Highly-sensitive NO,NO2,and NH3 measurements with an open-multipass cell based on mid-infrared wavelength modulation spectroscopy

A compact prototype based on mid-infrared wavelength modulation spectroscopy（WMS）is developed for the simul-taneous monitoring of NO,NO2,and NH3 in the urban area.Three quantum cascade lasers（QCLs）with central frequencies around 1900.0 cm^-1,1600.0 cm^-1,and 1103.4 cm^-1are used for NO,NO2,and NH3detections,respectively,by timedivision multiplex.An open-path multi-pass cell of 60-m optical path length is applied to the instrument for high sensitivity and reducing the response time to less than 1 s.The prototype achieves a sub-ppb detection limit for all the three target gases with an average time of about 100 s.The instrument is installed in the Jiangsu environmental monitoring center to conduct performance tests on ambient air.Continuous 24-hour measurements show good agreement with the results of a reference instrument based on the chemiluminescence technique.

Prediction Modeling and Mapping of Soil Carbon Content Using Artificial Neural Network, Hyperspectral Satellite Data and Field Spectroscopy

Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyperspectral image, field and laboratory scale data sets (350 - 2500 nm) were generated which consisted of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflectance data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and all three datasets (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods had a great potential for estimating and mapping spatial SOC content. The study concluded that ANN model was potential tools in predicting SOC distribution in agricultural field using hyper-spectral remote sensing data at image-scale, field-scale and lab-scale.

Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

The application of Fourier transform mid-infrared(FTIR) spectroscopy to identify variation in cell wall composition of Setaria italica ecotypes

Cell wall composition in monocotyledonous grasses has been identified as a key area of research for developing better feedstocks for forage and biofuel production.Setaria viridis and its close domesticated relative Setaria italica have been chosen as suitable monocotyledonous models for plants possessing the C4 pathway of photosynthesis including sorghum,maize,sugarcane,switchgrass and Miscanthus×giganteus.Accurate partial least squares regression（PLSR）models to predict S.italica stem composition have been generated,based upon Fourier transform mid-infrared（FTIR）spectra and calibrated with wet chemistry determinations of ground S.italica stem material measured using a modified version of the US National Renewable Energy Laboratory（NREL）acid hydrolysis protocol.The models facilitated a high-throughput screening analysis for glucan,xylan,Klason lignin and acid soluble lignin（ASL）in a collection of 183 natural S.italica variants and clustered them into classes,some possessing unique chemotypes.The predictive models provide a highly efficient screening tool for large scale breeding programs aimed at identifying lines or mutants possessing unique cell wall chemotypes.Genes encoding key catalytic enzymes of the lignin biosynthesis pathway exhibit a high level of conservation with matching expression profiles,measured by RT-q PCR,among accessions of S.italica,which closely mirror profiles observed in the different developmental regions of an elongating internode of S.viridis by RNASeq.

Research on Determination of Nitrogen Content in Petroleum Using Mid-infrared Spectroscopy

Nitrogen content is an important parameter for petroleum refining processes. The combined use of mid-infrared attenuated total reflection spectroscopy and multivariate calibration allows accurate determination of nitrogen content in petroleum and its products. The calibration models of nitrogen content in crude oils have been established by partial least squares (PLS) method. The results predicted by this method were very close to those determined by standard methods. Compared with standard methods, this method is provided with advantages such as high-speed, simplicity and good-repeat- ability without any needs for pretreatment.

Unsupervised calibration for noninvasive glucose-monitoring devices using mid-infrared spectroscopy

Noninvasive,glucose-monitoring technologies using infrared spectroscopy that have been studied typically require a calibration process that involves blood collection,which renders the methods somewhat invasive.We develop a truly noninvasive,glucose-monitoring technique using midinfrared spectroscopy that does not require blood collection for calibration by applying domain adaptation(DA)using deep neural networks to train a model that associates blood glucose concentration with mid-infrared spectral data without requiring a training dataset labeled with invasive blood sample measurements.For realizing DA,the distribution of unlabeled spectral data for calibration is considered through adversarial update during training networks for regression to blood glucose concentration.This calibration improved the correlation coeffcient between the true blood glucose concentrations and predicted blood glucose concentrations from 0.38 to 0.47.The result indicates that this calibration technique improves prediction accuracy for mid-infrared glucose measurements without any invasively acquired data.

