Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration

Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality seedlings.Nearinfrared spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor N.Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index(VSI).Here,we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emargiaata and developed a new VSI for this task.Plants were grown in a hydroponics system with 0,2.75,5.5or 11 mM N for 45 days.Then we measured gas exchange,chlorophylla fluorescence,and pigments in leaves;analyzed complete leaf nutrients,and recorded spectral data for leaves at 966 to 1685 nm using NIRS.With a statistical learning approach,the dimensionality of the spectral data was reduced,then models were generated using two classes(N deficiency,N)or four classes(0,2.75,5.5,11 mM N).The best combination of techniques for dimensionality reduction and classification,respectively,was stepwise regression(PROC STEPDISC)and linear discriminant function.It was possible to detect N deficiency in seedlings leaves with 100%precision,and the four N concentrations with93.55%accuracy before photosynthetic damage to the plant occurred.Thereby,NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A.emarginata.

Identifying the geographical origin and processing technology of Moyao(Myrrh)on the basis of near-infrared spectroscopy combined with chemometrics

OBJECTIVE:To evaluate the quality of Moyao(Myrrh)in the identification of the geographical origin and processing of the products.METHODS:Raw Moyao(Myrrh)and two kinds of Moyao(Myrrh)processed with vinegar from three countries were identified using near-infrared(NIR)spectroscopy combined with chemometric techniques.Principal component analysis(PCA)was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories.A classical chemometric algorithm(PLS-DA)and two machine learning algorithms[K-nearest neighbor(KNN)and support vector machine]were used to conduct a classification analysis of the near-infrared spectra of the Moyao(Myrrh)samples,and their discriminative performance was evaluated.RESULTS:Based on the accuracy,precision,recall rate,and F1 value in each model,the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results.In all of the chemometric analyses,the NIR spectrum of Moyao(Myrrh)preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins,and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best.The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.CONCLUSIONS:NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao(Myrrh)and can also provide a reference for evaluations of its quality and the clinical use.

NEAR-INFRARED,BROAD-BAND SPECTRAL IMAGING OF THE HUMAN BREAST FOR QUANTITATIVE OXIMETRY:APPLICATIONS TO HEALTHY AND CANCEROUS BREASTS

We have examined ten human subjects with a previously developed instrument for near-infrared diffuse spectral imaging of the female breast.The instrument is based on a tandem,planar scan of two collinear optical fibers(one for illumination and one for collection)to image a gently compressed breast in a transmission geometry.The optical data collection features a spatial sampling of 25 points/cm2 over the whole breast,and a spectral sampling of 2 points/nm in the 650-900nm wavelength range.Of the ten human subjects examined,eight are healthy subjects and two are cancer patients with unilateral invasive ductal carcinoma and ductal carcinoma in situ,respectively.For each subject,we generate second-derivative images that identify a network of highly absorbing structures in the breast that we assign to blood vessels.A previously developed paired-wavelength spectral method assigns oxygenation values to the absorbing structures displayed in the second-derivative images.The resulting oxygenation images feature average values over the whole breast that are significantly lower in cancerous breasts(69±14%,n=2)than in healthy breasts(85±7%,n=18)(p<0.01).Furthermore,in the two patients with breast cancer,the average oxygenation values in the cancerous regions are also significantly lower than in the remainder of the breast(invasive ductal carcinoma:49±11%vs 61±16%,p<0.01;ductal carcinoma in situ:58±8%vs 77±11%,p<0.001).

