精料补充料中肉骨粉含量的近红外光谱检测

为了保证饲料安全,精料补充料中肉骨粉的检测是十分必要的。该文探讨了精料补充料中肉骨粉含量的近红外光谱分析方法,123个样品作为校正集,采用偏最小二乘法(PLS),分别对光谱进行散射校正和卷积平滑、一阶微分、二阶微分预处理建立校正模型,以最大的决定系数(R²)和最小的标准差(RMSEC)为选择依据,通过比较,以多元散射校正和卷积平滑处理与二阶微分相结合的处理效果最好,其预测值与测量值的决定系数(R²)和标准差(RMSEC)分别为0.9751和0.437。34个样品作为检验集进行外部验证,决定系数(r²)和标准差(RMSEP)分别为0.9749和0.420,平均绝对误差和相对误差分别为0.326和13.89％。结果表明,利用近红外分析技术可以检测精料补充料中肉骨粉的含量。     

鱼粉中肉骨粉的可见-近红外光谱快速定性判别方法（简报）

为了能更加快速准确的定性判别鱼粉中是否掺有肉骨粉（MBM）,该文收集中国常用的鱼粉和肉骨粉,制备定标集样品201个,其中111个为掺有不同肉骨粉质量分数（1%～33%）的样品,90个为纯鱼粉,并独立制备113个验证集样品,其中74个为掺有不同肉骨粉质量分数（1%～33%）的样品,39个为纯鱼粉。在400～2 498 nm波长范围内进行光谱扫描,选择合适的光谱预处理方法和光谱范围,采用DPLS方法建立判别分析模型。建立的判别分析模型：数学预处理方法为2-8-6-1,散射校正方法为变量标准化处理（SNV）,光谱范围为全谱（408～ 2 492 nm）,定标模型的正确判断率为95.7%,外部验证正确判断率为95.6%,对于掺入量≥5%MBM时,正确判断率为100%。研究结果证明近红外反射光谱可以提供一种快速鉴别鱼粉中MBM的方法。     

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。     

基于近红外光谱的板栗水分检测方法

含水率是影响板栗贮藏、加工的关键指标之一,该文应用近红外光谱技术对板栗含水率进行快速无损检测。试验对240个板栗样本的带壳光谱和栗仁板栗光谱采用SPXY算法进行样本集划分,利用偏最小二乘法建立含水率定量检测模型,并对微分、多元散射校正、变量标准化等多种预处理方法对建模结果的影响进行比较。结果表明：栗仁和带壳板栗的光谱经一阶微分预处理后所建模型性能最佳,其中栗仁的水分检测模型校正集和验证集的相关系数分别为0.9359和0.8473,校正均方根误差为1.44%,验证均方根误差为1.83%;带壳板栗光谱所建模型校正集和验证集的相关系数分别为0.8270和0.7655,校正均方根误差为2.27%,验证均方根误差为2.35%。受栗壳的影响,带壳板栗光谱模型对含水率的预测精度低于栗仁光谱模型的预测精度。研究表明,近红外光谱分析技术可用于板栗含水率的快速无损检测。     

土壤可见光-近红外反射光谱与重金属含量之间的相关性

发展基于反射光谱技术的快速、简便、低成本的土壤重金属信息提取方法是区域土壤重金属污染治理所需要的。选择江西贵溪铜冶炼厂污染区,分析了9种重金属元素(Cu、Pb、Zn、Cd、Co、Ni、Fe、Mn及Cr)与土壤可见光-近红外反射光谱之间的相关性及其相关的原因。研究表明,研究区土壤中存在Cu(含量介于66.71~387 mg kg-1之间)和Cd(含量介于0.36~6.019 mg kg-1之间)的强烈富集。土壤重金属含量与反射光谱之间存在显著相关,污染元素Cu的最高相关系数为-0.87,Pb、Zn、Co、Ni、Fe的最高相关系数达到高度相关(|r|>0.80),Cr、Cd、Mn的最高相关系数达到显著相关(|r|>0.70)。微分光谱适于获取土壤中的重金属元素信息,利用组合波段能显著提高相关性。Cu与反射光谱之间的相关性主要受有机质的影响;Pb、Zn、Co、Ni主要受黏土矿物和铁锰氧化物的影响;Cr与反射光谱之间的相关性同时受有机质和黏土矿物的影响。     

