namely JWH-018, JWH-073, JWH-081, JWH-250, JWH-210, JWH-019, and AM-694). All the results were in agreement with GC-MS, which was used as the reference technique.     

基于高光谱成像的南瓜叶片叶绿素分布可视化研究

叶绿素浓度是植物生长的指示剂,而叶片的SPAD值则可以反映植物叶绿素含量,从而监测植物的生长状况。本文采用可见-近红外(380～1 030 nm)高光谱成像技术可以实现南瓜叶片SPAD值的可视化,同时根据叶片霜霉病疫情与叶绿素含量呈显著正相关进而可以快速诊断霜霉病疫情。通过测定健康叶片和感染不同霜霉病疫情的叶片光谱曲线,采用竞争性自适应重加权算法(CARS)进行特征波段的选择,可以得到10条特征波段,再结合偏最小二乘回归法(PLSR)进行南瓜叶片SPAD的预测。结果表明,通过对48个样本的训练,对23个样本进行预测,可以得到南瓜叶片SPAD较好的预测效果,其中R_C=0.918,RMSECV=3.932; R_CV=0.846,RMSECV=5.254; R_P=0.881,RMSEP=3.714。根据叶片光谱特征波段与SPAD之间的线性回归方程可以计算叶片各个像素点的SPAD值,最后采用图像处理技术可以得到南瓜叶片SPAD的可视化分布图,同时也反映了霜霉病的感染分布,进而判断南瓜叶片的霜霉病疫情。该研究为监测植物生长状况及判别南瓜叶片霜霉病疫情奠定了理论基础。     

可见光谱图像联合区间的黄瓜白粉病分割与检测

黄瓜白粉病是黄瓜病害中爆发频率较高的一种,传播速度极快,常常导致产量降低,无法获得预期的经济效益。特别是在病害爆发严重的年份,黄瓜减产量高达20%。提出了一种基于可见光谱图像联合区间的偏最小二乘回归判别模型（SI-PLSR）,用于黄瓜白粉病无损检测。采用Canon EOS 800D和Ocean Optics USB2000+光纤光谱仪采集了200个黄瓜白粉病感病叶片的可见光谱图像和反射率曲线。首先,采用基于小波降噪和H分割的分水岭分割算法从实时采集的黄瓜白粉病感病叶片可见光谱图像中提取目标叶片;其次,通过高斯拟合优化的Otsu算法分割目标叶片的可见光谱图像,获取白粉病病斑;然后,对350~1 100 nm全波段光谱反射率曲线建立偏最小二乘回归模型并计算交叉验证均方根误差RMSE_CV,同时将全波段等分为20个子区间,分别建立偏最小二乘回归模型,选取RMSE_CV小于全波段反射率曲线建模RMSE_CV的子区间组成联合区间;最后,将光谱联合区间与白粉病病斑分割结果融合建立SI-PLSR模型。从实验结果可知,感病目标叶片的提取成功率高达94.00%,200幅感病叶片可见光谱图像中成功提取188幅,其中157幅目标叶片的完整性参数高于95%,31幅目标叶片完整性参数在90%~95%之间。188幅目标叶片的病斑分割结果显示,平均错分率为5.81%,其中平均False negative为1.55%,平均False positive为4.26%。对20个子区间分别建立偏最小二乘回归模型发现,第5,6,7,11,12,13和19子区间的RMSE_CV值小于全波段光谱反射率曲线建模的RMSE_CV值,说明这7个子区间的光谱信息对白粉病的判别有较大的贡献,这与呈现波峰的470~520,530~580和700~780 nm波段相对应,因此选取这7个子区间的光谱反射率曲线建立联合区间。对联合区间建立SI-PLSR模型,其主成分数为7,校正集和验证集的相关系数和标准误差分别是0.975 2,0.907 3和0.919 5,1.091。与全波段PLSR模型相比,SI-PLSR的相关系数更接近于1,且标准误差更小。结果表明,所提出的SI-PLSR模型有效去除了可见光谱数据中冗余信息,加强了模型的稳定性,可以实现对黄瓜白粉病的快速无损准确识别,为黄瓜病害诊断提供了方法和参考依据。     