Spectral Correction Method of Multi-Channels Near-Infrared Spectrometer and Applications

Near-infrared (NIR) spectrometer based on semiconductor lasers can combine light source and splitter into one, which is an important direction for development of miniature instruments. In order to avoid random interference caused by inconsistency between light sources, the novel evaluation indicators for global stability of multi-channels spectral system were proposed based on the correlation between dynamic deviation spectra of any two channels. The NIR analysis of moisture for corn powder samples based on the partial least squares combined with Savitzky-Golay (SG) smoothing was taken as an example, and a spectral correction method for enhancing prediction performance of multi-channels spectral system was further provided using above evaluation indicators. The experiment results showed that the global stability evaluation indicators significantly increased after SG smoothing correction. Meanwhile, the root-mean-square errors of prediction for corn moisture reduced from 0.373 to 0.283 (%), and the correlation coefficient between predicted and actual values was improved from 0.702 to 0.855. The above results indicated that by improving global stability indicators, the prediction ability of multi-channels spectral system can be improved. The proposed method provided a valuable reference for designing multi-channels diminutive spectrometer with high prediction performance, which had significance for large-scale application of NIR technology.

Global Optimization of Norris Derivative Filtering with Application for Near-Infrared Analysis of Serum Urea Nitrogen

Near-infrared (NIR) spectroscopy combined with chemometrics methods was applied to the rapid and reagent-free analysis of serum urea nitrogen (SUN). The mul-partitions modeling was performed to achieve parameter stability. A large-scale parameter cyclic and global optimization platform for Norris derivative filter (NDF) of three parameters (the derivative order: d, the number of smoothing points: s and the number of differential gaps: g) was developed with PLS regression. Meantime, the parameters’ adaptive analysis of NDF algorithm was also given, and achieved a significantly better modeling effect than one without spectral pre-processing. After eliminating the interference wavebands of saturated absorption, the modeling performance was further improved. In validation, the root mean square error (SEP), correlation coefficient (RP) for prediction and the ratio of performance to deviation (RPD) were 1.66 mmol?L-1, 0.966 and 4.7, respectively. The results showed that the high-precision analysis of SUN was feasibility based on NIR spectroscopy and Norris-PLS. The global optimization method of NDF is also expected to be applied to other analysis objects.

Simulation of near-infrared photodiode detectors based on β-FeSi_2/4H-SiC heterojunctions

In this paper,we propose a near-infrared p-type β-FeSi2/n-type 4H-SiC heterojunction photodetector with semiconducting silicide（β-FeSi2） as the active region for the first time.The optoelectronic characteristics of the photodetector are simulated using a commercial simulator at room temperature.The results show that the photodetector has a good rectifying character and a good response to near-infrared light.Interface states should be minimized to obtain a lower reverse leakage current.The response spectrum of the β-FeSi2/4H-SiC detector,which consists of a p-type β-FeSi2 absorption layer with a doping concentration of 1×1015cm-3 and a thickness of 2.5 μm,has a peak of 755 mA/W at 1.42 μm.The illumination of the SiC side obtains a higher responsivity than that of the β-FeSi2 side.The results illustrate that the β-FeSi2/4H-SiC heterojunction can be used as a near-infrared photodetector compatible with near-infrared optically-activated SiC-based power switching devices.

In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

As unsafe components in herbal medicine(HM),saccharides can affect not only the drug appearance and stabilization,but also the drug efficacy and safety.The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using nearinfrared(NIR)spectroscopy.NIR spectra in the 4000–10,000-cm^(-1)wavelength range are acquired in situ using a transflectance probe.These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation.Calibration models based on partial least squares(PLS)regression have been developed for the three saccharide impurities,namely glucose,fructose,and sucrose.Model errors are estimated as the root-meansquare errors of cross-validation(RMSECVs)of internal validation and root-mean-square errors of prediction(RMSEPs)of external validation.The RMSECV values of glucose,fructose,and sucrose were 1.150,1.535,and 3.067 mg·mL^(-1),and the RMSEP values were 0.711,1.547,and 3.740 mg·mL^(-1),respectively.The correlation coeffcients(r)between the NIR predictive and the reference measurement values were all above 0.94.Furthermore,NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation.The results demonstrate that,as an alternative process analytical technology(PAT)tool for monitoring batch alcohol precipitation processes,NIR spectroscopy is advantageous for both efficient determination of quality characteristics(fast,in situ,and requiring no toxic reagents)and process stability,and evaluating the repeatability.

