近红外光谱检测技术及其在林业中的应用

近红外光谱是20 世纪80 年代以来发展最快、最引人注目的光谱分析技术。文章介绍了近红外光谱技术的发展历程和在国外的应用概况,重点列举了近红外光谱在林业中的成功应用实例。资料表明, 近红外光谱以其快速、无损伤、操作简单、稳定性好、效率高等特点,在国外的水果品质检测、木材性质检测和林业育种中的应用已成为一个活跃的研究领域。而近红外光谱技术在我国林业上的研究和应用则刚刚起步,因此本文对国内外利用近红外光谱技术在林业上的应用作一综述,并对近红外检测技术在林业上的应用进行了展望,以推动近红外光谱技术在我国林业科技和生产中的应用。     

Sugar Determination in Grapes Using NIR Technology

In the last years, the interest in near infrared (NIR) technology is growing. Among the advantages offered by NIR spectroscopy are: speed, low cost, high repeatability, high reproducibility, and nondestructive. The purpose of this paper is to study the possibility of using NIR reflectance spectroscopy for correlation with sugar contents in grapes of the following varieties: Garnacha and Viura. These procedures were performance: fruits were hand-harvested and 30 grapes of each variety were selected. Mass, sugar and NIR (800-2500 nm) reflectance spectra of all the samples were obtained. The harvesting lasted long to obtain a wide range of the sugar content. Data were analysed using SPSS and SAS. It is possible a correlationship between NIR spectra and sugar (°Brix) so we can predict °Brix of grape with NIR technology. Multilinear Regressions were obtained. So, Viura multilinear regression used 33 variables (R² = 0,925; standard error of estimate = 1,0446) and Garnacha, 63 variables (R² = 0,89; standard error of estimate = 1,0508).     

近红外光谱技术(NIRS)在反刍动物营养研究中的应用现状

近红外光谱技术(NIRS)分析样品时以其方便、快捷和准确等诸多优点在动物营养研究中得到了广泛的应用。用NIRS技术预测家畜日粮中有机物消化率时所产生的标准偏差(SECV)在1.6%～2.8%之间,而预测干物质的消化率时所产生的SECV在1.6%～3.5%之间。NIRS能够准确地预测饲料中的化学成分和生物学组分以及反刍家畜十二指肠微生物蛋白的流量,但对于预测饲料在瘤胃降解率的动态特性时与实际相差很大。NIRS技术预测舍饲家畜采食量与体内法得到的结果相似,但在预测放牧家畜采食量时其预测误差为14%左右。上述结果表明,NIRS技术在预测反刍家畜消化代谢、日粮营养评价、采食量等方面已取得了很大的进展,并在反刍动物营养研究领域中有着广阔的应用空间。     

可见/近红外光估测完整柑橘水果可溶性固形物含量的研究

研究用可见/近红外光谱(Vis/NIR spectroscopy)漫透射方式对柑橘类水果的可溶性固形物含量(SSC)进行了无损、快速定量分析。通过自行设计的Vis/NIR光谱系统测定了110个柑橘样品的SSC。82个柑橘样品用来建模,其余28个用来验证模型的性能。对实验室测得的柑橘水果的SSC实际值与Vis/NIR光谱数据进行相关性分析,用光谱定量分析软件中集成的偏最小二乘回归法(PLS)和主成分回归法(PCR)建立校正和预测模型。对比了不同光谱预处理方法, 如微分处理, 标准归一化处理(SNV), 多元散射校正(MSC)和Savitzky-Golay 光谱平滑方法)对模型检测结果的影响。根据预测平方根误差(RMSEP)和相关系数(r2)进行不同模型的预测性能评价,建立的最好的柑橘水果SSC预测模型的RMSEP＝0.538%,r2＝0.896。结果表明Vis/NIR可以作为一种准确、快速的无损检测方法来评价柑橘类水果的可溶性固形物含量。     