可见光光谱和机器学习的温室黄瓜霜霉病严重度定量估算

温室黄瓜霜霉病严重度的准确估算是科学防治霜霉病的前提条件,对于减少农药使用量、提升温室黄瓜品质和农民经济效益具有重要意义。机器学习在植物病害诊断领域的应用越来越广泛,已经取得了丰富的研究成果,病害严重程度的估算萌发了新的思路。利用霜霉病可见光图像并结合机器学习方法,开展温室黄瓜霜霉病严重度快速准确定量估算研究。利用数码相机采集温室黄瓜霜霉病叶片图像并进行预处理,剔除病害图像的背景。以黄瓜霜霉病叶片图像为输入,构建基于卷积神经网络(CNN)的估算模型。利用可见光光谱颜色特征(CVCF)结合支持向量机进行温室黄瓜霜霉病病斑图像分割,采用SURF(speeded up robust features)特征及形态学操作对分割结果进行优化。在获取黄瓜霜霉病病斑分割图像后,提取病斑图像RGB, HSV, L~*a~*b~*, YCbCr和HSI共5个颜色空间15个颜色分量的平均值和标准差2个颜色特征,以及在此基础上结合灰度共生矩阵提取各颜色分量的对比度、相关性、熵和平稳度4个纹理特征,共计90个特征;利用Pearson相关性分析进行特征优选,采用与温室黄瓜霜霉病严重程度实际值相关性高的图像特征构建浅层机器学习估算模型,包括支持基于向量机回归(SVR)的估算模型和基于BP神经网络(BPNN)的估算模型。基于以上3种估算模型开展黄瓜霜霉病严重度定量估算,采用决定系数(R^2)和归一化均方根误差(NRMSE)对估算模型准确率进行定量评价。结果表明,模型估算的温室黄瓜霜霉病严重度与实际值之间具有良好的线性关系,其中,基于CNN的估算模型准确率最高,模型的R^2为0.919 0, NRMSE为23.33%,其次是基于BPNN的估算模型,其R^2为0.890 8, NRMSE为24.64%,基于SVR的估算模型的准确率最低,其R^2为0.8901, NRMSE为31.08%。研究结果表明,利用黄瓜霜霉病可见光图像数据,结合卷积神经网络估算模型,实现了温室黄瓜霜霉病严重度的准确估算,能够为温室黄瓜霜霉病的科学防治提供参考,提高病害防治效率,减少农药使用。     

Molecular distribution of some commercial nonylphenol ethoxylates using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Nonylphenol ethoxylates (NPEs) belong to a group of nonionic surfactants that are collectively referred to as alkylphenol ethoxylates (APEs). APEs find widespread use in heavy-duty commercial and household cleaning formulations, shampoos, and industrial processing, i.e. textile manufacture. Their environmental impact depends on the molecular distribution and the extent of their biodegradation in municipal sewage systems, waterways and rivers. We have established two sample preparation methods that have enabled the determination of the molecular distributions of six commercial nonylphenol ethoxylates using matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS). In both methods, alpha-cyano-4-hydroxycinnamic acid, dissolved in acetonitrile/tetrahydrofuran, was used as the matrix. In one set of experiments, the NPEs were dissolved in an acetonitrile/tetrahydrofuran solvent system prior to mixing with the matrix solution, and the resulting MALDI-TOF mass spectra produced mostly sodiated molecules [M + Na](+). The NPEs, all having the formula 4-(C(9)H(19))-C(6)H(4)-(OCH(2)CH(2))(n)-OH, are Surfonic (R)N-95, N-100, N-102, N-120, N-150 and N-300. Surfonic N-95 and N-100 gave n values of 5-20; Surfonic N-102, N-120, N-150, and N-300 gave n values of 5-21, 5-22, 8-25 and 15-40, respectively. In order to develop a sample preparation method that could be used with less polar NPEs, we dissolved the NPEs (except N-300) in pentane prior to mixing with the matrix solution, and found that the MALDI spectral quality was unaffected by the solvent systems. Copyright 1999 John Wiley & Sons, Ltd.     