Rapid Detection of Accelerants in Fire Debris Using a Field Portable Mid-Infrared Quantum Cascade Laser Based Analyzer

Arson presents a challenging crime scene for fire investigators worldwide. Key to the investigation of suspected arson cases is the analysis of fire debris for the presence of accelerants or ignitable liquids. This study has investigated the application and method development of vapor phase mid-Infrared (mid-IR) spectroscopy using a field portable quantum cascade laser (QCL) based system for the detection and identification of accelerant residues such as gasoline, diesel, and ethanol in fire debris. A searchable spectral library of various ignitable fluids and fuel components measured in the vapor phase was constructed that allowed for real-time identification of accelerants present in samples using software developed in-house. Measurement of vapors collected from paper material that had been doused with an accelerant followed by controlled burning and then extinguished with water showed that positive identification could be achieved for gasoline, diesel, and ethanol. This vapor phase mid-IR QCL method is rapid, easy to use, and has the sensitivity and discrimination capability that make it well suited for non-destructive crime scene sample analysis. Sampling and measurement can be performed in minutes with this 7.5 kg instrument. This vibrational spectroscopic method required no time-consuming sample pretreatment or complicated solvent extraction procedure. The results of this initial feasibility study demonstrate that this portable fire debris analyzer would greatly benefit arson investigators performing analysis on-site.

Test on oxygen and benzene contents in gasoline by mid-infrared spectroscopy

This paper aims at testing oxygen and benzene contents in gasoline by mid-infrared spectroscopy.The experimental results prove that infrared spectroscopy(IR)is reliable.Compared with gas chromatography(GC)technology,this paper draws a conclusion that IR has several advantages,including rapid analysis,excellent repeatability and low analysis cost.

Near-Infrared Imaging Using a High-Speed Monitoring Near Infrared Hyperspectral Camera(Compovision)

This review paper reports near-infrared(NIR)imaging studies using a newly-developed NIR camera,Compovision.Compovision can measure a significantly wide area of 150mm×250mm at high speed of between 2and 5s.It enables a wide spectral region measurement in the 1 000~2 350nm range at 6nm intervals.We investigated the potential of Compovision in the applications to industrial problems such as the evaluation of pharmaceutical tablets and polymers.Our studies have demonstrated that NIR imaging based on Compovision can solve several issues such as long acquisition times and relatively low sensitivity of detection.NIR imaging with Compovision is strongly expected to be applied not only to pharmaceutical tablet monitoring and polymer characterization but also to various applications such as those to food products,biomedical substances and organic and inorganic materials.

Active hyperspectral imaging with a supercontinuum laser source in the dark

An active hyperspectral imaging(HSI) system was built with a supercontinuum(SC) laser illuminator and a visible/near-infrared hyperspectral camera, which was used for object spectrum detection in the dark. It was demonstrated that the Gaussian-like distribution of the SC illuminator can still be used for accurate reflectance spectrum measurement once the illuminator was characterized in advance. The validity of active HSI results was demonstrated by comparison with passive results. Then, the active HSI system was used to acquire reflectance spectra of different objects in just one pushbroom measurement successfully. With algorithms of principal component analysis clustering and unsupervised K-means spectral classification, this active HSI system with high spectral and spatial resolutions was demonstrated to be efficient and applicable for specific spectrum detections.

Single-shot mid-infrared incoherent holography using Lucy-Richardson-Rosen algorithm

In recent years,there has been a significant transformation in the field of incoherent imaging with new possibilities of compressing three-dimensional(3D)information into a two-dimensional intensity distribution without two-beam interference(TBI).Most of the incoherent 3D imagers without TBI are based on scattering by a random phase mask exhibiting sharp autocorrelation and low cross-correlation along the depth.Consequently,during reconstruction,high lateral and axial resolutions are obtained.Imaging based on scattering requires an astronomical photon budget and is therefore precluded in many power-sensitive applications.In this study,a proof-of-concept 3D imaging method without TBI using deterministic fields has been demonstrated.A new reconstruction method called the Lucy-Richardson-Rosen algorithm has been developed for this imaging concept.We believe that the proposed approach will cause a paradigm-shift in the current state-of-the-art incoherent imaging,fluorescence microscopy,mid-infrared fingerprinting,astronomical imaging,and fast object recognition applications.

