Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology

Hao, Lin; Wang, Zhuo; Ahmad, Waqas; Man, Zhong-xiu; Duan, Yaxian

doi:10.1016/j.jspr.2019.101523

Cited by 18 publications

(4 citation statements)

References 27 publications

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“…Scholars at home and abroad have conducted several studies on the application of this field. Imaging technology based on chemo-responsive dyes has been proposed to evaluate the states of some commodities or agro-products such as detection of cereal (Lin, Kang, et al, 2019;Lin, Wang, et al, 2020), wine and vinegar flavored foods (Guan et al, 2013(Guan et al, , 2014Lin et al, 2018;Lin, Jiang, et al, 2021), poultry meat (Alimelli et al, 2007;Huang et al, 2015), aquatic products, fruits and vegetables, and tea.…”

Section: Application In Sensing Food Quality and Safetymentioning

confidence: 99%

“…As mentioned above, the article has reflected some advances of odor imaging technology. In recent years, imaging detection technology has gradually been applied to the odor detection of some highly volatile foods such as vinegar, white wine, and mildew of grains (Chen, Liu, & Zhao, 2013; Chen, Hui, et al., 2014; Chen, Li, et al., 2014; Feng et al., 2010; Guan et al., 2014; Gu et al., 2019; Huo et al., 2014; Huang et al., 2011, 2014; Lin et al., 2017, 2018; Lin, Duan, et al., 2019; Lin, Kang, et al., 2019; Lin, Yan, et al., 2019; Lin, Kang, et al., 2020; Lin, Wang, et al., 2020; Khulal et al., 2016; Qin et al., 2012; Qian & Lin, 2015; Salinas et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

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Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Kang

Hao

Jiang

et al. 2021

Comp Rev Food Sci Food Safe

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Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemoresponsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application:The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

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Section: Application In Sensing Food Quality and Safetymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Kang

Hao

Jiang

et al. 2021

Comp Rev Food Sci Food Safe

Self Cite

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“…Colorimetric sensors detect particular changes which occurred due to colour variation. Colorimetric sensors are optical sensors that also change some stimuli in an image [6]. e colorimetric sensor array is used in the image segmentation process that provides an appropriate set of data for the segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Image Semantic Recognition and Segmentation Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Tang

Wang

Huang

et al. 2022

Computational Intelligence and Neuroscience

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Semantic feature recognition in colour images is required for identifying uneven patterns in object detection and classification. The semantic features are identified by segmenting the colorimetric sensor array features through machine learning paradigms. Semantic segmentation is a method for identifying distinct elements in an image. This can be considered a task involving image classification at the pixel level. This article introduces a semantic feature-dependent array segmentation method (SFASM) to improve recognition accuracy due to irregular semantics. The proposed method incorporates a deep convolutional neural network for detecting the semantic and un-semantic features based on sensor array representations. The colour distributions per array are identified for horizontal and vertical semantics analysis. In this analysis, deep learning classifies the uneven patterns based on colour distribution, i.e. the consecutive and scattered colour distribution pixels in an array are correlated for their similarity. This similarity identification is maximized through max-pooling and recurrent iterations, preventing detection errors. The proposed method classifies the semantic features for further correlation sections, improving the accuracy. The proposed method’s performance is thus validated using the metrics precision, analysis time and F1-Score.

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“…In order to respond to emergencies, refined grain storage has become a new development direction for grain storage [ 2 ]. Compared with the storage of raw grain, the storage area of refined grain products is smaller and the storage capacity is larger [ 3 ]. In case of large-scale natural disasters or public health events, the refined grain products can be quickly mobilized and used, which makes them more flexible.…”

Section: Introductionmentioning

confidence: 99%

Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

Jiang

Chen

2021

Sensors

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This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.

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Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology

Cited by 18 publications

References 27 publications

Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Advanced applications of chemo‐responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Image Semantic Recognition and Segmentation Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network

Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

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