Rapid analysis of Radix puerariae by near-infrared spectroscopy

A new, rapid analytical method using near-infrared spectroscopy (NIRS) was developed to differentiate two species of Radix puerariae (GG), Pueraria lobata (YG) and Pueraria thomsonii (FG), and to determine the contents of puerarin, daidzin and total isoflavonoid in the samples. Five isoflavonoids, puerarin, daidzin, daidzein, genistin and genistein were analyzed simultaneously by high-performance liquid chromatography-diode array detection (HPLC-DAD). The total isoflavonoid content was exploited as critical parameter for successful discrimination of the two species. Scattering effect and baseline shift in the NIR spectra were corrected and the spectral features were enhanced by several pre-processing methods. By using linear discriminant analysis (LDA) and soft independent modeling class analogy (SIMCA), samples were separated successfully into two different clusters corresponding to the two GG species. Furthermore, sensitivity and specificity of the classification models were determined to evaluate the performance. Finally, partial least squares (PLS) regression was used to build the correlation models. The results showed that the correlation coefficients of the prediction models are R = 0.970 for the puerarin, R = 0.939 for daidzin and R = 0.969 for total isoflavonoid. The outcome showed that NIRS can serve as routine screening in the quality control of Chinese herbal medicine (CHM).     

Rapid qualitative and quantitative evaluation of deer antler (Cervus elaphus) using near-infrared reflectance spectroscopy

Near-infrared (NIR) spectroscopy has been applied for both the qualitative and quantitative evaluation of the velvet deer antler. The most important parameters of determining the quality of velvet antler are the habitat (the country of origin) and ash content. Conventionally, the habitat is determined by examining the appearance of samples (by human eye), which lacks objectivity. Ash content is measured by an ignition method (measurement ash residue), however, it is too slow (4–5 h) to be used for rapid at-site measurement. Velvet antlers from three different habitats (China, New Zealand, and Russia), albeit the same species of Cervus elaphus, were evaluated in this paper. Soft independence modeling of class analogies (SIMCA) and partial least squares (PLS) were used for classification of habitat and determination of ash content. The habitat was successfully identified with over 80% accuracy, and the ash content prediction result using PLS regression showed good correlation with the reference ignition method with a standard error of prediction (SEP) of 1.264%.     

The performance of nu-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near-infrared spectroscopy

The ν-support vector regression (ν-SVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acousto-optic tunable filter near-infrared (AOTF-NIR) spectra. The performance of ν-SVR was compared with the partial least square regression (PLSR) and the back-propagation artificial neural networks (BP-ANN). The influence of SVR parameters on the predictive ability of model was investigated. The results indicated that the parameter ν had a rather wide optimal area (between 0.35 and 1 for the apple data). Therefore, we could determine the value of ν beforehand and focus on the selection of other SVR parameters. For analyzing SSC of apple, ν-SVR was superior to PLSR and BP-ANN, especially in the case of fewer samples and treating the noise polluted spectra. Proper spectra pretreatment methods, such as scaling, mean center, standard normal variate (SNV) and the wavelength selection methods (stepwise multiple linear regression and genetic algorithm with PLS as its objective function), could improve the quality of ν-SVR model greatly.     

Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy

The pharmaceutical industry faces increasing regulatory pressure to optimize quality control. Content uniformity is a basic release test for solid dosage forms. To accelerate test throughput and comply with the Food and Drug Administration's process analytical technology initiative, attention is increasingly turning to nondestructive spectroscopic techniques, notably near-infrared (NIR) spectroscopy (NIRS). However, validation of NIRS using requisite linearity and standard error of prediction (SEP) criteria remains a challenge. This study applied wavelet transformation of the NIR spectra of a commercial tablet to build a model using conventional partial least squares (PLS) regression and an artificial neural network (ANN). Wavelet coefficients in the PLS and ANN models reduced SEP by up to 60% compared to PLS models using mathematical spectra pretreatment. ANN modeling yielded high-linearity calibration and a correlation coefficient exceeding 0.996.     

Quantitative determination of soybean meal content in compound feeds: comparison of near-infrared spectroscopy and real-time PCR

Standard methods for determining the raw material content of compound feed are little exploited, except for the identification of meat and bone meal in feeds. In this work, near-infrared (NIR) spectroscopy and real-time polymerase chain reaction (PCR) were applied in order to establish new and fast methods for quantification of soybean meal content in compound feeds. The best prediction quality was achieved by using a model based on NIR spectroscopy (R ² = 0.9857, standard error of cross-validation 1.1065). Furthermore, a sensitive qualitative detection method by using the real-time PCR was developed (R ² = 0.976, slope −3.7599). Finally, the differences between the real-time PCR result and the NIR spectroscopy result for a given sample were also treated, and we found that the NIR spectroscopy method provided quite accurate results which approach closely those of the real-time PCR method. Hui Li and Xiaowen Lv contributed equally to this work.     

偏最小二乘-近红外漫反射光谱法测定西米替丁药片

研究了应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对西米替丁片剂药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品。     

Comparison of chemometric methods for brand classification of cigarettes by near-infrared spectroscopy

The combinations of NIR spectroscopy and three classification algorithms, i.e., multi-class support vector machine (BSVM), k-nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA), for discriminating different brands of cigarettes, were explored. The influence of the training set size on the relative performance of each algorithm was also investigated. A NIR spectral dataset involving the classification of cigarettes of three brands was used for illustration. Three performance criteria based on “correctly classified rate (CCR)”, i.e., “Average CCR”, “95 percentile of CCR” and “S.D. of CCR”, were defined to compare different algorithms. It was revealed that BSVM is significantly better than KNN or SIMCA in the statistical sense, especially in cases where the training set is relatively small. The results suggest that NIR spectroscopy together with BSVM could be an alternative to traditional methods for discriminating different brands of cigarettes.     

