近红外光谱技术在猫脑射频热凝毁损中的应用研究

目的：探索近红外光谱（nears）技术用于立体定向靶点毁损术中实时监测的可行性。方法：利用猫脑建立体内不同毁损时间、温度下的毁损灶体积模型,通过病理检测及近红外光谱仪观察并记录脑组织靶点毁损时的NIRS尤其是优化散射系数（）的变化情况。结果：不同温度、不同时间温度点下NIRS出现特征性变化曲线。并建立时间、温度及三维模型。结论：利用NIRS实时活体在位监测猫脑射频神经核团毁损术是科学、可行的,优化散射系数是监测的良好指标,比以往单凭经验的作法更科学、更准确。     

近红外光谱技术在神经外科患者脑氧和血流动力学监测中的应用研究进展

神经外科患者,尤其是脑血流自动调节功能受损的重症患者,脑氧饱和度是反应患者脑组织氧代谢情况的重要指标,实时、准确的脑氧饱和度监测方法对于指导选择有效的治疗措施和判断患者预后具有重要价值。基于血红蛋白不同氧合状态,即氧合血红蛋白(oxyhemoglobin,Hb O2),还原血红蛋白(deoxygenated hemoglobin,Hb)具有的差异性分子光谱,近红外光谱技术near infrared spectroscopy,NIRS)可监测人体局部组织氧饱和度。由于近红外射线能穿透颅骨直接获得脑组织内平均氧饱和度的特性,可协助临床实现无创持续监测脑氧饱和度的目的,近年来该技术在神经外科领域的应用研究获得了迅速发展,在颅脑创伤和其它神经外科疾病的应用研究中均取得了显著的进展,本文将对最新研究结果及其意义和未来发展方向进行综述。     

近红外光谱技术在运动脑功能研究中的应用

近红外光谱是一种评估生物组织氧合水平的无创性光学技术,这一技术以血液动力学原理为基础,能实时监测局部脑区的动态变化。近红外光谱作为一种客观的测量工具,在人体运动科学领域广泛运用。近红外光谱技术可用于区别体能水平、监控耐力训练和抗阻训练过程特征以及考察运动中的认知活动变化。本文综述了近红外光谱技术原理及其在抗阻运动、运动中枢疲劳和运动认知领域的运用,并对近红外光谱在运动科学领域的未来研究趋向作了分析。     

近红外光谱技术在烟草行业中的应用进展

介绍了近红外光谱分析技术在烟草常规化学成分分析、烟气分析、卷烟配方设计、不同产地模式识别和真假烟鉴别等方面的应用,认为近红外光谱分析技术在烟草行业中正扮演着越来越重要的角色,而且具有十分广阔的应用前景.     

近红外光谱技术在医学上应用进展

本文介绍了近红外光谱分析技术在医学领域的应用情况,并对该技术未来发展方向及其局限性进行了分析。     

近红外光谱技术在水果成熟期预测中的应用(综述)

近红外光谱技术以快速、准确和多组分同步分析等优势,近年来在水果果实发育、成熟期预测和品质检测等方面应用广泛,并在果实品质无损检测分析技术研究方面取得重要进展。本文综述近红外光谱技术的基本原理和特点,分析近红外光谱技术在果实成熟期预测中的研究现状和存在问题,并提出今后研究方向。     

近红外光谱技术（NIRS）在人体的应用与展望

近红外光谱技术(NIRS)在人体的应用与研究是近年来在国内外新兴的研究领域,因为其方便无创,成本低等优点,近20年来在不断发展和完善,引起大家的广泛关注。近红外光谱在700-900 mm范围内可以穿透一定深度的组织,组织内含氧血红蛋白、去氧血红蛋白对近红外光的吸收系数存在差异,经过传感器和计算机技术分析,得到组织的血氧参数。其测量参数为微动脉、微静脉和毛细血管中血液的血氧参数之加权平均,反应组织中的血氧参数,其中静脉血占主要地位,不同于普通脉搏式血氧监测仪的指端动脉血的血氧饱和度。基于近红外光谱技术的近红外组织血氧无创监测仪、功能性近红外光谱技术(f NIRS)、近红外光谱荧光技术等在临床医学,运动医学,神经生物学,认知科学,脑力疲劳,人机交互等新兴领域正发挥越来越重要作用。     

