An expeditious method for determining particle size distribution by near infrared spectroscopy: Comparison of PLS2 and ANN models

The particle size distribution of a solid product can be crucial parameter considering its application to different kinds of processes. The influence of particle size on near infrared (NIR) spectra has been used to develop effective alternative methods to traditional ones in order to determine this parameter. In this work, we used the chemometrical techniques partial least squares 2 (PLS2) and artificial neural networks (ANNs) to simultaneously predict several variables to the rapid construction of particle size distribution curves. The PLS2 algorithm relies on linear relations between variables, while the ANN technique can model non-linear systems.Samples were passed through sieves of different sieve opening in order to separate several size fractions that were used to construct two types of particle size distribution curves. The samples were recorded by NIR and their spectra were used with PLS2 and ANN to develop two calibration models for each. The correlation coefficients and relative standard errors of prediction (RSEP) have been used to assess the goodness of fit and accuracy of the results.The four calibration models studied provided statistically identical results based on RSEP values. Therefore, the combined use of NIR spectroscopy and PLS2 or ANN calibration models allows determining the particle size distributions accurately. The results obtained by ANN or PLS2 are statistically similar.     

Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy

Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results.     

Frequency self deconvolution in the quantitative analysis of near infrared spectra

In this paper a new model based on frequency self deconvolution (FSD) is proposed for the quantitative analysis of a near infrared (NIR) spectrum. The model couples FSD and partial least square regression (PLS). The grid search optimization method is used to select the optimal values of the full width at half height (FWHH) and the truncation point of the apodization function. The proposed FSD-PLS provides a significant improvement in the prediction ability of the PLS model. Furthermore, a modification of the new FSD-PLS method is introduced to enable the removal of the baseline variations from the NIR spectra. The proposed models were validated using absorbance spectra of mixtures composed from glucose, urea and triacetin in a phosphate buffer solution where the concentrations of the components are selected to be within their physiological range in blood. The whole experiments were carried out in a non-controlled environment to show that the model can suppress effectively most of the experimental variations. The results show that the standard error of prediction (SEP) decreases from 35.58 mg dL(-1) using 8 factors for the PLS model to 15.53 mg dL(-1) by using 12 factors for the modified FSD-PLS model. The proposed models are also shown to yield a slightly improved performance than a newly developed second derivative-PLS model without incurring the shortcoming associated with the derivative approach in not providing interpretable results and in degrading the SNR of the spectra at a faster rate.     

独立分量分析预处理法提高苹果糖度模型预测精度研究

为了提高苹果近红外光谱糖度预测模型精度,利用独立分量分析方法(ICA)对苹果近红外光谱进行了预处理,并且建立了糖度的偏最小二乘(PLS)预测模型。结果表明,独立分量分析不但能分离出噪声信号,而且所分离出来的光谱信号也比原始光谱信号光滑。在预处理后的最佳PLS糖度模型校正时的相关系数rc和标准偏差SEC分别为0.9549和0.3361,用于预测时的相关系数rp和标准偏差SEP分别为0.9071和0.4355。与普通的平均处理法的PLS模型相比,其精度有所提高,且模型更加简洁。     

近红外光谱法快速分析有机肥料中有机质、总养分和pH值

建立了近红外光谱法结合偏最小二乘(PLS)法测定126种有机肥料中有机质、总养分和p H值的快速方法。采用K–S法分类,选取S–G平滑、S–G导数、多元散射校正和均值平均化4种前处理方法对粉碎后样品的近红外光谱信息进行预处理,以PLS法建立定量分析模型。结果表明,有机肥料中总养分的RC,SEC,RP,SEP,RPD分别为0.990,1.272%,0.985,1.084%,5.9;p H值的RC,SEC,RP,SEP,RPD分别为0.910,0.344%,0.737,0.428%,2.9。有机质项目根据国标方法分为小于40%、小于55%和大于55%3种样品进行分析,3种样品的RP分别为1.000,0.989,1.000;RPD分别为18.9,17.5,8.8。对比国标方法,有机质和总养分的测定精度满足实验室精确分析要求,p H值测定法可用于定量分析。NIR–PLS法实现了对有机肥料进行无损快速的检测分析。     

