基于近红外光谱的红茶干燥中含水率无损检测方法

为实现工夫红茶干燥中含水率的快速检测,提出了基于近红外光谱红茶干燥中含水率无损检测方法。随机抽取6次干燥处理中的226个样本,进行波长1 000~17 99 nm近红外光谱扫描后按照国标法测定含水率。对原始光谱数据进行标准正态变量变换(SNVT)预处理,利用全局偏最小二乘法(PLS)、联合区间偏最小二乘法(si PLS),分别构建水分近红外预测模型并验证。结果表明:用两种方法检测含水率,其准确度都可靠,但利用si PLS法将全光谱划分为13个区间,联合4个区间用6个主成分数构建的水分预测模型效果更优,其预测集的相关系数R和预测均方根误差RMSEP值分别为0.9593和0.0395,说明模型预测精度高,可以实现红茶干燥中含水率的快速无损检测。     

基于可见–近红外光谱的水稻土全磷反演研究

采用PLSR偏最小二乘法回归结合留一法交叉验证,利用长期定位试验田以及直湖港小流域面上的水稻土土壤样本建立最优模型,研究了不同光谱预处理方式对水稻土全磷可见-近红外高光谱反演精度的影响,探索水稻土全磷光谱反演的可行性;并结合简单相关系数法以及PLSR模型回归系数法分析了水稻土全磷光谱反演的重要波段。结果表明,光谱预处理方法对土壤全磷反演精度的影响不大;基于PLSR建立的水稻土全磷光谱反演模型的校正决定系数达0.85,交叉验证决定系数为0.70,RPD为1.8,有较好的模型精度;440~740 nm为土壤全磷光谱反演的重要波段。利用PLSR对水稻土全磷进行光谱预测是可行的。     

应用红外光声光谱技术及支持向量机模型测定土壤有机质含量

快速测定土壤有机质含量对作物生产和土壤肥力评价具有重要意义,红外光声光谱技术的应用为土壤有机质快速测定提供了可能。本研究以江苏省南京市溧水区水稻土土样为材料,探究了红外光声光谱技术在有机质测定中的应用。采用主成分分析、偏最小二乘和独立成分分析,分别提取了土壤光谱的主成分、偏最小二乘潜变量和独立成分,并以提取的信息输入支持向量机,从而构建了三种支持向量机校正模型。同时,偏最小二乘也被用于建立校正模型,作为支持向量机模型的对照。预测结果表明,基于独立成分的支持向量机模型效果最好,预测相关系数R2、均方根误差RMSEP和实际测量值的标准差与光谱模型预测值标准差的比值即RPD值分别为0.808、0.575和2.28。F检验表明,该模型显著优于基于主成分的支持向量机模型,但与基于偏最小二乘潜变量的支持向量机模型,以及经典偏最小二乘模型没有显著差异。t检验表明,各校正模型对有机质的预测结果与化学测定结果没有显著差异。因此,红外光声光谱技术为土壤有机质的快速测定提供了新的技术手段。     

典型黑土区不同生态系统下土壤团聚体中胡敏酸的结构特征

为了揭示生态系统类型对土壤团聚体中腐殖质分子结构的影响,本研究以黑土区24 a长期定位试验处理下的自然草地(GL)、耕地(CL)和裸地(BL)土壤为研究对象,采用湿筛法将土壤团聚体分为2 mm、2～0.25 mm、0.25～0.053 mm和0.053 mm 4个粒级,运用元素分析、红外光谱和差热分析技术,对团聚体中胡敏酸(HA)的结构性质进行了研究。结果表明,生态系统类型对团聚体中HA的分子结构产生了影响。耕地各级团聚体中HA的O+S含量以及(O+S)/C比值(摩尔比)普遍高于草地相应粒级团聚体,反映了耕地土壤团聚体中HA的氧化程度高于草地;差热分析中,耕地和裸地土壤团聚体中HA的中温放热量低于草地相应粒级团聚体,且二者的热量高/中比值均高于草地,说明耕地和裸地土壤团聚体中HA的芳香性/脂族性高于草地;草地和耕地土壤团聚体中HA在2 920和2 850 cm-1处代表脂族聚亚甲基和末端甲基中C-H伸缩振动的吸收峰明显强于裸地,脂族碳/羧基碳(2 920/1 720)以及脂族碳/芳香碳(2 920/1 620)比值高于后者。与自然草地相比,耕地和裸地生态系统会增强土壤团聚体中HA的缩合度、氧化度和芳香性,提高其分子结构的稳定性。     

A new method of searching for concealed Au deposits by using the spectrum of arid desert plant species

