Identification of wheat grain in different states based on hyperspectral imaging technology

Abstract

Nondestructive identification of wheat grains in different states plays an important role in improving the quality of wheat products. This study investigated the possibility of using hyperspectral imaging techniques to discriminate healthy wheat grain, germinated wheat grain, mildewed wheat grain, and shriveled wheat grain (wheat grain infected with fusarium head blight). Both sides of individual wheat kernels were subjected to hyperspectral imaging (866.4–1701.0?nm) to acquire hyperspectral cube data. Spectral data were preprocessed by using standardization and multiple scattering correction. In addition, the principal component loading method was used to extract the characteristic wavelengths of both sides of wheat grains. The sample is divided into calibration set, test set, and validation set. The data of the calibration set are used to train the partial least squares discriminant analysis model, K-nearest neighbor model, and the support vector machine model, and the test set data are used to test the model. The results show that spectral data of both sides can achieve good classification results, while the reverse spectral data perform better. By comparing with each other, the support vector machine model is selected as the best classification model. Finally, using two hyperspectral images (reverse side) that are not involved in training and testing to verify the accuracy of the established support vector machine model, and the classification effect maps of the four wheat grains were visualized. The results indicate that nondestructive classification of wheat grains in different states is feasible based on hyperspectral imaging technology.     

Imaging Mass Spectrometry

Abstract

Mass spectrometry has been a valuable resource in science for decades. The ability to know exact masses of analytes allows for a more precise knowledge of the composition of materials. The technique has evolved to allow analysis of increasing numbers compounds in increasingly complex environments. Selected examples of biological applications of mass spectrometry imaging are discussed, in addition to a variety of tissue imaging and other medical applications. A number of inorganic applications are also described. Finally, a conclusion regarding the prospects for the future of imaging mass spectrometry completes this brief review.     

Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review

Abstract

In this review, various applications of near-infrared hyperspectral imaging (NIR-HSI) in agriculture and in the quality control of agro-food products are presented. NIR-HSI is an emerging technique that combines classical NIR spectroscopy and imaging techniques in order to simultaneously obtain spectral and spatial information from a field or a sample. The technique is nondestructive, nonpolluting, fast, and relatively inexpensive per analysis. Currently, its applications in agriculture include vegetation mapping, crop disease, stress and yield detection, component identification in plants, and detection of impurities. There is growing interest in HSI for safety and quality assessments of agro-food products. The applications have been classified from the level of satellite images to the macroscopic or molecular level.     

Imaging spectroscopy: Origin and future trends

Imaging spectroscopy, a useful tool for modern spectroscopic studies, can embed both spatial and spectral information to a set of images and derive the spectra from that. Such techniques are implemented to diversified fields like space observation and laboratory studies of solid materials. The present article intends to highlight the less noticed but significant fact that today's imaging spectroscopy in physical science originates from hyperspectral remote sensing, a technique for earth science that acquired maturity during the last three decades. It introduces several applications of spectral imaging for terrestrial and extraterrestrial objects and anticipates some future trends in this technique. Implementation of imaging spectroscopy to atomic and molecular physics is hoped to provide an indication of future research in this direction.     

