春秋季对近红外光谱模型预测奶牛场粪水氮磷含量结果的影响

为研究季节因素对规模化奶牛场粪水氮磷含量及其近红外光谱模型预测结果的影响,该研究采集了天津市春秋双季27家规模化奶牛场粪水处理全过程的250个粪水样品,解析了季节对粪水氮磷含量分布特征的影响,同时采集了所有样品的近红外光谱并进行主成分分析。采用偏最小二乘法(Partial Least Squares,PLS)建立了粪水氮磷季节内预测模型,包括春秋单季和双季融合模型以及季节间的相互预测模型。结果表明,粪水氮磷含量随季节变化呈现出差异性,季节内模型总体的预测效果较好,优于季节间模型;其中春季模型表现最佳,验证相关系数分别为0.98和0.90,剩余预测偏差(Residual Predictive Deviation,RPD)分别为4.67和2.03。研究表明,季节因素对粪水中氮磷含量的模型预测结果存在不同程度的影响,该研究可为建立全季节要素的综合模型提供依据。     

天津规模化奶牛场粪水运移中氮磷含量变化特征

为揭示粪水运移中氮磷含量的时空变化特征，探究不同管理方式下粪水氮磷含量的变化规律，在天津地区33家种养结合型规模化奶牛场定位监测，解析季节、地区、清粪方式、粪水处理工艺和运移环节粪水氮磷含量差异。结果表明：1）不同季节粪水总氮（Total Nitrogen，TN）和总磷（Total Phosphorus，TP）含量差异极显著（P<0.0001）；2）不同地区粪水TN含量差异不显著，TP含量差异显著（P<0.05）；3）不同清粪方式和粪水处理工艺下粪水TN和TP含量差异不显著，但干清粪+干清粪方式下粪水中TN和TP含量均略高于其他方式，厌氧发酵+沼液贮存处理工艺下粪水中TN和TP含量略高于其他处理工艺；4）不同运移环节粪水TN和TP含量差异极显著（P<0.0001）。该研究为系统摸清奶牛粪水中氮磷养分变化规律提供支撑，为粪水管控实用技术研发和路径选择提供依据。     

激光诱导击穿光谱技术检测土壤中的铅和镉含量

为了满足土壤重金属快速准确检测的需求,同时为土壤的重金属污染防治和农业的可持续发展提供理论指导,该研究利用激光诱导击穿光谱(laser-induced breakdown spectroscopy,LIBS)技术结合定标曲线法和化学计量学方法对土壤中重金属铅(Pb)和镉(Cd)元素进行定量分析。在获取LIBS数据之后,结合土壤LIBS发射谱线中Pb和Cd谱峰信息以及美国国家标准与技术研究院(national institute of standards and technology,NIST)的标准原子光谱数据库,选取了Pb和Cd的特征谱线分别为Pb I 405.78和Cd I 361.05 nm。首先对谱线信息进行预处理后,根据谱峰信息和元素的含量,分别建立了基于谱线峰强度、归一化后洛伦兹拟合强度、谱峰积分强度与对应元素浓度之间的关系模型和定标曲线。对于Pb元素的3种定标方法得到的线性关系的决定系数(R2)分别为0.983 85、0.970 97、0.993 21,且模型反演的结果与实际值的相对误差较小;而Cd元素的3种定标方法没有得到明显线性关系。然后运用偏最小二乘回归(partial least-squares regression,PLSR)建立了土壤Pb和Cd元素的定量分析模型,Pb元素PLSR模型的结果与定标曲线法的结果类似,其预测的相关系数(RP)为0.948 5,预测均方根误差(RMSEP)为2.044 mg/g;而Cd元素的PLSR模型的结果比起定标曲线法有较大提升,其预测的相关系数(RP)为0.994 9,预测均方根误差(RMSEP)为97.05μg/g,结果说明PLSR方法在光谱化学分析领域中比定标曲线法进行定量分析有更好的适用性。研究表明,LIBS技术能够实现对土壤重金属Pb和Cd含量的定量检测,为开发实时便携式LIBS土壤重金属检测仪提供了理论基础。     

