Visual detection of melamine in milk products by label-free gold nanoparticles

A simple, rapid, field-portable colorimetric method for the detection of melamine based on melamine-induced color change of label-free gold nanoparticles (Au NPs) was developed in this study. Melamine can induced the aggregation of Au NPs and results in the color change from wine-red to purple, which provided a platform for rapid and field-portable colorimetric detection of melamine. The proposed method can be used to detect melamine in liquid milk and infant formula with a detection limit of 1.0 and 4.2 ppm, respectively, within 30 min by naked eyes observation without the aid of any advanced instrument and the need of any complex pretreatment, and detect as low as 0.15 ppm of melamine in liquid milk and 2.5 ppm of melamine in infant formula with UV-vis-spectroscopy. The proposed method is promising for on-site screening of melamine adulterant in milk products.     

Molecularly imprinted polymer as SPE sorbent for selective extraction of melamine in dairy products

In this paper, a highly selective sample cleanup procedure combining molecular imprinting and solid-phase extraction (MI-SPE) was developed for the isolation of melamine in dairy products. The molecularly imprinted polymer (MIP) was prepared using melamine as the template molecule, methacrylic acid as the functional monomer and ethylene glycol dimethacrylate as the cross-linking monomer. The melamine imprinted polymer was used as selective sorbent for the solid-phase extraction of melamine from dairy products. An off-line MI-SPE method followed by high-performance liquid chromatography with diode-array detection for the detection of melamine was also established. The mean recoveries of melamine from ultra-heat treatment (UHT) milk and milk powders were 92.9-98.0% and 91.6-102.8%, respectively. Good linearity was obtained from 0.5 μM to 10 μM (r > 0.999) with a quantitation limit of 0.5 μmol/L (0.06 ppm) which was sufficient to analyse melamine at the maximum level permitted by U.S. Food and Drug Administration (1 ppm) in dairy products. It was demonstrated that the proposed MI-SPE-HPLC method could be applied to direct determination of melamine in dairy products.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares

Near-infrared (NIR) spectra in the region of 5000-4000 cm⁻¹ with a chemometric method called searching combination moving window partial least squares (SCMWPLS) were employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations. SCMWPLS is proposed to search for the optimized combinations of informative regions, which are spectral intervals, considered containing useful information for building partial least squares (PLS) models. The informative regions can easily be found by moving window partial least squares regression (MWPLSR) method. PLS calibration models using the regions obtained by SCMWPLS were developed for HSA, γ-globulin, and glucose. These models showed good prediction with the smallest root mean square error of predictions (RMSEP), the relatively small number of PLS factors, and the highest correlation coefficients among the results achieved by using whole region and MWPLSR methods. The RMSEP values of HSA, γ-globulin, and glucose yielded by SCMWPLS were 0.0303, 0.0327, and 0.0195 g/dl, respectively. These results prove that SCMWPLS can be successfully applied to determine simultaneously the concentrations of HSA, γ-globulin, and glucose in complicated biological fluids such as CS IIB solutions by using NIR spectroscopy.     

Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pretreatments and estimation of multivariate detection limits

Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600 ppm in an industrial ester. The detection limit obtained, round 100 ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes.     

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).     

Determination of the percentage of milk (cow's, ewe's and goat's) in cheeses with different ripening times using near infrared spectroscopy technology and a remote reflectance fibre-optic probe

In the present work we studied the use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the analysis of the percentage of milk (cow's, ewe's and goat's) used in the elaboration of cheeses with different ripening times. To do so, cheeses with known and varying percentages of cow's, ewe's and goat's milk were elaborated (112 samples with milk collected in winter and 112 samples with milk collected in summer) and used as reference material, and ripening controls were performed over 6 months. The method allows immediate control of the cheese without prior sample treatment or destruction by direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). Of all the samples (224), 200 formed to so-called calibration set and the other 24 were used for external validation. The calibration results obtained using 200 samples of cheese allowed the percentage of cow's, ewe's and goat's milk to be measured. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP(C)) obtained were respectively, 0.834 and 11.6% for cow's milk; 0.871 and 9.8% for goat's milk; 0.880 and 10.6% for ewe's milk. The ratio performance deviation (RPD) values obtained indicate that the NIRS equations can be applied to unknown samples.     

