Fast determination of milk fat content using Raman spectroscopy

In our work, we have demonstrated the capability of VIS Raman spectroscopy in combination with partial least square regression (PLS) as a rapid technique for direct milk fat determination. Raman spectra of milk samples revealed contributions from proteins, but mainly from their fat content with different spectral characteristics. Three different methods of sample preparations were applied: (i) liquid milk contained in an open dish, (ii) dried milk droplets on glass plates covered with Al foil, and (iii) liquid milk contained in quartz cuvettes. Methods (i) and (ii) showed a good PLS model for milk fat prediction with low root mean square errors and high correlation coefficients. The main advantage of milk sample contained in the dish lies in its simplicity as well as the fact that the open container maximizes the signal of interest avoiding background contributions. Our results show that Raman spectroscopy is suited for in-line monitoring purposes.     

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

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

基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。     

Raman spectroscopic quantification of milk powder constituents

Raman spectroscopy has significant potential for the quantification of food products. Milk powder is an important foodstuff and ingredient that is produced on large scale (over 20 million tonnes per annum). Raman spectroscopy, unlike near- and mid-infrared spectroscopies, has not been used extensively to quantify milk powder constituents. The effect of sample presentation on spectroscopic calibrations of protein and fat for 136 New Zealand milk powders was assessed using Raman spectroscopy. Prediction models were produced to quantify a protein concentration range of 32.19-37.65% w/w for skim milk powder, and a protein concentration range of 23.34-25.02% w/w and a fat concentration range of 26.26-29.68% w/w for whole milk powder (where ratios of prediction to deviation exceeded 2.6 with one exception). The resultant calibrations were not influenced by sample orientation; the sample temperature during data collection did affect the calibrations. Calcium fortification in the form of calcium carbonate was identified within a sub-set of samples, reinforcing the efficacy of Raman spectroscopy for identifying both crystalline and non-crystalline constituents within milk powder.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

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 total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.     

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular—for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76 ± 0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm—partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)—are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis.     

表面增强拉曼光谱法同时检测奶粉中三聚氰胺和二聚氰胺

本文建立了奶粉中同时检测三聚氰胺及二聚氰胺的表面增强拉曼光谱法.奶粉样品经15％三氯乙酸溶液提取,中性氧化铝吸附杂质后进行拉曼光谱检测.三聚氰胺线性范围为0.0050～0.075 mg/L,检出限为0.0015 mg/L,回收率在79.5％～124％之间,相对标准偏差小于8.8％(n=5);二聚氰胺线性范围为0.50～l0mg/L,检出限为0.15 mg/L,回收率在76.5％～112％之间,相对标准偏差小于9.4％(n=5).该方法相比常规表面增强拉曼光谱法,仅通过一种样品前处理手段即可对奶粉中的三聚氰胺或二聚氰胺进行定性检出及定量分析,相比色谱等检测方法具有样品前处理过程简单、耗时短等优点,在奶粉质量监控方面具有良好的应用前景.     

Determination of amylose content in starch using Raman spectroscopy and multivariate calibration analysis

Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm^?1, assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

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.     

Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection

This paper proposes a methodology for the classification and determination of total protein in milk powder using near infrared reflectance spectrometry (NIRS) and variable selection. Two brands of milk powder were acquired from three Brazilian cities (Natal-RN, Salvador-BA and Rio de Janeiro-RJ). The protein content of 38 samples was determined by the Kjeldahl method and NIRS analysis. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations were used to predict the total protein. Soft independent modeling of class analogy (SIMCA) was also used for full-spectrum classification, resulting in almost 100% classification accuracy, regardless of the significance level adopted for the F-test. Using this strategy, it was feasible to classify powder milk rapidly and nondestructively without the need for various analytical determinations. Concerning the multivariate calibration models, the results show that PCR, PLS and MLR-SPA models are good for predicting total protein in powder milk; the respective root mean square errors of prediction (RMSEP) were 0.28 (PCR), 0.25 (PLS), 0.11 wt% (MLR-SPA) with an average sample protein content of 8.1 wt%. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for the determination of total protein in milk powder.     

