Guimei Dong

Synchronous–asynchronous two-dimensional correlation spectroscopy for the discrimination of adulterated milk

A novel approach for discriminant analysis of adulterated milk is proposed using synchronous–asynchronous two-dimensional (2D) correlation spectroscopy and multi-way partial least squares discriminant analysis (NPLS-DA). The NIR transmittance spectra of pure milk and adulterated milk with a level of urea varying from 0.1 to 3 g L−1 were collected at room temperature. The synchronous and asynchronous 2D NIR (4200–4800 cm−1) correlation spectra of all samples were calculated and normalized. A new synchronous–asynchronous 2D correlation matrix was obtained by computing the sum of the upper triangular part of the normalized synchronous matrix and the strictly lower triangular part of the normalized asynchronous matrix. This new matrix preserves information contained and eliminates redundancy in synchronous and asynchronous 2D correlation matrices. Synchronous–asynchronous 2D correlation matrices of all samples were used to construct a discriminant model to classify adulterated milk and pure milk. For comparison, the NPLS-DA models were built based on the normalized synchronous and asynchronous 2D correlation spectra, respectively. Comparison results showed that the NPLS-DA model could provide better results using the synchronous–asynchronous 2D correlation spectra as compared to using synchronous or asynchronous 2D correlation spectra.

Two-dimensional hetero-spectral mid-infrared and near-infrared correlation spectroscopy for discrimination adulterated milk.

A new approach for discriminant analysis of adulterated milk is proposed based on two-dimensional (2D) hetero-spectral near-infrared (NIR) and mid-infrared (IR) correlation spectroscopy along with multi-way partial least squares discriminant analysis (NPLS-DA). NIR transmittance spectra and IR attenuated total reflection spectra of pure milk and adulterated milk with level of melamine varying from 0.03 to 3 g·L(-1) were collected at room temperature. The synchronous 2D hetero-spectral IR/NIR correlation spectra of all samples were calculated to build a discriminant model to classify adulterated milk and pure milk. Also, the NPLS-DA models were built based on synchronous 2D homo-spectral NIR/NIR and IR/IR correlation spectra, respectively. Comparison results showed that the NPLS-DA model could provide better results using 2D hetero-spectral IR/NIR correlation spectra than using 2D homo-spectral NIR/NIR and 2D IR/IR correlation spectra.

Multivariate methods for the identification of adulterated milk based on two-dimensional infrared correlation spectroscopy

The discrimination analysis of adulterated milk has been carried out based on two-dimensional (2D) infrared correlation spectroscopy along with multivariate methods like kernel orthogonal projection to latent structure (K-OPLS), multi-way partial least squares discriminant analysis (NPLS-DA), and unfolded partial least squares discriminant analysis (PLS-DA). 2D correlation spectroscopy, due to high spectral resolution and good spectral interpretation capabilities, is suitable for the analysis of complex biological data. 64 pure milk samples and 64 adulterated milk samples were measured in the mid-infrared range of 900–1700 cm−1. Then, the synchronous 2D correlation spectra of all samples were calculated in the region of between 900–1200 cm−1 and 1200–1700 cm−1. Finally, the K-OPLS, NPLS-DA, and unfolded PLS-DA models were developed based on the synchronous 2D correlation spectra of adulterated milk and pure milk. The classification accuracy rates of unknown samples for K-OPLS, NPLS-DA, and unfolded PLS-DA models were 95%, 92.5%, and 92.5%, respectively. The results indicated that 2D correlation infrared spectroscopy combined with multivariate methods were feasible and efficient for discrimination of adulterated milk.

Characterization of Adulterated Milk by Two-Dimensional Infrared Correlation Spectroscopy

A new approach for discrimination of adulterated milk is reported using two-dimensional infrared (IR) correlation spectroscopy by multiway principal component analysis (MPCA) and least squares support vector machines (LS–SVM). First, the synchronous two-dimensional spectra of pure and adulterated milk were calculated. Then, MPCA was used to reduce the dimensions, extract features of two-dimensional correlation data set, and distinguish adulterated milk and pure milk. Finally, a LS-SVM model was developed using the scores of the first thirteen principal components from synchronous two-dimensional correlation spectra computed by MPCA as the input variables. The ratios of correct classification were 100% and 96.3% for calibration set and prediction set, respectively. The area under the receiver operating characteristic curves (ROC) of 0.991 for prediction set was obtained by LS–SVM. The results indicate that two-dimensional correlation infrared spectra combined with MPCA–LS–SVM may be a rapid screening technique for discrimination of adulterated milk with good accuracy.

Classification of adulterated milk with the parameterization of 2D correlation spectroscopy and least squares support vector machines

A new discriminant method to classify adulterated milk and pure milk is proposed by combining the parameterization of two-dimensional (2D) correlation spectroscopy with least squares support vector machines (LS-SVM). 120 pure milk samples and 120 milk samples adulterated with melamine, urea and glucose were prepared and their synchronous 2D correlation spectra were calculated. Then 5 statistical parameters, which were mean, variance, standard deviation, skewness and kurtosis, were extracted based on the parameterization theory. Finally, the discriminant model of adulterated milk and pure milk was built combining these statistical parameters with LS-SVM. The ratios of correct classification 96.3% and 90% for calibration set and prediction set, respectively, were obtained. The results show that this method can not only extract effectively feature information of adulterant in milk, but also reduce the input dimension of LS-SVM and computational time demands, and so better fitted to realize discriminant analysis of adulterated milk and pure milk.

