Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea

Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R2 0.85) is better than the whole seed (0.33 R2 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R2_whole seed = 0.78, R2_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.

Simulation and Modelling of near Infrared Spectroscopy (NIRS) as Brain Monitor

Near infrared spectroscopy (NIRS) is a method for non-invasive monitoring of cerebral oxygenation and haemodynamics. Different devices provide information on changes of oxygenated (HbO2) and deoxygenated haemoglobin (HHb), oxidized cytochrome aa3 (CytOx) or regional oxygen saturation (rSO2). NIRS has been used during adult and paediatric cardiac surgery.

Investigation of Prefrontal Cortex Activity in University Students with Presenteeism: A Near-Infrared Spectroscopy (NIRS) Study

Presenteeism refers to impaired performance attributed to attending work with health problems. There has been no study examining the state of presenteeism with objective measures. We compared cerebral hemodynamic changes, measured by near-infrared spectroscopy (NIRS), during neuropsychological tests conducted by university students with presenteeism and healthy controls. Twenty-two university students participated in the study;11 of them with impaired performance caused by mental health problem were allocated to the presenteeism group and 11 without health problems to the control group. Presenteeism was assessed by the Presenteeism Scale for Students. To evoke hemodynamics changes, the participants completed a Word Fluency Test (WFT) and a Trail Making Test (TMT). The NIRS probes were located over the bilateral prefrontal area. Students with presenteeism had significantly higher incidences of depression than controls. However, there was no significant difference in behavioral performance examinations between the two groups. With regard to hemodynamics changes, the repeated measures analysis of covariance of the NIRS signals revealed significant interactions between group and task activation. Although we observed a significant increase in oxygenated hemoglobin concentration during the WFT among controls (simple main effect;left channel, F(1, 19) = 27.34, P F(1, 19) = 22.05, P < 0.001), no changes were found in students with presenteeism during either the WFT (simple main effect;left channel, F(1, 19) = 0.12, P F(1, 19) = 0.08, P t = ﹣0.94, P with Bonferroni correction = 0.745;right channel, t = ﹣2.19, P with Bonferroni correction < 0.113). This is the first study to reveal differences in activity in the cerebral cortex associated with presenteeism. The fact that students with presenteeism have prefrontal dysfunction might reinforce the concept of presenteeism.

Correlation of prefrontal activity measured by near-infrared spectroscopy (NIRS) with mood, BDNF genotype and serum BDNF level in healthy individuals

Depression has been known to reduce the prefrontal activity associated with the execution of certain cognitive tasks, although whether a temporarily depressed or anxious mood in healthy individuals affects the prefrontal blood oxygen level during cognitive tasks is unknown. Combining the measurement of prefrontal activity with near-infrared spectroscopy (NIRS) and the two cognitive tasks, namely the letter version of the verbal fluency test (VFT-l) and the Stroop test, we measured the effect of a depressed or anxious mood and gender on the changes in the prefrontal oxygenated hemoglobin (Oxy-Hb) levels during those cognitive tests in healthy individuals. Depressed mood or anxious mood was assessed by the Hospital Anxiety and Depression Scale (HADS). Thereby we aimed to explore the possibility of NIRS measurement for detecting the early subclinical manifestation of major depression. Moreover, we examined the possible relationships between prefrontal activation and the functional Val66Met polymorphisms of the brain derived neurotropic factor (BDNF) gene and serum BDNF level. As a result, the increased prefrontal Oxy-Hb levels during cognitive tasks were significantly correlated with the severity of depressed mood in males. The course of the prefrontal Oxy-Hb increase was different depending on the cognitive tasks, i.e., the VFT-l or the Stroop test, in both genders. Correlations of BDNF genotype and serum BDNF level with the prefrontal Oxy-Hb levels during those cognitive tasks were negative. Our results suggest that the early subclinical manifestation of depressed mood in males might be detected by the NIRS measurement, which is not correlated with the individual properties of BDNF.

Determination of Protein and Starch Content in Whole Maize Kernel by Near Infrared Reflectance Spectroscopy

Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.

