柑桔叶片黄龙病光谱特征选择及检测模型

为探索高光谱技术诊断黄龙病及分类的可行性,通过变量筛选方法组合为高维数据实用化提供参考。采集柑桔叶片高光谱图像并进行普通(polymerase chain reaction,PCR)鉴别分为轻度、中度、重度、缺锌和正常5类样品。用无信息变量消除算法(uninformative variable elimination,UVE)剔除无关信息,组合遗传算法(genetic algorithm,GA)和连续投影算法(successive projections algorithm,SPA)筛选变量,对数据进行降维。结合极限学习机(extreme learning machine,ELM)和最小二乘支持向量机(least squares support vector machine,LS-SVM)构建柑桔黄龙病判别模型。对预测样品进行诊断分类,来评价模型判别能力。经对比发现,UVE组合SPA筛选变量后的LS-SVM模型效果最好,该模型以Link_kernel函数为核函数,惩罚因子(γ)最小为1.07,误判率最低为0。用全谱作输入变量时LS-SVM模型复杂程度最高且预测能力最差,误判率最高为11.9%,可能是包含无用信息和冗余信息变量造成的。研究显示,UVE组合SPA筛选变量,结合LS-SVM对柑桔黄龙病诊断并分类具有一定可行性,为高维度数据实用化提供一定参考价值。     

HPLC method for analysis of free amino acids in fish using o-phthaldialdehyde precolumn derivatization

Precolumn derivatization applying o-phthaldialdehyde (OPA) was used to analyze free lysine, histidine, and ornithine, precursors of the respective biogenic amines cadaverine, histamine, and putrescine, which are considered indicators of fish quality and safety. This method uses 75% methanol to eliminate the use of strong acids as the extraction solution. Each analysis took 35 min, was reproducible, and allowed separation of primary amino acids in fish samples. A binary solvent delivery system coupled with a fluorescence detector and an Ultrasphere ODS column were utilized for HPLC separation. Linearity of the calibration curves was very good (r(2) = 0.99) for the amino acids of interest. Minimum concentrations of detection were 40 pmol/mL for histidine and lysine and 70 pmol/mL for ornithine. Average recoveries were 72% for lysine, 93% for histidine, and 98% for ornithine. This method used solvent gradient elution to study the levels of these analytes in mahi-mahi, bigeye tuna, and flounder.     

Variable selection and the interpretation of principal subspaces

Principal component analysis is widely used in the analysis of multivariate data in the agricultural, biological, and environmental sciences. The first few principal components (PCs) of a set of variables are derived variables with optimal properties in terms of approximating the original variables. This paper considers the problem of identifying subsets of variables that best approximate the full set of variables or their first few PCs, thus stressing dimensionality reduction in terms of the original variables rather than in terms of derived variables (PCs) whose definition requires all the original variables. Criteria for selecting variables are often ill defined and may produce inappropriate subsets. Indicators of the performance of different subsets of the variables are discussed and two criteria are defined. These criteria are used in stepwise selection-type algorithms to choose good subsets. Examples are given that show, among other things, that the selection of variable subsets should not be based only on the PC loadings of the variables.     

基于蒙特卡罗特征投影法的小麦蛋白质近红外光谱测量变量选择

为了实现小麦蛋白质的无损检测,简化便携式小麦蛋白质检测设备的预测模型,提高模型预测精度,该文针对小麦采集波长范围为950～1690nm的近红外漫透反射光谱,结合蒙特卡罗采样(MCS,monte carlosampling)技术与特征投影图(LPG,latent projective graph)方法对波长变量进行选择。根据模型集群分析(MPA,model population analysis)思想,采用MCS技术建立样本子空间,利用主成分分析(PCA,principal componentanalysis)得到LPG,假定LPG中共线性光谱变量对建模作用相同,选出少数波长变量建立子预测模型,选出预测均方根误差(RMSEP,root-mean-square error of prediction)较小的子模型,统计分析其变量的出现频次,选取频次最高的波长变量作为影响变量(IVs,influential variables)。研究结果表明,利用IVs建模可以将RMSEP值由0.5245减小到0.2548,采用蒙特卡罗采样技术的特征投影图方法(MC-LPG,monte carlo-latent projective graph)进行变量选择,对于提高模型预测精度是可行的。     

