Chen-Bo Cai

Untargeted detection and quantitative analysis of poplar balata (PB) in Chinese propolis by FT-NIR spectroscopy and chemometrics.

This paper investigates the feasibility of using FT-NIR spectroscopy and chemometrics for rapid analysis of poplar balata (PB) in Chinese propolis. Because practical adulterations usually involve addition of certain known active components, together with commercial PB, the commonly targeted analysis methods are insufficient to identify PB-adulterated propolis. Untargeted analysis of PB was performed by developing class models of pure propolis using one-class partial least squares (OCPLS). Quantitative analysis of PB was performed using partial least squares regression (PLSR). For untargeted analysis, the most accurate OCPLS model was obtained with SNV spectra with sensitivity 0.960 and specificity 0.941. OCPLS could detect adulterations with 2% (w/w) or more PB. For quantitative analysis, the root mean squared error of prediction (RMSEP) value of PB was 0.902 (w/w, %) with SNV-PLS. FT-NIR spectrometry and chemometrics demonstrate potential for rapid analysis of PB adulterations in Chinese propolis.

Untargeted Detection of Illegal Adulterations in Chinese Glutinous Rice Flour (GRF) by NIR Spectroscopy and Chemometrics: Specificity of Detection Improved by Reducing Unnecessary Variations

This paper aimed at developing a nondestructive and rapid method to detect adulterations in Chinese glutinous rice flour (GRF) using near-infrared (NIR) spectroscopy and chemometrics. Because various known and unknown ingredients can be potentially used for food adulteration, the commonly used targeted analytical methods focused on detecting one or more known/suspected adulterants usually cannot catch up with the constant “updating” of new adulterants. Therefore, this paper attempted to achieve untargeted detection by modeling the NIR spectra of pure GRF and analyzing those of test samples. Soft independent modeling of class analogy (SIMCA) and a recently suggested one-class partial least squares (OCPLS) was used to develop class models of pure GRF. To highlight the slight variations in NIR spectra caused by low-level doping and enhance the specificity for detecting extraneous adulterants, unwanted variations in pure GRF spectra should be removed. Smoothing, taking second-order derivative (D2), standard normal variate (SNV), and D2-SNV were performed to improve the raw spectra. One hundred thirty pure GRF samples from six main producing areas were prepared and used for training class models. To validate the specificity of class models, 215 adulterated GRF samples were prepared by blending the pure objects with different levels (1, 2, 4, 8, and 10xa0% (w/w)) of wheat flour, non-GRF, and an illegal food additive, talcum powder, which have been frequently used for GRF adulteration. The best OCPLS model was obtained with D2 spectra with prediction sensitivity of 1.000 and specificity of 0.916; SIMCA with D2-SNV obtained prediction sensitivity of 1.000 and specificity of 0.902. It was demonstrated that adulterations of GRF with 2xa0% or higher levels of wheat flour, non-GRF, and talcum powder can be safely detected with D2, SNV, or D2-SNV spectra. The analysis results indicate the specificity of untargeted detection of the three adulterants in GRF can be improved by removing the unwanted within-class variations.

Protected Geographical Indication Identification of a Chinese Green Tea (Anji-White) by Near-Infrared Spectroscopy and Chemometric Class Modeling Techniques

This paper reports a rapid identification method for a Chinese green tea with PGI, Anji-white tea, by class modeling techniques and NIR spectroscopy. 167 real and representative Anji-white tea samples were collected from 8 tea plantations in their original producing areas for model training. Another 81 non-Anji-white tea samples of similar appearance were collected from 7 important tea producing areas and used for validation of model specificity. Diffuse NIR spectra were measured with finely ground tea powders. OCPLS and SIMCA were used to describe the distribution of representative Anji-white tea objects and predict the authenticity of new objects. For data preprocessing, smoothing, derivatives, and SNV were applied to improve the raw spectra and classification performance. It is demonstrated that taking derivatives and SNV can improve classification accuracy and reduce the complexity of class models by removing spectral background and baseline. For the best models, the sensitivity and specificity were 0.886 and 0.951 for OCPLS, 0.886 and 0.938 for SIMCA with SNV spectra, respectively. Although it is difficult to perform an exhaustive analysis of all types of potential false objects, the proposed method can detect most of the important non-Anji-white teas in the Chinese market.