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.

隧道不良地质识别:方法、现状及智能化发展方向

随着隧道施工对于不良地质识别精度要求的不断提高以及人工智能技术的发展,融合多源信息的不良地质智能化识别已成为发展趋势。本文首先阐述了常见的6种隧道不良地质类型及其地质成因,回顾分析了隧道主要的不良地质识别方法及现状,详细介绍了笔者在不良地质智能化识别方面的探索性研究:基于机器学习利用图像识别技术对隧道围岩岩性与裂隙特征进行智能识别;融合图像和光谱特征进行不良地质识别;将地化分析融入到传统的超前钻探中,融合随钻参数和地化信息进行不良地质随钻识别,既可以发挥超前钻探在感知岩体质量和地层信息变化方面的优势,又可以发挥地化分析在岩性和不良地质异常识别方面的优势;基于地质与物探联合反演进行不良地质识别,旨在实现掌子面前方不良地质体“形”(位置、形态、规模)和“性”(性质和类型)的精确识别。最后,对隧道不良地质智能化识别的发展趋势进行了展望。

基于专利文本多粒度深层语义的技术演化路径智能识别方法研究

[目的/意义]专利文本是技术演化分析的可靠知识来源。利用领域多层本体和Sentence-BERT深度学习预训练模型可分别从词和句子两个角度挖掘多粒度深层文本语义信息,提升专利文本语义挖掘的全面性,进而提高技术演化路径识别的准确性。[方法/过程]以深度学习、基于本体的相似度计算及谱聚类算法等大数据和人工智能技术为基础,实现准确、高效、全面的技术演化路径智能识别。构建领域词典和领域本体,根据领域词典抽取专利摘要中的领域术语,根据领域本体中不同术语的最近共同祖先节点的深度,从词语级别计算专利摘要间的语义相似度;利用Sentence-BERT对专利摘要进行向量化表示,计算句子层面的语义相似度;结合两种相似度的计算结果构建相似度矩阵并进行谱聚类,根据谱聚类结果和专利时序特征识别技术演化路径。最后以光刻领域专利数据为例进行实证研究。[结果/结论]实验结果表明,谱聚类得到的技术类别划分结果的准确率、精确率、召回率和F1值的平均值均达到了85%以上,说明文章提出的基于专利文本多粒度深层语义的技术演化路径智能识别方法是有效的,其有助于科研人员、企业决策者和政府决策者探究技术发展历程,研判技术创新方向,推动关键技术研发。

联合运用多光谱和激光雷达技术构建的林分生物量估算模型

以广东省广州市从化石门国家森林公园为研究区域,选择4种不同林分类型(针阔混交林、阔叶林、针叶林、竹林),各林分类型选择3个20 m×20 m地块作为样方;结合激光雷达、多光谱图像、实测数据,构建多元非线性反演模型和多元线性回归模型,估算森林地上生物量,并选择最佳模型进行精度评价。结果表明:(1)依据多源数据建立的4种不同林分类型的多元非线性地上生物量反演模型的精度最高,针阔混交林样地地上生物量预测值为42.79 t·hm^(-2)、阔叶林样地地上生物量预测值为60.46 t·hm^(-2)、针叶林样地地上生物量预测值为32.99 t·hm^(-2)、竹林样地地上生物量预测值为1.92 t·hm^(-2)。(2)研究区中4种不同林分类型的多元非线性地上生物量反演模型的拟合精度,由大到小依次为竹林(决定系数为0.919)、阔叶林(决定系数为0.813)、针叶林(决定系数为0.786)、针阔混交林(决定系数为0.713),均符合精度要求。采用多光谱和激光雷达数据结合的方式,能够较精准地提取林分地上生物量信息,可准确估算针阔混交林、阔叶林、针叶林、竹林的地上生物量。

