Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

The maturity of Camellia oleifera fruit is one of the most important indicators to optimize the harvest day, which, in turn, results in a high yield and good quality of the produced Camellia oil. A hyperspectral imaging (HSI) system in the range of visible and near-infrared (400–1000 nm) was employed to assess the maturity stages of Camellia oleifera fruit. Hyperspectral images of 1000 samples, which were collected at five different maturity stages, were acquired. The spectrum of each sample was extracted from the identified region of interest (ROI) in each hyperspectral image. Spectral principal component analysis (PCA) revealed that the first three PCs showed potential for discriminating samples at different maturity stages. Two classification models, including partial least-squares discriminant analysis (PLS-DA) and principal component analysis discriminant analysis (PCA-DA), based on the raw or pre-processed full spectra, were developed, and performances were compared. Using a PLS-DA model, based on second-order (2nd) derivative pre-processed spectra, achieved the highest results of correct classification rates (CCRs) of 99.2%, 98.4%, and 97.6% in the calibration, cross-validation, and prediction sets, respectively. Key wavelengths selected by PC loadings, two-dimensional correlation spectroscopy (2D-COS), and the uninformative variable elimination and successive projections algorithm (UVE+SPA) were applied as inputs of the PLS-DA model, while UVE-SPA-PLS-DA built the optimal model with the highest CCR of 81.2% in terms of the prediction set. In a confusion matrix of the optimal simplified model, satisfactory sensitivity, specificity, and precision were acquired. Misclassification was likely to occur between samples at maturity stages two, three, and four. Overall, an HSI with effective selected variables, coupled with PLS-DA, could provide an accurate method and a reference simple system by which to rapidly discriminate the maturity stages of Camellia oleifera fruit samples.     

全波长扫描式多功能读数仪-分光光度法测定土壤中的无机氮

快速准确测定土壤中铵态氮、硝态氮含量对监测土壤肥力水平和生态环境,指导作物氮肥施用非常重要。选择30份土样,利用全波长扫描式多功能读数仪(酶标仪)结合靛酚蓝分光光度法、硫酸肼还原法测定土壤中铵态氮和硝态氮含量,探讨利用酶标仪测定土壤无机氮含量的可行性。结果显示,利用酶标仪测定土壤铵态氮、硝态氮含量与连续流动分析仪测定结果之间无明显差异,彼此间呈显著线性相关。铵态氮回归直线方程为Y(连续流动分析仪-NH_4~+-N)=0.997 6 X(酶标仪-NH_4~+-N)-0.012 3,相关系数R=0.961 9(n=30,P0.01);硝态氮回归方程为Y(连续流动分析仪-NO_3~--N)=0.959 3 X(酶标仪-NO_3~--N)+0.021 9,相关系数R=0.964 0(n=30,P0.01)。酶标仪测定铵态氮回收率在96.2%～108%,相对标准偏差在10%以内;硝态氮测定回收率为94.9%～110%,且相对标准偏差在5%以内,酶标仪测定土壤铵态氮和硝态氮方法检出限分别为0.068mg/L和0.028mg/L。酶标仪测定土壤无机氮速度快,精密度、准确度较高,消耗试剂少,可用于大批量土壤浸提液中铵态氮和硝态氮含量的快速分析。     

基于高光谱成像技术的不同时期番茄植株的快速无损判别研究

利用近红外高光谱成像技术对不同浓度盐胁迫下的番茄叶片进行了定性判别。采集192个叶片样本的平均光谱反射率数据,并对原始光谱数据分别进行多元散射校正（MSC）、标准正态化（SNV）、正交信号校正（OSC）、相关优化偏移（COW）4种预处理,建立了偏最小二乘回归（PLSR）模型。建模结果显示：OSC预处理光谱的建模效果最优。分别采用间隔变量迭代空间收缩法（iVISSA）、间隔随机蛙跳法（IRF）、遗传偏最小二乘算法（GAPLS）、竞争性自适应加权算法（CARS）、变量组合集群分析（VCPA）等方法提取特征波长,建立PLSR模型。结果表明：VCPA提取特征波长所建立的模型最优。将VCPA法提取的11个特征波长（945、975、990、1 002、1 005、1 067、1 204、1 326、1 595、1 642、1 660 nm）用于建立番茄叶片定性判别预测模型,最优预测模型的决定系数（R2P）与预测均方根误差（RMSEP）分别为0.917、0.456。该研究为在线监测植物长势提供了技术支撑。     