近红外透反射光谱测定单粒稻种的蛋白质含量

为探索NIR光谱技术在水稻种子蛋白质含量分析中的应用,本研究细致分析了单粒稻种在不同光谱采集方式下的近红外光谱(NIRS)特征,并利用离子束诱变育种得到的水稻9311突变体库的种子,建立准确性较好的单粒糙米和单粒稻种的蛋白质定量模型。结果表明,与漫反射光谱采集方式下的单粒糙米蛋白质模型相比,透反射和透射光谱采集方式下能得到相关性较好的糙米蛋白质模型,其中单粒糙米蛋白质最优定量模型的决定系数(R²)为0.97,预测均方根误差(RMSEP)为0.27%。在单粒稻种中,由于种壳的反射作用,漫反射光谱采集方式下依然无法建立准确性高的蛋白质模型,透反射光谱采集方式下能够建立具有一定预测能力的蛋白质定量模型(RMSEP=0.81%),透射光谱采集方式下能够建立准确性高的蛋白质定量模型(R²=0.96,RMSEP=0.24%)。本研究结果为无损快速分析单粒稻种提供了一种解决方法。     

基于可见-近红外反射光谱的土壤碳酸钙含量与反演效果关系研究

土壤可见-近红外反射光谱中包含了大量的土壤属性信息,研究人员根据土壤属性信息在光谱上的特征,对土壤属性进行定量反演。是否属性值越高,反演精度越高?目前对于属性含量与反演效果的定量关系尚不清楚。采集了我国西北地区黑河流域69个代表性干旱土剖面(292个发生层土样),以气量法测定其碳酸钙含量,使用Cary 5000分光光度计测定其可见-近红外光谱反射率,以样本量和离散度(变异系数)作为数据集划分标准,分别建立了11个相同样本量子集(A)和5个相近离散度子集(B),应用偏最小二乘回归(PLSR)算法对各子集进行土壤碳酸钙含量反演,以此探究碳酸钙含量与反演效果的定量关系。结果表明,碳酸钙可增加可见-近红外波段的光谱反射率,但利用可见近红外光谱反演土壤碳酸钙含量,其反演效果与碳酸钙含量关系不显著。     

近红外光谱式联合收割机谷物蛋白质含量检测系统设计

为了实现谷物联合收割机收获时实时在线检测谷物的蛋白质含量并记录采样地理位置信息,研发了一种基于近红外光谱原理的谷物蛋白质含量在线检测系统,系统主要由近红外光谱传感器模块、螺旋采样输送机构、控制模块、GPS/北斗定位模块、工控显现一体机等组成。谷物联合收割机近红外光谱式蛋白质含量在线检测系统工作时,当联合收割机出粮搅笼排出的谷物经过螺旋采样输送机构,采样机构的步进电机根据检测速率要求由控制器控制并间断进行谷物输送,控制器同时控制近红外光谱传感器在步进电机停止转动时进行光谱采样,谷物的近红外光谱和GPS/北斗定位模块位置信号等数据由RS485总线传输至上位机。编制了近红外传感器和采样机构等的控制与数据处理分析软件,经谷物蛋白质含量预测模型处理后,将谷物蛋白质、采样位置信息等实时显示在终端上并保存。为了验证谷物蛋白质含量预测模型及在线检测系统的性能,开展了室内标定和田间系统动态测试试验,小麦蛋白质含量预测模型的决定系数R2为0.865,绝对误差范围为-0.96%～1.22%,相对误差范围在-7.30%～9.53%,预测标准差值为0.638%;水稻蛋白质含量预测模型的决定系数R2为0.853,绝对误差范围为-0.60%～1.00%,相对误差范围为-8.47%～9.71%,预测标准差值为0.516%。系统田间测试试验表明,小麦蛋白质含量的最大相对误差为-6.69%,水稻蛋白质含量的最大相对误差为-8.02%,采样分析时间间隔对系统测试精度的影响不显著,系统稳定性和检测精度达到田间谷物蛋白质在线检测需要,为精准农业作业提供了科学依据。     

