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1.
为了研究输送带速度和光谱仪安装高度对近红外在线检测秸秆-煤混燃物的影响。该研究收集并制备秸秆样品80个、煤样品9个,制备秸秆质量分数为70%～99%的秸秆-煤混燃物样品120个(质量分数增量为1%)、秸秆质量分数为1%～30%的秸秆-煤混燃物样品120个(质量分数增量为1%)。在输送带速度分别为300、600、1 000和1 400 mm/s,光谱仪安装高度分别为50、56、59和65 mm的条件下,使用Thermo Fisher Scientific Antaris Target FT-NIR型光谱仪获取样品近红外光谱。使用线性判别分析法建立定性分析模型,使用偏最小二乘法建立定量分析模型。结果表明,光谱仪安装高度建议为50～65 mm,输送带速度建议小于300 mm/s。该研究可为近红外光谱法在线定性和定量检测生物质-煤混燃物的方法学研究和相关仪器设计提供参考。  相似文献   

2.
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。  相似文献   

3.
利用近红外光谱技术检测掺假豆浆   总被引：3，自引：1，他引：2  
为了对豆乳内在营养指标及掺假豆乳进行快速检测,试验运用近红外光谱技术,利用偏最小二乘法进行回归分析,分别建立83个真伪豆浆样品的蛋白质和总固形物含量定标模型,并对模型的预测性能进行分析。结果表明:选取主成分数为12和14,蛋白质和总固形物含量的近红外光谱预测值与化学实测值之间的相关系数R分别为0.9756和0.9489,校正均方根误差分别为0.186和0.175,预测集样品的预测值和实测值之间的残差值均较小、接近零,残差之和分别为-0.074和-1.191,说明建立的定标模型可以准确预测豆浆中蛋白质和总固形物含量,且预测性能较好;通过对预测集样品的预测值与豆浆行业标准规定值相比较,确定预测集样品中掺假豆浆的正确判别率为100%,说明建立的蛋白质和总固形物含量定标模型可以应用于掺假豆浆的判别检测,且判别结果准确率高。本试验表明利用近红外光谱技术可实现对豆浆主要品质指标的快速无损检测,也可准确进行真伪豆浆的快速判别,本检测方法可为豆乳行业健康持续发展提供一定的技术支撑。  相似文献   

4.
安徽省是国家重要商品粮生产基地,作为农业大省其粮食作物秸秆产量居于全国前列。明晰全省产粮大县主要粮食作物秸秆理论资源量和可收集资源量时空分布特征,并准确测算秸秆就地还田对土壤养分输入的贡献,可为秸秆全量化差异化利用策略优化及秸秆还田情景下的农田养分平衡调控提供决策依据。研究表明,2011-2019年安徽省产粮大县三大粮食作物秸秆资源总量呈现出稳步增长的态势,而不同作物秸秆产量年际变化趋势各异：小麦秸秆先升后稳,水稻秸秆波动不大,玉米秸秆逐年递增。2019年安徽省产粮大县三大粮食作物秸秆理论资源量为3 878万t,其中小麦、水稻和玉米秸秆所占比例分别为47.3%、36.3%和16.4%。淮北区为小麦和玉米秸秆资源集中分布区,占比分别为73.0%和88.3%,水稻秸秆主要产自江淮区（41.7%）、皖西区（21.3%）及沿江区（19.7%）。主要粮食作物秸秆资源总量分布表现为淮北区（52.5%）?江淮区（24.3%）?皖西区（10.5%）?沿江区（9.1%）?皖南区（3.6%）。2019年全省产粮大县小麦、水稻和玉米秸秆可收集资源量分别为1 338万、1 041万和542万t,淮北区单位播种面积小麦和玉米秸秆可收集资源量分别为4 505～6 310和4 171～5 395 kg/hm2,江淮区、皖西区和沿江区单位播种面积水稻秸秆可收集资源量分别为4 487～5 326、4 570～5 028和4 329～5 778 kg/hm2。2019年安徽省产粮大县三大粮食作物秸秆氮（N）、磷（P2O5）和钾（K2O）养分资源总量分别为25.3万、10.9万和90.1万t。在秸秆就地全量还田情景下,小麦玉米主产区（淮北区）小麦秸秆还田的氮（N）、磷（P2O5）和钾（K2O）养分输入量分别为35.8～50.1、14.1～19.8和139.8～195.8 kg/hm2,玉米秸秆还田的养分输入量分别为42.7～55.2、16.9～21.8和93.4～120.9 kg/hm2;水稻主产区（江淮区、皖西区和沿江区）水稻秸秆还田的养分输入量分别为38.0～50.8、18.8～25.0和151.6～202.3 kg/hm2。研究结果对进一步提升安徽省产粮大县秸秆资源综合利用效率及推动农业绿色高质量发展具有重要的现实意义。  相似文献   

