Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy

Zhu, Ling; Sun, Juan; Wu, Gangcheng; Wang, Yanan; Wang, Li; Qian, Haifeng; Qi, Xiguang

doi:10.1016/j.jcs.2018.06.010

Cited by 62 publications

(25 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Raman spectroscopy has been gradually used for identification of freshness, transgenic, origin, and adulteration of rice in recent years due to its advantages of high sensitivity, simple operation, short testing time, nondestructive, and so forth . But its application in rice variety identification is rare, and the common problem in these researches is that the characteristic information causing the difference between rice varieties has not been studied. Due to the high dimensions of Raman spectrum data, the unprecedented expansion of sample number, such identification methods are becoming more and more time‐consuming and will not be able to meet the needs of social development.…”

Section: Introductionmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

View full text Add to dashboard Cite

show abstract

“…However, the epidermis displays fewer peaks. The most prominent feature is that it contains the aromatic ring stretching vibration at 1606 cm −1 and the C C stretching vibration [ 11 , 12 ] near 1635 cm −1 with high strength, indicating that ferulic acid is abundant in the scale epidermis. As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of nutrition components in Lanzhou lily bulb by confocal Raman microscopy

Zhang

et al. 2021

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

View full text Add to dashboard Cite

Lanzhou lily is a famous lily variety in China, which has many advantages different from other lily varieties. It is rich in nutrients and can be used as medicine or food. The present study is performed to evaluate the quality of Lanzhou lily by Raman spectroscopy. Here, Raman spectra of lily bulbs were collected by confocal Raman microscopy. Through study of a variety of samples, we found that Raman peaks of several important nutrients including starch, sucrose and amino acids were clearly observed from scales of lily bulb, while strong characteristic peaks of ferulic acid were observed at the epidermis of the same scale due to the stimulation of the external environment. We also compared lily bulbs with various sizes and shapes using an average Raman spectrum of selected area. Then, changes of nutrients were quantitively analyzed in different storage period. The results show that the nutrient components including starch, sucrose, amino acids and ferulic acid can be evaluated by Raman spectroscopy. Then the quality of Lanzhou lily can be evaluated by Raman spectroscopy. This is valuable for quality evaluation of lily using non-destructive methods.

show abstract

“…The sample was washed with deionized water, and then dried at 40 o C until the weight was unchanged, and all the rice kernel samples were ground with a sample miller (LM-3100, Perten, Sweden) to obtain fine powder. [12] 2.2. Methods…”

Section: Methodsmentioning

confidence: 99%

Identification of rice varieties specialties in Vietnam using Raman spectroscopy

Giang

Trung

Yen

2020

Vietnam Journal of Chemistry

View full text Add to dashboard Cite

The characteristics and quality of rice are significantly affected by its variety. However, discrimination between varieties is an urgent but difficult and time‐consuming effort in Vietnam. In this study, an effective and reliable identification method was established by Raman spectroscopy (RS). Total Raman spectra of 32 rice samples were acquired from 400 to 1600 cm‐1 and the sensitive fundamental vibrations of less polar groups and bonds in rice were analyzed. Initially, the raw Raman spectra were processed by standard normal variety (SNV) combined with Savitzky Golay (SG) smoothing algorithm. The positive influence of SNV‐SGD2 on the ability to classify rice varieties has been confirmed by principal component analysis (PCA). Next, multivariate analysis methods included PCA, hierarchical cluster analysis (HCA), and K‐nearest neighbor (KNN), that have been compared with each other on the ability to classify rice varieties. All three methods give the ability to classify four rice varieties very well. The PCA method identifies four main factors were starch chains, amylose, amylopectin, and protein contents which are used to distinguish among four rice varieties. While HCA only distinguishes well between rice with high and low amylopectin content and does not provide the main components.

show abstract

Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy

Cited by 62 publications

References 24 publications

Improving Raman spectroscopic identification of rice varieties by feature extraction

Improving Raman spectroscopic identification of rice varieties by feature extraction

Evaluation of nutrition components in Lanzhou lily bulb by confocal Raman microscopy

Identification of rice varieties specialties in Vietnam using Raman spectroscopy

Contact Info

Product

Resources

About