A strategy for enhancing the quantitative determination ability of the diffuse reflectance near-infrared spectroscopy

Near-infrared diffuse reflectance spectroscopy (NIRDRS) has been proved to be a convenient and fast quantitative method for complex samples. The high detection limit or the low sensitivity of the method, however, is a big problem obstructing its application in the analysis of low concentration samples. A strategy for quantitative determination of low concentration samples was developed by using NIRDRS. The method takes an adsorbent as a substrate for gathering the analytes from a solution, and uses the multivariate calibration technique for quantitative calculation. So, the detection limit can be improved and the interferences can be eliminated when complex samples are analyzed. Taking benzoic and sorbic acids as the analyzing targets and the alumina as the adsorbent, partial least squares (PLS) model is built from the NIRDRS of the adsorbates. The results show that the concentrations that can be quantitatively detected are as low as 0.011 and 0.013 mg mL(-1) for benzoic and sorbic acids, respectively, and the co-adsorbates do not interfere each other.     

Construction of a universal model for non-invasive identification of penicillins for injection using near-infrared diffuse reflectance spectroscopy

A universal NIR model for identification of 24 types of penicillins for injection has been developed. A total of 194 batches of 24 products from 87 manufacturers in China were used in the study. The classification model is a principal component analysis (PCA) based model consisting of a primary identification library with four sub-libraries. The spectral frequency regions used were 6000–6400 cm⁻¹ and 8400–8900 cm⁻¹ in the main library, 6000–6800 cm⁻¹ in sub-library 1, 4100–12,000 cm⁻¹ in sub-libraries 2 and 3, and 6200–6400 cm⁻¹ and 4700–5000 cm⁻¹ in sub-library 4. The data preprocessing method is the first derivative with nine-point smoothing followed by vector normalization. The distances between spectra were calculated using factors 2–5 for the primary identification library, factors 4–7 for sub-library 1, and factor 2 for sub-libraries 2–4. The specificity of the model was validated, and it had a correct identification rate of approximately 99%. This study has not only confirmed, but also improved the strategy described in our early report (Chong et al. (2009) [11]) to build such a library for the identification of different medicines by NIR.     

Construction of a universal model for non-invasive identification of cephalosporins for injection using near-infrared diffuse reflectance spectroscopy

A universal classification model has been developed for identification of 26 different products of cephalosporins for injection using near-infrared (NIR) spectroscopy. A total of 324 batches of the 26 products, from 166 manufacturers in China, were used in the study. The classification model was a principle component analysis (PCA)-based method consisting of a primary identification library with four sub-libraries. The accuracy and specificity of the model were tested and were both found to be approximately 95%. The transfer of the classification model between different instruments of the same brand and the same instrument model has been investigated in this study. This study has shown that it is feasible to build a universal classification model to quickly screen for counterfeit drugs in the open market and distribution channels.     

Construction of a universal model for non-invasive identification of penicillins for injection using near-infrared diffuse reflectance spectroscopy

A universal NIR model for identification of 24 types of penicillins for injection has been developed. A total of 194 batches of 24 products from 87 manufacturers in China were used in the study. The classification model is a principal component analysis (PCA) based model consisting of a primary identification library with four sub-libraries. The spectral frequency regions used were 6000–6400 cm⁻¹ and 8400–8900 cm⁻¹ in the main library, 6000–6800 cm⁻¹ in sub-library 1, 4100–12,000 cm⁻¹ in sub-libraries 2 and 3, and 6200–6400 cm⁻¹ and 4700–5000 cm⁻¹ in sub-library 4. The data preprocessing method is the first derivative with nine-point smoothing followed by vector normalization. The distances between spectra were calculated using factors 2–5 for the primary identification library, factors 4–7 for sub-library 1, and factor 2 for sub-libraries 2–4. The specificity of the model was validated, and it had a correct identification rate of approximately 99%. This study has not only confirmed, but also improved the strategy described in our early report (Chong et al. (2009) [11]) to build such a library for the identification of different medicines by NIR.     

Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy

It has been evaluated the potential of near-infrared (NIR) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a way for non-destructive measurement of trace elements at μg kg⁻¹ level in foods, with neither physical nor chemical pre-treatment. Predictive models were developed using partial least-square (PLS) multivariate approaches based on first-order derivative spectra. A critical comparison of two spectral pre-treatments, multiplicative signal correction (MSC) and standard normal variate (SNV) was also made. The PLS models built after using SNV provided the best prediction results for the determination of arsenic and lead in powdered red paprika samples. Relative root-mean-square error of prediction (RRMSEP) of 23% for both metals, arsenic and lead, were found in this study using 20 well characterized samples for calibration and 13 additional samples as validation set. Results derived from this study showed that NIR diffuse reflectance spectroscopy combined with the appropriate chemometric tools could be considered as an useful screening tool for a rapid determination of As and Pb at concentration level of the order of hundred μg kg⁻¹.     

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%     

Feasibility and extension of universal quantitative models for moisture content determination in beta-lactam powder injections by near-infrared spectroscopy

In present work, we investigated the feasibility of universal calibration models for moisture content determination of a much complicated products system of powder injections to simulate the process of building universal models for drug preparations with same INN (International Nonproprietary Name) from diverse formulations and sources. We also extended the applicability of universal model by model updating and calibration transfer. Firstly, a moisture content quantitative model for ceftriaxone sodium for injection was developed, the results show that calibration model established for products of some manufacturers is also available for the products of others. Then, we further constructed a multiplex calibration model for seven cephalosporins for injection ranging from 0.40 to 9.90%, yielding RMSECV and RMSEP of 0.283 and 0.261, respectively. However, this multiplex model could not predict samples of another cephalosporin (ceftezole sodium) and one penicillins (penicillin G procaine) for injection accurately. With regard to such limits and the extension of universal models, two solutions are proposed: model updating (MU) and calibration transfer. Overall, model updating is a robust method for the analytical problem under consideration. When timely model updating is impractical, piecewise direct standardization (PDS) algorithm is more desirable and applied to transfer calibration model between different powder injections. Both two solutions have proven to be effective to extend the applicability of original universal models for the new products emerging.     

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.     

An approach by using near-infrared diffuse reflectance spectroscopy and resin adsorption for the determination of copper, cobalt and nickel ions in dilute solution

Near-infrared spectroscopy (NIRS) has been proved to be a powerful analytical tool and used in various fields, it is seldom, however, used in the analysis of metal ions in solutions. A method for quantitative determination of metal ions in solution is developed by using resin adsorption and near-infrared diffuse reflectance spectroscopy (NIRDRS). The method makes use of the resin adsorption for gathering the analytes from a dilute solution, and then NIRDRS of the adsorbate is measured. Because both the information of the metal ions and their interaction with the functional group of resin can be reflected in the spectrum, quantitative determination is achieved by using multivariate calibration technique. Taking copper (Cu²⁺), cobalt (Co²⁺) and nickel (Ni²⁺) as the analyzing targets and D401 resin as the adsorbent, partial least squares (PLS) model is built from the NIRDRS of the adsorbates. The results show that the concentrations that can be quantitatively detected are as low as 1.00, 1.98 and 1.00 mg L⁻¹ for Cu²⁺, Co²⁺ and Ni²⁺, respectively, and the coexistent ions do not influence the determination.     

Comparison between different data pre-treatment methods in the analysis of forage samples using near-infrared diffuse reflectance spectroscopy and partial least-squares multivariate calibration method

Moisture and protein content of alfalfa samples from Catalonia (Spain) have been analyzed by near-infrared (NIR) diffuse reflectance spectroscopy and multivariate calibration methods. In order to remove systematic variation in experimental data, such as base-line and multiplicative scatter effects, the evaluation of different data pre-processing methods is performed. Different figures of merit are used for quality assessment and comparison of these pre-treatment methods.     

Development and validation of a method for active drug identification and content determination of ranitidine in pharmaceutical products using near-infrared reflectance spectroscopy: a parametric release approach

In this paper we describe the strategy used in the development and validation of a near-infrared diffuse reflectance spectroscopy method for identification and quantification of ranitidine in pharmaceutical products (granulates, cores and coated tablets) at-line, with a fiber optic probe. This method was developed in a pharmaceutical industry for routine application, to replace reference methods and was submitted and approved to the National Medicine Regulatory Agency (Infarmed). We consider that this is the first step of a broader parametric release approach to pharmaceutical products.     