5.
二维相关光谱结合偏最小二乘法测定牛奶中的掺杂尿素   总被引：9，自引：5，他引：4  
为了检验牛奶中是否掺杂尿素并将其量化测定,配置含有尿素质量浓度范围为1～20g/L之间40个牛奶样品,以掺杂物尿素浓度为外扰,分别研究了掺杂尿素牛奶的二维相关(近红外-近红外,中红外-中红外,近红外-中红外)光谱特性,在此基础上,分别选择随浓度变化大的4200～4800cm-1和1400～1704cm-1为建模区间,采用偏最小二乘方法建立定量分析模型。研究结果表明:4200～4800cm-1建模分析效果优于1400～1704cm-1建模结果,其交叉验证均方根误差为0.266g/L,对未知样品集预测相关系数达到0.999,预测均方根误差为0.219g/L,这表明所建模型具有较好的预测效果。该方法无需样品处理,成本低,为快速判别牛奶是否掺杂提供了一种新的可能的方法。  相似文献   

6.
黑土土壤中全氮含量的高光谱预测分析   总被引：16，自引：5，他引：11  
为实现快速、准确估测土壤氮素含量水平,推动土壤信息化管理进程,该研究利用ASD2500高光谱仪在室内条件下测定了风干土壤样品的可见—近红外光谱。结果表明,通过不同的变换,光谱反射率对数的一阶导数与土壤全氮含量相关性得到增强,以400～600 nm波段范围内相关性最好。该文确定了以反射率对数的一阶导数光谱预测黑土全氮（TN）含量的最佳回归模型,模型所用的波段为可见光波段的556 nm、近红外的1 642和2 491 nm。同时,也确定了利用由可见光波段550和450 nm组成的归一化光谱指数预测黑土TN含量的最佳预测模型。模型通过验证达到较好的效果：利用反射率对数的一阶导数、归一化光谱指数对土壤TN的预测R2分别为0.863、0.829,均方根误差RMSE分别为0.122、0.152。  相似文献   

7.
基于可见/近红外光谱的菠萝水心病无损检测   总被引：1，自引：1，他引：0  
水心病近年严重危害菠萝产业,探究一种菠萝水心病的无损检测方法对保证上市果品、指导采后处理、促进产业提升具有重要意义。该研究采用自行搭建的菠萝可见/近红外光谱无损智能检测平台,考虑实际应用成本与效果,搭载覆盖不同波段（400～1 100、900～1 700和400～1 700 nm）的检测器对菠萝样本进行采样,随后人工标定水心病发生程度。研究结果表明,3种不同光谱波段对菠萝水心程度检测的较优方法均为：采用全波段进行多项式平滑（Savitzky Golay,SG）处理,再进行标准正态变量校正（Standard Normal Variate,SNV）,最后结合概率神经网络（Probabilistic Neural Network,PNN）建模识别。其中,400～1 100 nm所建模型对菠萝水心病训练集的回判正确率为98.51%,对验证集的检测正确率为91.18%;900～1 700 nm所建模型对菠萝水心病训练集的回判正确率为100%,对验证集的检测正确率为62%;400～ 1 700 nm所建模型对菠萝水心病训练集的回判正确率为100%,对验证集的检测正确率为91.18%。主成分分析（Principal Component Analysis,PCA）和偏最小二乘回归（Partial Least Squares Regression,PLSR）分析结果均显示,采用400～ 1 700 nm能轻微提升400～1 100 nm的检测效果。综合考虑实际应用成本与效果,实际应用建议采用400～1 100 nm光谱结合SG + SNV + PNN对菠萝水心病进行识别。研究结果证明可见/近红外光谱技术可为菠萝水心病无损、快速、智能检测提供有效的解决方案,为相关领域提供参考。  相似文献   