LESS-INVASIVE LASER THERAPY AND DIAGNOSIS USING A TABLETOP MID-INFRARED TUNABLE LASER

Since numerous characteristic absorption lines caused by molecular vibration exist in the midinfrared(MIR)wavelength region,selective excitation or selective dissociation of molecules is possible by tuning the laser wavelength to the characteristic absorption lines of target molecules.By applying this feature to the medical fields,less-invasive treatment and non-destructive diagnosis with absorption spectroscopy are possible using tunable MIR lasers.A high-energy nanosecond pulsed MIR tunable laser was obtained with difference-frequency generation(DFG)between a Nd:YAG and a tunable Cr:forsterite lasers.The MIR-DFG laser was tunable in a wavelength range of 5.5–10μm and generated laser pulses with energy of up to 1.4mJ,a pulse width of 5 ns,and a pulse repetition rate of 10 Hz.Selective removal of atherosclerotic lesion was successfully demonstrated with the MIR-DFG laser tuned at a wavelength of 5.75μm,which corresponds to the characteristic absorption of the ester bond in cholesterol esters in the atherosclerotic lesions.We have developed a non-destructive diagnostic probe with an attenuated total reflection(ATR)prism and two hollow optical fibers.An absorption spectrum of cholesterol was measured with the ATR probe by scanning the wavelength of the MIR-DFG laser,and the spectrum was in good agreement with that measured with a commercial Fourier transform infrared spectrometer.

Hyperspectral Reflectance Characteristics of Cyanobacteria

Potentially harmful cyanobacterial blooms are an emerging environmental concern in freshwater bodies worldwide. Cyanobacterial blooms are generally caused by high nutrient inputs and warm, still waters and have been appearing with increasing frequency in water bodies used for drinking water supply and recreation, a problem which will likely worsen with a warming climate. Cyanobacterial blooms are composed of genera with known biological pigments and can be distinguished and analyzed via hyperspectral image collection technology such as remote sensing by satellites, airplanes, and drones. Here, we utilize hyperspectral microscopy and imaging spectroscopy to characterize and differentiate several important bloom-forming cyanobacteria genera obtained in the field during active research programs conducted by US Geological Survey and from commercial sources. Many of the cyanobacteria genera showed differences in their spectra that may be used to identify and predict their occurrence, including peaks and valleys in spectral reflectance. Because certain cyanobacteria, such as Cylindrospermum or Dolichospermum, are more prone to produce cyanotoxins than others, the ability to differentiate these species may help target high priority waterbodies for sampling. These spectra may also be used to prioritize restoration and research efforts to control cyanobacterial harmful algal blooms (CyanoHABs) and improve water quality for aquatic life and humans alike.

Hyperspectral Analysis for a Robust Assessment of Soil Properties Using Adapted PLSR Method

Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.

Light-Induced Mid-Infrared Emission of Liquid Carbon Tetrachloride and Benzene

Light-induced infrared emission spectroscopy (LIRES) is a novel technique that permits to receive high-quality spectra in the mid-infrared region. Low-intensity visible light connected to a highly sensitive FTIR spectrometer is more advantageous for studying any samples, including biological samples without any damage. This technique permits obtaining unique information on the molecule structure via vibrational excitation fundamental frequencies, overtones, and combination modes. It also enables a direct observation of vibrational radiation transitions in vibrationally excited molecules as well as the channels of vibration energy redistribution, which is not allowed with any other method. In this work, the LIRES is being tested as a technique for studying of vibrationally-excited molecules of carbon tetrachloride and benzene in the liquid phase. On the other hand, using transparent liquids, we had tried to understand some of the physical phenomena that can drive emission in mid-IR. The characteristics of the infrared emission of both liquid species produced by different wavelength radiation from various types of light systems (100 - watt Xe-lamp and Nd:YAG laser;lambda = 1064 nm (8 mW) and lambda = 532 nm (4 mW)) are presented. We demonstrated that the IR-signal, as well as spectral properties of carbon tetrachloride and benzene, was dependent on the wavelength and power of excitation beam. Results obtained with different light sources show that the visible light produces a nonlinear IR-emission signal in transparent liquids. We believe that the visible light is the source of the nonlinear response and is producing the vibration excitation as well as photostimulated transformations of the molecules possessing the high activity for the nonlinear response.

Hyperspectral Imaging Based on Compressive Sensing: Determining Cancer Margins in Human Pancreatic Tissue <i>ex Vivo</i>, a Pilot Study

Cancer is the second-leading cause of death in the United State and surgery remains the primary treatment for most solid mass tumors. However, accurately identifying tumor margins in real-time remains a challenge. In this study, the design and testing of hyperspectral imaging (HSI) system based on a single-pixel camera engine is discussed. The primary advantage of a single pixel architecture over traditional scanning HSI techniques is its high sensitivity and potential to function at low light levels. The objective for the imaging system described here is to detect changes in the reflectance spectra of tissue and to use these differences to delineate tumor margins. This paper presents the results of a 19-patient pilot study that assesses the ability of the HSI system to use reflectance imaging to delineate adenocarcinoma tumor margins in human pancreatic tissue imaged ex vivo. Pancreatic tissue excised during pancreatectomy was imaged immediately after being sent to the pathology lab. A pathologist sectioned the tissue and placed samples into standard tissue embedding cassettes. These tissue samples were then imaged using the HSI system. After imaging, the samples were returned to the pathologist for processing and analysis. The HSI was later compared to the histological analysis. The spectral angle mapping (SAM) and support vector machine (SVM) algorithms were used to classify pixels in the HSI images as healthy or unhealthy in order to delineate margins. Good agreement between margins determined via HSI (using both SAM and SVM) and histology/white light imaging was found.