The Feasibility of Using Near-Infrared Spectroscopy and Chemometrics for Untargeted Detection of Protein Adulteration in Yogurt: Removing Unwanted Variations in Pure Yogurt

Untargeted detection of protein adulteration in Chinese yogurt was performed using near-infrared (NIR) spectroscopy and chemometrics class modelling techniques. sixty yogurt samples were prepared with pure and fresh milk from local market, and 197 adulterated yogurt samples were prepared by blending the pure yogurt objects with different levels of edible gelatin, industrial gelatin, and soy protein powder, which have been frequently used for yogurt adulteration. A recently proposed one-class partial least squares (OCPLS) model was used to model the NIR spectra of pure yogurt objects and analyze those of future objects. To improve the raw spectra, orthogonal projection (OP) of raw spectra onto the spectrum of pure water and standard normal variate (SNV) transformation were used to remove unwanted spectral variations. The best model was obtained with OP preprocessing with sensitivity of 0.900 and specificity of 0.949. Moreover, adulterations of yogurt with 1% (w/w) edible gelatin, 2% (w/w) industrial gelatin, and 2% (w/w) soy protein powder can be safely detected by the proposed method. This study demonstrates the potential of combining NIR spectroscopy and OCPLS as an untargeted detection tool for protein adulteration in yogurt.

Calibrating the Shelf-life of Chinese Flavored Dry Tofu by FTIR Spectroscopy and Chemometrics: Effects of Data Preprocessing and Nonlinear Transformation on Multivariate Calibration Accuracy

This paper demonstrates the application of FTIR spectroscopy coupled with chemometric methods to rapid analysis of the shelf-life of a traditional Chinese food, flavored dry bean curd (tofu). Transmittance FTIR spectra ranging from 4,000 to 400xa0cm−1 of 151 dry tofu samples were measured. Sample preparation involved finely grinding of samples and formation of thin KBr disks (under 10xa0MPa for 5xa0min). The shelf-life of dry tofu samples by spectroscopic analysis ranged from 29 to 161xa0days. Two different strategies were investigated to tackle the nonlinear problem: least squares support vector machines and partial least squares (PLS) with nonlinear transformation of spectral data by (a) B-spline function and (b) Gaussian kernel function. Different options of data preprocessing and calibration models were investigated to develop a nonlinear model with good generalization performance. Compared with linear models, both the two proposed strategies can improve the modeling and prediction accuracy with standard normal variate and second-order derivative spectra. B-spline-transformation PLS with second-order derivative spectra obtained a root mean squared error of prediction value of 9.6xa0days and was recommended as a simple-to-use nonlinear method for shelf-life prediction.

Rapid Quantification of Melamine in Different Brands/Types of Milk Powders Using Standard Addition Net Analyte Signal and Near-Infrared Spectroscopy

Multivariate calibration (MVC) and near-infrared (NIR) spectroscopy have demonstrated potential for rapid analysis of melamine in various dairy products. However, the practical application of ordinary MVC can be largely restricted because the prediction of a new sample from an uncalibrated batch would be subject to a significant bias due to matrix effect. In this study, the feasibility of using NIR spectroscopy and the standard addition (SA) net analyte signal (NAS) method (SANAS) for rapid quantification of melamine in different brands/types of milk powders was investigated. In SANAS, the NAS vector of melamine in an unknown sample as well as in a series of samples added with melamine standards was calculated and then the Euclidean norms of series standards were used to build a straightforward univariate regression model. The analysis results of 10 different brands/types of milk powders with melamine levels 0~0.12% (w/w) indicate that SANAS obtained accurate results with the root mean squared error of prediction (RMSEP) values ranging from 0.0012 to 0.0029. An additional advantage of NAS is to visualize and control the possible unwanted variations during standard addition. The proposed method will provide a practically useful tool for rapid and nondestructive quantification of melamine in different brands/types of milk powders.