8.
基于小波变换的番茄总糖近红外无损检测   总被引：1，自引：2，他引：1  
分别采用小波消噪、常数偏移消除等11种光谱预处理方法,对番茄总糖含量（质量分数）的近红外光谱进行预处理,通过偏最小二乘法定量校正模型预测值比较得出,小波消噪是适合番茄近红外光谱的最佳预处理方法,小波消噪的总糖质量分数近红外光谱优选区域为11 998.9～6 097.8 cm-1和4 601.3～4 246.5 cm-1,在此光谱区内建立的番茄总糖质量分数偏最小二乘法模型预测值与实测值的相关系数为0.930,内部交叉验证均方差为0.466%,校正标准差为0.469%,预测标准差为0.260%。试验结果表明：小波消噪后建立的近红外光谱模型能准确地对番茄总糖含量进行快速无损检测。  相似文献   

9.
近红外光谱法测定玉米秸秆饲用品质   总被引：6，自引：1，他引：5  
为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价,该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料,应用近红外光谱（NIRS）技术和偏最小二乘法（PLS）,采用一阶导数+中心化+多元散射校正的光谱数据预处理方法,构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802,交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342,外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191,各项标准差（SEC、SECV和SEP）为0.935～1.904,相对分析误差（RPD）均大于3。结果表明,各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。  相似文献   

10.
为了探明秸秆还田对宁南旱区土壤有机碳及土壤碳矿化的影响,为该区作物生产及土壤培肥制度的建立提供参考,通过4a（2007—2010年）秸秆还田定位试验,设置不同秸秆还田量处理,谷子秸秆按3000kg·hm-（2低L）、6000kg·hm-（2中M）、9000kg·hm-（2高H）粉碎还田,玉米秸秆按4500kg·hm-（2低L）、9000kg·hm-（2中M）、13500kg·hm-（2高H）粉碎还田,对照为秸秆不还田,对不同处理条件下土壤有机碳、土壤碳矿化速率、累积矿化量及其与不同形态碳素之间的相关性进行了分析。结果表明,土壤总有机碳、活性有机碳含量均随土层的加深而减少;各处理0～60cm土层土壤有机碳和活性有机碳含量分别比CK显著提高了24.2%、20.8%、9.5%和50.3%、46.6%、34.8%（P〈0.05）;秸秆还田不仅增加了土壤活性有机碳含量,同时也显著提高了0～20cm土层活性有机碳占总有机碳含量的比重,提高幅度达21.1%～23.1%（P〈0.05）;土壤碳矿化速率和累积矿化量在0～60cm各土层内随着秸秆还田量的增加大小顺序均为高量秸秆还田〉中量秸秆还田〉低量秸秆还田〉秸秆不还田,各秸秆还田处理较CK差异显著（P〈0.05）。相关性分析表明,土壤碳累积矿化量与不同形态碳素之间均存在极显著相关性。因此,在宁南半干旱区采用秸秆还田对提高土壤有机碳含量和碳矿化具有明显作用。  相似文献   

