Evaluation of nutritional parameters in infant formulas and powdered milk by Raman spectroscopy

It has been made a critical evaluation of the application of near infrared Fourier transform-Raman spectroscopy for the simultaneous determination of the most important nutritional parameters such as energetic value, carbohydrate, protein and fat contents of infant formula and powdered milk samples based on the use of partial least squares (PLS) regression analysis. A highly heterogeneous population of 23 samples, covering a wide range of infant food formula and powdered milk, were obtained from the Spanish market. Raman spectra, obtained by excitation with a Nd:YAG laser at 1064 nm, show no disturbing fluorescence effects; therefore sample spectra can be recorded without any previous preparation step. After correcting the spectra, hierarchical cluster analysis was performed in order to select a representative calibration set and the corresponding validation sample set. Different PLS models and several spectral windows were tested in order to evaluate their prediction capabilities for the validation set. Considering a calibration set comprised of three replicate spectra of 15 samples and a validation data set of eight samples, the procedure developed provided figures of merit which complied with the statutory values declared by the United States Food and Drug Administration (US FDA).     

Membrane sampling with microdialysis coupled to HPLC/UV for on-line simultaneous determination of melamine and cyanuric acid in non-dairy coffee creamer

An on-line microdialysis/high-performance liquid chromatography method was developed for the simultaneous determination of melamine and cyanuric acid in non-dairy coffee creamer. To collect these analytes from aqueous samples, the microdialysis system featured a microdialysis probe incorporating a polyarylethersulfone membrane and employed 0.05 M HCl in 0.1% (v/v) MeOH as the perfusate, with optimal efficiency obtained at a flow rate of 1 μL min(-1). The chromatographic conditions were optimized when using a reverse-phase phenyl column and a mobile phase of phosphate buffer solution in 10% (v/v) MeOH, buffered at pH 3.0. Good linearity relationship (r(2) > 0.9987), intra- and inter-day precisions (RSDs < 6.6%), recoveries (96.9 - 105.0%), and limits of detection (melamine, 3 ppb; cyanuric acid, 150 ppb) were observed for the two analytes. This method has been successfully applied to simultaneous determination of melamine and cyanuric acid in commercial creamers with the recoveries in the range of 97.5 to 102.6%.     

FTIR imaging coupled with multivariate analysis for study of initial diffusion of different solvents in cellulose acetate butyrate films

Fourier transform infrared (FTIR) spectroscopic imaging was used to study the initial diffusion of different solvents in cellulose acetate butyrate (CAB) films containing different amounts of acetyl and butyryl substituents. Different solvents and solvent/non-solvent mixtures were also studied. The FTIR imaging system allowed acquisition of sequential images of the CAB films as solvent penetration proceeded without disturbing the system. The interface between the non-swollen polymer and the initial swelling front could be identified using multivariate data analysis tools. For a series of ketone solvents the initial diffusion coefficients and diffusion rates could be quantified and were found to be related to the polar and hydrogen interaction parameters in the Hansen solubility parameters of the solvents. For the solvent/non-solvent system the initial diffusion rate decreased less than linearly with the weight-percent of non-solvent present in the solution, which probably was due to the swelling characteristic of the non-solvent. For a given solvent, increasing the butyryl content of the CAB increased the initial diffusion rate. Increasing the butyryl content from 17 wt.% butyryl to 37 wt.% butyryl produced a considerably larger increase in initial diffusion rate compared to an increase in butyryl content from 37 wt.% to 50 wt.% butyryl.     

利用探针分子红外光谱识别和表征铂族金属基单原子催化剂

单原子催化是提高贵金属利用率的有效手段,而表征单原子催化剂是理解单原子催化的基础.探针分子红外光谱可用于识别和定量催化剂样品中孤立的Pt族金属物种的浓度,从而得到负载的孤立的Pt族金属物种的局部几何形状、稳定性、活性及其分散性.本文讨论了该技术用于识别和表征含负载型孤立的Pt族金属原子催化剂的效能、应用、以及未来的发展方向.     

Detection of melamine in milk by surface-enhanced Raman spectroscopy coupled with magnetic and Raman-labeled nanoparticles

A new method based on surface-enhanced Raman spectroscopy (SERS) has been developed for sensitive and rapid detection of melamine. Spherical magnetic-core gold-shell nanoparticles (AuNPs) and rod-shaped gold nanoparticles (nanorods) labeled with a Raman-active compound were used to form a complex with the melamine molecules. 5,5'-Dithiobis(2-nitrobenzoic acid) was used as Raman-active compound because it is readily adsorbed by a gold nanoparticle surface forming a self-assembled monolayer (SAM) and has strong Raman scattering at 1330 cm(-1), because of the symmetric NO(2) stretch. The calibration curve was obtained by plotting Raman band area at 1330 cm(-1) against melamine concentration. A linear relationship was obtained with a high determination coefficient (R(2)=0.997). The method was validated for linearity, sensitivity, precision (intra-day and inter-day repeatability), and recovery. In the model system, the limits of detection (LOD) and quantification (LOQ) were 0.38 and 1.27 mg L(-1), respectively. For melamine-spiked milk samples, LOD and LOQ values were 0.39 mg L(-1) and 1.30 mg L(-1), respectively. Intra and inter-day precision were 3.73 and 4.94 %, respectively. This method was applied to samples of skimmed milk that had been spiked with melamine at different concentrations. The recovery of the method was 95-109 % in the concentration range 2-15 mg L(-1), and average RSD was 1.71 %. Total analysis time was less than 15 min.     