基于玻璃化转变的稻谷干燥变温控制系统的设计与试验

针对在稻谷变温干燥过程中变温节点不明确、温度波动范围大和响应时间慢等问题,该研究设计了一种基于玻璃化转变的稻谷变温干燥控制系统。根据稻谷玻璃化转变曲线,确定变温控制策略,运用Logistic回归分析建立混配阀门开度和稻谷温度之间的控制模型并通过最小二乘法辨识模型参数。利用遗传算法对模糊隶属度函数进行优化,目标函数值迭代至0.118收敛,寻得最优幅宽。在Simulink仿真试验中,稻谷温度设定为42 ℃时,模糊PID控制的响应时间为66.43 s,且超调量为3.600%,优化后的模糊PID控制响应时间为37.06 s,且超调量为0.120%;在150 s加入5 s的外部信号干扰,优化后的模糊PID控制比模糊PID控制的调节时间少4.19 s且超调量减小0.050%;在稳态时输入升温信号,优化后的模糊PID控制比模糊PID控制的调节时间少16.79 s且超调量低0.338%。利用自主研制的干燥试验台进行变温试验,在变温响应试验中,优化后的模糊PID控制比模糊PID控制在目标温度和梯度升温调节时间中分别缩短了37.56 s和18.63 s;在温度稳定性试验中,稻谷温度变化范围为41.9～42.1 ℃,平均相对误差小于0.4%,变异系数小于0.5%;在建三江国家农业高新技术示范区浓江农场进行生产性验证,优化后的模糊PID控制系统响应时间小于30 s,稳态温度误差在±0.15 ℃,平均相对误差小于0.5%。测试数据表明变温干燥控制系统性能稳定,满足实际干燥作业的生产工艺需求。     

Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection

Purpose

Fast and real-time prediction of leaf nutrient concentrations can facilitate decision-making for fertilisation regimes on farms and address issues raised with over-fertilisation. Cacao (Theobroma cacao L.) is an important cash crop and requires nutrient supply to maintain yield. This project aimed to use chemometric analysis and wavelength selection to improve the accuracy of foliar nutrient prediction.

Materials and methods

We used a visible-near infrared (400–1000 nm) hyperspectral imaging (HSI) system to predict foliar calcium (Ca), potassium (K), phosphorus (P) and nitrogen (N) of cacao trees. Images were captured from 95 leaf samples. Partial least square regression (PLSR) models were developed to predict leaf nutrient concentrations and wavelength selection was undertaken.

Results and discussion

Using all wavelengths, Ca (R²_CV?=?0.76, RMSE_CV?=?0.28), K (R²_CV?=?0.35, RMSE_CV?=?0.46), P (R²_CV?=?0.75, RMSE_CV?=?0.019) and N (R²_CV?=?0.73, RMSE_CV?=?0.17) were predicted. Wavelength selection increased the prediction accuracy of Ca (R²_CV?=?0.79, RMSE_CV?=?0.27) and N (R²_CV?=?0.74, RMSE_CV?=?0.16), while did not affect prediction accuracy of foliar K (R²_CV?=?0.35, RMSE_CV?=?0.46) and P (R²_CV?=?0.75, RMSE_CV?=?0.019).

Conclusions

Visible-near infrared HSI has a good potential to predict Ca, P and N concentrations in cacao leaf samples, but K concentrations could not be predicted reliably. Wavelength selection increased the prediction accuracy of foliar Ca and N leading to a reduced number of wavelengths involved in developed models.

     

基于Bayes判别分析模型的水土保持区划

为了更加合理地对甘肃、宁夏、青海3省份的黄土丘陵沟壑区进行水土保持区划,基于Bayes判别分析理论建立水土保持区划的数学模型.结合甘肃、宁夏、青海3省份的自然条件,社会经济情况和水土资源流失特点,以垦殖率、林草覆盖率、人口密度、单位面积粮食产量、降雨量和蒸发量等6个指标作为区划判别因子.计算分析表明,该模型与按土壤侵蚀类型进行区划的效果一致,与甘肃、宁夏、青海地区实际区划情况吻合,准确率为100％,可在其他水土保持区划中进行推广.     