稻谷新陈度近红外快速无损检测的研究

利用近红外技术结合化学计量学的方法建立了稻谷新陈度近红外光谱定量模型。对90个稻谷样本,通过近红外光谱仪扫描获得了从950～1 650 nm的光谱信息。运用UNSCRAMBLER9.7软件进行计算,选择全谱区,结合偏最小二乘法(PLS)算法,得到光谱最佳预处理方法为一阶导加Savizky-Golay平滑,最佳主成分数为7。进行内部交叉验证,决定系数r2为0.967 9,预测误差为54.51,且预测结果与真实值通过t检验,说明模型是可行的。为稻谷新陈度的快速无损检测提供了一种新的方法。     

苹果糖度近红外光谱分析模型的温度补偿

温度变化对水果品质近红外评价有很大影响,需要补偿温度波动对模型的影响。文章研究了温度变化(2～42 ℃)对苹果近红外漫反射光谱的影响,采用剔除温度变量法和内校正法补偿温度对模型的影响,提高预测精度。研究表明,温度与光谱信息存在一定相关性, 其模型R2=0.985,RMSEC=1.88,RMSEP=2.32;未进行温度校正模型的预测标准偏差达到2.55;采用复合预处理方法和改进的遗传算法对光谱数据优化,剔除温度变量法模型的R2=0.954,RMSEC=0.63,RMSEP₁=0.72,RMSEP₂=0.74;内校正法的模型R2=0.952,RMSEC=0.64,RMSEP₁=0.69,RMSEP₂=0.68;相比未进行温度补偿模型均提高了预测精度。结果显示：温度对苹果近红外光谱影响呈非线性变化,剔除温度变量法和内校正法可用于补偿温度对模型的影响,可提高模型预测精度。     

动物粪便近红外光谱的应用潜力

近红外光谱技术(NIRS)以其低成本、高效、环保、无损的优势在日粮和畜产品品质分析中得到广泛应用。饲料品质参数(如粗蛋白)的获取对于日粮营养的合理搭配不可或缺,传统的近红外光谱技术将日粮的近红外光谱与日粮品质参数的化学测定值进行回归,建立预测模型对日粮品质参数进行检测。然而,在放牧条件下日粮近红外光谱的难以获取限制了这一传统建模方法在动物营养中的应用。作为动物代谢性产物,动物粪便不仅包含有日粮信息也携带有动物自身信息。近年来,基于动物粪便近红外光谱和日粮信息(日粮植物学成分、化学成分及其消化率)建立的回归模型,也已被用于动物日粮信息的检测。简要介绍F.NIRS在动物性别、品种鉴定及繁殖和寄生虫感染状态检测中的应用。NIRS在动物粪肥分析中的应用结果表明NIRS能够对粪肥中主要成分含量进行快速分析。尽管F.NIRS预测准确性有待提高,但已显示出其作为快速、有效的分析工具用于检测日粮品质参数及动物生理状态的潜力。相关的英文评述性文章业已出版,但在中国F.NIRS的研究和应用非常欠缺。期望本文的呈现有助于F.NIRS在国内的发展。推荐将NIRS作为快速分析工具用于对粪肥主要成分(DM,OM,TS,VS,TN,TKN和NH₃-N)含量进行检测。     

近红外光谱技术在土壤成分检测中的研究进展

土壤是十分复杂的物质,有机物和无机物共存,土壤所含的信息量巨大。近红外光谱技术具有检测速度快、分析效率高、成本低、操作简单的特点,并且能同时检测多种土壤成分。介绍了近红外光谱技术检测土壤的基本流程和光谱数据处理方法,建模方法。重点是概括近年来近红外技术测定土壤成分的最新研究进展及土壤成分便携式近红外仪器的发展情况,并针对NIR在数字化农业方面的意义和未来发展方向进行了讨论。     

Mid‐ and Near‐Infrared Spectroscopic Determination of Carbon in a Diverse Set of Soils from the Brazilian National Soil Collection