近红外光谱快速检测食用油必需脂肪酸

为了建立食用油必需脂肪酸快速检测的方法,该研究提出了基于近红外光谱技术检测食用油中α-亚麻酸和亚油酸含量的快速测定方法。对光谱信息分别采用偏最小二乘回归方法(PLS)和最小二乘支持向量机(LS-SVM)建立模型。比较了多种光谱预处理方法对模型预测能力的影响。结果表明对于亚油酸含量的预测,采用Savitzky-Golay平滑法结合多元散射校正(MSC)的光谱预处理所建立的LS-SVM模型最优。预测集的决定系数(R2)、预测均方根误差(RMSEP)和剩余预测偏差(RPD)分别达到了0.989,0.0161和9.4783。对于α-亚麻酸含量的预测,采用Savitzky-Golay平滑法结合标准正态变换(SNV)的光谱预处理所建立的LS-SVM模型最优。α-亚麻酸含量预测结果的R2、RMSEP和RPD为0.972,0.0036和6.0561,据此表明,应用近红外光谱技术能够检测食用油中α-亚麻酸和亚油酸的含量,为快速检测食用油的必需脂肪酸提供了参考。     

应用近红外光谱法测定土壤的有机质和pH值

为了满足精细农业对土壤快速实时测试的需要,对未经过粉碎、过筛等处理的土壤,采集了4000～12500 cm-1范同的近红外光谱.研究了土壤的光谱特性,并采用偏最小二乘回归分析方法建立了一阶微分光谱的光谱吸光度与有机质含量和pH值之间的定量分析模型.试验分析表明:有机质的预测相关系数为0.818,预测标准偏差SEP为0.069,预测均方根误差为RMSEP为0.085;pH值的预测相关系数为0.834,SEP为0.095,RMSEP为0.114.表明采用近红外光谱仪经一阶微分处理可以很好地预测经过简单处理的上样中的有机质含量和pH值,该结论为今后田间快速土壤特性光谱测量奠定了基础.     

衰减全反射-中红外光谱法定量测定小麦粉中的石灰类添加物

为了对市售小麦粉中的石灰类添加物进行现场快速检测,该文采用衰减全反射-中红外光谱(mid-infrared spectroscopy combined with attenuated total reflection,ATR-MIR)结合偏最小二乘算法建立了小麦粉中掺入氧化钙、氢氧化钙、碳酸钙、氧化钙+氢氧化钙以及石灰总量的定量校正模型,并采用外部验证集对各模型进行验证。结果表明,氧化钙、氢氧化钙、碳酸钙、氧化钙+氢氧化钙和石灰总量模型的决定系数均大于0.98;校正均方根误差(root mean square error of calibration,RMSEC)均小于0.3;交互验证均方根误差(root mean square error of cross validation,RMSECV)均小于0.5;外部验证的预测均方根误差(root mean square error of prediction,RMSEP)均小于0.95;各模型的相对预测性能(ratio performance deviation,RPD)均大于4.5,该模型具有较高的精度,可以满足小麦粉中石灰含量的现场快速检测要求。该研究可为市售小麦粉的快速质量安全筛查提供新的方法,对小麦粉市场质量监控具有重要意义,并且对小麦粉中其他违禁添加物的检测亦有参考价值。     

贮备饲料近红外光谱模型快速预测青绿饲料营养成分含量

为快速检测饲料的营养成分,该研究利用贮备饲料的近红处技术(near-infrared,NIR)快速分析模型预测青绿饲料的营养成分含量。基于贮备饲料的NIR定标模型,将建模优化模式转移应用到青绿饲料的营养成分定量检测,以判断模型转移能力。在实验室环境下扫描并记录新鲜的青绿饲料样本和储存的贮备饲料样本的近红外反射光谱,利用230个贮备饲料样本进行光谱定标训练,以修正偏最小二乘(modified-partial least squares,M-PLS)建模方法,结合随机局部样本、局部选参、局部非连续性可调、交叉检验等技术相结合的方式建立局部优化模型,分别测试120个贮备饲料样本和120个青绿饲料样本中的氮(nitrogen,N)、中性洗涤纤维(neutral detergent fiber,NDF)、酸性洗涤纤维(acid detergent fiber,ADF)含量。将贮备饲料的定标校正模型应用于贮备饲料验证样本的营养成分测定,其标准误差(square error of prediction,SEP):N为1.02、NDF为16.56和ADF为13.47,相关系数均在0.9以上,相对预测偏差(relative prediction derivation,RPD)均大于3;该模型具有对青绿饲料样本的营养成分预测能力,其预测SEP:N为0.90、NDF为14.11和ADF为9.98,预测相关系数均在0.9以上,预测RPD均大于3,达到快速检测误差标准。由于局部建模过程中考虑了数据的潜在非线性结构和具有近似光谱响应的样本之间的不均匀性,相对全局建模方式而言具有更好的数据驱动性质,其建模效果优于全局建模方法。结果表明,基于贮备饲料样本建立的NIR定标校正模型可以用于青绿饲料营养成分的预测,特别是局部分析模型的应用能够提高NIR快速分析的预测精度。     

