The Study of Using Near-infrared Diffuse Reflectance Spectroscopy Rapid Identify Wheat Drought Resistance-Ⅱ

[Objective] The aim was to build an evaluation method rapidly identifying wheat drought tolerance with near infrared diffuse reflectance spectroscopy. [Method] In the research, 36 wheat varieties in 2007-2009 were chosen and drought-tolerance degrees of wheat were graded and identified according to Winter-wheat Drought Tol- erance Evaluation Technical Standards （GB/T 21127-2007）, and harvest wheat grains underwent spectrum collection, with a full-spectrum analyzer, to establish a database. [Result] Based on qualitative analysis and full-spectrum correlation research, the coef- ficient of determination （RSQ） and cross-validation coefficient of determination （1-VR） were concluded at 0.697 5 and 0.600 2, showing near-infrared diffuse reflectance spectroscopy is of significant differences among wheat varieties and of significant or extremely significant correlation with drought-tolerance indices. [Conclusion] The re- search indicates that to evaluate drought-tolerance of wheat with near-infrared diffuse reflectance spectroscopy is a rapid and feasible way, which is simple, convenient without damages on grains, and of practical values for construction wheat drought-tol- erance evaluation index system and identification of breeding materials.

Supercritical water syntheses of transition metal-doped CeO_2 nano-catalysts for selective catalytic reduction of NO by CO:An in situ diffuse reflectance Fourier transform infrared spectroscopy study

In the present study,we synthesized CeO2 catalysts doped with various transition metals(M=Co,Fe,or Cu)using a supercritical water hydrothermal route,which led to the incorporation of the metal ions in the CeO2 lattice,forming solid solutions.The catalysts were then used for the selective catalytic reduction(SCR)of NO by CO.The Cu‐doped catalyst exhibited the highest SCR activity;it had a T50(i.e.,50%NO conversion)of only 83°C and a T90(i.e.,90%NO conversion)of 126°C.Such an activity was also higher than in many state‐of‐the‐art catalysts.In situ diffuse reflectance Fourier transform infrared spectroscopy suggested that the MOx‐CeO2 catalysts(M=Co and Fe)mainly followed an Eley‐Rideal reaction mechanism for CO‐SCR.In contrast,a Langmuir‐Hinshelwood SCR reaction mechanism occurred in CuO‐CeO2 owing to the presence of Cu+species,which ensured effective adsorption of CO.This explains why CuO‐CeO2 exhibited the highest activity with regard to the SCR of NO by CO.

Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs

Background： The ability of near-infrared reflectance spectroscopy（NIRS） to determine the digestible energy（DE）and metabolizable energy（ME） content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set（n = 88） and a validation set（n = 29）. The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez（J Anim Sci. 71：3389–98,1993）. The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square（MPLS） regression method based on77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.Results： The NIRS equations obtained from actually determined DE, the coefficient of determination for calibration（RSQcal）, cross-validation（R^2CV）, and validation（RSQv） were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R^2CV, and RSQvvalues were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME（RPD_v= 2.38, 〈 2.50）, the other three equations built from actually determined energy and calculated DE produced good prediction performance（RPD_vranging from 2.53 to 2.69, 〉 2.50） when applied to validation samples.Conclusion： These results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.

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.

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

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.

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.

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.

Evaluation of Fourier Transform Infrared Spectroscopy for Diagnosis of Peroxisomal Diseases with Abnormal Very-Long-Chain Fatty Acid Metabolism

Very long chain fatty acids (VLCFAs) are accumulated in cells and blood in patients with peroxisomal diseases, such as adrenoleukodystrophy (ALD) and Zellwger Syndrome (ZS). The purpose of this study is to investigate usefulness of Fourier transform infrared spectroscopy (FTIR) with attenuated total reflection (ATR) analysis method for clinical diagnosis of those diseases, thereby we measured the infrared spectra of the sera of patients and healthy controls. Correlation coefficients between 2nd derivative FTIR spectra of the serum samples and the VLCFA content ratio which is used as a clinical parameter to date were comprehensively calculated to investigate which wavenumber showed high correlation with the VLCFA ratio. Multiple regression analysis using the serum FTIR spectra showed that high correlations were observed with VLCFA ratios (C26:0/C22:0 ratio), and we could construct a suitable regression model (R2 = 0.97, p ﹣19). In addition, the model system using various VLCFAs in newborn bovine serum also showed that several FTIR peaks in 800 ~ 900 cm﹣1 region were found to have good correlation with VLCFA ratios. Our results support that FTIR analysis is useful for diagnosis of peroxisomal diseases.

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg 1, Rb 1, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-forward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg 1, Rb 1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.