11.
The presence of relatively inert organic materials such as char has to be considered in calibrations of soil C models or when calculating C‐turnover times in soils. Rapid and cheap spectroscopic techniques such as near‐infrared (NIRS) or mid‐infrared spectroscopy (MIRS) may be useful for the determination of the contents of char‐derived C in soils. To test the suitability of both spectroscopic techniques for this purpose, artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals) and forest‐floor Oa material were produced. The total C content of these mixtures (432 samples) ranged from 0.5% to 6% with a proportion of char‐derived C amounting to 0%, 20%, 40%, 50%, 60%, or 80%. All samples were scanned in the visible and near‐IR region (400–2500 nm). Cross‐validation equations for total C and N, C and N derived from char (Cchar, Nchar) and Oa material were developed using the whole spectrum (first and second derivative) and a modified partial least‐square regression method. Thirty‐six samples were additionally scanned in the middle‐IR and parts of the near‐IR region (7000–400 cm–1 which is 1430–25,000 nm) in the diffuse‐reflectance mode. All properties investigated were successfully predicted by NIRS as reflected by RSC values (ratio of standard deviation of the laboratory results to standard error of cross‐validation) > 4.3 and modeling efficiencies (EF) ≥ 0.98. Near‐infrared spectroscopy was also able to differentiate between the different coals. This was probably due to structural differences as suggested by wavelength assignment. Mid‐IR spectroscopy in the diffuse‐reflectance mode was also capable to successfully predict the parameters investigated. The EF values were > 0.9 for all constituents. Our results indicated that both spectroscopic techniques applied, NIRS and MIRS, are able to predict C and N derived from different sources in soil, if closed populations are considered.  相似文献   

12.
Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.  相似文献   

13.
Kava ( Piper methysticum Forst f., Piperaceae) has anxiolytic properties and the ability to promote a state of relaxation without the loss of mental alertness. The rapid growth of the nutraceutical market between 1998 and 2000 has been stopped by a ban in Europe and Australia because of some suspicion of liver toxicity. It is now important to develop a fast, cheap, and reliable quality test to control kava exports. The aim of this study is to develop a calibration of the near-infrared reflectance spectroscopy (NIRS) using partial least-squares (PLS) regression. Two hundred thirty-six samples of kava roots, stumps, and basal stems were collected from the Vanuatu Agricultural Research and Technical Centre germplasm collection and from four villages. These samples, representing 45 different varieties, were analyzed using NIRS to record their absorption spectra between 400 and 2500 nm. A set of 101 selected samples was analyzed for their kavalactone content using HPLC. The results were used for PLS calibration of the NIRS. The NIRS prediction of the kavalactone content and the dry matter were in agreement with the HPLC results. There were good correlations between these two series of results, and coefficients ( R (2)) were all close to 1. The measurements were reproducible and had repeatability on par with the HPLC method. The NIRS system has been calibrated for the six major kavalactone content measurements, and it is suggested that this method could be used for quality control in Vanuatu.  相似文献   

14.
The purpose of the present study was to evaluate the capability of near infrared spectroscopy (NIRS) as a simple method to monitor the lipid content of garbage compost, which is a potential inhibitor of plant growth. We conducted a cultivation experiment with vegetable mock pak choy ( Brassica rapa L. Parachinensis Group) using two application rates of four garbage composts that differed in lipid content. The input of lipid from the compost to the field showed a significant negative correlation with germination rate and plant height in the initial growth stage. Reflectance spectra of untreated and freeze-dried and milled compost samples were taken using a scanning monochromator. Second-derivative spectra and multiple regression analysis were used to develop calibration equations for lipid and moisture contents. The calibration was carried out with the short wavelength region ([SWR] 800–1100 nm) and the long wavelength region ([LWR] 1100–2500 nm) separately. The calibration equations with the LWR were more accurate than those with the SWR for lipid and moisture determinations. The accuracies of the calibration equations for untreated samples were comparable to those for freeze-dried and milled samples. In conclusion, we suggest that the application rate of garbage compost can be determined by measuring the lipid content of untreated samples by NIRS.  相似文献   