近红外光谱技术在食品产地溯源中的应用进展

食品产地溯源是食品安全追溯制度的重要工作。近红外光谱技术(near infrared spectroscopy,NIRS)作为一种兼具快速、简便、不破坏试样、分析过程无试剂消耗等优点的新兴绿色检测技术,近年来被逐步应用于食品产地溯源的研究中。简要介绍了应用于食品产地溯源研究中近红外光谱技术常用的化学计量学技术及软件平台,同时概述了近年来该技术在国内外食品产地溯源中的研究进展,分析了在目前产地溯源研究中的优势和存在的问题,以期为近红外光谱溯源技术的进一步发展提供参考。     

近红外光谱技术在稻米特性检测中的应用(综述)

近红外光谱技术是一种新型的检测分析技术,广泛应用于农业、林业、工业、医药以及食品等多个行业领域。文章综述近红外光谱技术在稻米特性检测中的应用概况,包括对大米淀粉、蛋白质和脂肪酸等营养物质的测定,大米糊化特性、粘稠度和食味特性的分析,水稻生长过程中氮、磷、钾和其他营养元素含量的分析,育种研究与品种鉴别,病害、重金属等有害物质以及其他方面。同时,指出该技术在当前检测应用中存在的一些问题,并针对目前发展趋势展望该技术的前景。     

近红外光谱技术在生物学研究中的应用

分析了利用近红外(NIR)光谱技术进行生物学研究的原理与优势,概述了这一技术在生物体成分的定量分析、生物生理与病理信息的获取以及分类鉴定等领域中的应用,并讨论了其在生物学研究中的局限性与前景.     

Comparison of optically‐derived biomarkers in healthy and brain tumor tissue under one‐ and two‐photon excitation

The surgical outcome of brain tumor resection and needle biopsy is significantly correlated to the patient's prognosis. Brain tumor surgery is limited to resecting the solid portion of the tumor as current intraoperative imaging modalities are incapable of delineating infiltrative regions. For accurate delineation, in situ tissue interrogation at the submicron scale is warranted. Additionally, multimodal detection is required to remediate the genetically and molecularly heterogeneous nature of brain tumors, notably, that of gliomas, meningioma and brain metastasis. Multimodal detection, such as spectrally‐ and temporally‐resolved fluorescence under one‐ and two‐photon excitation, enables characterizing tissue based on several endogenous optical contrasts. In order to assign the optically‐derived parameters to different tissue types, construction of an optical database obtained from biopsied tissue is warranted. This report showcases the different quantitative and semi‐quantitative optical markers that may comprise the tissue discrimination database. These include: the optical index ratio, the optical redox ratio, the relative collagen density, spectrally‐resolved fluorescence lifetime parameters, two‐photon fluorescence imaging and second harmonic generation imaging.     

Process control in cell culture technology using dielectric spectroscopy

In the biopharmaceutical industry, mammalian and insect cells as well as plant cell cultures are gaining worldwide importance to produce biopharmaceuticals and as products themselves, for example in stem cell therapy. These highly sophisticated cell-based production processes need to be monitored and controlled to guarantee product quality and to satisfy GMP requirements. With the process analytical technology (PAT) initiative, requirements regarding process monitoring and control have changed and real-time in-line monitoring tools are now recommended. Dielectric spectroscopy (DS) can serve as a tool to satisfy some PAT requirements. DS has been used in the medical field for quite some time and it may allow real-time process monitoring of biological cell culture parameters. DS has the potential to enable process optimization, automation, cost reduction, and a more consistent product quality. Dielectric spectroscopy is reviewed here as a tool to monitor biochemical processes. Commercially available dielectric sensing systems are discussed. The potential of this technology is demonstrated through examples of current and potential future applications in research and industry for mammalian and insect cell culture.     

Microscope laser light scattering spectroscopy of single biological cells

A microscope laser light scattering setup was developed, allowing us to do intensity autocorrelation spectroscopy on the light scattered from a volume as small as (2 μm)³. This non-invasive technique makes cytoplasmic studies possible inside single live biological cells. The effect of osmotic swelling and shrinking on the diffusion coefficient of hemoglobin inside intact red blood cells is shown as an illustrative example of the applicability and sensitivity of this new experimental method.     

