Observation of Atherosclerotic Plaque Phantoms through Saline or Blood Layers by Near-Infrared Hyperspectral Imaging

We observed atherosclerotic plaque phantoms using a novel near-infrared (NIR) hyperspectral imaging (HSI) technique. Data were obtained through saline or blood layers to simulate an angioscopic environment for the phantom. For the study, we developed a NIR-HSI system with an NIR supercontinuum light source and mercury-cadmium-telluride camera. Apparent spectral absorbance was obtained at wavelengths of 1150 - 2400 nm. Hyperspectral images of lipid were constructed using a spectral angle mapper algorithm. Bovine fat covered with saline or blood was observed using hyperspectral images at a wavelength around 1200 nm. Our results show that NIR-HSI is a promising angioscopic technique with the potential to identify lipid-rich plaques without clamping and saline injection.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Rapid determination of oil content of single peanut seed by near-infrared hyperspectral imaging

Oil content is a crucial indicator for evaluating the quality of peanuts.A rapid and non-destructive method to determine oil content of individual peanut seed can provide robust technical support for breeding high-oil-content peanut varieties.In this study,we established a rapid determination method using near-infrared hyperspectral imaging and chemometrics to assess the oil content of single peanut seed.After selecting key wavelengths through competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and random frog(RF),we constructed an oil content calibration model based on partial least squares regression for single peanut seed.Validation results demonstrated that the correlation coefficient was 0.8393 with a root mean square error of 1.7771 in the calibration set,while it was 0.7915 with a root mean square error of 2.2943 in the independent prediction set.Most samples exhibited relative errors below 5%,confirming the reliability of this model in predicting oil content of single peanut seed.

Research on the detection of early caries based on hyperspectral imaging

Objective:We applied hyperspectral imaging(HSI)system to distinguish early caries from soundand pigmented areas.It will provide a theoretical basis and technical support,for research anddevelopment of an instrument that could be used for screening and detection of early dentalcaries.Methods:Eighteen extracted human teeth(molars and premolars),with varying degrees ofnatural pathology and no degree of decay involving dentin were obtained.HSI system with awavelength range from 400 to 1000nm was used to obtain images of all 18 teeth containingsound,carious and pigmented areas.We compared the spectra of the wavebands at both 500 nmand 780 nm from the different tooth states,and the reflectance diference bet ween sound versuscarious lesions and sound versus pigmented areas,respectively.Results:There was a slight diference in refectance bet ween carious areas and pigmented areas at500 nm.A substantial difference was additionally noted in refectance bet ween carious areas andpigmented areas at 780 nm.Conclusion:The results have shown that the interference of tooth surface pigment can be elim-inated in the near-infrared(NIR)waveband,and the caries can be effectively identifed from the pigmented areas.Thus,it could be used to detect carious areas of teeth in place of the traditionalvisual inspection method or white light endoscopy.Clinical significance:The NIR difused light signal enables the identification of early caries frompigment and other interference,providing a reasonable detection tool for early detection andearly treatment of teeth diseases.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung beans.Raw hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm,and all images were calibrated by the white and dark reference images.The spectral reflectance values were extracted from the region of interest(ROI)of each calibrated hyperspectral image,and then they were treated as the independent variables.The dependent variables of four varieties of mung beans were set as 1,2,3 and 4,respectively.The extreme learning machine(ELM)model was established using full spectral wavelengths for classification.Modified gram-schmidt(MGS)method was used to identify effective wavelengths.Based on the selected wavelengths,the ELM and linear discriminant analysis(LDA)models were built.All models performed excellently with the correct classification rates(CCRs)covering 99.17%-99.58% in the training sets and 99.17%-100%in the testing sets.Fifteen wavelengths(432 nm,455 nm,468 nm,560 nm,705 nm,736 nm,760 nm,841 nm,861 nm,921 nm,930 nm,937 nm,938 nm,959 nm and 965 nm)were recommended by MGS.The results demonstrated that hyperspectral imaging could be used as a non-destructive method to classify mung bean varieties,and MGS was an effective wavelength selection method.