土壤中铵态氮的测定条件研究

对氧化镁浸提-扩散法测定土壤中铵态氮的条件进行了研究,优化了氧化镁浸提-扩散温度和时间等实验条件,用振荡器代替人工转动扩散皿,最终确定较佳的氧化镁浸提-扩散温度为26℃,时间为15h。在优化条件下,方法的相对标准偏差(RSD,n=7)在1.1%~3.3%,NH4Cl的加标回收率在92.83%~103.1%,方法操作简单,测定结果准确可靠,精密度高,尤其适用于大批量样品的检测分析。     

Non-Destructive Detection of Moldy Walnuts Based on Hyperspectral Imaging Technology

Walnuts with their shells are a popular agricultural product in China. However, mildew from growth can sometimes be processed into foods. It is difficult to visually determine which walnuts have mildew without breaking the shells. A non-destructive method for detecting walnuts with mildew was studied by combining spectral data with image information. A total of 120 “Lüling” walnuts with shells were used for the mildew experiment. The characteristics of the spectral data from six surfaces of all samples were collected in the range of 370–1042 nm on days 0, 15, and 30. The spectrum was pretreated using SNV, and the feature bands were extracted using PCA and modeled using a support vector machine (SVM). The results show that the overall classification accuracy was 93%, with an of accuracy of 100% for INEN walnuts (normal internally and externally). The accuracy for IMEM walnuts (mildew internally and externally) reached 87.29%. There was an accuracy of 78.6% for IMEN walnuts (mildew internally and normal externally). The non-destructive detection of mildewed walnuts can be undertaken using hyperspectral imaging technology, which provides a new technique for exploring the mechanisms of walnuts with mildew.     

Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations

(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850–1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R² = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R² = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.     

Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging

Hyperspectral images contain both spectral and spatial image information and were investigated to characterize the freshness of fish. However, most studies of this application have focused on spectral signals rather than image features. The goal of this work was to investigate the ability of spectral and image textural variables for predicting the chemical and physical qualities of fish, respectively, and to optimize the variables for the specific quality determination. The chemical (total volatile basic nitrogen, TVB-N) and physical (texture profile analysis, TPA) properties were investigated. Partial least square (PLS) was applied to develop fish quality prediction models with the spectral and textural variables from the hyperspectral images. The results showed that the TVB-N content of fish fillets was accurately predicted using the spectra. Meanwhile, the TPA parameters were determined through the image textural features with high accuracy, which indicated image textural features were highly related with the TPA parameters. Moreover, spectral and textural features were also extracted from fish eyes and gills and were further used to predict the intact fish quality, taking advantage of the freshness sensitivity of the eyes and gills. The results illustrate that spectra from fish eyes and gills are a potential tool to predict the TVB-N content and TPA parameters for intact fish.     

Mapping the Distribution of Melanin Concentration in Different Fitzpatrick Skin Types Using Hyperspectral Imaging Technique

Measuring skin melanin concentration in order to assess skin phototype according to Fitzpatrick's classification is a constant research goal. In this study, a new approach for assessing skin melanin concentration based on hyperspectral imaging combined with an appropriate analytical model that exploits specific spectral bands to generate maps of melanin content distribution on different Fitzpatrick skin phototypes is presented. Hyperspectral images from the proximal inner side of the forearms of 51 young volunteers covering the first four classes of Fitzpatrick's phototypes were acquired using a hyperspectral imaging system. The images were analyzed using a modified Beer–Lambert law that segregates the contribution of melanin from the other constituents to the skin absorption spectrum. The performance of the model was evaluated using the coefficient of determination (r-squared). The results revealed that the approach proposed in this study generated accurate melanin concentration distribution maps that allowed a correct classification of skin phototype. In conclusion, the proposed approach for assessing skin melanin concentration proved to be very reliable for classifying skin phototypes, and, as it provides maps that are easily read, it has the advantage of a possible extension of its applications to other research concerning skin pigmentation.     

近红外光谱技术结合竞争自适应重加权采样变量选择算法快速测定土壤水解性氮含量

为了能够快速准确地掌握整个昆明地区土壤水解性氮含量的情况,收集963个不同类型的土壤样品,采用竞争自适应重加权采样(Competitive adaptive reweighted sampling,CARS)变量选择方法筛选波长变量,并建立水解性氮的偏最小二乘法(Partial least squares,PLS)分析模型。结果表明,采用CARS方法优选波长变量后,模型参数有所改善,交互验证标准偏差(Root mean square error of cross validation,RMSECV)由31.63降至25.55,交互验证相关系数(Correlation coefficientof cross validation,R_cv)由0.78提升至0.84,且模型外部验证结果与内部交叉验证结果基本一致。研究结果表明近红外光谱技术结合CARS分法,在大量代表性样品建模下,能够有效建立昆明地区不同土壤类型的水解性氮含量的近红外数学模型,方法可推广应用于土壤其他组分的近红外检测,具有重要的指导意义。     