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图的定量校正模型。所建立的苯甲酰新乌头原碱、苯甲酰乌头原碱、苯甲酰次乌头原碱、新乌头碱、次乌头碱、乌头碱、单酯型生物碱总量和双酯型生物碱总量LS-SVM模型的相对预测偏差(RPD)分别为3.3、3.2、4.1、7.7、8.8、7.6、4.0和8.6;验证集相关系数(rpre)分别为0.9486、0.9475、0.9668、0.9909、0.9946、0.9969、0.9669和0.9927,且LS-SVM模型优于PLS模型,说明NIRS模型验证集与HPLC测定值具有良好的非线性关系,模型预测效果良好。采用NIRS技术结合LS-SVM模型可以快速对附子中的上述6个生物碱含量以及单酯型生物碱总量和双酯型生物碱总量进行检测,方法操作简便,对控制附子中的生物碱含量具有一定的指导作用。     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

Determination of human serum albumin and γ-globulin in a control serum solution by near-infrared spectroscopy and partial least squares regression

Near infrared (NIR) spectra in the 1300– 1850 nm region were measured for control serum solutions containing both albumin and γ-globulin of various concentrations. Partial least squares two (PLS2) regression was applied to the NIR spectra to determine simultaneously the concentrations of both proteins. For albumin, the correlation coefficient (R) of 0.988, the standard error of calibration (SEC) of 1.61 g/L, the standard error of prediction (SEP) of 1.29 g/L, the relative standard deviation (RSD) of 0.026 and the ratio of standard deviation of reference data in prediction to SEP (RPD) of 12.2 were obtained. For γ-globulin, the corresponding values were 0.997, 1.36 g/L, 1.35 g/L, 0.0365 and 8.66, respectively. The regression coefficients (RCs) of PLS factors were compared between albumin and γ-globulin, and the observed differences in the RCs were discussed based upon the differences in the hydration between albumin and γ-globulin. In order to explore the effects of various metabolites such as glucose, and cholesterol on the chemometrics models, the RCs for albumin and γ-globulin in the control serum solutions were also compared with those for albumin and γ-globulin in phosphate buffer solutions previously studied. The results of our experiments show that NIR spectroscopy with the use of PLS2 regression has considerable promise in nondestructive determination of the concentrations of blood serum proteins.     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

Rapid determination of sucrose in chocolate mass using near infrared spectroscopy

This paper reports the results of a rapid method to determine sucrose in chocolate mass using near infrared spectroscopy (NIRS). We applied a broad-based calibration approach, which consists in putting together in one single calibration samples of various types of chocolate mass. This approach increases the concentration range for one or more compositional parameters, improves the model performance and requires just one calibration model for several recipes. The data were modelled using partial least squares (PLS) and multiple linear regression (MLR). The MLR models were developed using a variable selection based on the coefficient regression of PLS and genetic algorithm (GA). High correlation coefficients (0.998, 0.997, 0.998 for PLS, MLR and GA-MLR, respectively) and low prediction errors confirms the good predictability of the models. The results show that NIR can be used as rapid method to determine sucrose in chocolate mass in chocolate factories.     

An enrichment device of silica-based monolithic material and its application to determine micro-carbaryl by NIRS

Silica-based monolithic column material was synthesized and an enrichment device was fabricated with the material by assembling the material inside a glass column.The enrichment device was applied for the determination of micro-carbaryl with near-infrared spectroscopy(NIRS).The aqueous solutions of carbaryl passed through the device and the carbaryl was enriched on the surface of the material where diffuse reflection NIR spectra were measured.These procedures of enrichment and measurement ensured to conc...     