融合HSI的深度残差收缩网络鉴别变造文件字迹油墨研究

经济犯罪和各类民事纠纷等案件中,字迹油墨检验对质疑文书同一认定有重要意义,相关研究一直是法庭科学安全领域的重要课题。鉴于传统方法效率和精度较低,提出一种结合高光谱图像的深度残差收缩网络快速且无损鉴别字迹油墨种类的新方法。首先,采集了30支不同品牌型号的黑色签字笔油墨的高光谱图像,对每支中性油墨的高光谱图像进行分割,提取笔迹区域进行10×10像素融合,获取了共计13942像素点的反射率数据作为样本集。其次,结合残差网络、软阀值化与注意力机制,提出适用于处理高光谱数据的一维深度残差收缩网络模型,同时将其与卷积神经网络和传统机器学习模型进行比较。实验得出,支持向量机、逻辑回归、随机森林3个模型的像素反射率值测试准确率分别为59.1%、57.8%和51.7%,卷积神经网络为64.2%,损失函数值下降到1.536,而深度残差收缩网络验证识别率最高,达到75.4%,损失函数值最终下降到0.920,达到收敛。实验结果表明,光谱检测方法具有无损、成像快速、操作简单的优点,提出的光谱检测深度残差收缩网络模型在笔迹油墨的分类效果和精度上具有明显优势,可实现黑色签字笔油墨种类高光谱数据的深度挖掘和准确分类,结合笔迹检验技术能为法庭科学中质疑文书检验提供技术支撑。

基于SMOTE-UVE-SVM的小麦种子纯度高光谱图像检测

为了解决基于高光谱成像技术的小麦种子纯度检测过程中样本不均衡及波段信息冗余导致纯度检测模型性能下降的问题,提出了一种融合合成少数类过采样技术(SMOTE)、非信息变量剔除(UVE)和支持向量机(SVM)的种子纯度高光谱检测模型。该模型利用SMOTE算法对小麦种子少数类(杂质)样本进行扩充,改善样本的不均衡性;同时利用UVE对高维的高光谱特征进行选择,并构建SVM模型作为分类器,以进一步提高分类的性能。结果表明,5类小麦种子的平均准确率、精确率和负样本检出率分别达到95.98%、94.94%和89.32%,较传统方法分别提高了3.89%、7.18%和12.42%。所提出的方法在基于高光谱成像技术的小麦种子纯度检测中具有较好的应用前景。

白纹肉与木质肉品质安全无损检测研究进展

随着我国居民生活水平日益提升,消费者对食品品质及食品安全问题日渐关注。鸡肉味美价廉,肉质鲜美,深受消费者喜爱。为了满足消费者的鸡肉供应需求,禽肉业一味追求快速出栏率,导致鸡肉已出现一系列肌肉缺陷问题,发生率最高的白纹肉(white striping,WS)和木质肉(woody/wooden breast,WB)目前对禽肉业及消费者影响最为严重。常用的判别白纹肉、木质肉的方法是对其进行视觉观察和感官触碰,这种方法存在着主观、效率低和繁琐等缺点,而化学实验法会对鸡肉样本带来污染,显然不适用于后续的快速、在线检测及分选。近年来,无损检测技术发展迅速,其凭借快速、准确和安全等特点已广泛应用于食品品质安全领域。该文综述了近年来国内外关于近红外光谱、机器视觉以及高光谱成像等无损检测技术在白纹肉、木质肉检测中的最新研究进展,对各项技术现阶段存在的问题及未来的发展前景进行了总结和展望,以期为我国肉类食品品质安全检测提供参考和借鉴。