土壤总氮近红外光谱分析的波段优选

利用移动窗口偏最小二乘( MWPLS)和Savitzky-Golay(SG)平滑方法优选土壤总氮的近红外(NIR)光谱分析模型.从全部97个土壤样品中随机选出35个样品作为检验集;基于偏最小二乘交叉检验预测偏差(PLSPB),将余下62个样品划分为具有相似性的建模定标集(37个样品)、建模预测集(25个样品).最优波段为1692～2138 nm,SG平滑的导数阶数(OD)、多项式次数(DP)、平滑点数(NSP)分别为0,6,69,PLS因子数为11,建模预测均方根偏差(M-RMSEP)、建模预测相关系数(M-Rp)分别为0.015％,0.931,检验预测均方根偏差(V-RM-SEP)、检验预测相关系数(V-RP)分别为0.018％,0.882.其结果可为设计专用NIR仪器提供有价值的参考.     

土壤铵态氮中氮稳定同位素比值测定前处理方法优化研究

土壤氮素在土壤养分供给和植物生长发育中起着重要作用。利用氮稳定同位素自然丰度或富集标记技术,开展土壤氮循环转化、化肥利用效率和生物有效性等方面的研究,涉及NH4+-N稳定同位素比值的精准测定。本研究在前人基础上,开发了一种快速高通量的铵态氮蒸馏分离方法,并使用次氯酸盐替代了次溴酸盐优化了铵态氮化学转化条件,将转化效率由原方法的25%左右,提高至60%以上。利用新建立的前处理方法结合气体预浓缩装置与稳定同位素质谱仪（PreCon-IRMS）联用系统,分析了不同浓度条件下自然丰度、15N富集和标记的NH4+-N标准溶液中的氮稳定同位素比值。结果表明,新的反应体系下,当铵态氮浓度为0.5 μmol /mL以上时,所有NH4+-N标准样品均能获得较理想的分析精度,其中自然丰度和15N富集参比溶液的NH4+-N的δ15N的精度可控制在0.5‰以内,而15N标记的标准溶液的15N atom%测试精度为0.001~0.006 atom%（CV在0.1~0.3%之间）。所有NH4+-N标准样品的15N测定值与参考值一致,无分馏现象发生。将该方法应用于两种不同土地类型的旱地和稻田土壤浸提液中NH4+-N稳定同位素丰度的测定,也可获得较好的重现性（CV<0.5%）,与原方法测定结果基本一致,且精度更优。优化后的前处理方法操作简单,耗时短,重复性好,适用于土壤溶液铵态氮15N丰度测定的快速、批量前处理。     

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.     

基于拉曼高光谱成像的乳粉真伪非定向筛查新方法研究

乳粉真伪问题是我国食品安全的突出问题之一,其非定向筛查是分析科学领域的前沿热点。该研究提出一种稳健建模驱动的拉曼高光谱成像方法(RMD-RHIM),借助其图谱合一的数据特征,将乳粉中未知掺杂物识别问题转化为奇异样本识别问题,有效解决了乳粉中掺杂物的不确定性问题。在RMD-RHIM中,首先采用自适应迭代重加权惩罚最小二乘算法(airPLS)扣除拉曼光谱的背景信息,再通过改进迭代自权重偏最小二乘法(mIRPLS)准确识别乳粉拉曼高光谱成像信号中的畸变像素点,并转化为可视化的二值图像,实现了乳粉真伪的非定向筛查。结果表明,RMD-RHIM方法对阳性和阴性样品的识别率分别达到了98.3%和93.3%,可满足乳粉工业快速筛查的需求,并为其它食品样本的非定向筛查提供了一种新手段。     

元素分析仪测定土壤氮、碳含量的不确定度评定

用实例对元素分析仪测定土壤氮、碳含量不确定度进行评定.分析讨论了测定过程中不确定度的来源、不确定度分量的计算.结果表明,影响土壤碳、氮含量测定不确定度的主要因素是标准物质的引入和标准曲线的绘制.     

离子选择电极阵列测定土壤中硝态氮

土壤硝态氮反映土壤短期氮素供应水平,实时了解土壤硝态氮的含量为精准农业和农业面源污染防控提供支撑,因此,在线实时检测土壤硝态氮方法突破就显得十分迫切。土壤硝态氮中的硝酸根离子在土壤中的高水溶性和流动性为全固态硝酸根离子选择电极高敏感检测土壤中硝态氮提供了条件,固态硝态氮离子选择电极的离子选择膜反应硝酸根离子在被测溶液中的浓度。采用全固态硝酸根离子选择电极ELIT NO₃^-,且与温度电极和pH电极融合组成电极阵列对土壤饱和溶液中的硝酸根离子进行检测。设计了高输入阻抗运算放大电路对电极信号进行采集,并通过微处理控制蠕动泵完成土壤硝态氮待测溶液连续流动测定及实时传输结果。实验结果表明,电极响应时间≤15 s,斜率-51.63 mV/decade,线性范围10^-5~10^-2.2 mol/L,最低检测限10^-5.23 mol/L。相对标准差在0.78%~4.5%,加标回收率均在90.0%~110%。与紫外可见分光光度法测试结果相比,相关系数(R²)为0.9952,为土壤硝态氮在现场检测奠定技术基础。     

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.     