On-Line Measurement of the Chemical Oxygen Demand in Wastewater in a Pulp and Paper Mill Using Near Infrared Spectroscopy

Although near infrared (NIR) spectroscopy has been evaluated for numerous applications, the number of actual on-line or even on-site industrial applications seems to be very limited. In the present paper, the attempts to produce online predictions of the chemical oxygen demand (COD) in wastewater from a pulp and paper mill using NIR spectroscopy are described. The task was perceived as very challenging, but with a root mean square error of prediction of 149 mg/l, roughly corresponding to 1/10 of the studied concentration interval, this attempt was deemed as successful. This result was obtained by using partial least squares model regression, interpolated reference values for calibration purposes, and by evenly distributing the calibration data in the concentration space. This work may also represent the first industrial application of online COD measurements in wastewater using NIR spectroscopy.

Geographic Classification of Chinese Grape Wines by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.

Lateralization of Receptive Language Function Using near Infrared Spectroscopy

In recent decades, functional magnetic resonance imaging (fMRI) has proven to be more effective than the Wada test in the evaluation of language lateralization in special populations such as epileptic patients and children. However, fMRI requires that subjects remain motionless during data acquisition, making the assessment of receptive and expressive language difficult in young children and population with special needs. Near-Infrared spectroscopy (NIRS) is a non- invasive technique that has proven to be more tolerant to motion artifacts. The aim of the present study was to investigate the use of NIRS to assess receptive language patterns using a story listening paradigm. Four native French-speakers listened to stories read aloud by a bilingual speaker in both French and Arabic. To determine if the signal recorded was affected by episodic memory processes, a familiar story and an unknown story were presented. Results showed that listening to stories in French elicited a significantly higher left lateralized response than listening to stories in Arabic, independently of the familiarity of the story. These results confirm that NIRS is a useful non-invasive technique to assess receptive language in adults and can be used to investigate language lateralization among children and epileptic patients slated for epilepsy surgery.

Visual Perception of Incomplete Human-Like Body Postures: A Near-Infrared Spectroscopy Study

Previous studies have reported that the mirror neuron system plays a crucial role in social cognition. We examined whether the higher-order cognitive functions are involved in the activations in the mirror neuron area when we perceive simplified pseudo-postures. We measured 14 participants’ brain activation during the posture-recognition task using near-infrared spectroscopy. The participants’ task was to observe five sequentially presented target pseudo-postures and judge whether a test pseudo-posture was identical to one of the preceding five target pseudo-postures. The results in the majority of participants (n = 10/14) revealed that the activity in the inferior frontal mirror neuron area is modulated by perception of human-likeness, but not in the remaining four participants (n = 4/14). These results suggest that the degree of the activation of higher-order cognitive functions, which may be engaged in the inhibitory and/or facilitative processing of human body or bodily movement, leads to the distinctive activities in the inferior frontal mirror neuron area.

NIRS-XRF联用的煤炭发热量高稳定检测

实时获知煤炭发热量对于及时调整电站锅炉风粉配比和提高煤炭燃烧效率具有重要意义,为了实现电力生产中发热量的稳定快速检测,提出了一种近红外光谱(Near Infrared Spectroscopy, NIRS)与X射线荧光光谱(X-ray Fluorescence, XRF)联用的煤炭发热量高稳定检测方法,它结合了NIRS能高稳定检测煤中与发热量正相关的有机基团的优势与XRF能高稳定检测与发热量负相关的成灰元素的特点,大大提高了对煤炭发热量的测量重复性。在光谱预处理中,先将两套光谱融合作为偏最小二乘回归的输入变量进行全谱初步建模,依据回归系数选择NIRS光谱中的有效波段,再将它与XRF光谱中的成灰元素谱线一并融合进行归一化处理。建模时将预处理后的融合光谱数据作为输入变量,利用偏最小二乘回归对煤炭发热量进行建模。实验结果表明,NIRS-XRF联用方法对定标集煤样发热量预测的线性相关度系数(R^(2))为0.995,对验证集煤样发热量预测的最小均方根误差、平均相对误差和标准偏差分别为0.24 MJ/kg,0.61%和0.05 MJ/kg,测量重复性满足小于0.12 MJ/kg的国家标准。NIRS-XRF联用的煤炭发热量高稳定检测方法有望推广应用于火力发电、煤化工、冶金、水泥和焦化等“高碳”行业,助力我国按期实现碳中和目标。

Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

近红外光谱法快速测定晒青毛茶三种儿茶素组分含量

为了实现晒青毛茶儿茶素含量的快速检测,该研究利用高效液相色谱技术(High Performance Liquid Chromatography,HPLC)测定了50份晒青毛茶样品中的表没食子茶素没食子酸酯(Epigallocatechin Gallate,EGCG)、表没食子儿茶素(Epigallocatechin,EGC)、没食子酸(Gallic Acid,GA)三种儿茶素单体含量,结合样品的近红外光谱,分别建立了晒青毛茶三种儿茶素单体含量的偏最小二乘法(Partial Least Squares,PLS)模型,并对模型进行验证。实验结果表明,基于EGCG、EGC、GA含量所建立近红外模型的决定系数(Coefficient of Determination,R2)分别为99.99%、99.99%、99.92%;校正标准差(Root Mean Square Error of Calibration,RMSEC)分别为0.17、0.15、0.10;相对标准差(Relative Standard Deviation,RSD)分别为0.19%、0.27%、0.56%;外部验证的结果显示,三种儿茶素单体含量模型的预测值与真实值的平均绝对误差分别为0.13、0.12、0.07;平均相对误差分别为0.17、0.25、0.45。实验结果表明,该研究建立的近红外分析模型具有较高的预测准确度和稳定性,在快速检测晒青毛茶儿茶素方面具有潜在应用价值。

基于近红外光谱技术的六大茶类快速识别

为构建高质量的六大茶类识别模型,本研究中收集了370份样品,通过采集其近红外光谱(near-infrared spectroscopy,NIRS),结合光谱预处理、特征提取以及数据挖掘分类器算法,建立六大茶类快速识别模型。结果表明:1)支持向量机(support vector machine,SVM)与随机森林(random forest,RF)分类器皆适于六大茶类快速识别模型的构建;2)SVM分类器更适于结合原始光谱(original spectrum,OS)建模,预处理易使基于该分类器建立的模型鉴别性能减弱;3)随机森林(RF)分类器更适用于预处理后光谱建模,所得模型较OS模型在识别正确率(recognition accuracy,RA)及受试者工作特征曲线下面积(area under the curve,AUC)均得到明显提升;4)特征提取中线性判别分析(linear discriminant analysis,LDA)算法表现最好,所得模型的RA较OS模型明显提升,其中最佳模型OS-LDA-SVM的RA为100.00%,AUC为1.00,识别正确率高、泛化能力强、模型性能优异,可产业化应用。综上所述,近红外光谱结合预处理、特征提取算法及分类器建立模型,进行六大茶类识别的可行性强,模型的识别正确率高、性能优异,可为茶叶贸易的茶类快速识别提供科学、准确、高效的技术支撑,为国际茶类识别模型的产业化应用奠定基础。

近红外光谱技术(NIRS)在草地生态学研究中的应用

近红外光谱技术(NIRS)是一种快速、高效、无损的现代检测技术,已经在许多领域广泛应用。文章阐述了NIRS应用于草地生态学研究的意义,介绍了NIRS用于测定牧草营养成分、矿物质、土壤养分含量,分析了牧草混合物的组成、动物对所采食牧草的反应、牧草病虫害抗性等一些复杂特性,以及进行生化标记、同位素鉴别研究。综合这些研究可以看出,NIRS能够作为一种整体研究工具应用于草地生态学的许多研究领域中,可以检测各种常规化学成分、分析草地生态系统的各种动态指标和系统运行的多项整体特性。希望通过本文的总结分析,推动NIRS在中国草地生态学研究中的应用,加速该领域研究手段的现代化。

FT-NIR光谱法测定烟草中绿原酸、芸香苷、莨菪亭及总多酚含量

研究应用傅里叶变换近红外光谱法快速测定烟草中绿原酸、芸香苷、莨菪亭及总多酚含量的可行性。使用偏最小二乘法(PLS)为建模方法,选择7 500～4 000cm-1谱段,采用二阶导数和Norris滤波法进行光谱预处理,建立了烟草中绿原酸、芸香苷、莨菪亭及总多酚的近红外预测模型。组分最佳PLS预测模型的相关系数r分别为0.976 6,0.941 9,0.957 1和0.966 6,SEP均小于1.2倍的SEC,验证集样本的标准偏差(SD)/SEP均大于2。交叉检验的均方差(RMSECV)分别为1.938 9,1.046 2,0.047 9和2.745 2。实验验证了模型偏差的显著性,将近红外光谱技术与常规标准检测方法相比较。结果表明:两种方法测得值在显著水平0.05时,不存在显著性差异,近红外光谱技术可以准确地定量分析烟草中绿原酸、芸香苷、莨菪亭及总多酚。