基于判别分析法的金华火腿挥发性风味物质的形成过程分析

为了弄清金华火腿风味物质的形成过程,以60只杂交猪后腿为原料,按照传统工艺加工金华火腿,用气质联用仪(GC-MS)分析了6个工艺点的肌肉中挥发性风味物质的出峰面积,并用判别分析法分别研究了用挥发性风味物质的出峰面积和出峰面积占总出峰面积的百分率建立的判别函数判断金华火腿加工程度的可能性.研究结果表明,用金华火腿挥发性风味化合物的峰面积进行逐步判别分析,有20种挥发性化合物进入判别函数,而用峰面积占总出峰面积的百分率进行逐步判别分析,有17种挥发性化合物进入判别函数,建立的两套判别函数都能准确判别火腿的加工程度.结果提示:金华火腿风味化合物是在加工过程中逐步形成的,在不同加工阶段,火腿特征风味化合物的含量及其比例各具特征,可以用以辨别火腿的加工时间.     

Variable selection in near infrared spectra for the biological characterization of soil and earthworm casts

Near infrared reflectance spectroscopy (NIRS) was used to predict six biological properties of soil and earthworm casts including extracellular soil enzymes, microbial carbon, potential nitrification and denitrification. Partial least squares regression (PLSR) models were developed with a selection of the most important near infrared wavelengths. They reached coefficients of determination ranging from 0.81 to 0.91 and ratios of performance-to-deviation above 2.3. Variable selection with the variable importance in the projection (VIP) method increased dramatically the prediction performance of all models with an important contribution from the 1750–2500 nm region. We discuss whether selected wavelengths can be attributed to macronutrient availability or to microbial biomass. Wavelength selection in NIR spectra is recommended for improving PLSR models in soil research.     

基于超声成像技术的火腿肠质构分析与等级判别

为了研究超声成像技术在火腿肠质构分析与等级判别方面应用的可行性。通过对火腿肠蛋白质、淀粉等理化指标的测定将其分为特级、优级、普通级,并采集2个品牌3个等级的火腿肠共240份超声图像信息,在Matlab 7.0环境下提取图像角二阶矩、平均值等纹理特征值,最后利用线性判别式分析(linear discriminant analysis,LDA)和支持向量机(support vector machine,SVM)建立火腿肠的等级判别模型。结果表明:同品牌不同等级火腿肠超声图像、纹理特征值均具有较大差异,而同等级不同品牌火腿肠差异较小。建立的识别模型中:SVM优于LDA模型,当主成分为3时,SVM模型对应的校正集、预测集识别率均为100%,模型效果最佳。因此,超声成像技术可实现火腿肠内部质构的分析和等级的快速准确识别,研究结果可为超声成像技术在火腿肠内部质构分析和等级判别方面的应用提供参考。     

Modified method for the analysis of free fatty acids in fish

The objective of this study was the development of a method for the quantification of free fatty acids (FFA) using less aggressive reactants against the handler and the environment than those used in the classic method of Lowry and Tinsley. The modified procedure is a variation of the Lowry and Tinsley method employing cyclohexane in place of benzene. The use of benzene is prohibited in certain work processes and laboratories, and the competent authority in each country is actively promoting research into harmless or less harmful products that could replace benzene. A comparison with the traditional AOCS titration method for oil analysis was performed. FFA content in mackerel frozen at -10 degrees C was measured according to the three methods over a 12 month period. The results showed similar values, and good correlations were obtained.     

Confidence intervals for fish out-migration estimates using stratified trap efficiency methods

Marked fish placed above a smolt trap allow fisheries biologists to estimate the efficiency of the trap and hence to estimate the number of smolts migrating past the trap. We compared stratified maximum likelihood estimators and confidence intervals of total numbers of fish migrating to two other well-known estimators and associated confidence intervals using simulation. The Bailey adjusted Lincoln-Petersen estimator and its bootstrap confidence interval performed well overall.     