亲水作用色谱-串联质谱法同时测定液态奶中三聚氰酸和三聚氰胺

建立了亲水作用色谱-串联质谱同时测定液态奶中三聚氰酸和三聚氰胺的方法。液态奶样品经体积分数2.5%甲酸溶液提取、离心后乙腈稀释,亲水作用色谱柱分离,电喷雾串联四极杆质谱检测器检测,分别在负、正离子模式下测定三聚氰酸和三聚氰胺。三聚氰酸和三聚氰胺分别在0.5～100μg/L、0.1～50μg/L范围内线性关系良好。在0.25～15mg/kg、0.1～7.5mg/kg添加水平范围内,三聚氰酸平均回收率为84.5%～98.0%(RSD为2.1%～6.1%),三聚氰胺平均回收率为85.5%～88.9%(RSD为3.2～5.8%)。三聚氰酸、三聚氰胺定量限分别为0.25mg/kg、0.1mg/kg。     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

FT-MIR and Raman spectroscopy coupled to multivariate analysis for the detection of clenbuterol in murine model

A fast and simple screening method for the determination of clenbuterol at the ppb level in a murine model was demonstrated by Mid Infrared (MIR) and Raman spectroscopy in conjunction with multivariate analysis. In order to build the calibration models to quantify clenbuterol in rat meat, mixtures of rat meat and clenbuterol were prepared in a range of 5-10,000 ppb. Partial Least Square (PLS) analysis was used to build the calibration model. The results shown that Mid Infrared and Raman spectroscopy were efficient, but Mid Infrared (R(2) = 0.966 and SEC = 0.27) were superior to Raman (R(2) = 0.914 and SEC = 1.167). The SIMCA model developed showed 100% classification rate of rat meat samples with or without clenbuterol. The results were confirmed with contaminated meat samples from animals treated with clenbuterol. Chemometric models represent an attractive option for meat quality screening without sample pretreatments which can identify veterinary medicinal products at the ppb level.     

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV-visible spectroscopy and multivariate classification techniques

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L⁻¹. Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV.Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.     

Second-order and higher-order multivariate calibration methods applied to non-multilinear data using different algorithms

We discuss and evaluate the current state of second-order and higher-order multivariate calibration methods devoted to the determination of compounds in non-multilinear data systems. We examine possible causes of multilinearity deviations:

(1)

a non-linear relationship between signal and analyte concentration;

(2)

a signal for a given sample that is non-multilinear; and,

(3)

component profiles that are not constant across the different samples.

We discuss the advantages and the limitations of the algorithms available to cope with these different situations.The review covers relevant analytical problems found in samples of environmental and biological interest, highlighting some significant examples, and evaluating the advantages and the limitations of the different algorithms available.     

Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection

An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).     

A new approach to characterization of insulin derived from different species using 1H-NMR coupled with multivariate analysis

Most of the active components of polypeptides have a complex molecular structure, large molecular size. Such components may also be structurally heterogeneous. Therefore, development of a method that can confirm the consistency of polypeptides amino-acid sequences for product characterization is desirable. In general, it is extremely difficult to distinguish differences of a few amino acid residues in the 1H-NMR spectrum of polypeptides with molecular weights greater than several thousand. However, we have been able to distinguish between three insulin species differing in one to three amino acid residues using a combination of multivariate statistics and 1H-NMR spectra. These results demonstrate that this methodology could be useful for characterization of polypeptides.     

Discriminating five Polygonatum medical materials and monitoring their chemical changes associated with traditional process by FT-IR spectroscopy coupled with multivariate analysis

Several Polygonatum species are important medicinal materials as tonic to cure disorders in China. Because of their different medical effects, it is desired to distinguish them at species level. In addition, to ensure and control their medical quality, it is also important to monitor their chemical changes associated with traditional process. Taking the advantages of Fourier transform infrared spectroscopy (FT-IR) and multivariate analysis, we developed a convenient, fast and reliable approach to discriminate and quality control these materials. Despite similar absorption patterns, each species also presented spectral differences, especially on the FT-IR fingerprint range of 1800-600 cm⁻¹. Second derivative method obviously enlarged those differences and then showed more species-specific features. These spectral differences could be used as powerful discriminating points to distinguish them. PCA results showed that each species separated clearly with their biological replicates grouped together, which indicated that the variance between species is greater than within species, therefore, these species could be distinguishable. Using this approach, the five herbal materials were discriminated successfully in their raw, processed and ethanol extracted formats. On the other hand, visual inspecting infrared spectra of samples from 1 to 9 process steps, absorbance near 1737, 1259, 817 and 780 cm^-1 increased gradually but decreased gradually at 927 cm⁻¹. Besides, spectral contour near 1050 cm⁻¹ changed sharply with process treatment. These spectral changes indicated that hydrolyzing polysaccharides into oligo- and mono-saccharides, especially glucose and fructose, are the main chemical changes associated with traditional process. This is consistent with the traditional experience that the processed materials are dark as night and sweet as malt sugar. Meanwhile, our results also indicated that their chemical constituents changed profoundly after process, which might be the chemical basis for raw and processed materials have different medical effects. Based on absorbance at 817 and 780 cm⁻¹ and the color, taste, smell of processed materials followed by energy efficacy, raw materials had to be processed more than 21 h to ensure their quality. This research shows the potential of FT-IR spectroscopy coupled with multivariate analysis to discriminate different herbs and to monitor chemical changes with process and then control their quality. This could be very helpful to ensure the quality, safety, and efficacy of herbs on clinical practices.     

