近红外光谱分析技术在快速分析上的应用

简要介绍了近红外光谱的原理、特点,综述了近红外光谱在农业、食品、制药、石油化工、高分子等领域快速分析上的研究及应用现状,并对近红外光谱的应用前景进行了展望．     

近红外光谱混合模型定量分析不同物理状态样品的研究

近红外光谱(NIRS)以漫反射模式对非均质样本进行测量时,由于其光谱散射和吸收系数差异较大,建立的校正模型准确性和稳健性较低,因此,本研究提出了一种基于均质样本和模型转移方法建立混合模型的策略,解决非均质样本近红外光谱检测的问题.以烟叶样本为研究对象,分别建立了基于Shenk专利算法(Shenk′s)、分段直接标准化(PDS)和基于典型相关分析的模型转移算法(CTCCA)的烟粉+烟丝、烟粉+烟片混合模型,用于烟丝和烟片样本中烟碱含量的预测.结果表明,混合模型对烟丝和烟片样本的预测均方误差(RMSEP)较直接建模分别降低了1.39%和2.73%,预测结果有一定的改善,稳健性提高,3种方法中CTCCA表现最优.因此,采用近红外光谱均质模型和模型转移方法建立的混合模型对非均质样本的测定具有可行性,有利于在线近红外光谱分析技术的发展,可为近红外光谱模型的共享提供参考.     

Origin Identification of Hungarian Honey Using Melissopalynology,Physicochemical Analysis,and Near Infrared Spectroscopy

The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.     

近红外光谱法快速测定附子中6个生物碱的含量

应用近红外光谱(NIRS)技术结合偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)建立了附子中多指标成分的快速无损检测方法.选取38批样品建立了同时测定附子样品中6种成分含量的高效液相色谱(HPLC)方法;通过采集附子样品的NIRS图,分别采用PLS和LS-SVM建立了各个成分HPLC测定值与NIRS图的定量校...     

木薯品质分析的近红外光谱模型建立及其应用研究

应用近红外光谱法(NIRS)建立木薯中淀粉、水分定量分析的近红外光谱数学模型,探讨了修正偏最小二乘法(MPLS)、偏最小二乘法(PLS)以及主成分回归法(PCR)等优化处理对定标模型的影响,确定了修正偏最小二乘法(MPLS)是建立模型最适合的数学方法。并对模型预测结果的准确性进行了评价。结果表明:验证集样品的化学值和近红外预测值拟合存在较好的线性关系,相关性显著。淀粉模型预测标准偏差(Sep)为0.850,系统偏差(Bias)为-0.095,相关系数(r)为0.971。水分模型预测标准偏差(Sep)为0.075,系统偏差(Bias)为0.007,相关系数(r)为0.980。淀粉、水分定量分析的NIRS数学模型具有较高的预测准确性,可应用于木薯批量收购中的品质等分析。     

应用近红外技术测定黄豆粕中水分、蛋白质和粗脂肪

应用近红外分析仪（NIR）快速测定黄豆粕中水分、蛋白质和粗脂肪的含量,通过光谱扫描统计分析,分别找出测量黄豆粕水分、蛋白质、粗脂肪的波长及校准常数值,经数理统计分析结果表明：近红外法与常规水分测定法经典凯氏定氮法、索氏浸提法所测定的结果呈密切的线性相关,检测结果令人满意。     

Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market

The potential of the near infrared spectroscopy (NIRS) technique for the analysis of red paprika for aflatoxin B(1), ochratoxin A and total aflatoxins is explored. As a reference, the results from a chromatographic method with fluorescence detection (HPLC-FD) following an immunoaffinity cleanup (IAC) were employed. For the NIRS measurement, a remote reflectance fibre-optic probe was applied directly onto the samples of paprika. There was no need for pre-treatment or manipulation of the sample. The modified partial least squares (MPLS) algorithm was employed as a regression method. The multiple correlation coefficients (RSQ) and the prediction corrected standard errors (SEP(C)) were respectively 0.955 and 0.2 microg kg(-1), 0.853 and 2.3 microg kg(-1), 0.938 and 0.3 microg kg(-1) for aflatoxin B(1), ochratoxin A and total aflatoxins, respectively. The capacity for prediction of the developed model measured as ratio performance deviation (RPD) for aflatoxin B(1) (5.2), ochratoxin A (2.8) and total aflatoxins (4.4) indicate that NIRS technique using a fibre-optic probe offers an alternative for the determination of these three parameters in paprika, with an advantageously lower cost and higher speed as compared with the chemical method. Content of aflatoxin B(1) and total aflatoxins are the parameters currently employed by the food regulations to limit the levels of the four aflatoxins in many foodstuffs. In addition, aflatoxin B(1) itself is an excellent indicator for aflatoxins' contamination since it is always the most abundant and toxic.     

Application of the ANOVA-PCA method to stability studies of reference materials

Near infrared spectroscopy (NIRS) is an analytical technique that can be very useful for stability studies in particular because of its non destructive analytical capability. However, the spectral interpretation and treatment of this kind of multivariate data remains difficult without the use of chemometrics. In this article, a recent chemometrics method, analysis of variance - principal component analysis (ANOVA-PCA), was used for NIRS stability studies of sunflower and bread wheat external reference materials (ERM). It provided a practical tool for the study of the significance of various storage conditions according to an experimental design. Thus, the effect of the temperature, the nature of the atmosphere in the packaging and the storage duration were tested. ANOVA-PCA highlighted the influence of temperature and storage duration on the stability of the sunflower materials. For the bread wheat materials, the storage conditions did not have a significant effect on stability. Consequently, by applying ANOVA-PCA to near infrared spectral data, the sunflower materials were found to be considered stable for the time length of the study, i.e. 18 months stored in a cold room, while the bread wheat materials were found to be considered stable for the time length of the study, i.e. 12 months under the same conditions.     

A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations

The analytical determination of aminoglycosides in pharmaceutical formulations is very difficult due to the lack of chromophores or fluorophores. Several analytical methods have been developed along the years mainly based on derivatization reactions. The European Pharmacopeia (EP) and the United States Pharmacopeia (USP) describe a microbiological assay to the quantification of aminoglycosides. Near infrared spectroscopy (NIRS) can be used alternatively to analyse aminoglycosides without the need of derivatization reactions or other type of sample processing. A new NIRS based method was developed for the analysis of the aminoglycoside antibiotic neomycin. The method was developed with samples based on a commercial formulation containing neomycin sulphate and three excipients: lactose, talc and magnesium stearate. Synthetic and doped samples were manufactured for this purpose. Three lots of a commercial solid formulation were also used to assess the validity of the method to quantify neomycin sulphate in the industrial pharmaceutical product. The method proposes measurements in reflectance mode using a Fourier-transform near infrared (FT-NIR) spectrometer. Partial least squares regression was the multivariate method adopted to calibrate the NIR spectra with the neomycin sulphate mass fraction. The concentration of neomycin sulphate present in the commercial samples was confirmed by HPLC with pre-column derivatization with phenylisocyanate. Results show that neomycin sulphate was determined successfully in the commercial samples using the method calibrated with the doped samples (mass fraction error of 6.6%). Moreover, the synthetic samples were found to be unqualified to develop the method, producing a biased calibration.     

我国油料产品品质的近红外光谱快速检测技术研究进展

近红外光谱技术是一种快速无损检测技术,具有操作简单、检测成本低、无需化学试剂、绿色环保,以及可实现多品质参数同步检测等优点。该文综述了我国油料和食用植物油品质的近红外光谱速测技术研究进展,包括油料含油量、粗蛋白含量、脂肪酸含量等品质指标,食用油的理化指标,以及脂肪酸和食用油的真实性鉴别,并对油料产品品质的近红外光谱速测技术的发展前景进行了展望。     

Rapid Determination of Betulin in Betula platyphylla Outer Bark Using Near-Infrared Spectroscopy