基于高光谱成像和判别分析的黄瓜病害识别

利用光谱成像技术(400～720nm)识别黄瓜白粉病、角斑病、霜霉病、褐斑病和无病区域。构建高光谱图像采集系统进行样本图像的采集,预处理和光谱信息的提取。由于获得的原始光谱数据量很大,为了减少后续运算量,提高准确率,采用逐步判别分析和典型判别分析两种方法进行降维。逐步判别从55个波段中选择12个波段,典型判别从55个波段中提取2个典型变量。利用选择的光谱特征参数建立病害识别模型。逐步判别构建的模型对训练样本和测试样本的判别准确率分别为100%和94%,典型判别构建的模型对训练样本和测试样本的判别准确率均为100%。说明利用高光谱成像技术可以进行黄瓜病害的快速、准确识别,并为实现可见光谱范围内黄瓜病害的田间实时在线检测提供了可能。

基于高光谱成像技术的小黄瓜水分无损检测

利用近红外高光谱成像技术对小黄瓜的水分进行无损检测研究。采用多元散射校正和Savitzky-Golay卷积平滑对900~1700 nm波段范围内的原始光谱进行预处理，选取最优的预处理方法；运用偏最小二乘回归系数选择特征波长，建立全波段和特征波长下的偏最小二乘水分预测模型。结果表明，经过Savitzky-Golay卷积平滑处理后的光谱建模效果最好，且利用特征波长建立的小黄瓜水分校正和验证模型的相关系数和均方根误差分别为0.86，0.90和0.111，0.156，优于全波段建立的模型。研究表明，采用高光谱成像技术对小黄瓜水分的无损检测是可行的。

基于高光谱成像技术的猪肉新鲜度评价

该文研究利用高光谱成像技术预测猪肉新鲜度参数,挥发性盐基氮(TVB-N)和pH值。在470～1000nm波长范围内,从高光谱图像中提取的反射光谱,分别经过2次Savitzky-Golay(S-G)平滑、多元散射校正(MSC)处理后,建立PLSR(偏最小二乘法)的预测模型。对TVB-N的预测,使用2次S-G平滑处理、MSC光谱建立的PLSR预测模型相关系数分别为0.90和0.89,预测模型标准差分别为7.80和8.05。对pH值的预测,经过MSC处理比2次S-G平滑处理的结果好,相关系数为0.79,预测模型标准差为0.37。同时综合2个参数利用MSC处理后的预测模型对猪肉新鲜度进行评定,准确率达91%。研究结果表明,高光谱成像技术可以用于猪肉新鲜度快速无损检测。

基于高光谱成像的柑橘黄龙病无损检测

采用高光谱成像技术,结合最小二乘支持向量机（LS-SVM）和偏最小二乘判别分析（PLS-DA）2种方法,探索柑橘黄龙病快速无损检测的可行性。在380~1 080 nm光谱范围内,采集正常、轻度黄龙病、中度黄龙病、重度黄龙病和缺素5种柑橘叶片的高光谱图像。采用方差分析方法,分析了正常、轻度黄龙病、中度黄龙病、重度黄龙病和缺素5种叶片的叶绿素、淀粉和可溶性糖含量间的差异,表明3指标可作为判别黄龙病的指示性指标。采用偏最小二乘法,建立了叶绿素、可溶性糖及淀粉3指标含量的定量分析数学模型,模型预测均方根误差分别为7.46、5.51、5.88,提供了柑橘黄龙病高光谱成像快速检测依据。提取高光谱图像感兴趣区域的平均光谱,通过分析正常、轻度黄龙病、中度黄龙病、重度黄龙病和缺素5种叶片的代表性光谱,在750 nm处吸光度存在差异。采用2阶导数处理样品光谱,消除了450~650 nm和800~1 000 nm波段的基线漂移,放大了有效光谱信息。采用主成分分析（PCA）和连续投影算法（SPA）筛选柑橘黄龙病LS-SVM定性判别模型的输入变量,建立了LS-SVM定性判别模型,同时与PLS-DA进行对比。采用未参与建模的预测集样品评价模型性能,结果表明PLS-DA模型判别柑橘黄龙病的准确率更高,模型误判率为5.6%。实验结果表明,高光谱成像技术结合偏最小二乘判别分析方法可实现柑橘黄龙病快速无损检测与黄龙病病情等级判别。