Calibration of somatic cell count in milk based on near-infrared spectroscopy

A PLS model for prediction of somatic cell count (SCC) based on near-infrared (NIR) spectra of unhomogenized milk is presented in the study. Samples of raw milk were collected from cows in the early lactation period (from 7th to 29th day after parturition). The NIR spectra were measured in the region 400–1100 nm. As reference method a fluoro-opto-electronic method was applied. Different preprocessing methods were investigated. The robust version of PLS regression was applied to handle outliers present in the dataset and the uninformative variable elimination–partial least squares (UVE–PLS) method was used to eliminate uninformative variables. The final model is acceptable for prediction of SCC in raw milk.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

亲水作用色谱-电喷雾串联质谱法检测原料奶及奶制品中的三聚氰胺

建立了亲水作用色谱-电喷雾串联质谱测定原料奶及奶制品中三聚氰胺的方法。样品采用1%三氯乙酸水溶液-乙腈（体积比为1∶1）混合溶液提取,混合型阳离子交换反相固相萃取柱（MCX）富集净化,亲水作用色谱柱分离,电喷雾串联四极杆质谱仪进行检测。结果表明,三聚氰胺的质量浓度在0.05～10.0 mg/L范围内具有良好的线性关系。原料奶及奶制品中的三聚氰胺在0.5,2.5和10 mg/kg 3个添加水平下,平均回收率为76.3%～98.7%,相对标准偏差均小于6.8%;定量限（S/N＞10）为0.05 mg/kg。     

MALDI-MS and multivariate analysis for the detection and quantification of different milk species

The extensive consumption of milk and dairy products makes these foodstuffs targets for potential adulteration with financial gains for unscrupulous producers. Such practices must be detected as these can impact negatively on product quality, labelling and even health. Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF-MS) is a potentially useful technique, with proven abilities in protein identification and more recently through the use of internal standards for quantification purposes of specific proteins or peptides. In the current work, we therefore aim to explore the accuracy and attributes of MALDI-ToF-MS with chemometrics for the detection and quantification of milk adulteration. Three binary mixtures containing cows' and goats', cows' and sheep's, and goats' and sheep's milk and a fourth tertiary mixture containing all types of milk were prepared and analysed directly using MALDI-ToF-MS. In these mixtures, the milk concentrations of each milk varied from 0% to 100% in 5% steps. Multivariate statistical methods including partial least squares (PLS) regression and non-linear Kernel PLS regression were employed for multivariate calibration and final interpretation of the results. The results for PLS and KPLS were encouraging with between 2% and 13% root mean squared error of prediction on independent data; KPLS slightly outperformed PLS. We believe that these results show that MALDI-ToF-MS has excellent potential for future use in the dairy industry as a rapid method of detection and enumeration in milk adulteration.     

Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers

The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1 mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.     

Potential of front face fluorescence associated to PLS regression to predict nutritional parameters in heat treated infant formula models

This work shows that front face fluorescence spectroscopy associated to partial least squares (PLS) calibration is a fast and simple method to assess the nutritional impact of heat treatment on milk samples.Emission spectra of tryptophan (Trp) and of advanced Maillard products (AMP) were recorded on intact milk samples non-heated and heated at seven temperatures (72 °C, 80 °C, 87 °C, 95 °C, 100 °C, 110 °C and 115 °C) for six different times (from 2 min to 9.5 min) by means of front face fluorescence. PLS calibrations were constructed in order to indirectly quantify three indicators: vitamin C, protein denaturation and accumulation of Maillard products using the fluorescence of advanced Maillard products and soluble tryptophan method (FAST). The prediction models allowed obtaining an estimation of these indicators with a relative error of 12% for vitamin C and about 18% for the FAST index and soluble whey protein ratio.     

Development of nanofibrillated cellulose coated with gold nanoparticles for measurement of melamine by SERS

The objective of this study was to develop nanofibrillated cellulose (NFC)-based substrate for rapid detection of melamine in milk by surface-enhanced Raman spectroscopy (SERS). NFC were served as a highly porous platform to load with gold nanoparticles (AuNPs), which can be used as a flexible SERS substrate with nanoscale roughness to generate strong electromagnetic field in SERS measurement. The NFC/AuNP substrate was characterized by UV–Vis spectroscopy and electron microscopy. Milk samples contaminated by different concentrations of melamine were measured by SERS coupled with NFC/AuNP substrate. The spectral data analysis was conducted by multivariate statistical analysis [i.e. partial least squares (PLS)]. Satisfactory PLS result for quantification of melamine in milk was obtained (R = 0.9464). The detection limit for melamine extracted from liquid milk by SERS is 1 ppm, which meets the World Health Organization’s requirement of melamine in liquid milk. These results demonstrate that NFC/AuNP substrate has improved homogeneity and can be used in SERS analysis for food safety applications.     