The determination of combined vinyl acetate in polyvinyl chloride/polyvinyl acetate copolymer by near-infrared photo-acoustic spectrometry and diffuse reflectance spectrometry

The determination of combined acetate (0–15% w/w) in vinyl chloride/vinyl acetate copolymer by near-infrared photo-acoustic spectrometry is described. The results are compared with those obtained by the complementary technique of near-infrared diffuse reflectance spectrometry. Control of particle size is shown to be essential for both techniques.     

Studying the uptake of aniline vapor by active alumina through in-line monitoring a differential adsorption bed with near-infrared diffuse reflectance spectroscopy

Through non-invasive monitoring the uptake of aniline vapor by active alumina in a differential adsorption bed (DAB) with near-infrared diffuse reflectance spectroscopy (NIR-DRS), we have studied several features of the adsorption, including isotherm, kinetics and the chemical state of aniline molecules in aniline-alumina system. What is perhaps more important, since the information above is obtained synchronously, the proposed methodology could provide information about the type of adsorption (chemical or physical adsorption), the change of chemical state of aniline in the system during the adsorption process, whether the chemical adsorption and physical adsorption took place simultaneously, the rate of the chemical and physical adsorption, and so on.     

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited.The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.     

Near-infrared diffuse reflectance spectroscopy with sample spots and chemometrics for fast determination of bovine serum albumin in micro-volume samples

Near-infrared diffuse reflectance spectroscopy(NIRDRS) has attracted more and more attention in analyzing the components in samples with complex matrices.However,to apply this technique to micro-analysis,there are still some obstacles to overcome such as the low sensitivity and spectral overlapping associated with this approach.A method for fast determination of bovine serum albumin (BSA) in micro-volume samples was studied using NIRDRS with sample spots and chemometric techniques.10μL of sample spotted on a filter paper substrate was used for the spectral measurements. Quantitative analysis was obtained by partial least squares(PLS) regression with signal processing and variable selection.The results show that the correlation coefficient(R) between the predicted and the reference concentration is 0.9897 and the recoveries are in the range of 87.4%-114.4%for the validation samples in the concentration range of 0.61-8.10 mg/mL.These results suggest that the method has the potential to quickly measure proteins in micro-volume solutions.     

A simple and green analytical method for determination of glyphosate in commercial formulations and water by diffuse reflectance spectroscopy

This article describes a simple, inexpensive, and environmentally friendly method for the monitoring of glyphosate using diffuse reflectance spectroscopy. The proposed method is based on reflectance measurements of the colored compound produced from the spot test reaction between glyphosate and p-dimethylaminocinnamaldehyde (p-DAC) in acid medium, using a filter paper as solid support. Experimental designs were used to optimize the analytical conditions. All reflectance measurements were carried out at 495 nm. Under optimal conditions, the glyphosate calibration graphs obtained by plotting the optical density of the reflectance signal (AR) against the concentration were linear in the range 50-500 μg mL(-1), with a correlation coefficient of 0.9987. The limit of detection (LOD) for glyphosate was 7.28 μg mL(-1). The technique was successfully applied to the direct determination of glyphosate in commercial formulations, as well as in water samples (river water, pure water and mineral drinking water) after a previous clean-up or pre-concentration step. Recoveries were in the ranges 93.2-102.6% and 91.3-102.9% for the commercial formulations and water samples, respectively.     

Use of near-infrared reflectance spectroscopy for shelf-life discrimination of green asparagus stored in a cool room under controlled atmosphere