融合自适应稀疏表示和相关系数的高光谱伪装分类方法

目前比较成熟的高光谱成像手段有卫星遥感和航空成像技术,这两种成像方式侦察时间大致相同,入射光方向基本一致,因而地物的光谱曲线比较固定;在陆基条件下,地物的光谱曲线受成像环境的影响凸显,因此应该对适用于陆基条件下的高光谱图像分类方法进行研究。在陆基高光谱图像中,对每个地物进行类型以及种类的判别有利于后续对目标的识别和处理,不同于传统遥感图像分类,陆基条件下的高光谱图像目标分类训练样本不仅较难获得,并且在陆基条件下的高光谱图像中,训练样本之间的相关性随着目标类型、探测器参数以及成像环境等因素时刻发生变化。基于稀疏性表示的分类方法已经被广泛应用于处理图像问题以及各种机器视觉问题。对于陆基高光谱图像来说,基于固定范数约束的稀疏编码策略无法适应陆基条件下高光谱成像多变的环境,而自适应稀疏表示可以根据样本相关性自适应的调节范数约束,相关系数可以提高图像中的破坏因素（阴影、噪声点等）的识别精度。通过引入正则化参数,融合了自适应稀疏表示和相关系数,提出了一种新的高光谱图像分类方法。为了验证所提方法的有效性,分别在绿色植被背景和荒漠背景中设置伪装物,通过不同的分类方法对图像进行分类,实验结果表明,不管是分类精度还是分类一致性,该方法都有明显的优势,可以应用于陆基条件下的高光谱图像分类,为目标分类提供了理论基础。     

超光谱成像技术应用于神经分类的研究

在外科神经修复手术中,断端神经束性质的识别成为良好修复的关键。现有的一些神经束识别的方法不太理想。分子超光谱成像技术同时提供生物组织图像和光谱两方面的信息,对检测目标可进行定性、定量和定位的描述,可对不同的生物组织从光谱特性的角度识别、分类并在图像上定位;相比较于其他医学成像技术,具有独特的优势。本研究把超光谱成像技术应用于神经束的识别和分类研究中,以期通过不同神经束的特征光谱来识别并分类神经束,并借助图像光谱信息确定神经束在图像中的定位,以便更好的辅助外科手术人员开展神经修复手术。研究意义在于：提出一种全新的神经束识别和定位的方法,辅助外科人员提高神经修复的疗效;储备超光谱成像技术应用于生物组织的定性定量定位分析和研究的技术,加快超光谱成像技术向实用阶段进展的步伐。     

地面长波红外高光谱成像气体探测研究

报道了地面长波红外遥测的新进展 ,具体阐述了窗扫时空调制傅里叶光谱成像技术的实现过程.演示装置基于角锥反射镜M ichelson干涉具 ,构成了空间调制干涉 ;采用了制冷型长波红外焦平面探测器组件 ,通过对数据立方体的采集、重组、基线校正、切趾、相位校正和傅里叶变换等处理 ,实现了长波红外波段高光谱成像.自研的CHIPED-1长波红外高光谱成像原理实验装置的探测灵敏度指标噪声等效辐射通量密度NESR在单次采样时达到了5.6 × 10-8 W · (cm-1 · sr · cm2 )-1 ,与商品化时间调制干涉高光谱成像仪相当 ;反映了技术的先进性 ,并留有较大的改进空间.通过测试聚丙烯薄膜的透过率曲线 ,CHIPED-1红外高光谱成像原理实验装置的光谱响应范围达到了11. 5 μm.文章还以室外高楼和乙醚气体的探测实验为例 ,研究了二维分布化学气体VOC的高光谱成像探测方法.在复杂背景和低试验浓度情况下 ,从同一波数的红外光谱切片上 ,观察不出乙醚蒸气的存在 ,但是进行了差谱处理后 ,可以清楚看到乙醚蒸气的空间分布.高光谱方法应用在有机蒸气VOC的红外探测领域 ,相对于宽波段热成像方法 ,具有灵敏度高、抗干扰能力强和识别种类多等诸多优势.     

Diagnostic Application of Fluorescence Spectroscopy in Oncology Field: Hopes and Challenges

Abstract

Cancer is one of the big killers of world population. The majority of cancers are diagnosed at a late stage, making a cure almost impossible. Fluorescence spectroscopy is an emerging diagnostic tool for various medical diseases including premalignant and malignant lesions. Fluorescence spectroscopy is a noninvasive technique and has been applied successfully for the diagnosis of multisystem cancers with high sensitivity and specificity. Fluorescence spectroscopy minimizes the need for repetitive biopsy, which is routine practice for cancer patient follow-up. But there are many aspects of this new diagnostic technique that should be discussed in future research to overcome limitations and challenges faced by this technique for diagnosis of cancers.     

