Quantitative analysis and geographical traceability of black tea using Fourier transform near-infrared spectroscopy (FT-NIRS)

Ren, Guangxin; Wang, Shengpeng; Ning, Jingming; Xu, Rongrong; Wang, Yuxia; Xing, Zhiqiang; Wan, Xiaochun; Zhang, Zhengzhu

doi:10.1016/j.foodres.2012.10.032

Cited by 91 publications

(61 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In respect of tea samples, several studies aiming to identify the origin of teas have been performed (Ye, 2012). The chemical components were identified by high-performance liquid chromatography (HPLC) (Fernandez, Pablos, Martin, & Gonzalez, 2002;Fraser et al, 2013;Wang et al, 2014;Wu et al, 2014), gas chromatography-mass spectrometry (GCeMS) (Lin et al, 2013;Qin et al, 2013;Ye, Zhang, & Gu, 2012)， 1 HNMR (Lee et al, 2010) and fourier transform nearinfrared spectroscopy (FT-NIRS) (Ren et al, 2013) to trace the geographic origin of teas. Among the various analytical tools, metal element concentrations determined by inductively coupled plasma mass spectrometry (ICP-MS) is widely used and considered to be an effective and reliable tool to identify the geographic origin of teas.…”

Section: Introductionmentioning

confidence: 99%

Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: Taking Dongting Biluochun as an example

et al. 2016

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: Taking Dongting Biluochun as an example

et al. 2016

View full text Add to dashboard Cite

“…From 9,000 to 4,000 cm −1 , there were several NIR absorption bands for the “Xinfeng” walnut, namely 8,579, 8,266, 6,900, 5,900–5,600, 5,172, and 4,900–4,600 cm −1 . The wave peak, which was located in the vicinity of 8,266 cm −1 , was related to the absorption of the second overtone of the CH stretch (Kim and Kays, ; Ren et al, ). The 5,900–5,600 cm −1 absorption bands mainly displayed two obvious wave peaks, at around 5,874 and 5,674 cm −1 , respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Infrared spectroscopy forms a unique fingerprint map, which it does by scanning all components of products used for fingerprint traceability, and it offers lots of advantages, such as it is very quick, efficient, convenient, it is label‐free and provides real‐time detection, low‐cost consumption, and environmental protection safety. In previous studies, Ren et al () differentiated the geographical origins of black tea by Fourier transform near‐infrared (FT‐NIR) spectroscopy, with the identification accuracy reaching up to 94.3%. The geographical origins of lamb meat have been established, with 100% classification accuracy by Sun et al (), using near infrared (NIR) spectroscopy and PLS‐DA.…”

Section: Introductionmentioning

confidence: 99%

Multisource fingerprinting for region identification of walnuts in Xinjiang combined with chemometrics

Zhang

et al. 2018

J Food Process Engineering

View full text Add to dashboard Cite

Multiple techniques were employed to extract characteristic fingerprint information of walnuts from the different regions of Xinjiang. Analysis was conducted using near‐ and mid‐infrared spectroscopy, fatty acid and element data to establish geographic classification models of walnuts combined with chemometrics. The least squares discriminant analysis (PLSDA) model had a classification accuracy result of 98.8–100% for walnut samples from different geographical origins by NIR spectra. For the same variety of samples, PLSDA model also had high classification accuracy of 91–100% by MIR spectra. However, variance analysis showed that single element or fatty acid could not be differentiated between the three geographical origins of walnuts. In addition, the back propagation neural network classification model was used to analyze the elements found in the walnut specimens provided results with a mean accuracy of 89.7% for validation. This work demonstrated an acceptable feasibility of using multisource fingerprinting for differentiating regional and characteristic agriculture product to prevent frauds. Practical applications Currently, there is no effective, simple and rapid technology to prevent frauds of walnuts in Xinjiang occurring in the market, especially walnuts with adulteration or which has been counterfeited. Hence, in this research, we provided a feasible method to differentiate the walnut from different regions based on multiple fingerprinting and geographic traceability techniques, which could be applied to prevent frauds of walnuts occurring in the market in the future.

show abstract

“…NIRS regression models with modified partial least‐squares (MPLS) using the entire visible and near‐infrared wavelength range (400‐2500 nm) were also used to develop calibration for caffeine and individual catechins contents in tea leaves . In another study carried out by Ren et al, diffuse reflectance spectra in the range of 800 to 2500 nm were applied for quantitative analysis of caffeine, water extracts, total polyphenols, and free amino acid content in black tea . On 2012, good predictability with their NIR model to determine compounds in unfermented and sun‐dried cocoa beans was reported by Álvarez et al For caffeine, the main absorption bands of (a) 1570 nm, (b) 1650 nm, (c) 1684 nm, and (d) 2196 nm were selected based on highest beta calibration coefficients.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy

Chanda

Hazarika

Choudhury

et al. 2019

Journal of Chemometrics

View full text Add to dashboard Cite

Caffeine is an important component that determines the quality of tea, and its rapid estimation is very much needed for the industry. In this pursuit, a near‐infrared (NIR) spectroscopy‐based technique for the estimation of caffeine is developed and presented in this paper. On the basis of responses of the different bonds present in caffeine, four specific wavelength windows—(a) 1075 to 1239.5 nm (C―H stretch second overtone); (b) 1339.25 to 1440.75 nm (C―H stretch and C―H deformation); (c) 1640.25 to 1700 nm (C―H stretch first overtone, ═CH & amp; ―CH3 asymmetric); and (d) 900 to 1700 nm (whole range of the spectrometer)—were analyzed in details for model development and to obtain the effective wavelength (EW). Five different preprocessing techniques followed by two regression techniques—(a) the partial least‐squares (PLS) and (b) the support vector regression (SVR) were implemented on raw data for analysis. Comparing all the models, the wavelength band of 1075 to 1239.5 nm and 1339.25 to 1440.75 nm were found to produce satisfactory results. The best discrimination result was obtained using the combination of standard normal variate (SNV) preprocessing with SVR at the 1075 to 1239.5 nm wavelength region. The SVR regression with 105 samples in the training set and 15 samples in the testing set resulted in the performance parameters as RMSECV = 0.134, RMSEP = 0.069, rcv2 = 0.869, rp2 = 0.65, and RPD = 5.626 at 1075 to 1239.5 nm, whereas the PLS model produced the best RMSECV = 0.287, RMSEP = 0.077, rcv2 = 0.637, rp2 = 0.675, and RPD = 5.218 at 1339.25 to 1440.75‐nm wavelength band.

show abstract

Quantitative analysis and geographical traceability of black tea using Fourier transform near-infrared spectroscopy (FT-NIRS)

Cited by 91 publications

References 26 publications

Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: Taking Dongting Biluochun as an example

Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: Taking Dongting Biluochun as an example

Multisource fingerprinting for region identification of walnuts in Xinjiang combined with chemometrics

Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy

Contact Info

Product

Resources

About