This study sought to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to classify intact green asparagus, in refrigerated storage under controlled atmosphere, by storage time and post-harvest treatments applied. A total of 468 green asparagus (Asparagus officinalis, L., cultivar UC-157) were sampled after 7, 14, 21 and 28 days of refrigerated storage (2 °C, 95% R.H.) under three controlled atmosphere (CA) treatments: air (21 kPa O₂ + 0.3 kPa CO₂), CA₁ (5 kPa O₂ + 5 kPa CO₂) and CA₂ (10 kPa O₂ + 10 kPa CO₂). Two commercially available spectrophotometers were evaluated for this purpose: a scanning monochromator (SM) of 400-2500 nm and a combination of diode array and scanning monochromator (DASM) of 350-2500 nm. Models developed using partial least squares 2-discriminant analysis (PLS2-DA) correctly classified between 81-100% of samples by post-harvest storage time, depending on the instrument used. Using similar models, the DASM instrument correctly classified 85% of samples by post-harvest treatment, compared with 72% using the SM. These results confirmed that NIR spectroscopy, coupled with the use of chemometric techniques, provides a reliable, accurate method of predicting the shelf-life of asparagus under different storage conditions and as a function of post-harvest treatment applied; the method can be readily applied at industrial level.     

Improved detection limit for ammonium/ammonia achieved by Berthelot's reaction by use of solid-phase extraction coupled to diffuse reflectance spectroscopy

The proposed procedure is based on the extraction of the indothylmol blue into C₁₈ solid-phase extraction (SPE) membranes and direct quantification on the membrane surface by diffuse reflectance spectroscopy. The analytical performance of the proposed method has been evaluated for standard solutions of ammonium using reflectance values, R, as well as the Kubelka-Munk function, F(R). The results have been compared with those obtained by the conventional method, which uses UV-vis absorption spectroscopy with a sensor-based method. The described methodology provided satisfactory linearity and reproducibility within the ammonium concentration intervals 25-250 μg L⁻¹ and 25-500 μg L⁻¹ when using R and F(R), respectively. The limit of detection was around 10 μg L⁻¹, which is markedly lower than that of the classical procedure and than those provided by Nessler and OPA/thiol fluorimetric methods. For air samples the linear interval expressed as μg of ammonia is 0.24-2.4 or 0.24-4.7 employing R or F(R), respectively. The effect of potential interferences such as metals and aliphatic amines has also been evaluated. Finally, the proposed methodology has been adapted to the determination of ammonia in air and water samples. The method can be also used as a detector support for visual estimation.     

Near-infrared spectroscopy (NIRS) reflectance technology for the determination of tocopherols in alfalfa

The vitamin E (α- and (β+γ)-tocopherol) contents present in alfalfa (fresh or dehydrated) were analysed using near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. The range of vitamin E was 0.55–5.16 mg/100 g for α-tocopherol and 0.07–0.48 for (β+γ)-tocopherol. The regression method employed was modified partial least squares (MPLS). The equations developed using the fibre-optic probe for 69 samples of alfalfa (dehydrated and fresh) to determine the content of vitamin E in feeds had multiple correlation coefficients (RSQs) and prediction corrected standard errors (SEP (C)) of 0.946 and 0.321 mg/100 g for α-tocopherol and 0.956 and 0.022 mg/100 g for (β+γ)-tocopherol. The predicted values of vitamin E in feeds using NIRS technology applying the fibre-optic probe directly on the sample with neither previous treatment nor manipulation are comparable to those obtained using the chemical method, which included alkaline hydrolysis and hexane extraction of the vitamin from the unsaponifiable fraction before chromatographic determination.     

A method for determination of COD in a domestic wastewater treatment plant by using near-infrared reflectance spectrometry of seston

This paper proposes a method for determination of chemical oxygen demand (COD) in domestic wastewater. The proposed method is based on near-infrared reflectance (NIRR) measurements of seston collected from wastewater samples by filtration. The analysis does not require any special reagent, catalyst or solvent. Inherent baseline and noise features present in NIRR spectra are removed by a Savitzky-Golay derivative procedure followed by wavelet denoising. The resulting wavelet approximation coefficients are used for partial-least-squares modelling and subsequent prediction of COD values in new samples. The model is calibrated by using COD values obtained according to the American Public Health Association (APHA) reference method. The proposed method is applied to effluent samples from the anaerobic ponds of the Mangabeira municipal wastewater treatment plant in the city of João Pessoa (Paraíba, Brazil). By comparing the NIRR prediction results with the APHA reference values, a root-mean-square error of prediction (RMSEP) of 19 mg O₂ L⁻¹ and a correlation of 0.97 were obtained. Such results are deemed adequate in view of the joint estimate of the standard error of the reference method, which was calculated as 21 mg O₂ L⁻¹.