基于主成分分析和判别分析的白酒品牌鉴别方法

白酒的香气物质决定了白酒产品的差异。为了实现不同白酒产品的区分鉴别,提出了基于气相色谱分析技术结合模式识别实现白酒区分的方法。采集了7种产品共70个白酒样本的气相色谱数据,定性定量分析了己酸乙酯、乳酸乙酯等10种基本香气物质的含量,并对测定的物质进行主成分分析,验证区分效果,最后利用线性判别法建立判别函数,对不同白酒进行区分。结果表明,2种分析方法均可用于区分不同白酒,主成分分析结果显示,前3个主成分累计贡献率为86.527%,能有效描述香气物质和产品之间的复杂关系;线性判别分析对所有样本均得到准确的判别,正确率为100%,对预测样本的正确判别率达93.9%,建立的判别函数能准确区分不同白酒。研究表明,利用气相色谱技术结合模式识别的方法可用于不同白酒的区分鉴别。     

Characterization of fresh and decomposed dissolved organic matter using excitation-emission matrix fluorescence spectroscopy and multiway analysis

Fresh and decomposed dissolved organic matter (DOM) derived from 13 plant biomass and animal manure sources was characterized using multidimensional fluorescence spectroscopy with parallel factor analysis (PARAFAC), high-performance size-exclusion chromatography, and UV-vis spectroscopy. The PARAFAC analysis modeled seven fluorescence components: tryptophan-like, tyrosine-like, and five humic substance-like components. For most of the plant-derived DOM solutions, decomposition significantly affected the concentration of three humic substance-like-associated components, increasing two and decreasing one. The effect of decomposition upon DOM derived from animal manures was dependent on the manure source. For a majority of the DOM extracts, the ratio of fluorescence intensity to absorptivity at 254 nm increased following decomposition, indicating that fluorescing DOM compounds were generally more resistant to biodegradation than nonfluorescing UV-absorbing compounds. Molar absorptivity, humification index (HIX), and apparent molecular weight (MWAP) increased by 38.0, 38.8, and 370%, respectively, following decomposition. Spearman correlation analysis showed a strong positive relationship between the humic substance-like components and the DOM MWAP, absorptivity, and HIX. The results of this study support the use of multidimensional fluorescence spectroscopy with PARAFAC as a method to monitor the decomposition of carbon-rich soil amendments such as crop residues, green manures, and animal manures.     

混流式水轮机转轮设计变量耦合强度分析

在开展混流式水轮机转轮的多学科优化时通常需要进行设计变量的学科耦合强度分析,以便为建立简洁、高效的多学科优化求解策略提供依据。该文提出了一种基于全局相对灵敏度的转轮设计变量耦合强度分析方法,该方法基于参数化全三维反问题设计理论实现对转轮几何的参数化控制,并通过改进后的MorrisOAT法进行各学科目标函数对转轮设计变量的全局相对灵敏度计算,然后以各设计变量的全局相对灵敏度集合为论域,引入模糊隶属度函数量化计算设计变量对各学科目标函数的隶属度。最后,以各设计变量的隶属度值为基础,提出了设计变量的耦合强度判定准则,为设计变量的耦合程度属性确定提供了参考。采用该文所提出的分析方法对某混流式水轮机模型转轮的设计变量开展了耦合强度分析,并根据变量的耦合强度分析结果开展了转轮的优化设计,优化后的转轮不仅使得水轮机在3个优化工况下的水力效率分别提高0.2%、0.82%和1.2%,同时叶片的空化和强度性能也得到改善,该结果表明该文所提出的混流式水轮机转轮设计变量耦合强度分析方法能可靠有效的界定各设计变量的耦合程度属性,从而验证了方法的可行性。     