Determination of Methanol in Alcoholic Beverages by Two-Dimensional Near-Infrared Correlation Spectroscopy

ABSTRACT An innovative method for the determination of concentration of methanol in white spirit was proposed using two-dimensional near-infrared (NIR) correlation spectroscopy in combination with multivariate calibration. A total of 38 white spirit samples were adulterated with level of methanol varying from 0.1 to 10%. A one-dimensional NIR spectra of all samples were collected. Spectral characteristics of white spirit adulterated with methanol were studied based on one-dimensional NIR spectra and two-dimensional NIR correlation spectra. The origin of 4396 cm−1 peak of methanol was verified in terms of the existence or absence of cross-peaks in synchronous and asynchronous two-dimensional NIR correlation spectra. For comparison, the quantitative analysis models were constructed to determine content of methanol in white spirit using synchronous two-dimensional NIR correlation spectra and traditional one-dimensional NIR spectra, respectively. The prediction statistics criteria for synchronous two-dimensional NIR correlation spectra using N-way partial least squares, average relative error and root mean square error, were 2.97 and 0.064%, respectively. For traditional one-dimensional NIR spectra using partial least squares method, average relative error and root mean square error of prediction were 5.3 and 0.079%, respectively. Comparison results show that the proposed new method has stronger predictive ability and can provide better results than traditional one-dimensional NIR spectra using partial least squares.

Adulteration of sesame oil with corn oil detected by use of two-dimensional infrared correlation spectroscopy and multivariate calibration

ABSTRACT An innovative methodology was developed to detect adulteration of sesame oil with corn oil based on two-dimensional mid-infrared correlation spectroscopy with multivariate calibration. Forty pure sesame oils and 40 adulterated sesame oils with corn oil were prepared and the infrared absorption spectra were measured at room temperature, respectively. The synchronous two-dimensional mid-infrared correlation spectra were calculated to develop multivariate calibration models for adulteration of sesame oil with corn oil. The results showed the higher classification accuracy of 96.3% for the prediction set using two-dimensional mid-infrared correlation spectra and N-way partial least square discriminant analysis, versus 88.9% using traditional one-dimensional mid-infrared spectra and partial least squares discriminant analysis. Also, the multivariate calibration models were developed for quantitative analysis of sesame oil adulteration with corn oil. The root mean square error of prediction was 0.98% v/v using two-dimensional mid-infrared correlation spectra and N-PLS, and 1.15% v/v using traditional one-dimensional mid-infrared spectra and PLS. The results of our analyses indicated that the proposed method could provide better predictive results than traditional one-dimensional mid-infrared spectra and multivariate calibration.

Feasibility of the simultaneous determination of polycyclic aromatic hydrocarbons based on two-dimensional fluorescence correlation spectroscopy

A new approach for quantitative determination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on two-dimensional (2D) fluorescence correlation spectroscopy in conjunction with multivariate method. 40 mixture solutions of anthracene and pyrene were prepared in the laboratory. Excitation-emission matrix (EEM) fluorescence spectra of all samples were collected. And 2D fluorescence correlation spectra were calculated under the excitation perturbation. The N-way partial least squares (N-PLS) models were developed based on 2D fluorescence correlation spectra, showing a root mean square error of calibration (RMSEC) of 3.50μgL-1 and root mean square error of prediction (RMSEP) of 4.42μgL-1 for anthracene and of 3.61μgL-1 and 4.29μgL-1 for pyrene, respectively. Also, the N-PLS models were developed for quantitative analysis of anthracene and pyrene using EEM fluorescence spectra. The RMSEC and RMSEP were 3.97μgL-1 and 4.63μgL-1 for anthracene, 4.46μgL-1 and 4.52μgL-1 for pyrene, respectively. It was found that the N-PLS model using 2D fluorescence correlation spectra could provide better results comparing with EEM fluorescence spectra because of its low RMSEC and RMSEP. The methodology proposed has the potential to be an alternative method for detection of PAHs in environment.

Discrimination of Three-Dimensional Fluorescence Spectra of PAHs Mixture Using Parallel Factor Analysis

Polycyclic aromatic hydrocarbons (PAHs), as one of petroleum pollutants, have increasingly threatened the ecological environment and human health due to its strong carcinogenicity, whose fast and efficient detection has consequently become a global subject. Nowadays, fluorescence spectrometry, especially three-dimensional fluorescence spectroscopy has been used to directly detect PAHs in the environment for the high sensitivity and selectivity. Parallel factor analysis is used for separation from the spectrum of different fluorescence matters. In the paper, taking anthracene, pyrene and phenanthrene as PAHs research object, parallel factor analysis was adopted to resolve respective characteristic spectrum from three-dimensional fluorescence spectrum of PAHs mixture. The experimental results showed that this kind of method achieved good separation for mixture of anthracene, pyrene and phenanthrene, which proved its feasibility and validity to discriminate overlapping spectra of multiconstituents of PAHs.