title: Study on discrimination of white tea and albino tea based on near-infrared spectroscopy and chemometrics.
abstract: BACKGROUND
  White tea and albino tea have their own nutritional characteristics, but from the appearance they are quite similar to each other. It is not easy to distinguish them with existing analytical tools or by visual inspection. The current study proposed a rapid method to discriminate them based on near-infrared (NIR) spectroscopy associated with supervised pattern recognition methods.
  
  
  RESULTS
  For this purpose, discriminant partial least-squares (DPLS) and discriminant analysis (DA) were employed to build classification models on the basis of a reduced subset of wavenumbers and different pretreatment methods. A completely independent validation set was also used to test the model performance. The results of the DA model showed that with the SNV Karl Norris derivative spectral pre-treatment samples from the two different origins could be 100% correctly discriminated. Similarly, for the DPLS model, the best classification results were obtained with the multiplicative scattering correction (MSC) + first derivative spectral pre-treatments; the accuracy of identification was 98.48% for the calibration set and 100% for the validation set.
  
  
  CONCLUSIONS
  The overall results demonstrated that NIR spectroscopy with pattern recognition could be successfully applied to discriminate white tea and albino tea quickly and non-destructively without the need for various analytical determinations.
authors: State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, 330047, People's Republic of China.
authors: Yi
authors: Y
authors: Chen
authors: Jing
authors: J
authors: Deng
authors: Yuanxing
authors: Y
authors: Wang
authors: Boping
authors: B
authors: Liu
authors: Jian
authors: J
authors: Ding
authors: Xuejin
authors: X
authors: Mao
authors: Juan
authors: J
authors: Zhang
authors: Haitao
authors: H
authors: Hu
authors: Jing
authors: J
authors: Li
mesh_terms: D002138:Calibration; D028241:Camellia sinensis / Q000737:chemistry* / Q000254:growth & development; D002681:China; D016002:Discriminant Analysis; D005103:Expert Systems; D005511:Food Handling; D005513:Food Inspection / Q000379:methods*; D055951:Functional Food / Q000032:analysis*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D010858:Pigmentation; D018515:Plant Leaves / Q000737:chemistry* / Q000254:growth & development; D018520:Plant Shoots / Q000737:chemistry* / Q000254:growth & development; D011786:Quality Control; D012815:Signal Processing, Computer-Assisted; D019265:Spectroscopy, Near-Infrared
keywords: albino tea; discriminant analysis; discriminant partial least squares; identification; near-infrared diffuse reflection spectroscopy; white tea