15.
[目的]证实生物质炭中的水溶性有机碳对作物生长和品质的作用,为生物质炭的应用开辟新的途径。[方法]在实验室制取400,450,500℃小麦秸秆生物质炭,以KOH溶液浸提水溶性有机碳获得生物质炭浸提液W_(400),W_(450),W_(500),并应用于盆栽大蒜试验,研究其对大蒜生长、品质及土壤的影响。[结果](1)W_(400),W_(450),W_(500)对大蒜的生长都没有显著影响;(2)W_(400)处理使大蒜可溶性糖的含量显著提高了27.53%,土壤有机质和速效磷含量分别提高37%,26%;(3)W_(450)处理下大蒜维生素C、大蒜素含量分别显著提高34.9%和8.2%。[结论]生物质炭浸提液对大蒜生长没有影响,但可以提高大蒜品质。  相似文献   

16.
This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R2val = 0.61, RPDval = 1.60, RMSEval = 0.59) and good prediction for %C (R2val = 0.82, RPDval = 2.30, RMSEval = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.  相似文献   

17.
秸秆覆盖对盐渍化土壤水盐影响的试验研究   总被引：8，自引：3，他引：5  
研究了不同秸秆覆盖量处理对盐渍化土壤蒸发量、含水率及含盐量动态变化的影响。结果表明,（1）随着秸秆覆盖量增加,土壤蒸发量逐渐减少,当秸秆覆盖量为7 500 kg/hm2时,日蒸发量减少幅度趋于稳定。（2）秸秆覆盖量为10 500,7 500,4 500,1 500 kg/hm2时,蒸发抑制率依次为80.54%,79.01%,62.45%,37.93%。（3）随着秸秆覆盖量增加,不同土层含水率逐渐增加。当秸秆覆盖量为7 500 kg/hm2时,0—10 cm土层含水率达到最大值,比对照实验增加了9.28%;0—10 cm土层含盐量下降幅度最大,含盐量减少了3.21%。因此,秸秆覆盖盐渍土壤能够减少土壤水分蒸发损失,提高水分利用效率,抑制盐渍化土壤可溶性盐分的表聚作用,对改良盐渍化土壤具有显著效果。  相似文献   

18.
The authentication of rice (Korean domestic rice vs. foreign rice) has been attempted using near‐infrared spectroscopy (NIRS). Two sample sets (n1 = 280 and n2 = 200) were used to obtain calibration equations and the spectral regions used for this study were 500–600 nm, 700–900nm, and 980–2,498 nm. Modified partial least square (MPLS) regression was used to develop the prediction model. The standard error of cross validation (SECV) and the r2 were 0.165 and 0.91 respectively for 1st calibration set and 0.165 and 0.93 for 2nd calibration set respectively. The results of the independent validation (n3 = 80) showed that all of 80 samples were identified correctly. Even though authentication of rice was performed successfully using NIRS, the calibration statistics in this study showed that further effort is needed for implementation of NIRS for authentication of rice for industry purposes.  相似文献   

19.
The percentage of dark hard vitreous (DHV) kernels in hard red spring wheat is an important grading factor that is associated with protein content, kernel hardness, milling properties, and baking quality. The current visual method of determining DHV and non‐DHV (NDHV) wheat kernels is time‐consuming, tedious, and subject to large errors. The objective of this research was to classify DHV and NDHV wheat kernels, including kernels that were checked, cracked, sprouted, or bleached using visible/near‐infrared (Vis/NIR) spectroscopy. Spectra from single DHV and NDHV kernels were collected using a diode‐array NIR spectrometer. The dorsal and crease sides of the kernels were viewed. Three wavelength regions, 500–750 nm, 750–1,700 nm, and 500–1700 nm were compared. Spectra were analyzed by using partial least squares (PLS) regression. Results suggest that the major contributors to classifying DHV and NDHV kernels are light scattering, protein content, kernel hardness, starch content, and kernel color effects on the absorption spectrum. Bleached kernels were the most difficult to classify because of high lightness values. The sample set with bleached kernels yielded lower classification accuracies of 91.1–97.1% compared with 97.5–100% for the sample set without bleached kernels. More than 75% of misclassified kernels were bleached. For sample sets without bleached kernels, the classification models that included the dorsal side gave the highest classification accuracies (99.6–100%) for the testing sample set. Wavelengths in both the Vis/NIR regions or the NIR region alone yielded better classification accuracies than those in the visible region only.  相似文献   

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