Prospective sur les besoins « défense » en détecteurs infrarougeMid-term defence needs for infrared detectors

In this paper, we propose to present the prospects for mid-term needs for infrared detectors. These needs are derived from expected evolutions in imaging techniques as well as from operational requirements. The main trends that shall allow the direct development in infrared detection are as much the pursuit of greater range, a better discrimination of targets, as the efforts to minimize cost, volume, weigh and consumption. These trends will lead to an examination of the specific needs for some kind of ‘smart’ infrared detector. Among these applications, we will investigate more deeply the technological requirements for flash and 3D imaging, hyperspectral and uncooled imaging. To cite this article: J.-C. Peyrard, C. R. Physique 4 (2003).     

高光谱成像技术在彩绘文物分析中的研究综述

彩绘文物是文化遗产研究的重要内容之一。目前,许多的化学、光谱以及数字成像等分析技术应用于彩绘文物研究中,其中,高光谱成像技术集光谱分析与成像技术为一体,具有无损、快速成像以及"图谱合一"的特点。其技术特点使得高光谱成像技术在非接触、无样本的条件下对彩绘文物进行无损研究,既可以获得彩绘文物的整体形貌特征,还可以深入分析彩绘文物的光谱特征,是高光谱成像技术相比于其他彩绘文物研究方法的独特优势。利用高光谱成像技术研究彩绘文物分为数据采集、数据分析以及数据应用三步,其中数据分析与数据应用是研究的主要内容。通过对高光谱成像技术在彩绘文物中的相关研究成果进行总结归纳,其数据处理方法主要包括高光谱数据降维、光谱特征参量化、光谱解混合以及分类方法四个方面,并分别描述了四类处理方法的主要功能、常用方法和已有案例。从具体应用方向上,可归纳为视觉增强、隐含信息挖掘、保护监测和颜料分析四类,具体描述了四类应用方向所涵盖的内容以及所解决的问题。最后对相关研究中存在的挑战和发展前景进行了总结和展望。     

基于光谱曲线形态的高光谱影像检索方法研究

随着传感器技术、数据通讯技术的飞速发展,利用各种机载和星载传感器,己经获取到各种不同的海量遥感影像数据。巨大的数据量带来了数据存储和管理的问题,如何实现从海量影像数据中检索出我们所需要的信息显得十分迫切。影像检索最早由Chang于1980年提出,是对传统信息检索的扩展。针对海量遥感影像高效检索的需求和高光谱遥感影像波段数目多的特点,分析了影像检索中的影像距离函数和相似性度量问题,基于经典的曲线简化Douglas-Peucke算法(简称DP算法)提取光谱曲线的形态特征,利用“提取特征”的思想,提出了基于DP算法的光谱曲线和影像检索(简称DPSR)方法,将光谱形态特征应用于影像检索当中。DPSR利用光谱曲线上的特征点,减小了计算量,实现了有效地匹配和检索,适合高光谱遥感影像的光谱检索。文章选择了OMISI高光谱数据的四种易混分地类进行了相似性度量的对比实验。通过与常规的分析方法光谱角匹配(SAM)、光谱信息散度(SID)的对比可以看到,DPSR在较少计算量的情况下能保持较高的计算精度,提供了一种新的影像光谱高效检索方法。此外,文章还提出了尚待进一步研究的问题。     

基于EZW的高光谱图像压缩技术研究

随着高光谱遥感技术的发展,高光谱成像仪已应用于航天航空方面,与多光谱图像不同,高光谱图像是以纳米级的波段宽度对目标进行连续的光谱成像,所获取的图像光谱分辨率非常高.但是,随着波段数的不断增加,光谱成像仪获取的数据量越来越大,要对这些数据进行存储与传输是我们必须面对的问题.随着小波压缩技术的发展,在图像压缩领域有许多人对图像采取EZW压缩并提出各种改进方法,本文将此方法应用于高光谱图像的空间维压缩,对于光谱维的压缩暂不涉及,从高光谱图像的压缩重构结果看,无论从峰值信噪比( PSNR)和光谱曲线比对,还是从人眼主观对原图与重构图的比较,效果都不错.如果先对图像进行光谱维压缩,相信压缩效果会更好.     