基于叶绿素荧光光谱指数的温室黄瓜病害预测

温室蔬菜病害的发生及大面积流行严重影响设施农业的生产管理, 大大降低设施农业的经济效益。为了实现温室蔬菜病害的无损准确预测, 以黄瓜霜霉病害为例, 利用激光诱导叶绿素荧光构建光谱特征指数, 建立了温室蔬菜病害的预测模型。在试验中采用对比分析的方法, 通过对作物健康叶片接种病菌孢子, 分别采集健康、接种2 d、接种6 d和出现明显病症共4组试验样本的光谱曲线, 定性分析了荧光强度随叶片样本感染病菌孢子的变化规律;利用光谱曲线不同波段峰谷值创建了叶绿素荧光光谱指数k₁=F₆₈₅/F₅₁₂和k₂=F₇₃₄/F₅₁₂, 根据数值的变化范围, 设定k₁和k₂分别为20和10时可以作为判断样本出现明显病症与未出现明显病症的特征值, 其判断的准确率分别达到96%和94%;利用构建的光谱指数与样本健康状况的分类结果, 选择光谱指数F₆₈₅/F₅₁₂,F₆₈₅-F₇₃₄,F₇₁₅/F₆₁₂可以定性判断样本健康状况, 并选择光谱指数F₆₈₅/F₅₁₂,F₇₃₄/F₅₁₂,F₆₈₅-F₇₃₄,F₇₁₅/F₆₁₂作为建立定量分析模型的输入量, 以预测集分类准确率作为评价标准, 对比判别分析、BP神经网络、支持向量机三种数据建模方法, 结果表明支持向量机作为霜霉病害预测的建模方法, 其预测能力达到91.38%。利用激光诱导叶绿素荧光构建光谱指数方法, 研究植物病害的预测问题, 具有很好的分类和鉴别效果。     

On-target separation of analyte with 3-aminoquinoline/α-cyano-4-hydroxycinnamic acid liquid matrix for matrix-assisted laser desorption/ionization mass spectrometry

3-Aminoquinoline/α-cyano-4-hydroxycinnamic acid (3AQ/CHCA) is a liquid matrix (LM), which was reported by Kumar et al. in 1996 for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. It is a viscous liquid and has some advantages of durability of ion generation by a self-healing surface and quantitative performance. In this study, we found a novel aspect of 3AQ/CHCA as a MALDI matrix, which converges hydrophilic material into the center of the droplet of analyte-3AQ/CHCA mixture on a MALDI sample target well during the process of evaporation of water derived from analyte solvent. This feature made it possible to separate not only the buffer components, but also the peptides and oligosaccharides from one another within 3AQ/CHCA. The MALDI imaging analyses of the analyte-3AQ/CHCA droplet indicated that the oligosaccharides and the peptides were distributed in the center and in the whole area around the center of 3AQ/CHCA, respectively. This 'on-target separation' effect was also applicable to glycoprotein digests such as ribonuclease B. These features of 3AQ/CHCA liquid matrix eliminate the requirement for pretreatment, and reduce sample handling losses thus resulting in the improvement of throughput and sensitivity.     

Analysis of cancer cell lipids using matrix-assisted laser desorption/ionization 15-T Fourier transform ion cyclotron resonance mass spectrometry

A combination of methodologies using the extremely high mass accuracy and resolution of 15-T Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) was introduced for the identification of intact cancer cell phospholipids. Lipids from a malignant glioma cell line were initially analyzed at a resolution of >200,000 and identified by setting the mass tolerance to ±1 mDa using matrix-assisted laser desorption/ionization (MALDI) 15-T FT-ICR MS in positive ion mode. In most cases, a database search of potential lipid candidates using the exact masses of the lipids yielded only one possible chemical composition. Extremely high mass accuracy (<0.1?ppm) was then attained by using previously identified lipids as internal standards. This, combined with an extremely high resolution (>800,000), yielded well-resolved isotopic fine structures allowing for the identification of lipids by MALDI 15-T FT-ICR MS without using tandem mass spectrometric (MS/MS) analysis. Using this method, a total of 38 unique lipids were successfully identified.     