Rapid Analysis of Geographical Origins and Age of Torreya grandis Seeds by NIR Spectroscopy and Pattern Recognition Methods

This paper develops a rapid method for discriminating the geographical origins and age of roasted Torreya grandis seeds by near infrared (NIR) spectroscopic analysis and pattern recognition. 337 samples were collected from three main producing areas and produced in the last two years. The objective of geographical origins analysis is to discriminate the seeds from Fengqiao with a protected geographical indication (PGI) from those of another two provinces. Age classification is aimed to detect the old seeds produced in the last year from the freshly produced ones. Partial least squares discriminant analysis (PLSDA) was used to develop classification models, and the influence of data preprocessing methods on classification performance was also investigated. Taking second-order derivatives of the raw spectra proves to be the most proper and effective preprocessing method, which can remove baselines and backgrounds and reduce model complexity. With second derivative spectra, the sensitivity and specificity were 0.939 and 0.871 for age discrimination, respectively. Perfect classification was obtained, and both sensitivity and specificity were 1 for discrimination of geographical origins.

Nonlinear Multivariate Calibration of Shelf Life of Preserved Eggs (Pidan) by Near Infrared Spectroscopy: Stacked Least Squares Support Vector Machine with Ensemble Preprocessing

This paper aims at developing a rapid and nondestructive method for analyzing the shelf life of preserved eggs (pidan) by near infrared (NIR) spectroscopy and nonlinear multivariate calibration. A major concern with a nonlinear model is that the noncomposition-correlated spectral variations among pidan objects of different batches and production dates would unnecessarily increase model complexity and cause overfitting and degradation of prediction. To reduce the negative influence of unwanted spectral variations, stacked least squares support vector machine (LS-SVM) with an ensemble of 62 commonly used preprocessing methods is proposed to automatically optimize data preprocessing and develop the nonlinear model. The analysis results indicate that stacked LS-SVM can obtain stable calibration model, and the prediction accuracy is improved compared with models with single-preprocessing methods. Since LS-SVM is much faster than its ordinary counterparts, stacked LS-SVM with ensemble preprocessing can be performed within an acceptable computational time. When the objects and spectral variations are very complex, the proposed method can provide a useful tool for data preprocessing and nonlinear multivariate calibration.

Nondestructive Discrimination of Lead (Pb) in Preserved Eggs (Pidan) by Near-Infrared Spectroscopy and Chemometrics

A major safety concern with pidan (preserved eggs) has been the usage of lead (II) oxide (PbO) during its processing. This paper develops a rapid and nondestructive method for discrimination of lead (Pb) in preserved eggs with different processing methods by near-infrared (NIR) spectroscopy and chemometrics. Ten batches of 331 unleaded eggs and six batches of 147 eggs processed with usage of PbO were collected and analyzed by NIR spectroscopy. Inductively coupled plasma mass spectrometry (ICP-MS) analysis was used as a reference method for Pb identification. The Pb contents of leaded eggs ranged from 1.2 to 12.8u2009ppm. Linear partial least squares discriminant analysis (PLSDA) and nonlinear least squares support vector machine (LS-SVM) were used to classify samples based on NIR spectra. Different preprocessing methods were studied to improve the classification performance. With second-order derivative spectra, PLSDA and LS-SVM obtained accurate and reliable classification of leaded and unleaded preserved eggs. The sensitivity and specificity of PLSDA were 0.975 and 1.000, respectively. Because the strictest safety standard of Pb content in traditional pidan is 2u2009ppm, the proposed method shows the feasibility for rapid and nondestructive discrimination of Pb in Chinese preserved eggs.