A robust electrical conductivity imaging method with total variation and wavelet regularization

PurposeThis study aims to develop and evaluate a robust conductivity imaging method that combines total variation and wavelet regularization to enhance the accuracy of conductivity maps.Theory and methodsThe proposed approach is based on a gradient-based method. The central equation is derived from Maxwell's equation and describes the relationship between conductivity and the transceive phase. A linear system equation is obtained via a finite-difference method and solved using a least-squares method. Total variation and wavelet transform regularization terms are added to the minimization problem and solved using the Split Bregman method to improve reconstruction stability. The proposed approach is compared with conventional and gradient-based methods. Numerical simulations are performed to validate the accuracy of the developed method, and the effects of noise are determined. Phantom and in vivo experiments are conducted at 3 T to verify the clinical applicability of the proposed method.ResultsNumerical simulations show that the proposed method is more robust than other methods and can suppress the effects of noise. The quantitative conductivity value of the phantom experiment agrees with the measured value. The in vivo experiment results present a clear structure, and the conductivity value of the tumor region is significantly higher than that around healthy tissues.ConclusionThe proposed electrical conductivity imaging method can improve the quality of conductivity reconstruction, and thus, has future clinical applications.     

Identification of microalgae by hyperspectral microscopic imaging system

Harmful algal blooms can lead to serious environment problems, and thus monitoring and classifying microalgae have received increasing attention. Microcystis aeruginosa, Chlorella pyrenoidosa, and Nannochloris oculata all are small in size and have a similar morphology, which lead to discriminant difficulties using a traditional optical microscope. In this experiment, an independently developed hyperspectral microscopic imaging system was used to obtain the hyperspectral images of microalgae samples, and a hyperspectral dataset was developed through pretreated steps. Then the Fisher algorithm was employed to identify the species of the microalgae, and its sensitivity and specificity were found to be high. The result demonstrated that this method can classify the microalgae in a quick and convenient manner; in addition, the quantity and spatial information of the samples can be acquired.     

NIR Spectroscopy and Imaging Techniques for Evaluation of Fish Quality—A Review

Abstract

Fish is a highly perishable product and it is particularly important to pay attention to its quality changes. Maintenance of the high quality of fish and fishery products necessitates development of means for precise and rapid quality evaluation. Near-infrared (NIR) spectroscopy and imaging techniques are proven technologies that can provide useful information for the estimation of quality attributes in fish and fishery products due to fast speed, noninvasiveness, ease of use, and minimal sample preparation. The aim of this review is to communicate perspectives and aspects relating to NIR spectroscopy and imaging techniques with regard to evaluation of chemical composition (fat, protein, and moisture), microbiological (freshness, spoilage, and nematodes), and sensory (flavor, texture, and color) attributes of fish and fishery products. Moreover, the usefulness of NIR spectroscopy and imaging techniques for fish authentication and classification are presented. Finally, some viewpoints on the current situation and suggestions for future research directions are proposed.     