Monitoring of melamine contamination in fat watery milk by the photoluminescence analysis

A photoluminescence method to detect the toxic melamine contamination in fat watery milk has been proposed. Despite the quite different luminescence origins of milk and melamine patterns, their wide emission spectra under UV excitation are similar and in the range of 2.2-3.5 eV. The complex milk photoluminescence spectrum composed of riboflavin, furosine, lactulose, Vitamin E and tryptophan emitting species can be modified if milk pattern is undergone by acid treatment (for example, in vinegar). At the same time the melamine emission is not subjected to any modification in vinegar. It allows quantitatively discriminating the melamine contamination in milk in linear range, at least, 0.05-7 g/l from different photoluminescence spectra of milk (water) with and without melamine. Limit of melamine detection achieves 0.01 g/l.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

气相色谱-质谱联用法同时测定乳及乳制品中的三聚氰胺及肌酐

建立了同时测定乳制品中三聚氰胺及肌酐的气相色谱-质谱联用方法。样品经1%三氯乙酸溶液萃取,混合型阳离子交换固相萃取净化,提取液用氮气吹干后加入N,O-双(三甲基硅基)三氟乙酰胺-三甲基氯硅烷(BSTFA-TMCS)硅烷化试剂,于75 ℃下衍生60 min,最后采用选择离子模式下的气相色谱-质谱测定。三聚氰胺和肌酐的定量限分别为0.10 mg/kg和0.20 mg/kg;在0.1～50 mg/L范围内的线性相关系数均大于0.99。实际样品中,肌酐在10～100 mg/kg和三聚氰胺在0.1～5.0 mg/kg添加范围内的回收率分别为80.7%～116.8%和77.6%～107.5%,相对标准偏差分别小于9.4%和8.5%。该方法能有效除去干扰,灵敏度高,回收率较好,可用于乳制品中三聚氰胺和肌酐的同时测定。     

Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

Textile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.     

Selective determination of melamine in milk samples using 3-mercapto-1-propanesulfonate-modified gold nanoparticles as colorimetric probe

A novel and sensitive colorimetric method for determination of melamine in milk samples was developed by a 3-mercapto-1-propanesulfonate-modified gold nanoparticles (MPS-GNPs) probe. Melamine molecule has multiple -NH₂ groups. These functional groups can interact with MPS to form strong hydrogen bonding and induce the aggregation of the MPS-GNPs, resulting in a dramatic color change from red to blue. Therefore, the concentration of melamine in milk samples can be quantitatively detected by the naked eyes or a UV-vis spectrometer. Moreover, investigations have revealed that the sensitivity of the detection could be clearly improved by adding NaCl to the modified GNPs solution, which leads to a more rapid color change in the NaCl-optimized GNPs system. It is worth noting that the absorption ratio (A₆₅₀/A₅₂₀) of the modified GNPs in the NaCl-optimized system exhibited a linear correlation with melamine concentration and the limit of detection is 8 nM, well below the safety limit (1 ppm for infant formula in China).     

Determination of melamine residues in milk products by zirconia hollow fiber sorptive microextraction and gas chromatography–mass spectrometry

News stories about the contamination of milk with melamine in China emerged on Sept 11, 2008, and the situation has since become an international health scare. In this work, a novel analytical method based on enrichment and pretreatment of analytes in the milk sample, hollow fiber sorptive extraction and gas chromatography–mass spectrometry has been developed for the rapid analysis of melamine in the dairy products. In the proposed method, melamine in the fresh milk was extracted by zirconia hollow fiber, enriching on zirconia coating of the hollow fiber, and then analyzed by GC–MS. The method validations including linearity, limit of detection, limit of qualification, recoveries at three different concentrations, precision, and repeatability were investigated. It was found that the proposed method provided linear range from 0.001 to 1000 μg/mL (r² = 0.9997), low detection limit of 0.001 μg/mL, and preferable recoveries at three different concentrations. The obtained results demonstrated that zirconia hollow fiber combined with GC–MS is a simple, rapid and solvent-free method for the analysis of melamine in the dairy products.