高光谱成像的豆腐形成过程中组分含量变化检测

豆腐作为我国传统食品,其生产已有两千多年的生产历史,但目前市场上的豆腐还是以经验式小作坊生产为主,难以保证豆腐质量和品质的均一性。水分和蛋白质含量是影响豆腐品质的重要因素,然而,水分与蛋白质的传统检测方法过程繁琐,耗时、费力,无法及时指导生产。因此,探索豆腐制备过程中水分和蛋白质分布的快速、无损、定量描述方法,可为精确调控豆腐制备工艺提供科学依据。应用高光谱成像技术结合化学计量学方法检测豆腐形成过程中豆浆、热浆、凝胶、豆腐四种不同状态下水分与蛋白质含量变化并实现其含量分布可视化。采集每种状态下120个样品在432~963 nm波段范围内的高光谱图像,利用ENVI软件选取感兴趣区域并计算样品的平均光谱数据。采用卷积平滑(savitzky-golay, SG)结合多元散射校正(multiplicative scatter correction, MSC)对原始光谱进行预处理,消除光谱噪声影响。用预处理后的光谱数据建立偏最小二乘回归(PLSR)和主成分回归(PCR)定量模型,比较发现豆浆、热浆、凝胶、豆腐样品的PCR模型对与水分和蛋白质的预测结果均低于PLSR模型。选用PLSR模型作为最优模型,采用连续投影算法(SPA)筛选豆浆、热浆、凝胶、豆腐样品的特征波长,分别选取13, 9, 8和9个特征波长建立基于特征波长下的PLSR模型。结果表明:与全波段下的PLSR模型相比基于特征波长建立的SPA+PLSR模型的预测效果更好,对水分的预测模型RP达到0.84~0.96,蛋白质的预测模型达到0.92~0.97。基于预测效果更好的SPA+PLSR模型计算豆浆、热浆、凝胶、豆腐图像中每个像素点的水分与蛋白质含量,将样品中的水分与蛋白质分布用不同的颜色直观显示,实现水分与蛋白质在不同状态下的分布。验证了高光谱技术对豆腐形成中水分与蛋白质含量检测的可行性,解决传统检测方法的缺陷,为豆腐生产的工业化和智能化提供理论依据。

高光谱成像技术检测肴肉新鲜度

连续8d测定开封后于4℃贮藏的肴肉中挥发性盐基氮（total volatile basic nitrogen,TVB-N）含量,并获取430～960 nm波长范围内的高光谱图像,从中提取光谱信息并经标准正态变量变换（standard normal variate transformation,SNV）预处理后,分别采用偏最小二乘法（partial least squares,PLS）、常规区间偏最小二乘法（interval PLS,iPLS）、向后区间偏最小二乘法（backward interval PLS,biPLS）和联合区间偏最小二乘法（synergy interval PLS,siPLS）建立TVB-N含量的预测模型.结果显示：siPLS模型对肴肉TVB-N含量预测结果最佳,其优选出的特征波段为430～461、555～586、929～960 nm,对TVB-N含量的预测相关系数和均方根误差分别为0.854 8、2.47,根据siPLS模型所预测的TVB-N含量值评定肴肉新鲜度等级的总体准确率达到87.5％.结果表明：利用高光谱成像技术对肴肉新鲜度进行快速、无损检测是可行的.

