Evaluation of sample pretreatment method for geographic authentication of rice using Raman spectroscopy

Sha, Min; Gui, Dongdong; Zhang, Zhengyong; Ji, Xinyan; Shi, Xiaojing; Liu, Jun; Zhang, Ding

doi:10.1007/s11694-019-00087-7

Cited by 19 publications

(5 citation statements)

References 20 publications

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“…Then, 20‐g rice was added into the grinder within 0.5 min and milled for another 2 min. Finally, rice flour with particle size of 100–140 mesh was obtained using sieves of 100 and 140 mesh because Raman spectrum of rice flour at this size had the best repeatability . A vertical grinder (type 15B, Baling Electric Appliance Co., Ltd, China) was used to crush rice samples.…”

Section: Methodsmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

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Raman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm−1) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm−1). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.

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Section: Methodsmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

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“…In addition, recent studies on geographical origin identification using Raman and THz techniques only focus on plant-derived products. Although Raman spectroscopy distinguished the geographical origin of rice from 12 Chinese provinces and three Chinese cities, the discrimination rates were different, with the former reaching 100 % and the latter reaching a maximum of 88.9%, possibly because the urban areas were smaller than the provincial regions ( Sha et al, 2019 ; Zhu et al, 2018 ). And as for the application of THz, Liu et al (2018a) assessed the feasibility of using THz to quickly distinguish EVOO from four geographical sources.…”

Section: Principle and Research Progress In Rapid Analysis Technologymentioning

confidence: 94%

Rapid analysis technologies with chemometrics for food authenticity field: A review

Zhang,

Li,

Zhao

et al. 2024

Current Research in Food Science

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“…These include laser-induced breakdown spectroscopy, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy and Raman spectroscopy [32]. These methods were successfully employed for both rice authentication [39][40][41][42] and detection of chemical contaminants and adulterants [43][44][45][46].…”

Section: Brief Overview Of Non-dna Based Methods For Rice Certificationmentioning

confidence: 99%

DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview

Vieira

Faustino

Lourenço

et al. 2022

Foods

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Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.

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Evaluation of sample pretreatment method for geographic authentication of rice using Raman spectroscopy

Cited by 19 publications

References 20 publications

Improving Raman spectroscopic identification of rice varieties by feature extraction

Improving Raman spectroscopic identification of rice varieties by feature extraction

Rapid analysis technologies with chemometrics for food authenticity field: A review

DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview

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