Abstract

Calibrations for soil carbon content measured by combustion (total carbon, TC) and chromate oxidation by a modified Walkley‐Black method (Walkley‐Black carbon, WBC) from the Brazilian National Soil Collection were made using Fourier‐transform near (1100 to 2500 nm; NIRS) and mid‐infrared diffuse reflectance (2,500 to 25,000 nm; DRIFTS) spectroscopy combined with partial least squares (PLS). Calibration sets of sample populations of different carbon ranges, soil taxonomic classes, and soil textural groups were established. These are for TC ranges between 0.4 to 555.0, 0.4 to 99.1, and 0.4 to 39.9 g kg^?1: for WBC 0.2 to 401.0, 0.2 to 66.0, and 0.2 to 66.0, and 0.2 to 30.0 g kg^?1: for soil taxonomic classes Ferralsols and Acrisols; and for soil textural groups very clayey, clayey, and medium textures were examined. Calibrations obtained for the largest TC and WBC ranges were better compared to the lower ones, but lower root mean squared deviation (RMSD) and relative difference (RD=RMSD/mean value) were found for the lower carbon ranges. Taxonomic soil class was not an adequate criterium for calibration set formation. Soil texture had effect on calibrations, especially using NIR, because of the particle size effect to which NIR was more sensitive than mid‐IR. In general, DRIFTS showed better performance than NIRS. NIRS only outperformed DRIFTS when used with calibration set fairly homogeneous in its particle size distribution. Results demonstrated that while calibrations can be developed using either DRIFTS or NIRS for even a very diverse set of soil samples, which will determine C over a wide range of concentrations inherent in such a diverse set, it is desirable to seperate sample populations by soil textural properties and choose the adequate spectral range (NIR or mid‐IR) based on the textural group, for calibration development to achieve more accurate results.     

近红外光谱技术(NIRS)在草地生态学研究中的应用

近红外光谱技术(NIRS)是一种快速、高效、无损的现代检测技术,已经在许多领域广泛应用。文章阐述了NIRS应用于草地生态学研究的意义,介绍了NIRS用于测定牧草营养成分、矿物质、土壤养分含量,分析了牧草混合物的组成、动物对所采食牧草的反应、牧草病虫害抗性等一些复杂特性,以及进行生化标记、同位素鉴别研究。综合这些研究可以看出,NIRS能够作为一种整体研究工具应用于草地生态学的许多研究领域中,可以检测各种常规化学成分、分析草地生态系统的各种动态指标和系统运行的多项整体特性。希望通过本文的总结分析,推动NIRS在中国草地生态学研究中的应用,加速该领域研究手段的现代化。     

近红外漫反射光谱检测梨内部指标可溶性固性物的研究

旨在建立近红外漫反射光谱与梨水果内部可溶性固形物之间的关系,以评价近红外漫反射光谱在测量梨水果内部指标可溶性固形物的应用价值。应用近红外光谱(350～1 800 nm),采用多元线性回归(MLR)、主成分回归(PCR)和偏最小二乘法(PLS)三种不同的数学校正方法对梨水果的可溶性固形物(SSC)进行了定量分析,并且对梨水果不同位置的吸光度原始光谱,一阶微分和二阶微分三种不同预处理情况下的模型进行了最优化分析。在梨水果赤道部位预测结果较为理想,采用一阶微分预处理方法下应用PLS方法。研究结果为预测集的相关系数为0.851 7, 预测样本均方根误差为0.879 3。研究表明,近红外漫反射光谱可以作为一种准确、可靠和无损的检测方法用于评价梨水果内部指标可溶性固形物。     

近红外漫反射光谱检测赣南脐橙可溶性固形物的研究

研究了应用可见-近红外漫反射光谱技术快速检测赣南脐橙可溶性固形物的方法。以40个赣南脐橙为标准样本,利用漫反射光谱测定法获取完整赣南脐橙的可见-近红外光光谱(350～2 500 nm),采用多种光谱校正算法,选取不同的光谱波段范围对水果样本的漫反射二阶光谱进行有效信息的提取和分析,并结合偏最小二乘法和主成分回归等定量校正方法,建立了赣南脐橙可溶性固形物的定量数学模型。实验结果为: 在361～2 488 nm波段范围内,偏最小二乘法校正模型的预测精度最好,校正模型的相关系数为0.929,校正标准偏差和预测标准偏差分别为0.517,0.592,其预测集样本的预测值与真实值的相关系数为0.791。实验结果表明：应用近红外漫反射技术对赣南脐橙可溶性固形物的快速无损检测具有可行性。     

Optimization of the optical and electrical properties of electron beam evaporated aluminum-doped zinc oxide films for opto-electronic applications