A simple, rapid, and nondestructive method for the determination of betulin in the outer birch bark was developed using near infrared spectroscopy (NIRS). NIRS data of the outer birch bark collected throughout the year was preprocessed and analyzed by principal component analysis, which led to clear discrimination of the samples according to their harvest times. The reference content of betulin, a major constituent of the outer birch bark, was determined using ultra performance liquid chromatography with a diode array detector (UPLC-DAD). The optimized and validated analytical conditions of UPLC-DAD provided better separation and faster analysis time compared to a conventional HPLC method. Betulin content also showed seasonal variation and was higher in the samples collected during the summer season. Partial least squares calibration techniques were employed to estimate the relationship between the NIRS data and betulin contents. The spectral data showed high correlation coefficients (over 0.700) with betulin content. These results indicate that NIRS combined with UPLC can be used to determine the quality and to quantify the betulin content of the outer birch bark.     

Rapid determination of Paeoniae Radix using near infrared spectroscopy

A rapid and nondestructive near infrared spectroscopy (NIRS) was used to differentiate different geographical Paeoniae Radix and quantitatively predict the content of main active components. Paeoniflorin, albiflorin and benzoylalbiflorin were analyzed simultaneously with an Agilent Zorbax SB-C₁₈ column by gradient elution under high-performance liquid chromatography-UV detection (HPLC-UV). Multiplicative scatter correction (MSC), first derivative and Savitsky-Golay were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra in order to give a better correlation with the results obtained by HPLC-UV. Multiplicative regression methods were discussed. The spectra calibration equations produced highest correlation coefficient values (R²) and lowest root mean square error of prediction (RMSEP) were used for the determination of paeoniflorin, albiflorin and benzoylalbiflorin. The RMSEP of paeoniflorin, albiflorin and benzoylabiflorin were 0.866 mg/g, 0.369 mg/g and 0.084 mg/g, respectively, and the R² of cross validation were 0.986, 0.939 and 0.971, respectively. Furthermore with the use of principle component analysis (PCA), Paeoniae Radix was clustered according to different cultivation area. The results indicated that the NIRS method could be used for the quality control of Chinese herbal medicine.     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R2cv)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

用于药品质量快速检测的近红外光谱模糊神经元分类方法

针对非线性且分类界线模糊的药品质量类别快速测定难题, 将近红外光谱分析与模糊神经网络相结合, 经研究提出近红外光谱模糊神经网络分类方法, 用于计算辨析中药等化学组成复杂药品的近红外光谱模式类别, 从而快速评定这类药品的质量. 以参麦注射液为典型分析对象, 以鉴别其生产厂家这一模式分类问题为例, 考核本文方法, 结果表明, 其分类准确率达到94.2%, 明显优于经典的BP神经网络分类方法(84.6%), 可望用于中药产品质量类别的快速检测与评价.     

Identification of transgenic foods using NIR spectroscopy: A review

The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA.     

中药材三七中皂苷类成分的近红外光谱快速无损分析新方法

提出了用近红外漫反射光谱快速无损测定三七中皂苷类成分的新方法采用 HPLC分析了中药材三七固皂昔R_1,人参皂苷Hg_1,Rb_1和Rd的含量,用吸附树脂 比色法测定了三七总皂苷（PNS）的含量,共获得R_1,Bg_1,Rb_1,Rd,PNS的含 量范围分别为1,58-5．08,21,68-46.13,11.46-40.41粉．在3500-1100cm~(-1) 扫描样品,以交叉验证误差均方根（RMsECV）为指标,通过筛选,近红外波段和光 谱预处理方法．采用偏最小二乘算法建立了近红外光谱与5个组分PHLC分析值之间 的校正模型,预测了8个未知样本．R_1,Rg_1,Rb_1,Rd及PNS校正模型的RMSECV 分别为0．40,1.47,1.94,0RMSEP分别为0．53,3．15,2．14,0．70,9．03． 该方法快速无损,结果可靠,为中药材复杂体系中化学组分的测定提供了新的绿色 分析手段．     

