Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Abstract Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared (NIR) and mid-infrared (MIR) technology combined with supervised pattern recognition based on partial least-squares discriminant analysis (PLSDA) was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct (MSC) fingerprint and second derivative (2nd derivative) fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces of A. catechu .

[1]  Yukihiro Ozaki,et al.  Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression , 2006 .

[2]  Nathalie Dupuy,et al.  Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions , 2011 .

[3]  A. J. Gaitán-Jurado,et al.  Proximate analysis of homogenized and minced mass of pork sausages by NIRS , 2007 .

[4]  R. W. Lutz,et al.  Metabolic profiling of glucuronides in human urine by LC-MS/MS and partial least-squares discriminant analysis for classification and prediction of gender. , 2006, Analytical chemistry.

[5]  W. Cai,et al.  Online near-Infrared Spectroscopy Combined with Alternating Trilinear Decomposition for Process Analysis of Industrial Production and Quality Assurance , 2011 .

[6]  G. Downey,et al.  Mid-infrared spectroscopy coupled with chemometrics: a tool for the analysis of intact food systems and the exploration of their molecular structure-quality relationships - a review. , 2010, Chemical reviews.

[7]  R. Jones,et al.  Volatile alkaloids from Areca catechu , 1998 .

[8]  B. Villegas,et al.  Prediction of the identity of fats and oils by their fatty acid, triacylglycerol and volatile compositions using PLS-DA , 2010 .

[9]  Colm P. O'Donnell,et al.  Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data , 2005 .

[10]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[11]  W. Cai,et al.  Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram , 2011 .

[12]  M. Peterson,et al.  Associations between betel nut (Areca catechu) and symptoms of schizophrenia among patients in Nepal: A longitudinal study , 2009, Psychiatry Research.

[13]  Hai-Long Wu,et al.  Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods , 2007 .

[14]  W. Cai,et al.  Discrimination of plant samples using near-infrared spectroscopy with a principal component accumulation method , 2012 .

[15]  Daniel Cozzolino,et al.  Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics. , 2011, Journal of agricultural and food chemistry.

[16]  Shuangyan Huan,et al.  Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apple samples. , 2011, Food chemistry.