基于Fisher判别法则的小麦品质多指标分级

小麦生理生化指标对研究其储藏品质具有重要的作用,但由于各指标间关系的复杂性,所表达的信息存在较大差别,这给小麦储藏品质分析带来很大的不便。针对该问题,该文提出了一种小麦储藏品质多指标分析模型,选取降落数值、发芽率、过氧化物酶、脂肪酸值、电导率、还原糖值、丙二醛7个生理生化指标作为分析的关键因素,通过相似性和主成分法对各指标进行分析计算,发现脂肪酸值最具代表性;基于脂肪酸值数据分布变化趋势,采用聚类分析方法对小麦分类;使用Fisher判别法对小麦数据进行训练,得到2类判别函数,其中判别函数1的贡献率达到89.7%,在该函数下,计算获得3种类别小麦的中心值为–5.699、1.316和3.945,从而为判断小麦的品质状况提供计算依据。试验计算结果表明,在18批储藏小麦中,该文判别模型对小麦的分类结果与实际参考标准分类结果的一致性达到88.9%,验证了本模型的合理性,研究结果可为小麦品质评价分类提供参考。     

基于GGE双标图和马克隆值选择的棉花区域试验环境评价

棉纤维马克隆值是与纤维成纱品质密切相关的重要品质指标,也是长江流域棉花品质改良的主要制约因子。选择利用对目标环境代表性强的试验点进行区域试验布局有助于提高马克隆值育种效率和节省试验成本。GGE双标图是进行试验点代表性评价和选择最有效的统计和图形展示工具,已经在多种作物区域试验中用于品种稳定性和试验环境相似性分析,但在基于马克隆值选择的棉花区域试验环境评价中应用报道较少。本文采用GGE双标图方法对2000—2010年期间27组长江流域国家棉花区域试验中的15个试验环境基于马克隆值选择的鉴别力、代表性和理想指数进行综合评价与分析。结果表明:各试验环境依理想度指数优劣排序为荆州市>黄冈市>南通市>九江市>岳阳市>射洪县>常德市>安庆市>武汉市>南阳市>南京市>慈溪市>襄阳市>简阳市>盐城市;从中筛选出最理想试验环境为湖北省荆州市,较理想试验环境为湖北省黄冈市、江苏省南通市和江西省九江市,这些试验环境对以长江流域为目标环境的广适性新品种选育和作为区域试验点鉴别理想品种的效率最高;而位于江浙沿海棉区的江苏省盐城市和浙江省慈溪市不适宜作为针对长江流域的马克隆值选择与推荐环境。本研究充分展示了GGE双标图在区域试验环境评价方面的应用效果,也为以长江流域棉区为目标环境的广泛适应性和特殊适应性品种的马克隆值选择和应用提供理论依据。     

基于PIT遥测技术的竖缝式鱼道过鱼效率及鱼类行为分析

PIT（Passive Integrated Transponder）遥测作为一种射频识别和电磁感应相结合的技术,是开展鱼类行为学原位观测的一项重要手段。采用该技术进行鱼道过鱼效率评估,需解决该技术监测有效性及鱼类洄游轨迹量化分析等问题。该研究以大渡河安谷竖缝式鱼道为试验案例,以该水域的优势物种（白甲鱼和唇?）为试验对象,试验鱼的全长范围为9.70～26.60 cm,均值为（15.79±3.58 cm）。基于 HDX（Half Duplex）半双工PIT标记遥测系统的构建,试验鱼PIT标记的腹腔植入,开展了2种标记试验鱼在鱼道6个典型监测断面的行为特征研究以及鱼道过鱼效率的量化评估。结果表明,标记试验鱼成活率为79.17%,放流的169尾标记试验鱼,有81尾进入鱼道,14尾完全游出了鱼道。其中白甲鱼通过鱼道的经历时长为1.46～17.38 h;唇?通过鱼道的经历时长为11.5～162.38 h。2种试验鱼均在鱼道转弯池室停留的时间最长,触发频次最高。此外,2种标记试验鱼在鱼道不同监测断面的通过效率均没有显著性差异,鱼道的入口效率和过鱼效率分别为47.93%和8.28%。该研究可为国内过鱼设施过鱼效率量化及鱼类行为学原位观测提供技术参考。