基于可见光谱和支持向量机的黄瓜叶部病害识别方法研究

以黄瓜叶部病害作为研究对象,基于可见光谱反射率差异识别黄瓜叶部病害,研究基于SVM的黄瓜叶部病害识别预测模型。采用小波变换进行数据预处理;选取Otsu、边缘分割法和K均值聚类三类分割方法进行病斑分割,比较错分率和运行时间,K均值聚类方法更适合黄瓜叶部病斑分割;提取纹理、颜色和形状特征参数,共15个特征参数;通过交叉验证选择最优参数c和g,对核函数参数进行优化处理,并通过比较线性核、多项式核、RBF核等不同核函数情况下SVM的正确识别率,确定RBF核SVM模式识别方法能够更精准地识别黄瓜叶部病害。并将基于SVM与另外两种常见的黄瓜叶部病害识别方法,BP神经网络和模糊聚类进行比较,结果表明,基于SVM的识别模型对霜霉病的正确识别率为95%,白粉病和褐斑病的正确识别率均为90%,平均诊断正确率为92%;该模式识别方法识别效果最佳,运行时间最短,为基于可见光谱的黄瓜病害识别模型提供参考。     

Mass Spectrometry,Review of the Basics: Ionization

Abstract: Mass spectrometry (MS) has become an integral tool in life sciences. The first step in MS analysis is ion formation (ionization). Many ionization methods currently exist; electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) are the most commonly used. ESI relies on the formation of charged droplets releasing ions from the surface (ion evaporation model) or via complete solvent evaporation (charge residual model). MALDI ionization, however, is facilitated via laser energy and the use of a matrix. Despite wide use, ESI cannot efficiently ionize nonpolar compounds. Atmospheric pressure chemical ionization (APCI) and atmospheric pressure photo ionization (APPI) are better suited for such tasks. APPI requires photon energy and a dopant, whereas APCI is similar to chemical ionization. In 2004, ambient MS was introduced in which ionization occurs at the sample in its native form. Desorption electrospray ionization (DESI) and direct analysis in real time (DART) are the most widely used methods. In this mini-review, we provide an overview of the main ionization methods and the mechanisms of ion formation. This article is educational and intended for students/researchers who are not very familiar with MS and would like to learn the basics; it is not for MS experts.     

基于高光谱成像的小麦白粉病与条锈病识别

小麦白粉病和条锈病是我国两种最普遍、最具破坏性的小麦病害,且田间常常混合发生。由于病源和发病机理不同,有必要对这两种病害进行准确区分和识别,以采取不同的防治措施。基于ImSpector V10E高光谱成像系统采集的条锈菌侵染叶片、白粉菌侵染叶片和健康叶片(共计320个)在375～1 017 nm范围内的高光谱图像,利用高斯平滑等预处理方法得到三种小麦叶片的平均光谱曲线,发现小麦白粉病和条锈病的敏感波段均集中在550～680 nm的色素强吸收位置,且趋势基本一致。针对两种病害的响应波段交叉重叠的问题,通过主成分分析-载荷法(PCA)、连续投影算法(SPA)和竞争性自适应重加权算法(CARS)对小麦叶片的光谱信息进行有效降维,分别优选出3、6、30个敏感波段和特征波长;在此基础上,采用最小二乘-支持向量机和极限学习机两种分类算法分别基于全波段、PCA、SPA和CARS的优选波段,建立白粉病、条锈病和健康叶片的判别模型。结果表明,8种模型的准确识别率均在94.58%以上。其中,主成分分析-载荷法结合极限学习机模型最优,训练集与校正集的正确识别率分别为99.18%和100%,且结构简单,仅含有三个变量(占全波段的1.1%)。最后,通过对小麦白粉病、条锈病以及健康叶片的显微结构分析,发现病菌入侵叶片,破环细胞结构,导致叶绿素含量减少,光合作用效能降低,进而使得小麦在可见光波段光吸收程度减弱,反射率增大。可见,利用作物的高光谱图像信息能够准确地识别不同类型的小麦病害,为研发作物病害在线识别的多光谱系统提供重要的理论依据。     

基于高光谱成像和判别分析的黄瓜病害识别

利用光谱成像技术(400～720 nm)识别黄瓜白粉病、角斑病、霜霉病、褐斑病和无病区域。构建高光谱图像采集系统进行样本图像的采集,预处理和光谱信息的提取。由于获得的原始光谱数据量很大,为了减少后续运算量,提高准确率,采用逐步判别分析和典型判别分析两种方法进行降维。逐步判别从55个波段中选择12个波段,典型判别从55个波段中提取2个典型变量。利用选择的光谱特征参数建立病害识别模型。逐步判别构建的模型对训练样本和测试样本的判别准确率分别为100%和94%,典型判别构建的模型对训练样本和测试样本的判别准确率均为100%。说明利用高光谱成像技术可以进行黄瓜病害的快速、准确识别,并为实现可见光谱范围内黄瓜病害的田间实时在线检测提供了可能。     