Quantification of perfusion and metabolism in an autism mouse model assessed by diffuse correlation spectroscopy and near-infrared spectroscopy

There is a need for quantitative biomarkers for early diagnosis of autism. Cerebral blood flow and oxidative metabolism parameters may show superior contrasts for improved characterization. Diffuse correlation spectroscopy (DCS) has been shown to be reliable method to obtain cerebral blood flow contrast in animals and humans. Thus, in this study, we evaluated the combination of DCS and fNIRS in an established autism mouse model. Our results indicate that autistic group had significantly (P = .001) lower (~40%) blood flow (1.16 ± 0.26) × 10⁻⁸ cm²/s), and significantly (P = .015) lower (~70%) oxidative metabolism (52.4 ± 16.6 μmol/100 g/min) compared to control group ([1.93 ± 0.74] × 10⁻⁸ cm²/s, 177.2 ± 45.8 μmol/100 g/min, respectively). These results suggest that the combination of DCS and fNIRS can provide hemodynamic and metabolic contrasts for in vivo assessment of autism pathological conditions noninvasively.     

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R²CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R²CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R²CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R² of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.     

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.     

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R(2)(cv)) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R(2)(val)) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R(2)(cv), RMSECV, R(2)(val) and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.     

近红外光谱技术快速检测原料乳中掺杂物

采用近红外光谱技术结合化学计量学方法,对原料乳中常见的2种掺杂物——大豆分离蛋白与植脂末进行定量分析研究。先通过不同光谱预处理方法结合偏最小二乘法（PLS）建模评价不同预处理方法的效果,结果表明通过平滑处理结合多元散射校正（MSC）进行光谱预处理效果最佳,大豆分离蛋白PLS定量模型相关系数（R2）与交叉验证均方差（RMSECV）分别为0.980 9、0.127 5,植脂末PLS模型分别为0.972 2、0.130 8。随后比较了不同建模方法的效果,结果发现：采用径向基神经网络（RBF）对大豆分离蛋白的建模效果最佳,R2为0.999 4,测试集均方根误差为0.003 1;采用广义回归神经网络（GRNN）方法对植脂末建模效果最佳,R2为0.998 9,测试集均方根误差为0.004 5。因此,合理结合近红外光谱技术与化学计量学方法可快速、准确检测原料乳中大豆分离蛋白和植脂末这2种掺杂物含量。     

Genetic parameters of fat quality in pigs measured by near-infrared spectroscopy

Subcutaneous fat from Norwegian Landrace (n=3230) and Duroc (n=1769) pigs was sampled to investigate the sources of variation and genetic parameters of various fatty acids, fat moisture percentage and fat colour, with the lean meat percentage (LMP) also included as a trait representing the leanness of the pig. The pigs were from half-sib groups of station-tested boars included in the Norwegian pig breeding scheme. They were fed ad libitum to obtain an average of 113 kg live weight. Near-infrared spectroscopy (NIRS) was applied for prediction of the fatty acids and fat moisture percentage, and Minolta was used for the fat colour measurements. Heritabilities and genetic correlations were estimated with a multi-trait animal model using average information-restricted maximum likelihood (AI-REML) methodology. Fat from Landrace pigs had considerably more monounsaturated fatty acids, polyunsaturated fatty acids (PUFAs) and fat moisture, as well as less saturated fatty acids (SFAs) than fat from Duroc pigs. The heritability estimates (s.e. 0.03 to 0.08) for the various fatty acids were as follows: Palmitic, C16:0 (0.39 and 0.51 for Landrace and Duroc pigs, respectively); Palmitoleic, C16:1n-7 (0.41 and 0.50); Steric, C18:0 (0.46 and 0.54); Oleic, C18:1n-9 (0.67 and 0.57); Linoleic, C18:2n-6 (0.44 and 0.46); α-linolenic, C18:3n-3 (0.37 and 0.25) and n-6/n-3 ratio (0.06 and 0.01). The other fat quality traits revealed the following heritabilities: fat moisture (0.28 and 0.33), colour values in subcutaneous fat: L* (whiteness; 0.22 and 0.21), a* (redness; 0.13 and 0.24) and b* (yellowness; 0.07 and 0.17) and LMP (0.46 and 0.47). LMP showed high positive genetic correlations to PUFA (C18:2n-6 and C18:3n-3), which implies that selecting leaner pigs changes the fatty acid composition and deteriorates the quality of fat. Higher concentrations of PUFA are not beneficial as the ratio of n-6 and n-3 fatty acids becomes unfavourably high. Owing to the high genetic correlation between C18:2n-6 and C18:3n-3 and a low heritability for this ratio, the latter is difficult to change through selection. However, a small reduction in the ratio should be expected if selection aims at reducing the level of C18:2n-6. Selection for more C18:1n-9 is possible in view of the genetic parameters, which are favourable for eating quality, technological quality and human nutrition. The NIRS technology and the high heritabilities found in this study make it possible to implement fat quality traits to achieve the breeding goal in the selection of a lean pig with better fat quality.