基于尺度不变特征变换筛选稳定特征波长的近红外光谱模型传递方法

该研究利用一维尺度不变特征变换(SIFT)算法寻找烟叶近红外光谱(Near infrared spectroscopy,NIRS)的稳定特征波长,根据样品精密度测试光谱筛选的波长计算重现率和重现度,采用L_9(3~3)正交表优化SIFT算法中的相关参数,使重现率和重现度尽可能高。基于优化的参数和主机上10个代表性样品的光谱,筛选出10个稳定特征波长集合,以这些波长集合并集的光谱响应为自变量,采用偏最小二乘(PLS)方法构建烟叶总植物碱NIRS模型(简称SIFT-PLS)。该模型直接传递到3台从机后,对3台从机样品总植物碱的平均相对预测误差(MRE)均满足小于6%的企业内控要求,而全光谱模型(WW-PLS)直接转移后仅1台从机的MRE满足要求,经分段直接校正(PDS)方法校正从机光谱后,WW-PLS模型也仅对1台从机的MRE小于6%。采用SIFT算法筛选稳定特征波长建立的NIRS模型可在3台从机直接共享,无需转移集,不需对从机光谱或光谱模型进行校正,实现了真正意义的无标样NIRS模型的直接转移。     

Improvement of Direct Calibration in spectroscopy

Several linear calibration methods have been proposed for predicting the concentration of a particular compound from a spectrum. Some methods are based on experimental data, such as Partial Least Square Regression. Other methods are based on expert data, e.g. Direct Calibration. This article proposes a new method, called Improved Direct Calibration, which uses expert and experimental information. It performs a projection onto the pure interest spectrum, after correcting it from influence factors. No calibration dataset is necessary to build this model. This method has been successfully applied to the quantification of ethanol in musts during fermentation, using near infrared spectrometry.     

近红外光谱法快速鉴定油菜杂交种的纯度

建立近红外光谱技术测定油菜杂交种纯度的方法。考察了样品杯类型、光谱预处理方法和波长范围对近红外模型预测性能的影响。结果发现,由不同样品杯采集近红外光谱所建立的校正模型,其预测性能存在较大的差异,旋转杯明显优于安瓿瓶;采用消除常数偏移量对光谱进行预处理能有效地提取光谱信息,选择5 000~8 000 cm–1波数范围作为建模谱区,其包含的有效信息率最高。在最佳条件下建立油菜杂交种纯度的校正模型,其决定系数(R2)为0.980 0,交互验证均方根误差(RMSECV)为0.008 59。利用该模型对预测集进行测定,预期均方根误差(RMSEP)为0.007 59,表明该模型具有很好的预测性能,近红外光谱法用于杂交种纯度的鉴定是可行的。     

Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection

This paper developed a rapid method using near infrared spectroscopy (NIRS) to differentiate two species of Cortex Phellodendri (CP), Cortex Phellodendri Chinensis (PCS) and Cortex Phellodendri Amurensis (PAR), and to predict quantitatively the content of berberine and total alkaloid content in all Cortex Phellodendri samples. Three alkaloids, berberine, jatrorrhizine and palmatine were analyzed simultaneously with a Thermo ODS Hypersil column by gradient elution with a new mobile phase under high-performance liquid chromatography-diode array detection (HPLC-DAD). Berberine content determined by HPLC-DAD was exploited as a critical parameter for successful discrimination between them. Multiplicative scatter correction (MSC), second derivative and Savitsky-Golay (S.G.) were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra as well as to enhance spectral features in order to give a better correlation with the results obtained by HPLC-DAD. With the use of principal component analysis (PCA), samples datasets were separated successfully into two different clusters corresponding to two species. Furthermore, a partial least squares (PLS) regression method was built on the correlation model. The results showed that the correlation coefficients of the prediction models were R = 0.996 for the berberine and R = 0.994 for total alkaloid content. The influences of water absorption bands present in the NIR spectra on the models were also investigated in order to explore the practicability of NIRS in routine use. The outcome showed that NIRS possibly acts as routine screening in the quality control of Chinese herbal medicine.