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Abstract: Licorice, the roots of Glycyrrhizia uralensis Fisch, is used as a medicinal herb and food additive in China. At present, the quality grading and authentication of licorice products is generally done by visual inspection, which is subjective and often biased. In this study, a rapid and nondestructive method for the classification of licorice samples according to their growing conditions, geographic areas and plant parts was developed using fiber optic diffuse reflectance near-infrared spectroscopy (FODR-NIR). With the use of multiplicative signal correlation (MSC) and Norris derivative filter, the differences of the NIR spectra among different licorice samples were enhanced even though the raw spectra were very similar. The results showed that the NIR spectra of the samples were moderately clustered in the principle component spaces, and pattern recognition technique of soft independent modeling of class analogy (SIMCA) provided satisfactory classification results. Additionally, a partial least square (PLS) method using HPLC data set as reference was constructed to predict the value of glycyrrhizic acid (GA) in licorice. The results showed that PLS models with both data normalization (DN) coupled with first derivative pretreatments, and MSC coupled with first derivative pretreatments provided acceptable results. [Copyright &y& Elsevier]

Abstract: Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650cm−1 and NIR spectra in the range of 10,000–4000cm−1 were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885cm−1 and NIR ranges of 6200–5400cm−1 between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique. [Copyright &y& Elsevier]