Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia ( Oreochromis niloticus ) pupil and gill color during storage at 4 °C

Shi, Ce; Qian, Jianping; Han, Shuai D.; Fan, Beilei; Yang, Xinting; Wu, Xiao-Ming

doi:10.1016/j.foodchem.2017.09.047

Cited by 63 publications

(38 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, another study has been classified the AQI instruments as Sensory Bionic Technologies (SBT), able to overcome the subjectivity and poor repeatability of sensory methods (Wu et al, 2019). SBT application is based on the artificial senses of smell, taste, and vision (Shi et al, 2018), and comprises (i) electronic noses (Loutfi, Coradeschi, Mani, Shankar, & Rayappan, 2015;Zaukuu, Bazar, Gillay, & Kovacs, 2019), (ii) electronic tongues (Jiang, Zhang, Bhandari, & Adhikari, 2018;Peris & Escuder-Gilabert, 2009;Zaukuu et al, 2019), (iii) computer vision techniques (Wu et al, 2019), and (iv) colorimetric sensor arrays (CSAs; Huang, Xin, & Zhao, 2011).…”

Section: Artificial Quality Index (Aqi)mentioning

confidence: 99%

“…Color is one of the most important food quality parameters and has a direct effect on consumer buying intention (Shi et al, 2018). On the other hand, this matrix is highly susceptible to color changes due to decay process (Shi et al, 2018), in which the discoloration is caused by the formation of metmyoglobin (Chaijan, Benjakul, Visessanguan, & Faustman, 2005). QI as a tool for color assessment is an expensive and time-consumption procedure (Dowlati, de la Guardia, Dowlati, & Mohtasebi, 2012).…”

Section: Machine Vision System (Mvs)mentioning

confidence: 99%

“…Moreover, the color measured by a panel is a subjective result, which is difficult to convert to reproducible numerical values (Hong, Yang, You, & Cheng, 2014). Therefore, other fast and noninvasive methods have been developed for a continuous fish evaluation, such as the MVS (Dowlati et al, 2012;Shi et al, 2018). MVS is an emerging technology employed to recognize objects, extract, and analyze quantitative information from digital images (Hong et al, 2014).…”

Section: Machine Vision System (Mvs)mentioning

confidence: 99%

See 2 more Smart Citations

Fish Quality Index Method: Principles, weaknesses, validation, and alternatives—A review

Bernardo

Rosário

Delgado

et al. 2020

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Fish is a high nutritional value matrix of which production and consumption have been increasing in the last years. Advancements in the efficient evaluation of freshness are essential to optimize the quality assessment, to improve consumer safety, and to reduce raw material losses. Therefore, it is necessary to use rapid, nondestructive, and objective methodologies to evaluate the quality of this matrix. Quality Index Method (QIM) is a tool applied to indicate fish freshness through a sensory evaluation performed by a group of assessors. However, the use of QIM as an official method for quality assessment is limited by the protocol, sampling size, specificities of the species, storage conditions, and assessor's experience, which make this method subjective. Also, QIM may present divergences regarding the development of microorganisms and chemical analysis. In this way, novel quality evaluation methods such as electronic noses, electronic tongues, machine vision system, and colorimetric sensors have been proposed, and novel technologies such as proteomics and mitochondrial analysis have been developed. In this review, the weaknesses of QIM were exposed, and novel methodologies for quality evaluation were presented. The consolidation of these novel methodologies and their use as methods of quality assessment are an alternative to sensory methods, and their understanding enables a more effective fish quality control.

show abstract

Section: Artificial Quality Index (Aqi)mentioning

confidence: 99%

Section: Machine Vision System (Mvs)mentioning

confidence: 99%

Section: Machine Vision System (Mvs)mentioning

confidence: 99%

See 1 more Smart Citation

Fish Quality Index Method: Principles, weaknesses, validation, and alternatives—A review

Bernardo

Rosário

Delgado

et al. 2020

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…Strong correlations between the leaf N concentration (LNC) and image color indices are observed [16]. Additionally, the significant relationship between deficiency of nitrogen and color characteristic has been proven in soybean plants in an outdoor environment [17].The color characteristic has been successfully employed to classify healthy plants versus unhealthy plants [18][19][20][21]. Visual inspection and image processing techniques are the major recognition methods for evaluating plant health conditions.…”

mentioning

confidence: 99%

“…The color characteristic has been successfully employed to classify healthy plants versus unhealthy plants [18][19][20][21]. Visual inspection and image processing techniques are the major recognition methods for evaluating plant health conditions.…”

mentioning

confidence: 99%

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Zhu

Guan

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

show abstract

Comparison of freshness prediction method for salmon fillet during different storage temperatures

et al. 2021

Self Cite

View full text Add to dashboard Cite

BACKGROUND Many new forecasting models have been applied to fish freshness prediction like support vector regression (SVR) and radial basis function neural network (RBFNN). In this study, RBFNN, SVR, and Arrhenius models were established and compared for predicting and evaluating the quality of salmon fillets during storage at different temperatures, based on thiobarbituric acid (TBA), total volatile basic nitrogen (TVB‐N), total viable counts (TVCs), K value, and sensory assessment (SA). RESULTS The TBA, TVB‐N, TVC, and K values increased during storage whereas SA decreased. Residuals of the three models are random and irregular, indicating that these models were suitable for predicting the freshness of salmon fillets. The RBFNN predicted quality of salmon fillets stored at different temperatures with relative errors all within ±5% (except for the TVC value at day 6). Relative errors of the SVR model for predicting TVB‐N and K value were within 10%, while the relative errors of the Arrhenius model fluctuated greatly (ranging from ±0.46 to ±38.29%) and most of it exceeded 10%. RBFNN model had the best predictive performance by comparing the residual and relative errors of the three models. CONCLUSION RBFNN is a promising method for predicting the freshness of salmon fillets stored at −2 to 10 °C in the cold chain. © 2021 Society of Chemical Industry

show abstract

Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia ( Oreochromis niloticus ) pupil and gill color during storage at 4 °C

Cited by 63 publications

References 33 publications

Fish Quality Index Method: Principles, weaknesses, validation, and alternatives—A review

Fish Quality Index Method: Principles, weaknesses, validation, and alternatives—A review

High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform

Comparison of freshness prediction method for salmon fillet during different storage temperatures

Contact Info

Product

Resources

About