ArticleFood chemistry: X2022
Rapid detection of adulteration in powder of ginger (
Article in Food chemistry: X, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 68 citations in OpenAlex.
- Rapid evaluation of the comprehensive quality of paeonol/cyclodextrin supramolecular complexes using CASSA based on near-infrared spectroscopy combined with artificial intelligence.Molecular diversity · 2026Article
- Reliable Quantification of Powdered Ginger Adulteration by Vis-NIR Spectroscopy and Chemometrics.Molecules (Basel, Switzerland) · 2026Article
- CounterfeitFood science & nutrition · 2026Article
- Machine Learning and the Use of Spectroscopy for Adulteration Detection in Turmeric Powder.Molecules (Basel, Switzerland) · 2026Article
- Advances in Optical Sensing Technologies for On-Site Detection of Harmful Residues in Food: Principles and Recent Applications.Foods (Basel, Switzerland) · 2025Review
- Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genusJournal of pharmaceutical analysis · 2025Review
- FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (Food chemistry: X · 2024Article
- Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes.Food chemistry: X · 2024Article
- Rapid determination of the geographical origin of kimchi by Fourier transform near-infrared spectroscopy coupled with chemometric techniques.Scientific reports · 2024Article
- Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data.Polymers · 2024Article
- Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art.Foods (Basel, Switzerland) · 2023Review
- Rapid Prediction of Adulteration Content inFoods (Basel, Switzerland) · 2023Article
- Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level.Food chemistry: X · 2023Article
- Variations in the morphological and chemical composition of the rhizomes ofFood chemistry: X · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.
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