Aluminum-doped zinc oxide (AZO) thin films have been deposited by electron beam evaporation technique on glass substrates. The structural, electrical and optical properties of AZO films have been investigated as a function of annealing temperature. It was observed that the optical properties such as transmittance, reflectance, optical band gap and refractive index of AZO films were strongly affected by annealing temperature. The transmittance values of 84% in the visible region and 97% in the NIR region were obtained for AZO film annealed at 475 °C. The room temperature electrical resistivity of 4.6×10⁻³ Ω cm has been obtained at the same temperature of annealing. It was found that the calculated refractive index has been affected by the packing density of the thin films, whereas, the high annealing temperature gave rise to improve the homogeneity of the films. The single-oscillator model was used to analyze the optical parameters such as the oscillator and dispersion energies.     

On the angular dependence of gaps in 1-D Si/SiO2 periodic structures

A multilayer of silicon and silicon dioxide was used to study the angular dependence of reflectance maxima originating from interference and bulk optical properties. Silicon dioxide has a lattice resonance in the infrared causing an interval of high reflectance for wavelengths around 9 μm. The multilayer was designed such that the interference maxima do not overlap/interact with the material related reflectance maximum. In this way the different angular behavior for the two types of reflectance maxima can be studied simultaneously. Experimental and calculated reflectance spectra for s- and p-polarized light for angles of incidence between 0° and 90° collected for every 5° are presented. The reflectance features caused by interference generally move to shorter wavelengths with increasing angle of incidence, and the materials related peak is widened for (s-polarized light) and excitation of the longitudinal modes was observed for p-polarized light.     

Gath-Geva联合模糊聚类的生菜近红外光谱聚类分析

生菜的新鲜程度是影响生菜品质的最重要因素之一,其主要取决于生菜的储藏时间,因此,对不同储藏时间的生菜进行准确鉴别具有重要研究价值。由于不同储藏时间生菜的近红外光谱数据具有差异性的特点,因而使用近红外为不同储藏时间的生菜进行鉴别分类是可行的。通过将联合模糊C均值聚类（allied fuzzy c-means, AFCM）中的欧式距离测度替换为指数距离测度从而提出了一种GG联合模糊聚类（Gath-Geva AFCM, GGAFCM）分析算法。GGAFCM通过迭代计算得到模糊隶属度值和典型值,再结合近红外光谱实现了对不同存储时间生菜的高效精准鉴别。以新鲜的生菜样本作为研究对象,使用傅里叶近红外光谱仪（Antaris Ⅱ型）每隔12 h对生菜样本采集漫反射光谱数据,光谱的波数范围介于10 000~4 000 cm-1之间。首先,通过主成分分析（principal component analysis, PCA）对采集到的1 557维生菜近红外光谱数据进行数据压缩将其降至22维,然后通过模糊线性判别分析（fuzzy linear discriminant analysis, FLDA）对降维后的近红外漫反射光谱数据的鉴别信息进行提取。设定鉴别向量数为2,即通过FLDA将22维的生菜近红外光谱数据转换为了2维数据。最后将模糊C均值聚类（fuzzy c-means, FCM）的聚类中心作为GGAFCM和AFCM的初始聚类中心,通过运行FCM,GGAFCM和AFCM完成对不同储藏时间生菜的鉴别分类,并对三种模糊聚类算法得到的聚类准确率、模糊隶属度、迭代次数进行分析。实验结果表明：在初始化条件相同的情况下,采用的GGAFCM算法与FCM和AFCM算法相比具有更高的鉴别准确率。在m=2的情况下,GGAFCM的鉴别准确率达到了95.56%,而AFCM的聚类准确率为91.11%。GGAFCM迭代4次达到收敛,而AFCM与FCM均需要8次迭代计算才能达到收敛。基于近红外光谱技术,通过GGAFCM结合PCA与FLDA算法可以高效快速且无损的完成对储存时间不同的生菜的准确鉴别分类,为生菜储存时间的准确、快速鉴别提供了实验依据和参考方法,具有一定的实际应用价值。     

A non-standard auxiliary integrating sphere and correction method for the realization of diffuse reflectance

To realize spectral diffuse reflectance scale in 0/d geometric condition at National Institute of Metrology in China (NIM), a facility based on a non-standard auxiliary integrating sphere method with special structure has been built up and a correction method for the influence on the thickness of PTFE (polytetrafluoroethylene) coating at the port of auxiliary integrating sphere is mainly discussed. The uncertainty of spectral diffuse reflectance in NIM is approximately 0.25% (k = 2) in 380–800 nm. This facility has been used to establish China diffuse reflectance scales in the VIS–NIR part of the spectrum. This system and correction method presented in this paper can be easily implemented on most commercial spectrophotometers with a lower cost.     