土壤有机质可见-近红外反射光谱特性研究

为优化土壤有机质含量预测模型运算速度,以50份土壤有机质为例,利用Pearson相关系数分析法对土壤有机质的特征波段进行提炼筛选。结果表明,土壤样品的反射光谱与土壤有机质含量在555~662 nm波段范围内呈较强负相关,显著特征波段为601 nm;经过不同预处理后的土壤反射光谱与土壤有机质含量呈较强的相关性,显著特征波段增加,主要体现在601、1221、1410、1665、1880、2110、2200 nm波段附近。对不同土壤含水率的反射光谱与土壤有机质含量之间的Pearson相关曲线进行分析发现,显著特征波段主要体现在601、1450、1930、2200 nm波段附近,随着土壤含水率的增加,土壤有机质的特征波段601 nm的相关性逐渐减弱,土壤水分的特征波段1450、1930、2200 nm的相关性逐渐加强;土壤含水率超过10%,土壤反射光谱与土壤有机质含量呈正相关。研究获取的土壤有机质特征波段和土壤水分影响波段,为土壤有机质快速检测模型的建立提供理论支撑。

Feasibility assessment of phenotyping cotton fiber maturity using infrared spectroscopy and algorithms for genotyping analyses

Background:Cotton fiber maturity is an important property that partially determines the processing and performance of cotton.Due to difficulties of obtaining fiber maturity values accurately from every plant of a genetic population,cotton geneticists often use micronaire(MIC) and/or lint percentage for classifying immature phenotypes from mature fiber phenotyp es although they are complex fiber traits.The recent development of an algorithm for determining cotton fiber maturity(MIR)from Fourier transform infrared(FT-IR)spectra explores a novel way to measure fiber maturity efficiently and accurately.However,the algorithm has not been tested with a genetic population consisting of a large number of progeny pla,nts.Results:The merits and limits of the MIC-or lint percentage-bas ed phenotyping method were demonstrated by comparing the observed phenotypes with the predicted phenotypes based on their DNA marker genotypes in a genetic population consisting of 708 F2 plants with various fiber maturity.The observed MIC-based fiber phenotypes matched to the predicted phenotypes better than the observed lint percenta ge-based fiber phenotypes.The lint percentage was obtained from each of F2 plants,whereas the MIC values were unable to be obtained from the entire population since certain F2 plants produced insufficient fiber mass for their measurements.To test the feasibiility of cotton fiber infrared maturity(MIR)as a viable phenotyping tool for genetic analyses,we me asured FT-IR spectra from the second population composed of 80 F2 plants with various fiber maturities,determined MIR values using the algorithms,and compared them with their genotypes in addition to other fiber phenotypes.The results showed that MIR values were successfully obtained from each of the F2 plants,and the observed MIR-based phenotypes fit well to the predicted phenotypes based on their DNA marker genotypes as well as the observed phenotypes based on a combination of MIC and lint percentage.Conclusions:The M,R value obtained from FT-IR spectra of cotton fibers is able to accurately assess fiber maturity of all plants of a population in a quantitative way.The technique provides an option for cotton geneticists to determine fiber maturity rapidly and efficiently.

Chemometric Feature Selection and Classification of <i>Ganoderma lucidum</i>Spores and Fruiting Body Using ATR-FTIR Spectroscopy

Ganoderma lucidum(G. lucidum) spores as a valuable Chinese herbal medicine have vast marketable prospect for its bioactivities and medicinal efficacy. This study aims at the development of an effective and simple analytical method to distinguish G. lucidum spores from its fruiting body, which is of essential importance for the quality control and fast discrimination of raw materials of Chinese herbal medicine. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with the appropriate chemometric methods including penalized discriminant analysis, principal component discriminant analysis and partial least squares discriminant analysis has been proven to be a rapid and powerful tool for discrimination of G. lucidum spores and its fruiting body with classification accuracy of 99%. The model leads to a well-performed selection of informative spectral absorption bands which improve the classification accuracy, reduce the model complexity and enhance the quantitative interpretations of the chemical constituents of G. lucidum spores regarding its anticancer effects.

Titanium dioxide as an adsorbent to enhance the detection ability of near-infrared diffuse reflectance spectroscopy

A method for quantitative determination of fish sperm deoxyribonucleic acid(fsDNA)was developed by using titanium dioxide(TiO2)as an adsorbent and near-infrared diffuse reflectance spectroscopy(NIRDRS).The selective enrichment of fsDNA was proved by comparing the adsorption efficiency of bovine serum albumin,tyrosine and tryptophan,and the low adsorption background of TiO2 was illustrated by comparing the spectra of four commonly-used inorganic adsorbents(alkaline aluminium oxide,neutral aluminium oxide,nano-hydroxyapatite and silica).The spectral feature of fsDNA can be clearly observed in the spectrum of the sample.Partial least squares(PLS)model was built for quantitative determination of fsDNA using 28 solutions,and 13 solutions with interferences were used for validation of the model.The results showed that the correlation coefficient(R)between the predicted and the reference concentration is 0.9727 and the recoveries of the validation samples are in the range of 98.2%-100.7%.

Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate

Near infrared diffuse reflectance spectroscopy(NIRDRS) has gained wide attention due to its convenience for rapid quantitative analysis of complex samples. A method for rapid analysis of triglycerides in human serum using NIRDRS with silver mirror as the substrate is developed. Due to the even and high reflectance of the silver mirror, the spectral response is enhanced and the background interference is reduced.Furthermore, both linear and nonlinear modeling strategies were investigated adopting the partial least squares(PLS) and least squares support vector regression(LS-SVR), continuous wavelet transform(CWT)was used for spectral preprocessing, and variable selection was tried using Monte Carlo uninformative variable elimination(MC-UVE), randomization test(RT) and competitive adaptive reweighted sampling(CARS) for optimization the models. The results show that the determination coefficient(R) between the predicted and reference concentration is 0.9624 and the root mean squared error of prediction(RMSEP) is 0.21. The maximum deviation of the prediction results is as low as 0.473 mmol/L. The proposed method may provide an alternative method for routine analysis of serum triglycerides in clinical applications.

ATR-FTIR结合DSC法快速鉴别食品接触用塑料包装制品

研究了一种衰减全反射红外光谱(ATR-FTIR)结合差示扫描量热法(DSC)快速鉴别食品接触用塑料包装制品单一材质的方法。红外光谱图能准确反应塑料制品的内部结构,结合DSC曲线得出其具体成分。该方法快速、准确且几乎不破坏样品。

基于ATR-FTIR研究药食同源植物多花黄精的产地分布

目的基于傅里叶变换衰减全反射红外光谱(attenuated total reflection-flourier transformed infrared spectroscopy,ATR-FTIR)研究药食同源植物多花黄精的产地分布。方法采用ATR-FTIR技术采集不同产地多花黄精粉末样本的红外光谱数据,通过红外谱图的波数分析其对应的官能团,并进行精密度、重复性、稳定性的方法学考察试验。谱图数据经预处理后,建立主成分分析(principal components analysis,PCA)模型和正交偏最小二乘-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)模型,进行多元统计数据分析。结果不同产地多花黄精ATR-FTIR图谱的峰形、峰位差异微小。运用PCA和OPLS-DA可视化处理不同产地多花黄精的数据,发现7种不同产地多花黄精代谢轮廓差异明显,可对不同产地的多花黄精进行辅助鉴别。结论ATR-FTIR技术操作简单,环保经济,可为多花黄精质量控制提供技术参考。

相对湿度对SO_(2)/NH_(3)在α-Fe_(2)O_(3)表面非均相反应的影响

采用原位漫反射傅里叶变换光谱技术研究SO_(2)/NH_(3)在α-Fe_(2)O_(3)颗粒物表面的非均相反应,探究相对湿度条件对NH_(3)作用下生成硫酸盐颗粒物影响.结果表明,干燥条件下颗粒物表面有硫酸盐(1271,1244,1163,1069cm^(-1))以及铵盐(1698,1435cm^(-1))特征峰的生成,NH_(3)能够与SO_(2)协同作用促进硫酸盐颗粒物的生成.随着相对湿度的增大,部分硫酸盐的峰位红移至硫酸氢盐(1239cm^(-1))处,表明湿润条件下有硫酸氢盐的生成.随着相对湿度从0%上升至78%,颗粒物表面硫酸盐的生成速率由9.38×10^(-17)ions/(g⋅s)降低至4.33×10^(-17)ions/(g⋅s),NH_(3)作用下SO_(2)在颗粒物表面的摄取系数(γ)由2.04×10^(-6)降低至9.43×10^(-7),表明水汽对SO_(2)/NH_(3)在α-Fe_(2)O_(3)颗粒物表面上的非均相反应有抑制作用.

氢氧化钠分子结构原位漫反射一维及二维漫反射中红外光谱研究

通过原位一维漫反射中红外(MIR)光谱开展了氢氧化钠分子结构研究。实验发现,氢氧化钠分子结构主要红外吸收模式包括OH基团伸缩振动模式(νOH-NaOH-_(一维))和OH基团面内摇摆振动模式(ρOH-NaOH-_(一维))。进一步开展了氢氧化钠分子结构的原位二维漫反射MIR光谱研究。采用原位漫反射一维及二维漫反射MIR光谱开展氢氧化钠结构研究,对氯碱工业具有重要意义。

红外偏振辐射模型对铬和铜元素的优化识别

针对优化激光诱导击穿光谱技术识别铬和铜元素的问题,本文提出了一种结合红外偏振检测理论的方法。利用菲涅尔反射定律和基尔霍夫理论分析了红外偏振产生机理,根据能量守恒定律验证了目标红外自发热辐射的偏振特性;利用红外偏振辐射传输模型及菲涅尔反射穆勒矩阵,结合温度的麦克斯韦-玻尔兹曼分布特性,推导出了等离子体红外偏振辐射信号模型,用于识别各激发能量下的重金属元素;利用5种不同能量的脉冲激光验证了模型的精准性。结果表明:在相同的激光能量下,红外偏振辐射光谱可以收集到更多的特征信号峰;在低能量激励下,红外偏振辐射光谱的优势更为突出。红外偏振辐射模型的构建是科学有效的,可用于优化重金属元素的快速检测。