光谱检测技术在毒品侦缉中的应用

世界毒品形势日益严峻。不断涌现的新型毒品和更加隐匿的运毒方式给毒品侦缉工作带来了新的挑战。传统的毒品检测方法存在样品预处理过程复杂、检测设备昂贵、难以搬运等问题,无法满足现场快速检测的需求。光谱检测技术凭借检测速度快、无需样品预处理、设备体积小等优势正逐步成为毒品侦缉过程中重要技术手段。结合近年来光谱检测技术的研究进展,根据毒品形态综述了几种光谱检测技术在毒品侦缉中的应用。针对海洛因、甲基苯丙胺等常见固态毒品,太赫兹光谱检测和能量色散X射线衍射技术凭借高穿透性、可视化等优势,可以实现对纸张、木头、橡胶等常见藏匿物品中毒品的表征,适用于公共安全检测中毒品的快速筛查;针对毒品溶液和吸毒者体液(尿液、唾液、血液等)中毒品成分的检测,综述了红外光谱检测和表面增强拉曼散射技术最新进展,表面增强拉曼散射由于具有高度特异性的分子指纹图谱和不受水溶液影响的优势,成为液态毒品识别与检测领域研究的热点,该方法有望用于液态毒品的便携式快速检测;针对笑气等气态物质的滥用,总结了多种基于光谱检测技术的痕量气体检测方法,综述了基于量子级联激光器的呼出笑气检测进展,该技术在气态毒品检测领域有着较好的应用前景。

基于毫米波通信技术的超高清视频传输方法研究

为了提高对超高清视频传输能力,提出基于毫米波通信的超高清视频传输方法。采用多频调制通信组网技术构建超高清视频传输的信道模型,建立超高清视频毫米波通信传输的自适应图像信息交互和采集模型;结合Lyapunov指数谱密度特征分析,实现谱特征分解和信道均衡模型构建;建立视频传输的视景转换和高阶谱特征提取模型;采用毫米波通信技术,实现滤波补偿和反馈增益调节;采用联合自相关的统计特征分析方法,得到动态均衡迭代函数,通过复杂信道环境下的抗干扰抑制和均衡调度,实现对超高清视频的无损传输和编码解码。实验结果表明,该方法进行超高清视频传输的误码率较低,平均为0.058 2,传输时延较低,平均为248.4 ms,峰值信噪比为49 dB。

基于“图像+光谱”技术的马铃薯早疫病智能诊断方法研究

为实现马铃薯早疫病的智能化诊断,提出基于“图像+光谱”技术的马铃薯早疫病智能诊断方法,该系统可针对马铃薯早疫病的症状进行智能分析。首先搜集马铃薯病斑的相关信息;然后对信息进行针对性处理,包括颜色调整和对图像裁剪,以提取病斑的关键特征;最后系统再利用病斑特征识别并自动诊断马铃薯早疫病。经过试验验证,此方法能有效地诊断马铃薯早疫病,切实为智慧农业中植物病害的智能监控和预警提供创新方法。

光谱分析技术用于畜禽养殖粪污成分检测的研究进展

作为一种“放错了地方的资源”,养殖业所产生的粪污排放量大、成分复杂,借助先进理化分析手段开展成分分析,进而针对性地开展发酵处理,实现“变废为宝”具有重要意义。传统的粪污成分理化检测方法存在耗时长、前处理过程复杂等问题,近年来,光谱分析技术因具有检测速度快、成本低等优点,在养殖粪污成分检测研究领域已开展较多研究,并在未来应用中展示出良好前景。本文介绍了传统的粪污检测方法及存在不足;综述了基于光谱分析技术检测养殖粪污中碳氮元素、金属元素、干物质/有机物和磷元素等成分含量的研究进展;并对光谱分析技术用于养殖粪污检测在预测模型构建、样品制备方法和检测仪器研发等方面存在的问题和改进方向进行了总结,可为养殖粪污资源化高效利用提供理论支撑。