拉曼光谱法无损检测蜂蜜中的果糖和葡萄糖含量

应用拉曼光谱结合化学计量学方法对蜂蜜果糖和葡萄糖含量进行了定量分析。用自适应迭代重加权惩罚最小二乘(adaptive iteratively reweighted penalized least squares,airPLS)算法进行基线校正,用竞争性自适应重加权采样(competitive adaptive reweighted sampling,CARS)算法筛选变量,分别用线性的偏最小二乘(partial least squares,PLS)回归算法和非线性的支持向量机(support vector machines,SVM)回归算法建立定量校正模型,并进行预测。2种模型都有较好的预测结果。对果糖,SVM模型预测值与高效液相色谱法(high performance liquid chromatography,HPLC)测定值的相关系数(R)和预测均方根误差(root mean square error of prediction,RMSEP)分别为0.902和1.401,略优于PLS模型(R为0.892,RMSEP为1.604);对葡萄糖,PLS模型的R和RMSEP分别为0.968和0.669,优于SVM模型(R为0.933,RMSEP为1.410)。结果表明拉曼光谱结合化学计量学方法可快速无损测定蜂蜜果糖和葡萄糖含量。     

赣南脐橙园土壤全磷和全钾近红外光谱检测

为建立一种能够同时快速检测土壤全磷和全钾的定量估计模型,该文采用近红外漫反射技术对赣南脐橙果园的土壤进行研究,对56个土样风干、过筛,然后进行光谱采集和化学分析。光谱经过Savitzky-Golay平滑后再用一阶微分变换的方法进行预处理,分别应用偏最小二乘回归（partial least square regress PLS）、主成分回归（principal component regression PCR）和最小二乘支持向量机（least squares support vector machine LS-SVM）3种方法,在4 000～7 500 cm-1波数范围内,建立赣南脐橙果园土壤全磷和全钾快速定量检测模型。结果发现在建立土壤全磷模型时,PLS和PCR的预测模型效果均不理想,但LS-SVM建立的模型较为理想, 其预测相关系数（correlation coefficient of prediction RP）为0.884,预测集均方根误差（the root mean square error of prediction RMSEP）为0.341,预测相对分析误差（residual predictive deviation RPD）为2.59。在建立土壤全钾模型时,PLS、PCR和LS-SVM 建立3种模型效果均理想,其中以LS-SVM模型最理想,其预测相关系数（RP）为0.971,预测集均方根误差（RMSEP）为0.714,预测相对分析误差（RPD）为5.12。研究表明,采用LS-SVM建立的土壤全磷和全钾模型对实现土壤全磷和全钾含量快速检测具有可行性。     

特征波长筛选在近红外光谱测定梨硬度中的应用

为了提高应用近红外光谱分析技术快速测定梨硬度的精度和稳定性,该研究采用联合区间偏最小二乘和遗传算法（siPLS-GA）在校正模型中用来筛选特征光谱区域和波长,通过交互验证法确定模型的主成分因子数和筛选的波长,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。基于siPLS-GA的最优模型包含4个光谱区、96个变量和10个主成分因子。该模型结果显示：最佳预测模型相关系数（Rp）和RMSEP分别为0.9083和0.5573。研究结果表明,近红外光谱技术结合siPLS-GA建模用于无损、快速测定梨的硬度是可行的。     

