Classification of Guizhou Aspidistra Plants by Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study was conducted to establish a near-infrared diffuse reflectance spectroscopy of Guizhou Aspidistra plants. [Method] Twenty three batch- es of Guizhou Aspidistra plants including A. chishuiensis, A. spinula, A. Caespitosa, A. sichuanensis, A. ebianensis, A. retusa, A. guizhouensis and A. liboensis were subjected to drying, pulverization and sieving and then directly determined for near- infrared reflectance spectrums; and the plants in this genus were classified by clus- ter analysis and principal component analysis （PCA）. [Result] The near-infrared re- flectance spectrums of the 23 batches of Guizhou Aspidistra plants showed very high similarity. The spectrums were processed by first derivative method, and the spectral range of 4 000-7 500 cm-1 was selected as the analytical range. Cluster analysis and PCA were employed to mass spectrum variables of plants in Aspidis- tra, fewer new variables became the linear combination of primary variables, and small differences between different varieties were enlarged, thereby facilitating intu- itive classification of plants in this genus. [Conclusion] Near-infrared diffuse re- flectance spectroscopy is nondestructive and rapid for determination of solid sam- pies, and provides a new method for the classification of Guizhou Aspidistra plants combined by information processing techniques.

Rapid Identification for Drought Resistance of Wheat Using Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study aimed to establish an identification system for drought-resistance in wheat by using near-infrared diffuse reflectance spectroscopy. [Method] In 2006-2007, 36 wheat varieties with different drought resistance were selected and were classified according to their drought resistance grades determined by the Technical Specification of Identification and Evaluation for Drought Resistance in Wheat （GB/T 21127-2007）. In addition, the harvested wheat seed samples were spectrally analyzed with FOSS NIRSystems5000 near-infrared spectrum analyzer for grain quality （full spectrum analyzer） and then the forecasted regression equations were established. [Result] After the establishment of a database and validation, dis- criminated functions were obtained. The determination coefficient （RSQ） and coeffi- cients of determination for cross validation （1-VR） in the discriminant function built with seed samples from water stress area were 0.846 0 and 0.781 8, respectively, which indicated that the consistency between drought resistance and spectral charac- teristics in wheat varieties was good, and there was high correlation between the near-infrared diffuse reflectance spectra of seeds and the drought resistance in wheat. [Conclusiou] Under water stress condition, it is feasible to establish a conve- nient, rapid and no-damage identification system for the drought resistance in wheat by using the near-infrared diffuse reflectance spectrum technique to scan wheat seeds.

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.

RAPID DETERMINATION OF PROTEIN IN MILLET BY FOURIER TRANSFORM NEAR-INFRARED(FTNIR)DIFFUSE REFLECTANCE SPECTROSCOPY

In this paper,the Fourier transform near-infrared(FTNIR)diffuse reflectance spectroscopy is applied for the rapid determination of protein in millet.The partial least-squares(PLS)regression is successfully used as an effective multivariate calibration technique.The calibration set is composed of 20 standard millet samples that the protein contents were determined by the traditional Kjeldahl method.The optimal model dimension is found to be 5 by cross-validation.22 millet samples were determined by the proposed FTNIR-PLS method.The correlation coefficient between the concentration values obtained by the FTNIR-PLS method and the traditional Kjeldahl method is 0.9805.The standard error of prediction(SEP)is 0.28% and the mean recovery is 100.2%.The proposed method has been successfully applied for the routine analysis of protein in about 10,000 grain samples.

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.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

As unsafe components in herbal medicine(HM),saccharides can affect not only the drug appearance and stabilization,but also the drug efficacy and safety.The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using nearinfrared(NIR)spectroscopy.NIR spectra in the 4000–10,000-cm^(-1)wavelength range are acquired in situ using a transflectance probe.These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation.Calibration models based on partial least squares(PLS)regression have been developed for the three saccharide impurities,namely glucose,fructose,and sucrose.Model errors are estimated as the root-meansquare errors of cross-validation(RMSECVs)of internal validation and root-mean-square errors of prediction(RMSEPs)of external validation.The RMSECV values of glucose,fructose,and sucrose were 1.150,1.535,and 3.067 mg·mL^(-1),and the RMSEP values were 0.711,1.547,and 3.740 mg·mL^(-1),respectively.The correlation coeffcients(r)between the NIR predictive and the reference measurement values were all above 0.94.Furthermore,NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation.The results demonstrate that,as an alternative process analytical technology(PAT)tool for monitoring batch alcohol precipitation processes,NIR spectroscopy is advantageous for both efficient determination of quality characteristics(fast,in situ,and requiring no toxic reagents)and process stability,and evaluating the repeatability.