With the increase of exploration depth, it is more and more difficult to find Au deposits. Due to the limitation of time and cost, traditional geological exploration methods are becoming increasingly difficult to be effectively applied. Thus, new methods and ideas are urgently needed. This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits. For this purpose, 48 plant (Seriphidium terrae-albae) and soil (aeolian gravel desert soil) samples were first collected along a sampling line that traverses an Au mineralization alteration zone (Aketasi mining region in an arid region of China) and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device (ASD) FieldSpec3 spectrometer. Then, the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated. Additionally, the characteristic bands were selected from plant spectra using four different methods, namely, genetic algorithm (GA), stepwise regression analysis (STE), competitive adaptive reweighted sampling (CARS), and correlation coefficient method (CC), and were then input into the partial least squares (PLS) method to construct a model for estimating the soil Au content. Finally, the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method. The results were compared with those of a model based on the full spectrum. The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information, and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil. The cross-validated coefficient of determination (R²) and the ratio of the performance to deviation (RPD) between the predicted value and the measured value reached the maximum of 0.8218 and 2.37, respectively, with a minimum value of 6.56 μg/kg for the root-mean-squared error (RMSE) in the full spectrum model. However, in the process of modeling, it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method. Compared with the GA, STE, and CC methods, CARS was the superior characteristic band screening method based on the accuracy and complexity of the model. When modeling with characteristic bands, the highest accuracy, R² of 0.8016, RMSE of 7.07 μg/kg, and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra (1/lnR)' (where it represents the first derivative of the reciprocal of the logarithmic spectrum) of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil. Thus, characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content. Finally, this study proposes a method of using plant spectra to find concealed Au deposits, which may have promising application prospects because of its simplicity and rapidity.     

湖南杉木人工林近自然经营问题探讨

德国人工林在19世纪,与湖南杉木人工林有相似之处,采用单一树种营造大面积人工林,造成地力消耗很大,无法实现林木的可持续利用。本文从杉木经营历史,分析了现有栽培制度下的炼山、幼林抚育、轮伐期短、林分结构单一等现状所导致的杉木人工林生产力下降、林地地力衰退。本研究引进德国森林近自然经营理念及原则改造杉木人工林,分别对杉木幼林、中林、成林进行第1代目标树经营,为第2代目标树形成创造有利条件,使林地地力得到恢复,达到可持续经营的目的,同时获得较大经济效益。     

寒地水稻直链淀粉含量测定方法的研究

水稻直链淀粉含量是水稻品质育种的重要衡量指标,为加速水稻品质育种进程,以10份寒地水稻品种为试验材料,采用近红外谷物分析仪测定法、改良的标准测定法以及单粒法3种测定方法分别对精米粒、糙米粒、精米粉和糙米粉进行直链淀粉含量的测定。结果表明:3种测定方法适用范围不同,且具有各自的分析特点。单粒法测定结果与改良的标准测定法结果平行可靠,可为水稻直链淀粉含量遗传分析和早期世代选择提供有效的测定方法,根据单粒法对糙米粉直链淀粉含量的测定结果重新建立了分级标准。     

基于近红外光谱技术对鹿茸鉴别方法的研究

通过对鹿茸和伪劣鹿茸近红外光谱的分析,选取信息量丰富的7000~4500cm-1区段的光谱作为分析对象,用PCA结合Fisher识别法建立针对鹿茸真伪的识别函数,实现了对鹿茸真伪的准确识别,其中定标集样本的识别准确率达到了98.3%,外部鹿茸样本验证集识别准确率达到了93.5%。     

火焰原子吸收光谱法测定牛大力中矿质元素的含量

[目的]测定中药牛大力中矿质元素的含量。[方法]采用硝酸-高氯酸消解,应用火焰原子吸收光谱法测定中药牛大力中矿质元素Ca、Mn、Mg、Fe、Cu和Pb的含量。[结果]中药牛大力中Ca、Mn、Mg、Fe、Cu、Pb的含量分别为3 279.05、2 008.30、381.53、450.04、16.36、1.96μg/g。该方法的加标回收率为96.99%～101.38%,相对标准偏差(RSD)小于2%,具有良好的准确度和精密度。[结论]中药牛大力中Ca、Mn、Mg、Fe含量丰富,Cu、Pb含量较低。该测定结果可为研究矿质元素与牛大力药理作用的关系提供依据。     

苹果可溶性固形物近红外在线光谱变量优选

为简化近红外光谱模型,提高对苹果可溶性固形物含量的预测精度,将移动窗口偏最小二乘法(MWPLS)与遗传算法、连续投影算法相结合优选特征变量,建立偏最小二乘回归校正模型。其中移动窗口偏最小二乘法和遗传算法相结合优选的36个光谱变量建立的校正模型预测结果最好,可以有效筛选近红外光谱特征波长,模型预测相关系数为0.90,模型的预测均方根误差为0.70°Brix。