Near-infrared reflectance spectroscopy and multivariate calibration techniques applied to modelling the crude protein, fibre and fat content in rapeseed meal

Near-infrared reflectance spectroscopy (NIRS) is often applied when a rapid quantification of major components in feed is required. This technique is preferred over the other analytical techniques due to the relatively few requirements concerning sample preparations, high efficiency and low costs of the analysis. In this study, NIRS was used to control the content of crude protein, fat and fibre in extracted rapeseed meal which was produced in the local industrial crushing plant. For modelling the NIR data, the partial least squares approach (PLS) was used. The satisfactory prediction errors were equal to 1.12, 0.13 and 0.45 (expressed in percentages referring to dry mass) for crude protein, fat and fibre content, respectively. To point out the key spectral regions which are important for modelling, uninformative variable elimination PLS, PLS with jackknife-based variable elimination, PLS with bootstrap-based variable elimination and the orthogonal partial least squares approach were compared for the data studied. They enabled an easier interpretation of the calibration models in terms of absorption bands and led to similar predictions for test samples compared to the initial models.     

高粱籽粒中多酚类物质的傅立叶变换近红外光谱分析

利用高效液相色谱(HPLC)法测定高粱籽粒中阿魏酸、原儿茶醛和花青素的含量,比色法测定总酚、总黄酮、缩合单宁的含量;运用偏最小二乘法建立NIR光谱与HPLC法和比色法分析值之间的多元校正模型,预测高粱籽粒中主要酚类物质的含量.结果表明,各成分近红外预测值与实测值之间的校正模型相关系数(R)、内部交叉验证均方差(RMSECV)、最佳主因子数分别为:总酚0.9737, 0.288, 4;总黄酮0.9660, 0.00671, 8;缩合单宁0.9558, 0.0289, 6;阿魏酸0.9818, 0.0391, 6;原儿茶醛0.9979, 0.0118, 5;花青素0.9977, 0.0523, 4;预测相对偏差(RSEP)分别为:总酚6.99%、总黄酮4.54%、 缩合单宁7.13%、阿魏酸2.68%、原儿茶醛5.46%、 花青素5.81%.结果表明,模型对样品NIR的预测值与其相应的化学值有较好的相关性,此模型可用来预测高粱籽粒中的各酚类物质的含量,在高粱优质育种和品质分析中具有广泛的应用价值.     

Discrimination of Scrophulariae Radix according to geographical origin and determination of active constituents by near infrared spectroscopy (NIRS)

The potential of near infrared reflectance spectroscopy (NIR) was investigated for its ability to non-destructively discriminate the geographic origins of Scrophularia spp., Andong, Uisung and China. Application of principal component analysis to NIR spectra leads to a clear separation of Andong sample from the others. Moreover, the contents of two neuroprotective constituents of Scrophularia spp., 8-O-(E-p-methoxycinnamoyl)-harpagide (HG), and E-p-methoxycinnamic acid (MCA), were determined by HPLC-DAD. Partial least squares (PLS) regression of NIR spectra combined with these analytical reference data yield the development of calibration models for the contents of the two constituents. The correlation coefficients of prediction models for HG and MCA were > 0.87. These outcomes indicated that the NIRS could be useful for the discrimination of Scrophularia spp.     

Fast determination of trace dimethyl fumarate in milk with near infrared spectroscopy following fluidized bed enrichment

Near infrared spectroscopy (NIRS) has been proved to be a powerful analytical tool in different fields. However, because of the low sensitivity in near infrared region, it is a significant challenge to detect trace analytes with normal NIRS technique. A novel enrichment technique called fluidized bed enrichment has been developed recently to improve sensitivity of NIRS which allows a large volume solution to pass through within a short time. In this paper, fluidized bed enrichment method was applied in the determination of trace dimethyl fumarate in milk. Macroporous styrene resin HZ-816 was used as adsorbent material, and 1?L solution of dimethyl fumarate was run to pass through the material for concentration. The milk sample was pretreated to remove interference matters such as protein, fat, and then passed through the material for enrichment; after that, diffuse reflection NIR spectra were measured for the analyte concentrated on the material directly without any elution process. The enrichment and spectral measurement procedures were easy to operate. NIR spectra in 900–1,700?nm were collected for dimethyl fumarate solutions in the concentration range of 0.506–5.060?μg/mL and then used for multivariate calibration with partial least squares (PLS) regression. Spectral pretreatment methods such as multiplicative scatter correction, first derivative, second derivative, and their combinations were carried out to select the optimal PLS model. Root mean square error of cross-validation calculated by leave-one-out cross-validation is 0.430?μg/mL with ten PLS factors. Ten samples in an independent test set were predicted by the model with the mean relative error of 5.33?%. From the results shown in this work, it can be concluded that the NIR technique coupled with on-line enrichment method can be expanded for the determination of trace analytes, and its applications in real liquid samples like milk and juice may also be feasible.     