Detection of sheep and goat milk adulterations by direct MALDI-TOF MS analysis of milk tryptic digests

In dairy field, one of the most common frauds is the adulteration of higher value types of milk (sheep's and goat's) with milk of lower value (cow's milk). This illegal practice has an economic advantage for milk producers and poses a threat for consumers' health because of the presence of hidden allergens as, for example, cow milk proteins, in particular, α(s1) -casein and β-lactoglobulin. The urgent need of sensitive techniques to detect this kind of fraud brought to the development of chromatographic, immunoenzymatic, electrophoretic and mass spectrometric assays. In the current work, we present a fast, reproducible and sensitive method based on the direct matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS analysis of milk tryptic digests for the detection of milk adulteration by evaluating specie-specific markers in the peptide profiles. Several pure raw and commercial milk samples and binary mixtures containing cows' and goats', cows' and sheep's and goats' and sheep's milk (concentrations of each milk varied from 0% to 100%) were prepared, and tryptic digests were analyzed by MALDI-TOF MS. The use of the new MALDI matrix α-cyano-4-chlorocinnamic acid allowed to detect cow and goat milk peptide markers up to 5% level of adulteration. Finally, from preliminary data, it seems that the strategy could be successfully applied also to detect similar adulterations in cheese samples. Copyright ? 2012 John Wiley & Sons, Ltd.     

Atomic force microscopy of Bacillus spore surface morphology

Bacillus spore surface morphology was imaged with atomic force microscopy (AFM) to determine if characteristic surface features could be used to distinguish between four closely related species; Bacillus anthracis Sterne strain, Bacillus thuringiensis var. kurstaki, Bacillus cereus strain 569, and Bacillus globigii var. niger. AFM surface height images showed an irregular topography across the curved upper surface of the spores. Phase images showed a superficial grain structure with different levels of phase contrast and significant differences in average surface morphologies among the four species. Although spores of the same species showed similarities, there was significant variability within each species. Overall, AFM revealed that spore surface morphology is rich with information, which can be used to distinguish a sample of about 20 spores from a similar number of spores of closely related species. Statistical analysis of spore morphology from a combination of amplitude and phase images for a small sample allows differentiation between, B. anthracis and its close relatives.     

Mapping of powdery mildew using multi-spectral HJ-CCD image in Beijing suburban area

Powdery mildew is one of the most serious diseases, which has a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study examines the potential of a moderate resolution multispectral satellite image in disease monitoring at regional scale. At the suburban area around Beijing, a large size ground survey sample (n = 90) and the corresponding HJ-CCD image were acquired at the grain filling stage of winter wheat. A number of spectral features were found to be sensitive to powdery mildew through an independent t-test. Based on these spectral features, classification models were established using both spectral information divergence (SID) and spectral angle mapper (SAM), respectively. The results showed that the overall accuracies of disease identification and severity estimation were moderate. The estimation of normal and seriously infected samples yielded higher accuracies than slightly infected samples. The single phase HJ-CCD can only be used for locating the infected areas of powdery mildew, whereas is unable to discriminate the severity levels of disease. The presence of several stressors and disturbances other than disease is a possible reason of the unsatisfactory performance of disease monitoring models. Therefore, the integration of multi-phase onboard data and some relevant ancillary data is necessary to improve the accuracy and reliability of disease monitoring at regional scale.     

Moderate hydrostatic pressure–temperature combinations for inactivation of Bacillus subtilis spores