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

Abstract

American Petroleum Institute (API) gravity is an important parameter in the crude oil industry and the nitrogen compounds are related to the toxic effects of the oil in refineries and the environment. In this paper, 194 crude oil samples with API gravities ranging from 11.4 to 57.5 were used for the purpose of estimating the physicochemical properties: API gravity, total nitrogen content (TNC) and basic nitrogen content (BNC). Initially, infrared spectra in the mid and near regions (MIR and NIR) were collected, then full-spectral partial least squares (PLS) and the orthogonal projections to latent structures (OPLS) chemometric models were developed and validated, as well as models using interval PLS (iPLS), synergy interval PLS (siPLS) and competitive adaptive reweighted sampling PLS (CARSPLS) as variable selection tools. For API gravity and TNC, the best calibration technique is the NIR CARSPLS with a root mean square error of prediction (RMSEP) values of 0.9 and 0.0275?wt%, respectively. For BNC, the best technique is MIR siPLS with a prediction error of 0.0134?wt%. The results were validated based on the evaluation of the figures of merit, a statistical evaluation of the accuracy, characterization of the systematic error and measurement for errors in the residues. The results were satisfactory considering the high variability of the data and the diversity of the samples, demonstrating suitable applicability for practical analysis.     

Using Native Chemical Ligation for Site-Specific Synthesis of Hetero-bis-lanthanide Peptide Conjugates: Application to Ratiometric Visible or Near-Infrared Detection of Zn2+

The interest in ratiometric luminescent probes that detect and quantify a specific analyte is growing. Owing to their special luminescence properties, lanthanide(III) cations offer attractive opportunities for the design of dual-color ratiometric probes. Here, the design principle of hetero-bis-lanthanide peptide conjugates by using native chemical ligation is described for perfect control of the localization of each lanthanide cation within the molecule. Two zinc-responsive probes, r-LZF1^Tb|Cs124|Eu and r-LZF1^Eu|Cs124|Tb are described on the basis of a zinc finger peptide and two DOTA (DOTA=1,4,7,10-tetraaza-cyclododecane-1,4,7,10-tetraacetic acid) complexes of terbium and europium. Both display dual-color ratiometric emission in response to the presence of Zn²⁺. By using a screening approach, anthracene was identified for the sensitization of the luminescence of two near-infrared-emitting lanthanides, Yb³⁺ and Nd³⁺. Thus, two novel zinc-responsive hetero-bis-lanthanide probes, r-LZF3^Yb|Anthra|Nd and r-LZF3^Nd|Anthra|Yb were assembled, the former offering a neat ratiometric response to Zn²⁺ with emission in the near-infrared around 1000 nm, which is unprecedented.     

Detection of Plasmon Coupling between Silver Nanowires Based on Hyperspectral Darkfield and SERS Imaging and Supported by DDA Theoretical Calculations

We report the unprecedented observation of plasmon coupling between silver nanowires, showing how the surface‐enhanced Raman scattering depends upon this interaction and how the spectrum can be shaped by the hot spot. Such observations were accomplished by Raman spectroscopy mapping of silver nanowires modified with rhodamine. The local spectra on the hot spots were measured by darkfield hyperspectral microscopy, a powerful but uncommonly used technique that is capable of determining the location, structure, and spectra of the hot spots. The result obtained by the simulation of two parallel nanowires based on the discrete dipole approximation (DDA) method was in excellent agreement with the results obtained experimentally.     

Detection of Reactive Oxygen and Nitrogen Species by Upconversion Nanoparticle-Based Near-Infrared Nanoprobes: Recent Progress and Perspectives

Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are essential oxidative metabolites of organisms, which are closely related to physiological, pathological and pharmacological processes. The accurate detection of ROS/RNS is important for the understanding of biological processes, monitoring of pharmacological effects, and predicting the course of disease. The recently developed NIR nanoprobes based on upconversion nanoparticles (UCNPs) hold great prospects in sensitive and deep-tissue detection of ROS/RNS, and considerable progress has been achieved so far. In this review, we systematically summarize the up-to-date advances of UCNPs-based near-infrared (NIR) probes for ROS/RNS sensing, and the potential challenges and perspectives for further research are also highlighted. We envision that such a research field will have a bright future for modern biomedical applications.