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

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Lu Xu ◽  
Peng-Tao Shi ◽  
Xian-Shu Fu ◽  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
...  
Keyword(s):  
Green Tea ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Rapid Identification ◽  
Classification Performance ◽  
Chinese Market ◽  
White Tea ◽  
Modeling Techniques ◽  
Tea Plantations ◽  
Model Training

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.

Measurement of the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion Based on near Infrared Spectroscopy

2011 ◽  
Vol 301-303 ◽  
pp. 1093-1097 ◽  
Author(s):  
Shi Rong Ai ◽  
Rui Mei Wu ◽  
Lin Yuan Yan ◽  
Yan Hong Wu
Keyword(s):  
Amino Acids ◽  
Infrared Spectroscopy ◽  
Correlation Coefficient ◽  
Green Tea ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tea Polyphenols ◽  
Strong Absorption ◽  
Tea Infusion

This study attempted the feasibility to determine the ratio of tea polyphenols to amino acids in green tea infusion using near infrared (NIR) spectroscopy combined with synergy interval PLS (siPLS) algorithms. First, SNV was used to preprocess the original spectra of tea infusion; then, siPLS was used to select the efficient spectra regions from the preprocessed spectra. Experimental results showed that the spectra regions [7 8 18] were selected, which were out of the strong absorption of H2O. The optimal PLS model was developed with the selected regions when 6 PCs components were contained. The RMSEP value was equal to 0.316 and the correlation coefficient (R) was equal to 0.8727 in prediction set. The results demonstrated that NIR can be successfully used to determinate the ration of tea polyphenols to amino acids in green tea infusion.

scholarly journals Screening of grated cheese authenticity by nir spectroscopy

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
Chiara Cevoli ◽  
Angelo Fabbri ◽  
Alessandro Gori ◽  
Maria Fiorenza Caboni ◽  
Adriano Guarnieri
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Performance ◽  
Integrating Sphere ◽  
Least Squares Regression ◽  
Test Set ◽  
Spectra Analysis ◽  
Protected Designation Of Origin ◽  
Parmigiano Reggiano

Parmigiano–Reggiano (PR) cheese is one of the oldest traditional cheeses produced in Europe, and it is still one of the most valuable Protected Designation of Origin (PDO) cheeses of Italy. The denomination of origin is extended to the grated cheese when manufactured exclusively from whole Parmigiano-Reggiano cheese wheels that respond to the production standard. The grated cheese must be matured for a period of at least 12 months and characterized by a rind content not over 18%. In this investigation the potential of near infrared spectroscopy (NIR), coupled to different statistical methods, were used to estimate the authenticity of grated Parmigiano Reggiano cheese PDO. Cheese samples were classified as: compliance PR, competitors, non-compliance PR (defected PR), and PR with rind content greater then 18%. NIR spectra were obtained using a spectrophotometer Vector 22/N (Bruker Optics, Milan, Italy) in the diffuse reflectance mode. Instrument was equipped with a rotating integrating sphere. Principal Component Analysis (PCA) was conducted for an explorative spectra analysis, while the Artificial Neural Networks (ANN) were used to classify spectra, according to different cheese categories. Subsequently the rind percentage and month of ripening were estimated by a Partial Least Squares regression (PLS). Score plots of the PCA show a clear separation between compliance PR samples and the rest of the sample was observed. Competitors samples and the defected PR samples were grouped together. The classification performance for all sample classes, obtained by ANN analysis, was higher of 90%, in test set validation. Rind content and month of ripening were predicted by PLS a with a determination coefficient greater then 0.95 (test set). These results showed that the method can be suitable for a fast screening of grated cheese authenticity.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Si-Min Yan ◽  
Chen-Bo Cai ◽  
Xiao-Ping Yu
Keyword(s):  
Least Squares ◽  
Inductively Coupled Plasma ◽  
Near Infrared ◽  
Safety Concern ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Nonlinear Least Squares ◽  
Classification Performance ◽  
Nondestructive Method ◽  
Support Vector

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 12.8 ppm. 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 2 ppm, the proposed method shows the feasibility for rapid and nondestructive discrimination of Pb in Chinese preserved eggs.

Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools

2016 ◽  
Vol 60 (1) ◽  
pp. 84-90 ◽  
Author(s):  
XinGang Zhuang ◽  
LiLi Wang ◽  
Qi Chen ◽  
XueYuan Wu ◽  
JiaXiong Fang
Keyword(s):  
Green Tea ◽  
Near Infrared ◽  
Nir Spectroscopy

scholarly journals Rapid Analysis of Geographical Origins and Age ofTorreya grandisSeeds by NIR Spectroscopy and Pattern Recognition Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Lu Xu ◽  
Si-Min Yan ◽  
Chen-Bo Cai ◽  
Wei Zhong ◽  
Xiao-Ping Yu
Keyword(s):  
Pattern Recognition ◽  
Sensitivity And Specificity ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Classification Performance ◽  
Model Complexity ◽  
Pattern Recognition Methods ◽  
Torreya Grandis ◽  
Second Derivative Spectra ◽  
Derivatives Of

This paper develops a rapid method for discriminating the geographical origins and age of roastedTorreya grandisseeds 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.

Rapid Detection of Adulteration in Dendrobium huoshanense Using NIR Spectroscopy Coupled with Chemometric Methods

2020 ◽  
Author(s):  
Jing-Wen Hao ◽  
Yue Chen ◽  
Nai-Dong Chen ◽  
Chao-Feng Qin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Quantitative Model ◽  
Chemometric Methods ◽  
Chinese Market ◽  
Scattering Correction ◽  
Dendrobium Huoshanense

Abstract Background Dendrobium huoshanense (DHS) is a typical traditional Chinese medicine with unique medical and high economic values; however, it may easily be adulterated with cheaper alternatives (e.g. Dendrobium henanese, DHN), because of their similar appearances and tastes. Objective In this study, adulteration of DHN in DHS was detected by near infrared (NIR) spectroscopy combined with chemometric methods. Methods By performing partial least squares (PLS) analysis, PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure DHS samples and those adulterated with DHN. Results The R2 value obtained for the PLS-DA model was 0.4898 with an RMSEP error of 0.1554, resulting in a 100% accuracy of validation sample sets. Similarly, a PLSR model was also developed to quantify the amount of DHN adulterant in DHS samples. Experimental results indicated that the good performance of the multiplicative scattering correction (MSC) model is the better model showing a prediction performance of RMSEP of 2.38 and R2 of 0.9946. Conclusions These results suggest that the combination of NIR spectroscopy and chemometric method provides a fast, simple and reliable method for detecting adulteration of DHS. Highlights The method of classification allowed identification of both authentic and adulterated DHS samples. Comparison of six different techniques for spectra preprocessing to improve quantitative model performance was obtained with MSC derivative spectra. The method can detect most of the current DHS adulterations in the Chinese market.

Use of NIR Spectroscopy Combined with Recognition Methods for the Identification of Black Teas from Different Regions

2012 ◽  
Vol 503-504 ◽  
pp. 1601-1604 ◽  
Author(s):  
Jing Ming Ning ◽  
Sheng Peng Wang ◽  
Zheng Zhu Zhang ◽  
Xiao Chun Wan
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Black Tea ◽  
Rapid Identification ◽  
K Nearest Neighbor ◽  
Identification Rate

Near-infrared (NIR) spectroscopy, combined with pattern recognition, was applied in this study for the rapid identification of Black tea from different origins.The K-Nearest Neighbor model recognition method was used for the establishment of a tea origin recognition model, which involved optimization of the principal component factors (PCs) and the identification rate using a cross-validation method. The experimental results showed that, after standard normal variant spectral preprocessing, an optimized model was obtained when the PCs were equal to three, with the cross-validation recognition rate and the predicted recognition rate reaching 98.1% and 93.3%, respectively.

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