规模化奶牛场粪污全量贮存及肥料化还田工艺设计

为推进粪污全量贮存和肥料化还田模式在规模化奶牛场的应用，该研究以存栏500头规模奶牛场为例，分析了粪污收集量、贮存工艺与设施和粪肥还田等内容，提出了粪污贮存池设计容积和粪肥还田配套土地面积等参数。结果表明：奶牛粪污全量收集量为17.33 t/d，全量贮存设施分为舍内贮存池和舍外贮存囊2种。单个舍内贮存池尺寸为85 m×12 m×2 m（长×宽×深），粪污存储期9个月，所需贮存池数量为5个，总容积10 200 m3；舍外贮存囊占地尺寸为90 m×30 m（长×宽），深2.2 m，总容积5 615 m3。粪肥全部还田所需土地面积与种植作物类型和种植制度相关，种植作物为小麦、玉米、小麦+玉米和水稻（1年2熟）时，需配套土地分别为248.4、400.6、122.8和127.0 hm2。粪肥还田成本为10.37万元/a，全部还田可节省化肥22.8万元/a，年可产生经济效益12.43万元。     

京郊畜禽粪污氮磷含量特征及影响因素分析

中国规模化养殖废弃物中养分资源数量可观,但缺乏循环利用技术,处置不当易引发环境污染问题。该文通过问卷调研和粪污样品检测,对京郊典型养殖场粪便和废水中的总氮、总磷含量特点进行分析,同时追踪典型规模化猪场废水中总氮、总磷含量变化的影响因素及其季节性变化特征。结果表明:所调研的养殖场中,猪粪、牛粪的总氮质量分数平均值分别为29.1,17.8 g/kg,总磷质量分数平均值分别为15.1,6.8 g/kg,猪粪便的总氮、总磷含量变异程度大于牛粪;猪场和牛场中废水总氮、总磷质量分数的平均值分别为892,540和82.4,53.3 mg/L,猪场废水总氮、总磷含量变异程度明显大于牛场。规模化猪场粪便总氮、总磷含量受到饲料配方的影响较大;受饲料和圈舍用水量的影响,现行饲养工艺及粪污处理方式下粪便对废水中总氮、总磷含量的影响较小;万头以上的规模化猪场废水中总氮、总磷含量存在季节性差异,且随着废水存储时间的推移其无机磷比例增加。这些变化特征可对畜禽粪便和养殖废水的资源化再利用提供了有用的参考。     

Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms

The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R²) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R² and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.     

Rapid determination of lignin content of straw using fourier transform mid-infrared spectroscopy

To determine lignin content in triticale and wheat straws, calibration models were built using Fourier transform mid-infrared spectroscopy combined with partial least-squares regression. The best model for triticale and wheat straws was built using averaged spectra with raw spectrum in spectrum format and constant in path length as spectral pretreatments. The values of r(2), root-mean-square error of prediction (RMSEP), and residual predictive deviation (RPD) for the triticale straw model were 0.935, 0.305, and 3.89, respectively. The r(2), RMSEP, and RPD values for the wheat straw model were 0.985, 0.163, and 8.50, respectively. Both models showed good predictive ability. A model built using both triticale and wheat straws indicated that the values of r(2), RMSEP, and RPD were 0.952, 0.27, and 4.63, respectively. This model also showed good predictive ability and could predict lignin contents in triticale and wheat straws with the same high accuracy.     

规模化养猪场粪污全量收集及贮存工艺设计

基于全量收集的粪污贮存技术具有粪尿收集方便、运行成本低廉和养分利用率高等特点,在欧美等发达国家得到了普遍应用,是一种适合在中国华北、西北等地区和土地匹配较充足的区域进行推广的粪污处理与还田利用技术。文章以规模化养猪场尿泡粪全量贮存技术为研究对象,分析了尿泡粪收集量、贮存工艺控制参数、贮存设施设计和投资运行成本等内容,旨在为该技术的推广应用提供参考。结果表明:每头生猪整个饲养周期内尿泡粪收集量为0.70 m~3;粪污贮存设施分为舍内贮存池和舍外贮存罐2种,粪污贮存方法可采取舍内贮存、舍外贮存和舍内结合舍外贮存3种。粪污pH值酸化至5.5～6.5,氨排放量最高可减少80%;粪肥还田前一般要求存储时间为6个月。以存栏5000头规模养猪场为例,舍内贮存池所需容积为6 600 m~3,投资660万元;舍外贮存罐所需容积为4 118 m~3,投资206万元;舍内结合舍外贮存设施所需容积为8 214 m~3,投资651万元;粪污处理成本为3.83万元/a,施肥成本为10.8万元/a;全部粪肥还田可满足133 hm~2农田用肥,节省化肥6.0万元/a,该研究可为粪污贮存及利用提供参考。     

Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale

This paper reports on the influence of the number of samples used for the development of farm‐scale calibration models for moisture content (MC), total nitrogen (TN) and organic carbon (OC) on the prediction error expressed as root mean square error of prediction (RMSEP) for visible and near infrared (vis‐NIR) spectroscopy. Fresh (wet) soil samples collected from four farms in the Czech Republic, Germany, Denmark and the UK were scanned with a fibre‐type vis‐NIR, AgroSpec spectrophotometer with a spectral range of 305–2200 nm. Spectra were divided into calibration (two thirds) and prediction (one third) sets and the calibration spectra were subjected to a partial least squares regression (PLSR) with leave‐one‐out cross‐validation using Unscrambler 7.8 software. The RMSEP values of models with a large sample number (46–84 samples from each farm) were compared with those of models developed with a small sample number (25 samples selected from the large sample set of each farm) for the same variation range. Both large‐set and small‐set models were validated by the same prediction set for each property. Further PLSR analysis was carried out on samples from the German farm, with different sample numbers of the calibration set of 25, 50, 75 and 100 samples. Results showed that the large‐size dataset models resulted in smaller RMSEP values than the small‐size dataset models for all the soil properties studied. The results also demonstrated that with the increase in sample number used in the calibration set, RMSEP decreased in almost linear fashion, although the largest decrease was between 25 and 50 samples. Therefore, it is recommended that the number of samples should be chosen according to the accuracy required, although 50 soil samples is considered appropriate in this study to establish calibration models of TN, OC and MC with smaller expected prediction errors as compared with smaller sample numbers.     

两种稻谷胶稠度测定方法的比较分析

为探索快速准确检测稻谷胶稠度的方法,本研究通过近红外漫反射红外光谱技术(NIRDRS)和傅里叶变换中红外漫反射红外光谱技术(FTIRDRS)结合偏最小二乘法(PLS),分别建立107个稻谷样品的胶稠度快速测定红外模型,而后利用区间偏最小二乘法(iPLS)及反向区间偏最小二乘法(BiPLS)对模型进行优化,得到较优的胶稠度测定分析通用模型。结果表明,DRIFTS原始光谱经7点平滑预处理和BiPLS优化,得到最佳模型的交互验证系数(R²)、交叉验证均方差(RMSECV)、预测均方差(RMSEP)及相对分析误差(RPD)分别为0.965 81、4.79、4.73及2.66。最佳近红外漫反射光谱模型是经多元散射校正(MSC)预处理、BiPLS优化后建立的,其R²、RMSECV、RMSEP及RPD分别为 0.964 58、4.35、3.68及3.42。10组外部验证性试验中NIRDRS模型的平均相对误差为1.93%,FTIRDRS模型的平均相对误差为2.60%,表明两种方法均对稻谷胶稠度含量有较强的预测能力和良好的预测效果,均有替代传统国标法测定稻谷胶稠度的潜力。     

Family Dairy Farms in Galicia (N.W. Spain): Classification by Some Family and Farm Factors Relevant to Quality of Life

A survey of 103 family dairy farms in Galicia (N.W. Spain) collected information on 94 variables (78 quantitative, 16 qualitative) belonging to seven groups dealing with the location of the farm, family structure, sources of income, production variables, characteristics of the farm house (‘housing quality’), characteristics of the complex comprising farm house and farm buildings (the ‘central area’), and the characteristics and proximity to the farm house of the routes used for transit of cattle, fodder, slurry, etc. Following elimination of redundant variables, principal components analysis identified four factors accounting for about 40% of the total variance: three dominated each by a single a priori group (house–farm separation, central area, and housing quality), and one (production capacity) that combined production and source-of-income variables. Using these factors, the farms studied were subjected to hierarchical clustering by means of the Ward aggregation strategy, and a typology was established accordingly. Finally, guidelines for the design and improvement of farm installations and housing on Galician family dairy farms are sketched.