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.

Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is compulsory.Thus,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality.The present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor set-up.To this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are investigated.The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield.The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression.The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of 0.9608.Thus,we applied a PAT approach to generate further insight into this established industrial process.

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。

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.

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。

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。

近红外光谱技术(NIRS)在反刍动物营养研究中的应用现状

近红外光谱技术（NIRS）分析样品时以其方便、快捷和准确等诸多优点在动物营养研究中得到了广泛的应用；用NIRS技术预测家畜日粮中有机物消化率时所产生的标准偏差（SECV）在1．6％～2．8％之间，而预测干物质的消化率时所产生的SECV在1．6％～3．5％之间。NIRS能够准确地预测饲料中的化学成分和生物学组分以及反刍家畜十二指肠微生物蛋白的流量，但对于预测饲料在瘤胃降解率的动态特性时与实际相差很大。NIRS技术预测舍饲家畜采食量与体内法得到的结果相似，但在预测放牧家畜采食量时其预测误差为14％左右。上述结果表明，NIRS技术在预测反刍家畜消化代谢、日粮营养评价、采食量等方面已取得了很大的进展，并在反刍动物营养研究领域中有着广阔的应用空间。

基于PLS算法的生物质秸秆元素分析NIRS快速检测

为探讨利用近红外光谱技术快速检测生物质秸秆中N、C、H、S和O 元素的可行性,采集并制备水稻、小麦、油菜和玉米秸秆样本199个,采用近红外光谱（NIRS）分析技术,结合偏最小二乘（PLS）化学计量学算法,在7400-5550cm^-1波段范围内,比较不同光谱预处理方法的定标效果,建立最优的生物质秸秆中N、C、H、S和O 元素的定量分析模型,并用独立的验证集样本对模型进行验证.验证结果表明所建立的N元素的定量分析模型可用于实际检测;O 元素的定量分析模型可进行实际估测;采用近红外技术用于C元素定量分析是可行的,但模型需要进一步优化;H、S元素采用NIRS技术无法进行定量分析.

棉籽17种氨基酸含量的NIRS定标模型构建与测定方法研究

选用氨基酸含量差异较大的多年份、多品种、多地点种植的445份棉花种子为材料，分别对其进行近红外光谱扫描。用一阶导数的数学处理（1，4，4，1）、标准正态变换和去趋势（SNV＋D）最佳组合的预处理方法，结合改良的偏最小二乘法（MPLS）构建棉籽17种氨基酸成分的近红外定标模型。定标结果表明，天冬氨酸、苏氨酸、谷氨酸、甘氨酸、丙氨酸、缬氨酸、异亮氨酸、亮氨酸、苯丙氨酸、赖氨酸、组氨酸和精氨酸等12种氨基酸含量的定标模型较好，其RPDc为3．735-7．132，外部检验，上为0．910-0．979，近红外光谱分析完全可以替代化学测定；而丝氨酸、蛋氨酸、酪氨酸和脯氨酸等四种氨基酸的定标模型次之，其RP—Dc为2．205-2．814，外部检验r^2为0．800-0．830，近红外分析技术虽不能完全替代化学测定的结果，但仍可用于大量样品的筛选。半胱氨酸定标方程的RPDc较小（RPDc=1．358），因此，棉籽中半胱氨酸含量不能用近红外分析方法进行检测。

FT-NIRS技术应用于稻米直链淀粉含量分析研究

运用近红外光谱快速分析技术,使用偏最小二乘法建立了近红外光谱和水稻糙米直链淀粉含量的数学模型,并进行糙米直链淀粉含量预测。结果表明糙米近红外光谱与其直链淀粉含量具有良好的相关性,决定系数r2=0.8429,最大绝对误差4.82%,平均误差2.30%。该方法在不破坏样品的前提下快速分析水稻直链淀粉含量,可用于稻种资源的快速鉴定,对于水稻优质育种及其相关研究具有重要意义。