斑潜蝇虫害叶片受害程度对其近红外反射光谱的影响

为探索实现作物虫害自动监测的方法，采用图像处理和光谱分析技术，测定了斑潜蝇虫害叶片的近红外反射光谱，计算了虫害叶片的破损率，对其破损率和干鲜比与近红外分光反射率的关系分别进行了回归分析。结果表明：在某些波段，叶片的破损率和干鲜比均与近红外分光反射率有较好的相关性。叶片的干鲜比与近红外分光反射率关系的决定系数：黄瓜为R²=0.79(在1452 nm)，番茄为R²=0.70(在1450 nm)。叶片的破损率与近红外分光反射率关系的决定系数，黄瓜为R²>0.81(在1436～1468 nm)，番茄为R²>0.69(在1436～1466 nm)。试验和分析结果证明斑潜蝇虫害叶片的虫害程度能很好地被近红外光谱信息反映。     

近红外反射技术建立合肥地区精米直链淀粉含量测定模型

以合肥地区种植的203份水稻材料为检测对象,用近红外反射技术采集光谱,常规化学方法测定精米直链淀粉含量。结果表明,定标样品的直链淀粉含量分布范围为3.439%～28.046%,代表性和连续性良好。采用多种计量数学处理方法和偏最小二乘法(PLS),优化建立了精米直链淀粉含量的定量分析预测模型。定标集(C-Set)样品数132个,相关系数(Rc)0.9278,定标标准差(SEC)1.6582;验证集(V-Set)样品数67个,相关系数(Rv)0.8736,预测标准差(SEP)1.9083,并证实所建立的模型在测定精米直链淀粉含量上具有很好的准确性和实用性,对合肥地区水稻品质育种及种质资源相关研究具有实用价值。     

近红外光谱分析技术在饲料质量安全评价中的应用研究进展

近红外光谱分析技术是饲料质量安全控制中不可或缺的一种技术,它不仅能够较准确的测定饲料产品营养价值的各相关参数指标,而且能科学的分析配合饲料的原料组成和来源,应用于饲料安全溯源管理;同时,利用近红外光谱分析技术获取强特异性的个性特征信息,可用于完善饲料产品注册系统。本文综述了近红外光谱分析技术在饲料质量安全评价方面的应用研究成果,以便使该技术更好的为饲料行业质量安全控制服务。     

近红外光谱法快速测定土壤碱解氮、速效磷和速效钾含量

运用偏最小二乘法(PLS)和人工神经网络(ANN)方法分别建立了0.9 mm筛分风干黑土土壤碱解氮、速效磷和速效钾含量预测的近红外光谱(NIRS)分析模型。使用偏最小二乘算法建立的碱解氮、速效磷和速效钾校正模型的决定系数R²分别为0.9520、0.8714和0.7300，平均相对误差分别为3.42%、13.40%和7.40%。人工神经网络方法建立的碱解氮、速效磷和速效钾校正模型的决定系数分别为0.9563、0.9493和0.9522，相对误差分别为2.67%、6.48%和2.27%，测试集仿真的相对误差分别为5.44%、16.65%和7.87%。结果表明，人工神经网络方法所建立的校正模型均优于偏最小二乘法所建模型；用近红外光谱分析法预测土壤碱解氮含量是可行的，而速效磷、速效钾模型的测试集样品仿真的相对误差较大，其预测可行性还需做进一步研究。     

Potential nitrogen and phosphorus leaching from soils fertilized with meat and bone meal