拉曼光谱同时测定乙醇与葡萄糖的方法研究

以共焦拉曼光谱技术为基础,结合96孔板,对比利用一元线性回归法、多元线性回归法、主成分回归法和偏最小二乘法建立定量分析模型,探索同时定量检测乙醇和葡萄糖的快捷方法。对同一组乙醇和葡萄糖混合标准溶液进行定量检测,并用木薯淀粉发酵液进一步验证。结果表明,偏最小二乘法定量结果的平均标准误差(SE)为0.179,平均相对标准偏差(RSD)为0.029,结合二阶导数后分别降至0.106和0.021,说明偏最小二乘法具有良好的紧密度和稳定性;t检验表明,在置信度为95%的条件下,实测值与标准值间不存在显著性差异。研究表明基于拉曼光谱技术的偏最小二乘回归定量方法,能满足实验和生产对乙醇和葡萄糖检测精度的要求,可用于乙醇和葡萄糖相关成分的同时快速检测。     

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.     

应用表面增强拉曼光谱技术快速检测尿样中的β-兴奋剂

应用表面增强拉曼光谱技术与化学计量法相结合分析克伦特罗、沙丁胺醇和莱克多巴胺3种β-兴奋剂的标准溶液.在取自10头猪的尿样中,分别添加5个不同浓度的莱克多巴胺(1～20 mg/L),采用快速的液液萃取法对样品进行前处理,再进行表面增强拉曼测试.结果表明,克伦特罗和沙丁胺醇标准溶液的最低检测浓度为2 μg/L,莱克多巴胺标准溶液的最低检测浓度为0.1 mg/L;通过偏最小二乘法建立模型进行定量分析,3种药物的实际值与预测值的相关系数(R2)为0.9134～0.9368;本方法可检测尿样中1 mg/L莱克多巴胺,经外部验证后模型的实际值与预测值的相关系数(R2)为0 881,相对分析误差(RPD)为2.83;分析尿液中的莱克多巴胺含量所需时间小于30 min,为快速检测莱克多巴胺提供新途径.     

Spreeta-based biosensor immunoassays to detect fraudulent adulteration in milk and milk powder

Biacore biosensors (Biacore AB, Uppsala, Sweden) have proven to be robust analytical tools for the automated immunochemical detection of different adulterants and contaminants in milk and milk powder. However, the significant cost of the instruments is a disincentive for their wide application in food control laboratories. Therefore, a low-cost alternative optical biosensor (Spreeta, Texas Instruments, Attleboro, MA) was built into an affordable liquid handling system. Using this prototype biosensor, an inhibition immunoassay for bovine K-casein was evaluated for the detection of cow's milk in ewe's and goat's milk and for the detection of bovine rennet whey powder in milk powder. Comparable sensitivities were obtained for both adulterants in the Spreeta-based prototype biosensor and a Biacore 3000 instrument. The limit of detection for cow's milk was 0.17% (v/v) and bovine rennet whey powder could be detected in milk powder above 1% (w/w). The Spreeta sensor was also useful in the control of fraudulent water additions to milk, simply by measuring differences in the bulk response.     

Near-infrared spectroscopy quantitative determination of Pefloxacin mesylate concentration in pharmaceuticals by using partial least squares and principal component regression multivariate calibration

Pefloxacin mesylate, a broad-spectrum antibacterial fluoroquinolone, has been widely used in clinical practice. Therefore, it is very important to detect the concentration of Pefloxacin mesylate. In this research, the near-infrared spectroscopy (NIRS) has been applied to quantitatively analyze on 108 injection samples, which was divided into a calibration set containing 89 samples and a prediction set containing 19 samples randomly. In order to get a satisfying result, partial least square (PLS) regression and principal components regression (PCR) have been utilized to establish quantitative models. Also, the process of establishing the models, parameters of the models, and prediction results were discussed in detail. In the PLS regression, the values of the coefficient of determination (R²) and root mean square error of cross-validation (RMSECV) of PLS regression are 0.9263 and 0.00119, respectively. For comparison, though applying PCR method to get the values of R² and RMSECV we obtained are 0.9685 and 0.00108, respectively. And the values of the standard error of prediction set (SEP) of PLS and PCR models are 0.001480 and 0.001140. The result of the prediction set suggests that these two quantitative analysis models have excellent generalization ability and prediction precision. However, for this PFLX injection samples, the PCR quantitative analysis model achieved more accurate results than the PLS model. The experimental results showed that NIRS together with PCR method provide rapid and accurate quantitative analysis of PFLX injection samples. Moreover, this study supplied technical support for the further analysis of other injection samples in pharmaceuticals.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.