宽光谱吸收应用中光谱扫描区间的选择

与传统的窄带扫描吸收光谱技术相比,宽光谱吸收技术可以获取更宽光谱范围内的丰富吸收数据,具有很大的技术优势。宽光谱吸收技术测量系统的硬件与传统的可调谐二极管吸收光谱技术测量系统基本一致,唯一不同的是宽光谱吸收技术则使用宽带调谐光源。实际应用中,宽光谱吸收光源一般都具有较宽的光谱覆盖范围(>20 nm),而使用全谱段扫描会对数据采集、存储和处理提出较高的要求,从而增加系统成本和测量工作量。因此,选择合适宽度的光谱扫描区间就成为宽光谱吸收应用中的一个关键问题。利用等效下能级概念,提出了针对宽光谱吸收应用的光谱扫描区间选择的数值方法,并针对1.3μm波段和2μm波段开展了扫描区间选择研究。在1.3μm波段,计算给出的优选扫描谱段与文献报道中使用的谱段相重合,验证了选择方法的可行性和正确性。在2μm波段,在给定工况条件下,通过实验数据验证了所选择扫描区间在温度反演精度方面的优越性。针对实际应用中面临的宽工况环境,通过计算给出了2μm波段优选的扫描光谱区间。计算结果表明,在1.8μm的短波段和1.9μm的长波段均存在适用的光谱扫描区间,其中,1.86μm附近测温精度最高;更宽的扫描带宽有助于降低系统在全光谱范围内的整体测温不确定度。相关研究可为宽光谱吸收技术在燃烧流场诊断的现场应用提供关键技术指导。

近红外光谱及成像在果品无损检测中的应用

近红外光谱及成像技术作为新兴传感无损检测技术,具有绿色、无污染、非破坏性等优势,为果品品质领域的快速、无损检测提供新的方法和手段。文章综述近红外光谱及成像技术在果品品质检测中的研究进展,介绍近红外光谱和成像技术的基本原理,分析近红外光谱技术在果品品质检测中的化学组成、成熟度评估和小型化设备开发的应用现状,探析近红外光谱成像技术在果品外观和质量、病虫害诊断中的应用现状,阐述其在果品组分分布和缺陷识别等方面的技术优势。同时,探讨近红外光谱及成像技术与人工智能、物联网结合应用的巨大潜力,以及对提高果品检测效率、精度和改善供应链智能管控等方面的应用前景做出展望,以期为果品无损检测的应用研究提供参考。

高压强下2μm波段CO_(2)宽光谱吸收测量与分析

利用宽带可调谐掺铥光纤激光器开展了覆盖152 kPa至932 kPa的2µm波段CO_(2)宽光谱吸收测量实验,通过将实测谱与基于HITRAN数据库计算的理论谱进行峰值匹配完成了波长定标,然后将基线表示为三次多项式并结合理论光谱数据,对原始信号做基线-吸收线整体拟合得到了与实验参数设置相吻合的压强、温度和CO_(2)浓度信息。其中压强计算误差低于5%,温度计算误差低于1%,实测谱与理论谱的整体残差在0.04以内。另外对比了窄带吸收谱与宽带吸收谱的吸收面积反演结果,验证了宽光谱吸收技术在高压测量环境下的可靠性,在温度、气体摩尔分数近似不变的情况下,宽带吸收谱反演的吸收面积与压强在测量范围内保持了良好的线性关系。

新型无损检测技术在番茄品质检测中的研究与应用进展

番茄是我国种植面积最广的蔬菜之一,受到广大消费者的青睐。近年来,随着人们对健康饮食需求的逐步提升,番茄的品质愈发受到关注。番茄形状较为规则,但不同品种间的大小、果型、颜色差异较大,蕴含的营养成分种类繁多、化学结构复杂,导致其品质检测存在一定难度。传统番茄品质检测方法大多存在主观性强、破坏性强、耗时费力的缺点,难以满足大规模品质检测的需求。近年来,随着各类无损检测技术的发展,机器学习、多光谱技术、电子鼻/电子舌等新型检测方法也已逐步应用于番茄品质的快速、无损检测中。本文在传统番茄品质检测技术的基础上,重点总结了基于图像识别的人工智能、电子鼻技术和光谱技术在番茄无损检测方面的发展与应用,为番茄品质检测的研究与发展提供参考。