Abstract

The nitrogen and phosphorus contents of meat and bone meal (MBM) make it a potentially valuable nutrient source in agriculture. Its narrow N:P ratio, however, makes it a potential environmental risk if it is not utilized with caution. A column-leaching experiment was conducted to assess potential nitrogen and phosphorus leaching from bare soils fertilized with MBM. This should give an idea of the efficacy of autumn or early spring MBM application on land devoid of vegetation cover. Earlier greenhouse and field experiments have indicated that nitrogen mineralization after MBM application takes place relatively rapidly. The column experiment conducted here has indicated that spring application of MBM prior to planting can result in the loss of mineral nitrogen, reducing the total amount of N available to the crop. A higher initial loss (up to 47 mg l^?1) of ammonium was found for the MBM treated soils, but this declined to less than 0.1 mg l^?1 by the end of the experiment, 11 weeks later. Nitrate loss was highest at the onset of the experiment for the soils that received mineral N fertilizer. With time, however, the largest nitrate losses were associated with the MBM-treated soils. Nitrate leaching continued to well beyond the end of the experiment. The effect will most certainly be exacerbated when the crop N (rather than crop P) requirement forms the basis for MBM application. The amount of P leached as ortho-P is probably small but may also represent an environmental risk if N remains to be the basis of MBM application. Based on the results presented here and considering the dangers of elevated nutrient losses, autumn and early spring MBM application may not be recommended.     

基于骨蛋白拉曼光谱特异性的肉骨粉种属鉴别方法

为了快速检测肉骨粉的种属来源,该研究开发了一种简便、可靠、科学、高效的肉骨粉种属鉴别方法。以87个肉骨粉(猪,鸡,牛和羊肉骨粉)为研究对象,利用拉曼光谱技术,结合化学计量学方法,建立了基于骨蛋白拉曼光谱特性的肉骨粉种属鉴别分析方法与模型。研究结果表明:根据偏最小二乘判别分析(PartialLeastSquaresDiscriminant Analysis,PLS-DA)模型,发现鸡和哺乳动物(猪,牛和羊)肉骨粉主要在1 659、2 930、2 852、1 246和1 455 cm-1附近的特征谱带具有差异性;猪和反刍动物(牛和羊)肉骨粉主要是在2 852、1 659和1 246 cm-1附近的特征谱带具有差异性;牛和羊肉骨粉主要是在878、853、2 930、2 852、1 246、1 455和1 659 cm-1附近的特征谱带具有差异性,并且PLS-DA模型鉴别肉骨粉的灵敏度和特异度均大于93.8%。研究结果可以丰富肉骨粉种属鉴别方法体系以及为开发便携式拉曼光谱仪提供参考。     

Utilization of near infrared reflectance spectroscopy (NIRS) to quantify the impact of earthworms on soil and carbon erosion in steep slope ecosystem: A study case in Northern Vietnam

This work focuses on a new approach to quantify the effects of above-ground earthworm's activity on soil erosion in steep slope ecosystems such as in Northern Vietnam. In these areas and in many others in the world, soil erosion becomes a major issue while the factors that determine it are still misunderstood. Earthworm's activity is believed to influence soil erosion rate, but we are still unable to precisely quantify their contribution to soil erosion. In this study, we used Near Infrared Reflectance Spectroscopy (NIRS) to quantify the proportion of soil aggregate in eroded soil coming from earthworm activity. This was done by generating NIRS signatures corresponding to different soil surface aggregates (above-ground soil casts produced by earthworms vs. surrounding topsoil).     

