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排序方式: 共有193条查询结果,搜索用时 15 毫秒
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近红外技术(NIR)在小麦商品粮收购中的应用研究 总被引:7,自引:0,他引:7
利用近红外谷物品质分析仪 (Perten DA910 0 ) ,对陕西省某县 6 2 4户农民 2 0 0 0年夏季上缴粮库的公购粮进行现场品质分析。结果认为 :人工主观评定的商品粮等级与其蛋白质含量、水分含量、硬度、沉淀值、烘烤体积无显著相关性 ;近红外谷物品质分析仪 (Perten DA910 0 )完全能够满足粮库验粮的需要 ;小麦籽粒硬度、蛋白质含量是商品粮收购定级的重要指标 相似文献
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近红外光谱分析中,异常样本的存在严重影响定标模型的预测性能和适配性。基于 X / Y 联合的ODXY异常样本识别和剔除方法,提出并证明了一种专用于多组分分析的MODXY异常样本识别方法。实验采用80组玉米近红外光谱数据,利用不同异常样本识别方法剔除异常样本后建立玉米含水率、含油率、蛋白质含量和淀粉含量4种组分的偏最小二乘预测模型,采用预测均方差和决定系数作为评价指标比较所建模型的性能,检验MODXY方法在多组分分析中的异常样本识别能力。实验结果表明:在近红外多组分分析中,MODXY方法在大多数情况下具有更好的异常样本识别能力;MODXY方法和ODXY方法均有一定的适用范围,它们更适合于相应组分化学值的相对标准偏差较大的情况。 相似文献
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ZHANG Hong-jiang WU Jin-hong LUO Li-jun LI Ying YANG Hua YU Xin-qiao WANG Xiao-shan CHEN Liang MEI Han-wei 《中国农业科学(英文版)》2007,6(8):941-948
The near infrared spectra of 178 recombinant inbred lines (RILs) from the cross of Ⅱ-32B/Yuezaoxian 6 (YZX6) and 511 varieties in rice were acquired. A total of 80 RILs and 96 cultivars were selected as modeling samples by comparing the spectra similarity primarily. Three partial least square (PLS) regression models were developed, based on the RILs (RIL-model), the varieties (Var-model) and their mixture (Mix-model), for protein content (PC) and amylose content (AC), respectively. Cross validation and outer prediction showed that the models were largely influenced by the range and distribution of modeling samples. The regression model of PC based on the cultivars and the model of AC based on RILs had higher coefficient of determination (r^2 ≥ 0.9) and lower root mean square error of cross validation (RMSECVs). The disadvantages of RIL samples for PC model and variety samples for AC model were probably caused by the narrow range of variance. Aberrant predictions were obtained for outer sample with PC or AC outside the range or within the distribution gap of modeling samples. The Mix-models gave more reliable prediction as the distribution of RIL and variety modeling samples were complementary to each other. 相似文献
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研究基于近红外光谱技术的木材密度预测。运用基于高斯核变换的非线性偏最小二乘法建立密度预测模型,并且对所建模型的评价参数进行了对比分析。结果表明该方法建立的预测模型能对样品的密度进行有效预测。研究表明样品近红外光谱信息与样品的实际密度值之间不是单纯的线性关系,非线性模型可以更好地表征二者之间的关系。 相似文献
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应用可见/近红外光谱技术快速鉴别山西陈醋品种 总被引:2,自引:0,他引:2
为了实现对山西老陈醋品种的快速鉴别,应用可见/近红外光谱透射技术,结合化学计量学方法,进行了山西老陈醋品种的判别分类试验研究。对4个不同品种共240个山西老陈醋样品采集其光谱数据,结合主成分分析和神经网络技术分别对山西陈醋原始光谱、一阶微分光谱、二阶微分光谱进行了判别分析。结果表明:可见/近红外原始光谱结合主成分分析神经网络判别分析法的分析结果最优,校正集正确分类的百分比达92.1%,预测集达85.0%;二阶微分光谱分析结果最差。 相似文献
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为探索快速高效测定大麦籽粒中抗性淀粉含量的方法,利用衰减全反射中红外(attenuated total reflection mid-infrared spectroscopy,ATR-MIR)和近红外(near-infrared spectroscopy,NIR)光谱技术,分别用3种不同方法进行预处理,建立大麦样品的抗性淀粉含量快速测定红外模型,通过不同预处理预测模型的校正和内部交叉验证结果的比较,依据决定系数(r)和均方根误差(RMSE)筛选出基于ATR-MIR和NIR光谱的最佳预测模型,再对最佳预测模型进行外部验证。结果表明,经基线位移校正+范围归一化(BOC+RN)预处理后的PLS模型为最佳ATR-MIR预测模型;经标准正态变换+Savitzky-Golay法一阶求导(SNV+1thD)的预处理模型为最佳NIR预测模型。用验证集材料对BOC+RN和SNV+1thD最佳预测模型的预测效果进行外部验证,光谱预测值与化学测定值之间没有显著差异,说明两种方法均可以用于大麦籽粒抗性淀粉含量测定;ATR-MIR光谱比NIR光谱具有更好的预测能力。 相似文献
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YU Huaqiang ZHAO Rongjun FU Feng FEI Benhua JIANG Zehui Research Institute of Wood Industry Chinese Academy of Forestry Beijing P.R.China 《中国林业科技(英文版)》2007,6(2):14-19
The visible and near infrared (NIR) (350-2500 nm) spectra and the MOE of 438 small clear wood samples from Chinese fir, eucalyptus and poplar 72 were measured. Using partial least-square (PLS) modeling, the NIR spectra could be used to predict MOE and MOR of the clear-wood samples from Chinese fir and eucalyptus solid wood. NIR spectra could only be used to Predict MOE but not MOR of poplar clear-wood samples. With the exception of MoR of poplar clear-wood samples, the correlations between NIR and the mechanical properties are very strong, and the calibration and test correlation coefficients are both above 0.80. 相似文献
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In industrial areas, heavy metals may accumulate in forest soil organic horizons, affecting soil microorganisms and causing
changes in the chemical composition of the accumulated organic matter. The objectives of this study were to test the ability
of near-infrared spectroscopy (NIRS) to detect heavy metal effects on the chemical composition of forest soil O horizons and
to test whether NIRS may be used to quantitatively determine total and exchangeable concentrations of Zn and Pb (Znt, Pbt, Znex, Pbex) and other chemical and microbial properties in forest soil O horizons polluted with heavy metals. The samples of O horizons
(n = 79) were analyzed for organic C (Corg), total N and S (Nt, St), Znt, Pbt, Znex, Pbex, basal respiration (BR), microbial biomass (Cmic) and Cmic-to-Corg ratio. Spectra of the samples were recorded in the Vis-NIR range (400–2,500 nm). To detect heavy-metal-induced changes in
the chemical composition of O horizons principal components (PC1–PC7) based on the spectral data were regressed against Znt + Pbt values. A modified partial least squares method was used to develop calibration models for prediction of various chemical
and microbial properties of the samples from their spectra. Regression analysis revealed a significant relationship between
PC3 and PC5 (r = −0.27 and −0.34, respectively) and Znt + Pbt values, indicating an effect of heavy metal pollution on the spectral properties of the O horizons and thus on their chemical
composition. For quantitative estimations, the best calibration model was obtained for Corg-to-Nt ratio (r = 0.98). The models for Corg, Nt, and microbial properties were satisfactory but less accurate. NIRS failed to accurately predict St, Corg-to-St, Znt, Pbt, Znex, and Pbex. 相似文献