Frictional behavior of brown rice grain during moderate processing

Yang, Liu; Fan, Yuchao; Song, Shaoyun; Wang, Deguo; Zhang, Yonglin; Cao, Meili

doi:10.1016/j.triboint.2021.107123

Cited by 13 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the endosperm structure of organic material with element (C H O) embeds into the concave zone during seed shear. This also corresponds to the images shown in Figure 12e,f, the endosperm plowing and tissue residue on iron surface (Yang et al, 2019; Yang et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Research on microstructural‐mechanical and shearing properties of castor seed during mechanical extraction

Yang

Chen

et al. 2023

Journal of Texture Studies

Self Cite

View full text Add to dashboard Cite

Castor seed oil, as an important biomass fuel, has attracted extensive attention worldwide due to inclusive applications. Castor seed screw mechanical extraction is in fact seed shear damage and oil output. Seed shearing mechanism has been investigated with a developed tribometer. Influences of pressing load, shearing speed, roller roughness were analyzed. Castor seed structural damage was in‐situ observed with optical microscope, and in‐depth analyzed with Scanning Electron Microscopy and Energy Dispersive Spectroscopy. The results reveal that shear interaction can be divided into three stages: coat damage, transition shearing and endosperm oil output. Seed shear mechanism includes coat peeling, endosperm plowing, tissue transferring and oil lubrication. High pressing load leads to more damage of coat and endosperm, causing more oil to flow out. With shearing speed increasing, coat is easily peeled, obvious endosperm shear plowing and oil lubrication happened in contact area. Coat damage by high roughness leads more oil output. Castor oil enters the contact area and work as lubricant, leading to the decrease of friction resistance.

show abstract

Section: Resultsmentioning

confidence: 99%

Research on microstructural‐mechanical and shearing properties of castor seed during mechanical extraction

Yang

Chen

et al. 2023

Journal of Texture Studies

Self Cite

View full text Add to dashboard Cite

show abstract

“…The validation of the optimized PDO method was performed on the following two aspects: (1) The evaluation of intra-day precision and inter-day precision of the optimized PDO method for the determination of cellulose content in the rice bran layer; (2) the comparison between the determination results of the optimized PDO method and the original PDO method.…”

Section: Validation Of the Optimized Pdo Methodsmentioning

confidence: 99%

“…With socioeconomic development and improving living standards, there has been a growing demand for deep-milled, polished white rice among consumers. However, this consumer trend has induced an over-milling issue within the rice processing industry [ 2 ], resulting in substantial food waste and posting a threat to global food security. According to Dhankhar’s study [ 3 ], commercial rice mills can lose up to 25% of rice weight during milling.…”

Section: Introductionmentioning

confidence: 99%

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Fan,

Qin,

et al. 2023

Foods

View full text Add to dashboard Cite

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models’ predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

show abstract

“…The husk is removed to obtain brown rice kernel, which consists of bran, endosperm, and embryo, and white rice is obtained when the bran is properly milled [2]. During processing, the brown rice kernel is affected by various mechanical forces, resulting in crack formation inside the grain, which reduces white rice quality and affects the classification of marketing grades, which is avoided [3]. The effective identification of cracks in rice grains will help further expand the use and economic value of rice grains in actual processing and production.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection of Brown Rice Kernel Based on Optimized ResNet-18 Network

Wang,

Hu,

Xiao

2023

IEEE Access

View full text Add to dashboard Cite

Brown rice kernel cracks significantly impact grain quality, and timely, accurate detection is crucial for enhancing the quality and taste of processed rice. In this study, we developed an optical observation platform and optimized the original ResNet-18 neural network structure to improve the detection and classification of grain cracks. We established image datasets for japonica and indica rice varieties, and employed image augmentation and model migration techniques during training. In addition, we compared the performance of the optimized model with DenseNet-121 and GoogLeNet. The results demonstrate a notable enhancement in crack detection accuracy for japonica, reaching 96%, which is a 3.67% improvement over the original model. Furthermore, we achieved a substantial reduction in average training time, reduced by 58.66%. For indica rice, after model optimization and migration, the accuracy reached 96.67%. It's important to note that the optimized model has limitations and is not suitable for mixed datasets with limited training data. This technology offers the capability to accurately identify and detect cracks in brown rice kernels under visible light conditions, presenting a promising solution for enhancing grain quality during processing.

show abstract

Frictional behavior of brown rice grain during moderate processing

Cited by 13 publications

References 29 publications

Research on microstructural‐mechanical and shearing properties of castor seed during mechanical extraction

Research on microstructural‐mechanical and shearing properties of castor seed during mechanical extraction

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Crack Detection of Brown Rice Kernel Based on Optimized ResNet-18 Network

Contact Info

Product

Resources

About