A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses

Xu, Li-Ping; Wang, Xiaodan; Huang, Yue; Wang, Ying; Zhu, Lin; Wu, Ruijia

doi:10.1016/j.foodres.2019.03.047

Cited by 15 publications

(12 citation statements)

References 40 publications

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“…We compared the prediction results of molecular odors between the models established in this study and the methods reported in the literature. As shown in Table 4 , the prediction accuracy of the MLP-Des model was higher than that of AI-RF/DBN ( Accuracy = 0.94) [ 5 ], GA-ANN ( Accuracy = 0.90) [ 21 ], MILP/MINLP ( Accuracy = 0.75) [ 4 ] and Olfactometer ( Accuracy = 0.97) [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

Insight into the Structure–Odor Relationship of Molecules: A Computational Study Based on Deep Learning

et al. 2022

Foods

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Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure–odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules.

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Section: Resultsmentioning

confidence: 99%

Insight into the Structure–Odor Relationship of Molecules: A Computational Study Based on Deep Learning

et al. 2022

Foods

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“…rough the interface test, function test, and performance test of the mobile teaching system, it is observed that the system can interact with the teacher normally, including the playing state of courseware and the synchronous operation of whiteboard [24]. At the same time, after students apply for control permission and become controllers, local operations can be synchronized to teachers and other students.…”

Section: System Test Resultsmentioning

confidence: 99%

Mobile Education System Based on Genetic Algorithm

Dang

2022

Mobile Information Systems

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In order to solve the problems existing in the current mobile education system, this paper proposes a method of mobile education system based on genetic algorithm. By analyzing the research status of mobile learning and the theory and application mode of mobile learning, this method explores the combination of mobile learning theory and application mode and China’s traditional primary and secondary education mode, then puts forward a mobile teaching model suitable for primary and secondary education, and builds a mobile teaching system on this basis. The results show that, through the analysis, it is found that most of them remain at about 10%. In practice, the occupation is slightly increased only when multiple people teach, but it also belongs to an acceptable normal level, which can meet more complex functions in the future. The function of mobile learning platform needs to be expanded. For example, mobile learning forum can provide learners with a platform for communication, discussion, and sharing learning materials.

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“…Benzaldehyde was first extracted from bitter almonds [36]. 16.159 1,6-octadien-3-ol and 3,7-dimethyl-1.50 × 10 7 9.20 × 10 6 9.33 × 10 6 2.35 × 10 6 3.87 × 10 6…”

Section: 585mentioning

confidence: 99%

“…Benzaldehyde has a characteristic odor and aromatic taste, similar to bitter almond, and is also produced during the ripening of apricots [39,50]. 16.143 1,6-Octadien-3-ol, 3,7-dimethyl-3.03 × 10 7 2.66 × 10 7 3.27 × 10 7 3.80 × 10 6 4.83 × 10 6…”

Section: 585mentioning

confidence: 99%

“…For this reason, it is not only important to identify new rapid technologies and devices to support the production of high quality, but also to increase knowledge on their aromatic product to better understand the evolution that can occur during the cooking of the fruit used as raw material. As a result, interest in new technologies based on chemical sensor arrays [16] has grown in recent years. Ample interest has been demonstrated by the numerous scientific publications that are distributed both between classes of foods such as meats, vegetables, cereals, etc., but also between raw materials and finished and packaged products, following the entire production chain from the fields to the fork.…”

Section: Introductionmentioning

confidence: 99%

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Nanomaterial Gas Sensors for Online Monitoring System of Fruit Jams

Núñez-Carmona

Abbatangelo

Zottele

et al. 2019

Foods

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Jams are appreciated worldwide and have become a growing market, due to the greater attention paid by consumers for healthy food. The selected products for this study represent a segment of the European market that addresses natural products without added sucrose or with a low content of natural sugars. This study aims to identify volatile organic compounds (VOCs) that characterize three flavors of fruit and five recipes using gas chromatography–mass spectrometry (GC–MS) and solid-phase micro-extraction (SPME) analysis. Furthermore, an innovative device, a small sensor system (S3), based on gas sensors with nanomaterials has been used; it may be particularly advantageous in the production line. Results obtained with linear discriminant analysis (LDA) show that S3 can distinguish among the different recipes thanks to the differences in the VOCs that are present in the specimens, as evidenced by the GC–MS analysis. Finally, this study highlights how the thermal processes for obtaining the jam do not alter the natural properties of the fruit.

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A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses

Cited by 15 publications

References 40 publications

Insight into the Structure–Odor Relationship of Molecules: A Computational Study Based on Deep Learning

Insight into the Structure–Odor Relationship of Molecules: A Computational Study Based on Deep Learning

Mobile Education System Based on Genetic Algorithm

Nanomaterial Gas Sensors for Online Monitoring System of Fruit Jams

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