基于高光谱漫透射成像整体检测番茄可溶性固形物含量

为了实现番茄可溶性固形物含量（soluble solids content,SSC）的有效检测,提出高光谱漫透射成像检测方法,对比该成像方式下不同姿态（果脐端面姿态BS、赤道圆周3姿态C1、C2、C3以及组合姿态C1C2C3）的检测效果。首先对采集的不同姿态光谱图像,通过剪裁消除图像边缘噪声。针对圆周赤道面姿态C1、C2和C3,进行了拼接处理,获得组合姿态图像C1C2C3。其后对以上5种姿态图像进行单波段背景分割,获取目标区域,并统计不同姿态下番茄漫透射平均光谱。最后利用漫透射光谱结合偏最小二乘回归（partial least squares,PLS）方法,对番茄SSC分别在450~720、720~990、450~990 nm 3个波段进行定量分析。结果表明,组合姿态C1C2C3在3个波段区域上整体检测效果优于单个姿态的检测效果,其模型验证集均方根误差（root mean squared error of prediction,RMSEP）分别为0.299%、0.133%、0.151%;相关系数rp分别为0.42,0.89,0.90。说明利用高光谱漫透射成像,获取组合姿态光谱图像,可以有效检测番茄SSC。

高光谱技术诊断马铃薯叶片晚疫病的研究

鉴于晚疫病可对马铃薯造成毁灭性灾害,对受晚疫病胁迫的马铃薯叶片进行了高光谱图像特征研究。旨在探索马铃薯叶片的高光谱图象特征与晚疫病害程度的关联,以实现准确、快速、无损的晚疫病诊断。采用60片马铃薯叶片,对其中48片采用离体方式接种晚疫病菌,所剩12片作为对照,染病前后连续观测7天,得到染病和健康样本。健康和染病样本按照染病时间和染病程度不同采用374~1 018nm波段范围的可成像高光谱仪分别采样,基于ENVI软件处理平台提取图像中感兴趣区的光谱信息,并采用移动平均平滑、导数处理、光谱变换、基线变换等预处理方法提高信噪比,建立了最小二乘支持向量机（LS-SVM）的识别模型。9个模型中,基于原始光谱（不预处理）和光谱变换预处理后的数据所建立的模型预测效果最好,识别率均达到了94.87%。表明基于高光谱成像技术可以实现晚疫病胁迫下马铃薯病害程度的有效区分。

哈密瓜坚实度的高光谱无损检测技术

提出利用高光谱对哈密瓜坚实度进行检测的方法,对比分析了不同波段范围、不同预处理法、不同光程校正法和不同定量校正算法对哈密瓜坚实度预测模型准确度的影响.实验结果表明,在500～820nm波段光谱区域,采用偏最小二乘法对经过标准正则变换校正的一阶微分处理的光谱建模效果较优,其校正集相关系数为0.873,校正均方根误差为4.18N,预测集相关系数为0.646,预测均方根误差为6.40N.研究表明,应用高光谱对哈密瓜坚实度的无损检测研究具有可行性.

高光谱成像技术在茶叶中的应用研究进展

高光谱成像技术是一种融合了图像和光谱的检测技术,能同时获取待测物体的空间信息和光谱信息,因此该技术既可以直观反映研究对象外观纹理特征,又可以像光谱信息一样间接反映物体内部物理结构和品质成分特性。茶叶含有多种对人体有益的功能成分,具有较高的开发和实用价值。本文简述了高光谱成像技术的检测原理,分类综述了该技术在茶叶栽培管理、病虫害监测、生产加工、等级划分等方面的应用及不足,并提出了相应的解决措施,同时对其在茶叶领域的发展进行了展望,以期为促进茶叶精细化发展提供参考。

基于近红外高光谱成像技术的马铃薯干物质含量无损检测

基于近红外高光谱成像技术对马铃薯干物质含量进行无损检测研究。运用偏最小二乘回归系数法对多元散射校正(MSC)预处理后的光谱优选出8个特征波长,采用粒子群算法优化支持向量机(PSO-SVM)和偏最小二乘回归算法(PLSR)分别建立特征波长预测模型并对比分析。结果表明,采用粒子群算法优化支持向量机(PSO-SVM)建立的特征波长预测模型优于偏最小二乘回归算法(PLSR)预测模型,校正和验证模型的相关系数和均方根误差分别为0.944 37、0.919 77和0.155 01、0.156 90,高光谱成像技术对马铃薯干物质含量无损检测是可行的。