Feasibility of near-infrared spectroscopy to detect and to quantify adulterants in cow milk.

Cow milk adulteration involves the dilution of milk with a less-expensive component, such as water or whey. Near-infrared spectroscopy (NIRS) was employed to detect the adulterations of milk, non-destructively. Two adulteration types of cow milk with water and whey were prepared, respectively. NIR spectra of milk adulterations and natural milk samples in the region of 1100 - 2500 nm were collected. The classification of milk adulterations and natural milk were conducted by using discriminant partial least squares (DPLS) and soft independent modelling of class analogy (SIMCA) methods. PLS calibration models for the determination of water and whey contents in milk adulteration were also developed, individually. Comparisons of the classification methods, wavelength regions and data pretreatments were investigated, and are reported in this study. This study showed that NIR spectroscopy can be used to detect water or whey adulterants and their contents in milk samples.     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.     

Determination of Ni2+ using an equilibrium ion exchange technique: Important chemical factors and applicability to environmental samples

The main purpose of this study was to investigate the relationship between some coffee roasting variables (weight loss, density and moisture) with near infrared (NIR) spectra of original green (i.e. raw) and differently roasted coffee samples, in order to test the availability of non-destructive NIR technique to predict coffee roasting degree. Separate calibration and validation models, based on partial least square (PLS) regression, correlating NIR spectral data of 168 representatives and suitable green and roasted coffee samples with each roasting variable, were developed. Using PLS regression, a prediction of the three modelled roasting responses was performed. High accuracy results were obtained, whose root mean square errors of the residuals in prediction (RMSEP) ranged from 0.02 to 1.23%. Obtained data allowed to construct robust and reliable models for the prediction of roasting variables of unknown roasted coffee samples, considering that measured vs. predicted values showed high correlation coefficients (r from 0.92 to 0.98). Results provided by calibration models proposed were comparable in terms of accuracy to the conventional analyses, revealing a promising feasibility of NIR methodology for on-line or routine applications to predict and/or control coffee roasting degree via NIR spectra.     

Determination of human serum albumin and γ-globulin in a control serum solution by near-infrared spectroscopy and partial least squares regression

Near infrared (NIR) spectra in the 1300– 1850 nm region were measured for control serum solutions containing both albumin and γ-globulin of various concentrations. Partial least squares two (PLS2) regression was applied to the NIR spectra to determine simultaneously the concentrations of both proteins. For albumin, the correlation coefficient (R) of 0.988, the standard error of calibration (SEC) of 1.61 g/L, the standard error of prediction (SEP) of 1.29 g/L, the relative standard deviation (RSD) of 0.026 and the ratio of standard deviation of reference data in prediction to SEP (RPD) of 12.2 were obtained. For γ-globulin, the corresponding values were 0.997, 1.36 g/L, 1.35 g/L, 0.0365 and 8.66, respectively. The regression coefficients (RCs) of PLS factors were compared between albumin and γ-globulin, and the observed differences in the RCs were discussed based upon the differences in the hydration between albumin and γ-globulin. In order to explore the effects of various metabolites such as glucose, and cholesterol on the chemometrics models, the RCs for albumin and γ-globulin in the control serum solutions were also compared with those for albumin and γ-globulin in phosphate buffer solutions previously studied. The results of our experiments show that NIR spectroscopy with the use of PLS2 regression has considerable promise in nondestructive determination of the concentrations of blood serum proteins. Received: 31 December 1997 / Revised: 9 April 1998 / Accepted: 27 April 1998