Ubiquinol formation in isolated photosynthetic reaction centres monitored by time-resolved differential FTIR in combination with 2D correlation spectroscopy and multivariate curve resolution

Two-dimensional correlation analysis was carried out in combination with multivariate curve resolution–alternating least squares (MCR-ALS) to analyse time-resolved infrared (IR) difference spectra probing photo-induced ubiquinol formation in detergent-isolated reaction centres from Rhodobacter sphaeroides. The dynamic 2D IR correlation spectra have not only allowed the determination of the concomitance or non-concomitance of different chemical events through known marker bands but also have helped identify new vibrational bands related to the complex series of photochemical and redox reactions. In particular, a strong positive band located at 1565 cm⁻¹ was found to be synchronous with the process of ubiquinol formation. In addition, a tailored MCR-ALS analysis was performed using a priori chemical knowledge of the system, in particular including the pure spectrum of one species obtained from an external measurement. Enhancing the MCR-ALS performance in this way in time-dependent processes is relevant, especially when other essential pieces of information, such as kinetic models, are unavailable. The results give evidence of four independent spectral contributions. Three of them show marker bands for a monoelectronic reduction of the primary quinone Q_A (Q_A⁻/Q_A transition, first contribution), for a monoelectronic reduction of a secondary quinone Q_B (Q_B⁻/Q_B transition, second contribution) and for ubiquinol formation (third contribution). The results obtained also confirm that a key rate-limiting factor is the slow ubiquinone and ubiquinol exchange among micelles, which strongly influences the kinetic profiles of the involved species.     

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.     

The determination of the fatty acid content of sea buckthorn seed oil using near infrared spectroscopy and variable selection methods for multivariate calibration

This study proposes an analytical method for the simultaneous near infrared (NIR) spectrometric determination of palmitic, oleic, linoleic and linolenic acids in sea buckthorn seed oil. For this purpose, four different combinations of multivariate calibration methods and variable selections were evaluated: partial least squares (PLS) with full spectrum; PLS with uninformative variables elimination (UVE); PLS with competitive adaptive reweighted sampling (CARS); and multiple linear regression (MLR) with uninformative variable elimination combined with successive projections algorithm (UVE-SPA). An independent set of samples was employed to evaluate the performance of the resulting models. The UVE-SPA-MLR model developed with a few spectral variables provided the best results for each parameter. The values of relative errors of prediction (REP) from the UVE-SPA-MLR model for palmitic, oleic, linoleic and linolenic acids are 1.77%, 1.20%, 1.02% and 1.40%, respectively. These results indicate that this method is a feasible and fast method for the determination of the fatty acid content of sea buckthorn seed oil.     

Rapid assessment of the impact of microwave heating coupled with UV-C radiation on the degradation of PAHs from contaminated soil using FTIR and multivariate analysis

The presence and fate of polyaromatic hydrocarbons (PAHs) in the environment are receiving a great concern. In this study, three oil-contaminated soils (industrial area, Dukhan city, and artificial soils) were utilized to examine the effect of microwave (MW) heating and UV-C irradiation on the PAHs degradation. A rapid assessment of the impact was evaluated using Fourier transform infrared (FTIR) and multivariate analysis. The total organic matter values for the maximum PAHs reduction were evaluated based on the FTIR spectra of the contaminated soils followed with the principal component analysis (PCA). The results showed that the highest total organic carbon reduction was achieved for the industrial soil sample that required a high MW power and long MW exposure time. On the other hand, the Dukhan city soil sample, which has the lowest total organic carbon, required a high MW power and short MW exposure time followed by UV-C treatment for 20 min to reach the maximal FTIR transmittance reduction. The cluster analysis was also used to evaluate the impact of MW heating, and MW heating followed by UV-C irradiation on the degradation of PAHs. The PCA results of the industrial city sample showed that neither MW treatment (100% MW, 15 min exposure time) followed by UV-C treatment for 20 min nor 10 min is significantly different from the MW treatment (100% MW, 15 min exposure time). However, for the Dukhan sample, the UV-C treatment at 10 min after high MW power and long exposure time (100% MW, 15 min exposure time) was the most efficient treatment.     

Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.