Development of a Computer Vision System and Novel Evaluation Criteria to Characterize Color and Appearance of Rice

Jinorose, Maturada; Prachayawarakorn, Somkiat; Soponronnarit, Somchart

doi:10.1080/07373937.2010.506174

Cited by 22 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rice samples with the ratio of chalky area higher than 0.5 can be classified as chalky rice. Based on the previous study (Jinorose et al 2010), chalky area with thresholding segmentation by white degree in Matlab was confirmed to be able to obtain results from visual inspection effectively. Chalkiness degree (CD) is the ratio of the area of chalky area (CA) to total area of rice samples (TA) and is calculated as follows:…”

Section: Spectral and Image Feature Extractionmentioning

confidence: 98%

“…Identifying the chalky area was done by selecting a suitable grey space with calculating colour parameter. The ratio of the chalky area to the total area of the whole rice could be used for calculating the proportion of the chalky area (Jinorose et al 2010). Rice samples with the ratio of chalky area higher than 0.5 can be classified as chalky rice.…”

Section: Spectral and Image Feature Extractionmentioning

confidence: 99%

“…Among the external quality attributes, shape (Rabiei et al 2004), colour (Lamberts et al 2007) and chalkiness (Liu et al 2009) are probably the most important indicators of rice quality (Jinorose et al 2010). Traditional analytical methods for assessing these quality measurements are complex, time-consuming and unstable, which also involves human errors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Wang

Liu

et al. 2014

Food Anal. Methods

View full text Add to dashboard Cite

This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', lengthwidth ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice.

show abstract

Section: Spectral and Image Feature Extractionmentioning

confidence: 98%

Section: Spectral and Image Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Wang

Liu

et al. 2014

Food Anal. Methods

View full text Add to dashboard Cite

show abstract

“…White is the color of cooked rice favored by consumers. [26] The color of cooked rice rose with decreased drying temperature and the increased interaction of both surface velocity and initial moisture content. The appearance of cooked rice is the overall observation in terms of shape, integrity, and transparency.…”

Section: Effect Of Drying Parameters On the Texture And Taste Attribumentioning

confidence: 98%

“…Once the optimum conditions had been determined and verified, they were used to dry wet rough rice. The consideration of taste value is important when trying to improve the drying quality of rice, due to breakage, [28] color, and appearance, [26] selected as evaluation indexes of rice quality in drying of rough rice. [29] CONCLUSIONS…”

Section: Effect Of Drying Parameters On the Texture And Taste Attribumentioning

confidence: 99%

Effect of Drying Conditions on the Texture and Taste Characteristics of Rough Rice

et al. 2011

View full text Add to dashboard Cite

The effects of drying conditions, viz. drying temperature, surface velocity, and initial moisture content, on the quality characteristics of cooked rice, including both texture and taste attributes, were investigated. A response surface methodology was employed to analyze the effects of drying conditions on the cooked rice quality and to optimize the drying parameters. The results show that moisture content is the most important positive factor, followed by drying temperature with a negative effect, and surface velocity is the least important, with a negative effect on taste. The drying conditions exert the significant effects on the texture and taste attributes of cooked rice. Drying temperature has a positive effect on the cohesiveness value of cooked rice and negative effects on the partial quality attributes, including hardness, gumminess, chewiness, stickiness, color, and flavor. The increase of surface velocity may cause an increase in cohesiveness and color and a decline in hardness, gumminess, smell, palatability, and flavor. Higher moisture content of rough rice contributes to improved color and palatability attributes and reduce the hardness and gumminess indexes. The overall taste value of cooked rice for the dried rough rice has a positive correlation with the hardness, gumminess, and stickiness (p < 0.05). Results suggested that the highest taste value for the rough rice was obtained under optimal conditions of drying temperature of 35.13 C and surface velocity of 0.41 m/s for rough rice with an initial moisture content of 19.69%. The result was verified by validation experiments.

show abstract