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

Quantitative determination of serum triglycerides was achieved in diffuse reflectance mode using silver mirror as the substrate to enhance the spectral features.     

Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods

Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin.     

Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging

A rapid method based on hyperspectral imaging for detection of Escherichia coli contamination in fresh vegetable was developed. E. coli K12 was inoculated into spinach with different initial concentrations. Samples were analyzed using a colony count and a hyperspectroscopic technique. A hyperspectral camera of 400-1000 nm, with a spectral resolution of 5 nm was employed to acquire hyperspectral images of packaged spinach. Reflectance spectra were obtained from various positions on the sample surface and pretreated using Sawitzky-Golay. Chemometrics including principal component analysis (PCA) and artificial neural network (ANN) were then used to analyze the pre-processed data. The PCA was implemented to remove redundant information of the hyperspectral data. The ANN was trained using Bayesian regularization and was capable of correlating hyperspectral data with number of E. coli. Once trained, the ANN was also used to construct a prediction map of all pixel spectra of an image to display the number of E. coli in the sample. The prediction map allowed a rapid and easy interpretation of the hyperspectral data. The results suggested that incorporation of hyperspectral imaging with chemometrics provided a rapid and innovative approach for the detection of E. coli contamination in packaged fresh spinach.     

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 of98.2%–100.7%     

Fast quantitative detection of sesame oil adulteration by near-infrared spectroscopy and chemometric models

Adulteration of foods has been known to exist for a long time and various analytical tests have been reported to address this problem. Among them, authenticity of sesame oil has attracted much attention. Near-infrared (NIR) spectral quantitative detection models of sesame oil adulterated with other oils are constructed by chemometric methods, i.e., competitive adaptive reweighted sampling (CARS), elastic component regression (ECR) and partial least squares (PLS). Sixty samples adulterated with different proportions of five kinds of other oils of lower price were scanned by a Fourier-transform-NIR spectrometer and the NIR spectra were collected in 4500–10000 cm⁻¹ region by transmission mode. All samples were divided into the training set and an independent test set. Model population analysis has also been carried out and confirms the importance of selecting representative samples. The experimental results indicate that the PLS model using only 10 variables from CARS and the ECR model show similar performance and both are superior to the full-spectrum PLS model. CARS focuses on selecting variables and ECR focuses on optimizing the parameters, implying that both roads lead to the same destination. It seems that NIR technique combined with CARS or ECR is feasible for rapidly detecting sesame oil adulterated with other vegetable oils.     

Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380–1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on ‘Manual Prawn Mask’ and ‘Automatic Prawn Mask’, respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round.     

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39–14.14 mg cm⁻². The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R²), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20 s without sample destruction.     

Simultaneous determination of mercury, lead and cadmium ions in water using near-infrared spectroscopy with preconcentration by thiol-functionalized magnesium phyllosilicate clay

Analysis of metal ions in environment is of great importance for evaluating the risk of heavy metal to public health and ecological safety. A method for simultaneous determination of metal ions in water samples was developed by using adsorption preconcentration and near-infrared diffuse reflectance spectroscopy (NIRDRS). A high capacity adsorbent of thiol-functionalized magnesium phyllosilicate, named Mg-MTMS, was prepared by co-condensation for preconcentration of Hg²⁺, Pb²⁺ and Cd²⁺ in aqueous solutions. After adsorbing the analytes onto the adsorbent, NIRDRS was measured and PLS models were established for fast and simultaneous quantitative prediction. Because the interaction of the ions with the functional group of the adsorbent can be reflected in the spectra, the models built with the samples prepared by river water were proven to be efficient enough for precise prediction. The determination coefficients (R²) of the validation samples for the three ions were found as high as 0.9197, 0.9599 and 0.9861, respectively. Furthermore, because the high adsorption efficiency of Mg-MTMS, the detected concentrations are as low as milligrams per liter for the three ions, and the concentration can be further reduced. Therefore, the feasibility of quantitative analysis metal ions in river water by NIRDRS is proven and this may provide a new way for fast simultaneous determination of trace metals in environmental waters.     

近红外光谱快速测定复方磺胺甲噁唑药效成分

基于近红外漫反射光谱技术,利用偏最小二乘多元校正方法建立了复方磺胺甲噁唑片中的两个有效成分磺胺甲噁唑(SMZ)和甲氧苄啶(TMP)含量的快速同时测定方法。对于SMZ和TMP定量分析模型,相关系数分别为99.969%与99.938%,校正集残差分别为0.217与0.159,而预测根均方差分别为0.310和0.418。该方法具有简单、快捷、两组分同时准确测定以及样品不经任何预处理等特点。     

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.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Rapid,single-step nucleic acid detection

A rapid detection method for nucleic acid based on bioluminescence resonance energy transfer (BRET) from the luminescence donor Renilla luciferase to an acceptor quantum dot upon oligonucleotide probe hybridization has been developed. Utilizing a competitive assay, we detected the target nucleic acid by correlating the BRET signal with the amount of target present in the sample. This method allows for the detection of as little as 4 pmol (20 nM) of nucleic acid in a single-step, homogeneous format both in vitro in a buffer matrix as well as in a cellular matrix. Using this method, one may perform nucleic acid detection in as little as 30 min, showing much improvement over time-consuming blotting methods and solid-phase methods which require multiple wash steps to remove unbound probe. This is the first report on the use of quantum dots as a BRET acceptor in the development of a nucleic acid hybridization assay. An erratum to this article can be found at     

A new accessory for the detection of trace components by FT-IR spectroscopy

We have developed a specific compartment to increase the sensitivity of a routine FT-IR spectrometer. A high performance MCT detector has been included in the system. The SNR of the spectrometer has been improved by a factor of 10.