Authentication of lotus root powder adulterated with potato starch and/or sweet potato starch using Fourier transform mid-infrared spectroscopy.

Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques were developed for classification and quantification of cheaper starches (potato and sweet potato starch) in lotus root powder (LRP). By performing principal component analysis (PCA), it was possible to distinguish between adulterated and non-adulterated LRP. The coefficient of determination (R(2)) and standard deviation ratio (SDR) of calibration set were found to be 0.9587-0.9898 and 3.63-10.2, depending on the pre-treatment of spectra. The external validation set gave a coefficient of determination (R(2)) and standard deviation ratio (SDR) of 0.9810 and 5.47, respectively. Moreover, the limit of detection (1%), the limit of quantification (3%), reasonable recovery (92.3-101.5%), satisfactory intra-assay (2.9-5.5%) and inter-assay (11.0-13.5%) precision illustrated the good performance of the present method. The results obtained in this study indicate that FT-MIR spectroscopy can be used as an easy, rapid and novel tool to detect the LRP adulterated with cheaper starches.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market.

A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics.

The feasibility of rapid analysis of glucose and fructose in lotus root powder by Fourier transform near-infrared (FT-NIR) spectroscopy was studied. Diffuse reflectance spectra were collected between 4000 and 12,432cm(-1). Calibration models established by partial least-squares regression (PLSR), interval PLS of forward (FiPLS) and backward (BiPLS), back propagation-artificial neural networks (BP-ANN) and least squares-support vector machine (LS-SVM) were compared. The optimal models for glucose and fructose were obtained by LS-SVM with the first 10 latent variables (LVs) as input. For fructose the correlation coefficients of calibration (rc) and prediction (rp), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.9827, 0.9765, 0.107%, 0.115% and 4.599, respectively. For glucose the indexes were 0.9243, 0.8286, 0.543%, 0.812% and 1.785. The results indicate that NIR spectroscopy technique with LS-SVM offers effective quantitative capability for glucose and fructose in lotus root powder.