ArticleScientific reports2016
Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data.Journal of food science and technology · 2023Article
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- Innovative Green Chemistry Approach to Synthesis of SnMolecules (Basel, Switzerland) · 2022Article
- Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review.Foods (Basel, Switzerland) · 2021Review
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- From Green Remediation to Polymer Hybrid Fabrication with Improved Optical Band Gaps.International journal of molecular sciences · 2019Article
- Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
3 authors.
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Abstract
This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea.
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