Identifying the geographical origin and processing technology of Moyao(Myrrh)on the basis of near-infrared spectroscopy combined with chemometrics

OBJECTIVE:To evaluate the quality of Moyao(Myrrh)in the identification of the geographical origin and processing of the products.METHODS:Raw Moyao(Myrrh)and two kinds of Moyao(Myrrh)processed with vinegar from three countries were identified using near-infrared(NIR)spectroscopy combined with chemometric techniques.Principal component analysis(PCA)was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories.A classical chemometric algorithm(PLS-DA)and two machine learning algorithms[K-nearest neighbor(KNN)and support vector machine]were used to conduct a classification analysis of the near-infrared spectra of the Moyao(Myrrh)samples,and their discriminative performance was evaluated.RESULTS:Based on the accuracy,precision,recall rate,and F1 value in each model,the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results.In all of the chemometric analyses,the NIR spectrum of Moyao(Myrrh)preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins,and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best.The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.CONCLUSIONS:NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao(Myrrh)and can also provide a reference for evaluations of its quality and the clinical use.

Development and implementation of incident response near-infrared models for analyzing contaminated medicines containing diethylene glycol solvent

Samples of preparations contaminated by diethylene glycol(DEG),diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra.Then the iden-tification models were developed using the collected spectra and the spectra of distilled water,propylene glyool and the preparations without diethylene glyool.Besides,the quantification model was also established for determining the concentration of diethylene glyool in the pre-parations.V alidation results show the identification and quantification models have ideal pre-diction performance.The emergency NIR models are rapid,easy to use and accurate,and can be implemented for identifying diethylene glycol raw material,screening the preparations contam-inated by diethylene glycol in the markets and analyzing the concentrations of DEG.

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 Cysteine Using Near-infrared Diffuse Reflectance Spectroscopy with Enrichment via Thiol-maleimide Click Reaction

Enrichment technique has been proved to be an efficient way to make the near-infrared diffuse reflectancespectroscopy（NIRDRS） suitable for micro analysis. However, low selectivity presented by conventional enrichmentmethods makes the quantitative analysis easy to be affected by the coexisting components. In this study, a specificenrichment method with chemical bonding via thiol-maleimide click reaction was used to achieve the reduction of theinterferences. Taking cysteine as the analyzing target, maleimide-functionalized SiO2 nanoparticles were prepared forthe enrichment of cysteine. Then determination of cysteine in aqueous solution and human serum was studied usingthe partial least squares model built from the NIRDRS spectra of the adsorbate. The results show that the concentra-tion that can be quantitatively detected is as low as 2.0 μg/mL, and the correlation coefficient（R） between thereference and predicted concentration is 0.9871 for the validation samples. The recoveries are in the range of89.5%-113.8% for human serum samples in the concentration range of 0--16.2 μg/mL.

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.

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.

A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets

Objectives:This study is aimed to explore the blending process of Dahuang soda tablets.These are composed of two active pharmaceutical ingredients(APIs,emodin and emodin methyl ether)and four kinds of excipients(sodium bicarbonate,starch,sucrose,and magnesium stearate).Also,the objective is to develop a more robust model to determine the blending end-point.Methods:Qualitative and quantitative methods based on near-infrared(NIR)spectroscopy were established to monitor the homogeneity of the powder during the blending process.A calibration set consisting of samples from 15 batches was used to develop two types of calibration models with the partial least squares regression(PLSR)method to explore the influence of density on the model robustness.The principal component analysis-moving block standard deviation(PCA-MBSD)method was used for the end-point determination of the blending with the process spectra.Results:The model with different densities showed better prediction performance and robustness than the model with fixed powder density.In addition,the blending end-points of APIs and excipients were inconsistent because of the differences in the physical properties and chemical contents among the materials of the design batches.For the complex systems of multi-components,using the PCA-MBSD method to determine the blending end-point of each component is difficult.In these conditions,a quantitative method is a more suitable alternative.Conclusions:Our results demonstrated that the effect of density plays an important role in improving the performance of the model,and a robust modeling method has been developed.

Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics

False seeds can often be seen in the maize seed market,leading to a serious decline in maize yield.Those existing variety identification methods are expensive,time consuming,and destructive to seeds.The aim of this study is to develop a cheap,fast and non-destructive method which can robustly identify large amounts of maize seed varieties based on near-infrared reflectance spectroscopy(NIRS)and chemometrics.Because it is difficult to establish models for every variety in the market,this study mainly investigated the performance of models based on a large number of samples(more than 40 major varieties in the market).The reflectance spectra of maize seeds were collected by two modes(bulk kernels mode and single kernel mode).Both collection modes can be applied to identification,but only the single kernel mode can be applied to purity sorting.The spectra were pretreated with smoothing,the first derivative and vector normalization;and then principal component analysis(PCA),linear discriminant analysis(LDA)and biomimetic pattern recognition(BPR)were applied to establish identification models.The environmental factors such as producing areas and years have a significant influence on the performance of the models.Therefore,the method to improve the robustness of the models was investigated in this study.New indexes(correct acceptance degree(CAD),correct rejection degree(CRD)and correct degree(CD))were defined to analyze the performance of the models more accurately.Finally,the models obtained a mean correct discrimination rate of over 90%,and exhibited robust properties for samples harvested from different areas and years.The results showed that NIR technology combined with chemometrics methods such as PCA,LDA,and BPR could be a suitable and alternative technique to identify the authenticity of maize seed varieties.

比较教学法在红外光谱分析实验教学中的探索与应用

紧密结合实验特性和教学目的,提出将比较教学法应用于实验课堂,深入探讨了其在红外光谱分析实验教学中的应用与实践。通过实施这种教学方法,学生不仅能深入理解透射法与衰减全反射红外光谱分析法的异同点,而且能掌握这两种方法的实际应用技巧。实践表明,比较学习法有效推动了高分子材料分析实验的教学改革和质量的提升,有助于强化学生实践操作能力,激发他们的科研兴趣,为培养未来的科研人才奠定坚实基础。

几种物理技术在农业中的应用及展望

物理技术在农业中的新品种培育、增产、提高农作物品质、抗病抗旱及产品的品质检测分级、环境保护等方面发挥着越来越重要的作用。以物理技术中的近红外技术、磁技术、静电技术为例,概述了其技术特点及在农业中的研究现状,探讨了其发展趋势,并进行了展望。

基于近红外光谱及BP神经网络分析法预测森林土壤有机碳含量

为快速测定森林土壤的有机碳含量，从取自小兴安岭带岭林业局东方红林场的120个土壤样品中采集350～2500 nm的土壤近红外光谱数据，对光谱做一定的预处理后，运用主成分分析法压缩提取前8个主成分，结合BP神经网络非线性方法建立土壤有机碳含量的预测模型并进行验证。结果表明，验证集的相关系数为0.78002，均方根误差为0.5002，预测集的相关系数为0.84941，均方根误差为0.4538。应用近红外光谱技术及BP神经网络非线性方法建模可以有效地预测土壤的有机碳含量，为野外大面积快速测定森林土壤碳含量提供了技术依据。

近红外光谱技术在天然草地牧草营养价值评价中的应用及展望

近红外光谱技术(NIRS)是近几十年迅速发展的测试分析技术,由于准确、高效、无损等检测优势,在牧草营养价值评价领域得到广泛应用,但是在天然草地牧草方面应用较少。快速、实时评价天然草地牧草营养价值,为研究天然草地营养供给和营养载畜量提供基础数据,对草地畜牧业生产具有重要意义。文章阐述了近红外光谱技术的基本原理和特点,介绍了直接法和间接法评价牧草营养价值,分别从常规营养成分、矿物元素、抗营养成分、营养物质消化率4个层次综述近红外光谱技术在2个方法中的应用,并做出展望,以期建立基于NIRS技术的天然草地牧草营养价值数据库,为天然草地的科学管理和合理利用发挥重要作用。