Selection of optimal combinations of inputs in a partial least squares model for prediction of soil organic matter

Abstract

Partial least squares model is widely used in estimation of soil physical and chemical parameters such as soil organic matter and moisture content, due to its advantages in dealing with collinearity of variables like hyperspectral reflectance. However, it is hard to determine optimal combination of partial least squares model input for soil organic matter prediction since there are lots of possibilities such as, different mathematical transformation of spectral reflectance, wavelength ranges, and spectral resolution. Laboratory hyperspectral reflectance of soils in Songnen plain were analyzed in this study, and the orthogonal experimental design method for deriving optimal combination of input variables for soil organic matter prediction models was introduced. For intercalating orthogonal experimental design table, five different levels which commonly used by researchers were assigned to factors. Results show that the optimal combination input for single black soil is using the derivative logarithmic reciprocal reflectance in the wavelength range selected by multiple stepwise regression at a spectral resolution of 5?nm (R²=?0.95, RMSE?=?0.21, and RPD?=?4.49), and different soils is using continuum removed in the wavelength range selected by MSR at a spectral resolution of 5?nm (R²?=?0.77, RMSE?=?0.74, and RPD?=?2.08). With optimal combination input, the partial least squares model prediction ability was evaluated as excellent for single black soil, possible for different soils. This study illustrates the orthogonal experimental design method can be an effective way to identify the optimal input variables of a partial least squares model for soil organic matter prediction, and multiple stepwise regression can be a preprocessing step to reduce hyperspectral data redundancy before using partial least squares to predict soil organic matter. Overall, this study provides a new approach for determining optimal input of partial least squares predicting model.     

Optical Fiber Sensor Technology in Portugal

Abstract

A general overview of the R&D activity in fiber optic sensing developed over the last fifteen years in Portugal is given. Different topics are addressed, including interferometric, intensity and Bragg grating based fiber optic sensors, signal processing and multiplexing techniques, optical current sensors, together with some references to field trials and applications. Possible guidelines for present and future national R&D activity on this subject are outlined.     

Classifying of Nellore cattle beef on Normal and DFD applying a non conventional technique

The aim of this study was to evaluate the accuracy in the Normal and DFD classification in Nellore beef using a bench-top hyperspectral imaging system. A hyperspectral imaging system (λ = 928–2524 nm) was used to collect hyperspectral images of the Longissimus thoracis et lumborum (n = 78) of Nellore cattle. The images were processed, being selected region of interest and extracted spectra image and were selected the wavelengths considered most important for the treats evaluated. Six linear discriminant models were developed to classify beef samples on Normal and DFD. The model using all wavelengths associated with the reflectance and absorbance spectrum transformed with the pretreatment 2nd derivative resulted in an overall accuracy of 93.6% for both pretreatments. In this configuration, the model was able to classify correctly 73 samples from a total of 78 samples. The results demonstrate that the hyperspectral imaging system may be considered a viable technology for beef classification on Normal and DFD.     

可见-近红外高光谱图像技术快速鉴别激光打印墨粉

为了使用快速、无损的方法区分激光打印文件使用的墨粉种类,利用高光谱成像技术结合化学计量法对6种激光打印墨粉的光谱数据进行建模和种类鉴别的研究。利用可见-近红外高光谱成像仪采集400~1 000 nm波段内的光谱数据,采用Savitzky Golay平滑、标准化、多元散射校正和标准正态变量变换4种方法分别对光谱数据进行预处理,而后分别建立随机森林(RF)、K最近邻(KNN)、支持向量机(SVM)、偏最小二乘判别分析(PLS-DA)和簇类独立软模式(SIMCA)模型,进而实现激光打印墨粉的种类鉴别。利用准确率、拒识率和误识率3个指标作为模型评价标准。实验结果显示,SVM和PLS-DA模型的效果最佳,准确率为100%,拒识率和误识率为0。基于可见-近红外高光谱成像技术可以实现激光打印墨粉的快速种类鉴别。     

Generalized Operator Yang-Baxter Equations,Integrable ODEs and Nonassociative Algebras

Abstract

Reductions for systems of ODEs integrable via the standard factorization method (the Adler-Kostant-Symes scheme) or the generalized factorization method, developed by the authors earlier, are considered. Relationships between such reductions, operator Yang-Baxter equations, and some kinds of non-associative algebras are established.