We report the effect of using moderate hydrostatic pressure, 40–140?MPa, at moderate temperature (38–58°C) to inactivate Bacillus subtilis spores in McIlvaine's citric phosphate buffer at pH 6. We have investigated several parameters: pressure applied, holding time, pressure cycling, and temperature. The kinetics of spore inactivation is reported. The results show that spore inactivation is exponentially proportional to the time the sample is exposed to pressure. Spore germination and inactivation occur at the hydrostatic pressures/temperature combinations we explored. Cycling the pressure while keeping the total time at high pressure constant does not significantly increase spore inactivation. We show that temperature increases spore inactivation at two different rates; a slow rate below 33°C, and at a more rapid rate at higher temperatures. Increasing pressure leads to an increase in spore inactivation below 95?MPa; however, further increases in pressure give a similar rate kill. The time dependence of the effect of pressure is consistent with the first-order model (R²?>?0.9). The thermal resistance values (Z_T) of B. subtilis spores are 30°C, 37°C, and 40°C at 60, 80, 100?MPa. The increase in Z_T value at higher pressures indicates lower temperature sensitivity. The pressure resistance values (Z_P) are 125, 125 and 143?MPa at 38°C, 48°C, and 58°C. These Z_P values are lower than those reported for B. subtilis spores in the literature, which indicates higher sensitivity at pressures less than about 140?MPa. We show that at temperatures <60°C, B. subtilis spores are inactivated at pressures below 100?MPa. This finding could have implications for the design of the sterilization equipment.     

Fourier Transform Mid-Infrared Photoacoustic Spectroscopy for Presymptomatic Detection of Powdery Mildew Infection in Rubus corchorifolius L.

A powdery mildew fungi-induced disease appearing on leaves of Rubus corchorifolius L. has been observed in China, which resulted in seriously influencing the yield and quality of the fruit, and a presymptomatic detection of powdery mildew infection is needed to guarantee the yield and quality through removing the fungi in an early stage. Depth-profiling Fourier transform mid-infrared photoacoustic spectroscopy was applied to characterize both the healthy leaves and powdery mildew-infected leaves of Rubus corchorifolius L. The profiled surface could be divided into out layer (depth of about 1.32 µm) and deep layer (depth of about 1.87 µm). There were numerous differences in the total spectral range (500–4000 cm^?1) between healthy leaves and infected leaves, especially the intensity of absorption bands of 2800–3000 cm^?1 (aliphatic C–H vibration) and 2250–2350 cm^?1 (CO₂) significantly decreased when the leaf was infected by powdery mildew. For the out layer the standard spectral variance between healthy leaf and infected leaf was 7.33 × 10², whereas it was 1.86 × 10⁴ for the deep layer; the standard spectral variance between out layer and deep layer for healthy leaf was 3.38 × 10³, whereas it was 1.84 × 10⁴ for infected leaf, which implied that both out layer and deep layer responded to powdery mildew infection. Combining spectral differences between healthy leaf and infected leaf and variances between out layer and deep layer, a presymptomatic detection of powdery mildew infection was successfully made, which provided an alternative option and noninvasive method for the fast diagnosis of powdery mildew infection on Rubus corchorifolius L.     

叶绿素荧光成像技术的葡萄霜霉病早期检测

葡萄霜霉病对葡萄生产构成严重威胁,尽早防治是治理霜霉病的关键.为了对该病进行早期检测,以PCR检测获取的霜霉病相对生物量作为霜霉病侵染的依据,从暗适应—光适应—暗弛豫3个光合生理状态连续变化过程中,采集80个人工接种霜霉菌叶片和80个健康对照叶片连续6 d的叶绿素荧光图像.对比健康和接种叶片叶绿素荧光动力学曲线、参数图...     

MALDI-TOF MS对难溶聚芳酰胺PA6T的分析表征

选用五种样品试剂三氟乙酸、四氢呋喃、甲酸、乙腈、60%乙腈/5%三氟乙酸和两种不同的制样方法,采用基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)表征难溶性聚合物聚对苯二甲酸己二酰胺(PA6T)的结构和分子量。发现以60%乙腈/5%三氟乙酸为样品试剂所得谱图的分子质量范围和信噪比最大,且溶剂法一步点样优于无溶剂法制样。获得了离子液体中合成产物PA6T的链结构,表明其主链为线性,未发现环状聚合物的存在,存在三种端基结构聚合体： 两端均为氨基或羧基、或一端为氨基另一端为羧基。采用MALDI-TOF MS和SEC两种方法对比测量了PA6T的平均分子量和分子量分布指数,表明MALDI-OF MS测得的平均分子量低于SEC法,分子量分布指数也低于SEC法,这是由于MS处理数据时忽略了低分子量部分,且高分子量部分因难以离子化而无法得到引起的。对于难溶的聚芳酰胺类聚合物的结构和分子量及其分布表征,MALDI-TOF MS仍不失为一个好方法。