水稻蛋白质含量NIR模型适配范围的研究

目的比较不同类型样品建立水稻蛋白质近红外模型的效果和适配范围。方法通过对178份来自“II-32B/岳早籼6号”的重组自交系和496份水稻品种的近红外反射光谱的比较分析,选择其中59个株系和76份品种作为建模样品,采用偏最小二乘法建立基于品种、重组自交系和混合样品的3个蛋白质含量回归模型。结果经模型内部交叉验证和对模型外部重组自交系和品种样品的验证结果的比较分析,发现基于分离群体的模型因蛋白质含量范围较窄,样品来源较单一,适应范围仅局限于本群体内样品蛋白质含量预测,而品种和混合模型对群体和品种样品都表现出良好的适应能力,交叉验证决定系数大于0.90,外部验证决定系数大于0.89,本试验可为近红外建模的样本集选择提供良好的指导意义。结论不同类型样品对建模效果有显著影响,品种模型和混合模型的适配范围显著大于群体模型,研究结果不能支持用背景变异较小的样品建立较高精度回归模型的设想。

近红外光谱技术(NIRS)在干草品质检测中的研究与应用

干草品质对动物的生产性能、牧草价值和最终盈利有着最直接的影响,检测干草品质的方法也在不断改进,近红外光谱技术(NIRS)这种快速、高效而无破坏性的现代检测技术已在饲草料中广泛应用。文章阐述了应用NIRS分析干草品质的可行性和高效性,详细介绍了NIRS用于检测牧草营养成分、真菌侵染程度、内生真菌产生的有毒物质以及一些难于用化学方法检测的微量成分等重要指标的研究成果。指出NIRS用于分析干草品质突破了过去常规化学方法的复杂、耗时、成本高等缺陷,是干草品质分析方法的一个重大进步,并期待着NIRS在牧草品质中的应用研究工作继续完善和发展,建立更精确的定标模型以及更系统的分析软件,促进畜牧业的快速发展。

近红外光谱技术(NIRS)在检测牧草霉菌毒素中的应用

具有快速、高效、无损、在线等优势的近红外光谱技术(nearinfrared reflectance spectroscopy technique,NIRS)已经在农业、食品、化工、医学等领域获得广泛应用。牧草在加工贮藏过程中,受多种真菌的侵染,引起霉菌毒素在牧草中积累。霉菌毒素会通过动植物产品进入人畜的食物链,引起人畜中毒。牧草中霉菌毒素的常规检测不但需要粉碎、浸提、层析等繁琐的样品前处理过程,还需要酶联免疫吸附法、高效液相色谱法、溥层色谱法等后续检测过程。通过发展高精度、低检出限的光谱仪器,建立相应霉菌毒素检测的软件技术与校正模型,能够在牧草利用中达到快速准确检测霉菌毒素的含量和性质,从而为牧草的合理加工与利用提供依据,促进NIRS技术在健康畜产品生产领域的应用。

近红外反射光谱法(NIRS)测定棉仁粉中蛋白质和棉酚含量的研究

选用49份不同蛋白质和棉酚含量的陆地棉种质资源和188份陆地棉重组近交系为材料,以多年份、多地点种植收获的种子材料组成原始样品集,分别对棉仁粉中蛋白质含量和棉酚含量进行化学测定,采用改进的偏最小二乘法(Modified PLS)和(2,4,4,1)的数学转换方法建立近红外反射光谱(NIRS)定标模型,以寻找棉籽蛋白质含量和棉酚含量的快速测定方法。结果表明,蛋白质含量的定标决定系数(RSQ=0.933)和交叉检验决定系数(1-VR=0.929)较高,定标标准误差(SEC=0.623)和交互校验标准误差(SECV=0.638)较小,预测模型的建模效果较好,可替代化学分析。棉酚含量预测模型的RSQ,1-VR,SEC和SECV分别为0.836,0.811,0.074和0.079,模型预测效果略差于蛋白质模型,但仍可用于棉仁粉中棉酚含量的测定。