Near infrared reflectance spectroscopy (NIRS) could be used for characterization of soil nematode community

Studying soil nematofauna provides useful information on soil status and functioning but requires high taxonomic expertise. Near infrared reflectance (NIR) spectroscopy (NIRS) has been reported to allow fast and inexpensive determination of numerous soil attributes. Thus the present study aimed at assessing the potential of NIRS for determining the abundance and diversity of soil nematodes in a set of 103 clayey topsoil samples collected in 2005 and 2006 from agricultural soils in the highlands of Madagascar.The morphological characterization of soil nematofauna involved extraction through elutriation then counting under binoculars and identification at family or genus level using microscopy, on ca. 150-g fresh soil samples. Taxa were assigned to five trophic groups, namely bacterial feeders, fungal feeders, obligate plant feeders, facultative plant feeders, and omnivores and predators (together). In addition, four ecological indexes were calculated: the Enrichment index, Structure index, Maturity index, and Plant parasitic index.Oven-dried (40 °C) < 2-mm sieved 5-g soil subsamples were scanned in the NIR range (1100-2500 nm), then spectra were fitted to nematofauna data using partial least square regression. Depending on the sample set considered (year 2005, year 2006, or both years), NIRS prediction of total nematode abundance was accurate (ratio of standard deviation to standard error of cross validation, i.e. RPD ≥ 2) or acceptable (RPD ≥ 1.6). Predictions were accurate, acceptable, or quasi-acceptable (RPD ≥ 1.4) for several of the six most abundant taxa, and to a larger extent, for most trophic groups (except facultative plant feeders); but they could not be made for taxa present in a small number of samples or at low abundance. By contrast, NIRS prediction of relative abundances (in proportion of total abundance) was poor in general, as was also the prediction of ecological indexes (except for the 2006 set). On the whole, these results were less accurate than NIRS predictions of soil attributes often reported in the literature. However, though not very accurate, NIRS predictions were worthwhile considering the labor-intensity of the morphological characterization. Most of all, NIRS analyses were carried out on subsamples that were probably too small (5 g) to allow representative sampling of nematofauna. Using larger samples for NIRS (e.g. 100 g) would likely result in more accurate predictions, and is therefore recommended. Scanning un-dried samples could also help improve prediction accuracy, as morphological characterization was carried out on samples not dried after sampling.Examining wavelengths that contributed most to NIRS predictions, and chemical groups they have been assigned to, suggested that NIRS predictions regarding nematofauna depended on constituents of both nematodes and preys’ food. Predictions were thus based on both nematofauna and soil organic properties reflected by nematofauna.     

Estimation of mineral content in natural grasslands by near infrared reflectance spectroscopy

Abstract

Near infrared reflectance spectroscopy (NIRS) was tested to predict the nitrogen (N) and mineral concentration [for the elements phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn)] in natural grassland samples. The samples wrere taken from different community types according to the topographic gradient at different maturation stages and during a period of four consecutive years. A subset of 95 samples was selected on the basis of the spectral variation. Chemical values from the calibration sample set were regressed on the corresponding spectral data using a stepwise multiple regression analysis. Another subset of 75 samples was used as the validation set. Standard errors of prediction and correlation coefficients, respectively, were: 0.71 and 0.97 (N), 0.22 and 0.73 (P), 1.83 and 0.84 (K), 0.83 and 0.92 (Ca), 0.15 and 0.92 (Mg), 3.94 and 0.66 (Na), 44 and 0.84 (Mn), 19 and 0.75 (Fe), 1.01 and 0.77 (Co), and 3.9 and 0.79 (Zn).     

紫花苜蓿幼苗耐盐性的近红外光谱鉴定

紫花苜蓿幼苗耐盐性快速鉴定对于耐盐种质资源筛选和耐盐新品种选育具有重要意义。脯氨酸和丙二醛是表征植物耐盐性的两种重要生化指标。研究应用便携式近红外仪和近红外光谱分析技术,结合偏最小二乘回归法,研究了40个不同紫花苜蓿品种幼苗耐盐性的脯氨酸和丙二醛含量,建立了新鲜样品和干燥样品的近红外漫反射光谱定量分析模型。研究结果表明：脯氨酸、丙二醛的近红外漫反射光谱分析效果均较好,校正模型决定系数R2 和验证集样品预测值与常规分析测定值的验证决定系数r2都大于0.85,两种样品脯氨酸的相对分析误差RPD值分别为1.72