便携式近红外光谱仪的苹果糖度模型温度修正

样品温度对近红外光谱有很大影响,在近红外技术评价水果品质的实际应用时,需要修正温度变化对模型预测结果的影响。便携式近红外光谱仪采集不同温度下(0～30℃)苹果的漫透射光谱,采用二阶导数和卷积平滑进行预处理。选取20℃下代表性样本的光谱数据,建立基准PLS模型。斜率/偏差法分别计算苹果糖度PLS模型在0,10和30℃下的修正方程。分析结果表明:斜率/偏差法对0,10和30℃下外部样本预测结果进行修正,预测精度得到显著提高,其修正前后的Q值分别为0.525cv 0.810,0.680cv0.822,0.669cv 0.802。温度修正模型可以有效提高预测精度,也扩展了近红外仪器的适用性,为自主研发便携式近红外光谱仪提供参考。     

Q band interferometer for high temperature measurements

A Q band interferometer for determining the complex permittivity of solids and liquids between 20°C and 1300°C has been developed. The Roberts and Von Hippel standing-wave method was used. The technique involves the utilization of a computer-assisted digital voltmeter for data acquisition and treatment. Results for a 67SiO₂–33Na₂O within the temperature range 20–400°C and in the liqid state (950°C) are presented.     

近红外光谱法测定紫花苜蓿青贮鲜样的营养价值

应用近红外光谱技术(NIRS)直接分析新鲜饲草的营养价值,如饲草中干物质(DM),粗蛋白(CP),中性洗涤纤维(NDF),酸性洗涤纤维(ADF)含量,对畜牧业生产具有重要意义。鲜草中由于含有较多的水分,不易制备均一的样品和进行光谱中有用信息的提取,因此难于进行近红外光谱分析。本试验应用偏最小二乘回归法(PLS)、傅里叶变换近红外光谱技术和液氮冷冻制样技术,建立了适合于不同品种,不同生育期,不同茬次和不同青贮方法即时测定青贮苜蓿鲜样中DM,CP,NDF,ADF的模型,以期对NIRS在测定紫花苜蓿青贮鲜样品这些成分测定的可行性进行分析。所建DM,CP,NDF和ADF模型的交叉检验决定系数(R2_cv)为0.884 6～0.989 8,交叉检验标准误(RMSECV)为3.9～9.7 g·kg-1鲜重。用50个样品对模型进行外部检验,预测相关系数(r)为0.939 7～0.994 9,预测标准误为1.9～8.3 g·kg-1鲜重。结果表明： 采用适当的样品处理方法和光谱分析技术可以实现近红外光谱技术对苜蓿青贮鲜样的营养价值评定。     

杏贮藏期间可溶性固形物和硬度的近红外光谱检测

以杏为材料,研究其贮藏期间可溶性固形物和硬度的近红外漫反射无损检测模型的建立方法。研究发现,定标建模最少样品量为100个。对于可溶性固形物,刚收获样品的校正模型对各贮藏阶段的预测效果均较好,决定系数(r2p)接近0.9、预测均方根误差(RMSEP)在0.6左右及相对分析误差(RPDp)达2.5以上;而且混合阶段模型的预测效果均优于采收及不同贮藏阶段的独立模型,r2p和RPDp分别达0.9和3.0以上、RMSEP在0.3—0.5之间。对于硬度,各阶段独立模型仅能粗略预测相应贮藏时期的样品,而混合阶段模型对各贮藏时期的样品均能实现快速分析,rp2和RMSEP分别在0.8和1.0左右、RPDp达2.0。结果表明近红外漫反射光谱可用于及时评价杏贮藏期间可溶性固形物和硬度的变化。