结合机器视觉和光谱技术的番茄综合品质检测方法

[目的]实现番茄内外部品质同时快速准确测定,提高番茄的分级效率和品质。[方法]基于机器视觉和光谱技术,提出一种结合外部品质和内部品质的番茄综合品质分级方法。通过对YOLOv8模型进行4个方面的优化(轻量化卷积、小目标检测层、CBAM注意机制和损失函数)完成外部缺陷检测,结合果形指数和番茄尺寸完成外部品质分级。通过预处理方法、特征提取方法和改进粒子群优化的最小二乘支持向量机完成番茄内部品质分级。通过试验对所提分级检测方法的性能进行分析。[结果]所提方法可以实现番茄综合品质检测,具有较高的准确性和效率。外部品质分级准确率>93.00%,内部品质分级准确率>86.00%,融合品质分级准确率>96.00%,平均分级时间<0.25 s。[结论]结合机器视觉和光谱检测技术可以实现番茄综合品质的快速、无损和准确评估。

遥感技术在地质找矿中的应用及发展前景

遥感技术在地质找矿中具有广域观测、多源数据整合、非侵入性和非接触性、高时间分辨率、快速、高效和经济性等优势。它能帮助识别和定量化不同类型的矿物质,确定潜在的矿产资源。在地质找矿中,遥感技术主要应用于矿物识别、构造特征分析、矿化带探测和环境监测。未来,随着遥感技术的发展,地质找矿工作将进一步改进高分辨率图像获取、多光谱和高光谱遥感、雷达和LIDAR技术以及数据处理和分析技术,从而提高矿产勘探的效率和精度。总体而言,遥感技术在地质找矿中有广阔的应用前景。

先进光谱技术在多金属矿物分析中的应用与发展

在开展地质工作时,多金属矿物分析是十分重要的环节,能够为矿产资源开采利用以及各项研究工作提供可靠依据。近年来,我国光谱技术持续发展,并大范围应用于农业、食品等领域,通过对光谱技术进行详细研究,其对多金属矿物分析和鉴定有着积极作用。基于此,本文首先对光谱技术进行介绍,其次,对光谱技术在多金属矿物分析中的应用策略进行分析,并结合实例,对多金属矿物分析中光谱技术的应用要点进行详细探究,并由此提出光谱技术的发展方向,以推广该项技术在多金属矿物分析中的应用。

基于红外光栅技术的高压场地作业区域人员入侵检测方法

为了提高作业区域人员入侵检测的准确度,提出了一种基于红外光栅技术的高压场地作业区域人员入侵检测方法。该方法利用红外光栅技术采集人员图像视觉信息,并通过对结构化相似度特征进行分析,结合监督对比学习和主干特征提取方法,实现特征分割和重构处理。同时,通过模糊度分析获得人员的边缘包围轮廓特征,实现对人员入侵行为的识别。经过实验验证,结果表明该方法在入侵人员检测方面具有高准确性,误报率(FPR)、漏报率(FNR)和虚警率(FAR)的平均值分别为2.69‰、3.13‰和3.21‰,检测速率平均为16.25帧/s。

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为实现缸套内表面微织构形貌的在线检测,设计一种非接触式测量系统。采用基于EtherCAT通信协议开发的伺服系统,通过高精度伺服电机带动光谱共焦位移传感器,一次采集多个微织构的不同截面轮廓,经特征提取与坐标变换,形成单个织构的三维点云数据。通过光强值出现局部极小值确定边缘位置,采用稳健高斯回归滤波方法滤除误差,使用移动最小二乘法拟合曲面,最终获得微织构的精准三维形貌。试验结果表明:该系统与超景深显微镜测得的直径最大差值为5.7μm,深度最大差值为0.45μm,具有较高的重复性和精度,可作为缸套内表面微织构形貌在线检测的一种手段。