Neural network in food analytics

References

• Acquarelli, J., T. van Laarhoven, J. Gerretzen, T. N. Tran, L. M. C. Buydens, and E. Marchiori. 2017. Convolutional neural networks for vibrational spectroscopic data analysis. Analytica Chimica Acta 954:22–31. doi: 10.1016/j.aca.2016.12.010.
   PubMed Web of Science ®Google Scholar
• Adali, T, and S. Haykin. 2010. Adaptive signal processing: next generation solutions. Hoboken, New Jersey, USA: John Wiley & Sons.
   Google Scholar
• Administration, U. F. A D. 2021. New era of smarter food safety. 82–90.
   Google Scholar
• Allard, M. W., E. Strain, D. Melka, K. Bunning, S. M. Musser, E. W. Brown, and R. Timme. 2016. Practical value of food pathogen traceability through building a whole-genome sequencing network and database. Journal of Clinical Microbiology 54 (8):1975–83. doi: 10.1128/JCM.00081-16.
   PubMed Web of Science ®Google Scholar
• Alom, M. Z., T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, and V. K. Asari. 2018. The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv Preprint arXiv:1803.01164.
   Google Scholar
• Altae-Tran, H., B. Ramsundar, A. S. Pappu, and V. Pande. 2017. Low data drug discovery with one-shot learning. ACS Central Science 3 (4):283–93.
   PubMed Web of Science ®Google Scholar
• Apetrei, I, and C. Apetrei. 2016. Application of voltammetric e-tongue for the detection of ammonia and putrescine in beef products. Sensors and Actuators B: Chemical 234:371–9. doi: 10.1016/j.snb.2016.05.005.
   Web of Science ®Google Scholar
• Arora, M., P. Mangipudi, and M. K. Dutta. 2021. Deep learning neural networks for acrylamide identification in potato chips using transfer learning approach. Journal of Ambient Intelligence and Humanized Computing 12 (12):10601–14. doi: 10.1007/s12652-020-02867-2.
   Web of Science ®Google Scholar
• Bai, Y., Y. Xiong, J. Huang, J. Zhou, and B. Zhang. 2019. Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features. Postharvest Biology and Technology 156:110943. doi: 10.1016/j.postharvbio.2019.110943.
   Web of Science ®Google Scholar
• Baldi, P. 2012. Autoencoders, unsupervised learning, and deep architectures. Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 37–9.
   Google Scholar
• Basile, T., A. D. Marsico, and R. Perniola. 2022. Use of artificial neural networks and NIR spectroscopy for non-destructive grape texture prediction. Foods 11 (3):281. doi: 10.3390/foods11030281.
   PubMed Web of Science ®Google Scholar
• Baykal, H, and H. K. Yildirim. 2013. Application of artificial neural networks (ANNs) in wine technology. Critical Reviews in Food Science and Nutrition 53 (5):415–21. doi: 10.1080/10408398.2010.540359.
   PubMed Web of Science ®Google Scholar
• Beltagy, I., M. E. Peters, and A. Cohan. 2020. Longformer: The long-document transformer. arXiv Preprint arXiv:2004.05150.
   Google Scholar
• Bengio, Y., P. Lamblin, D. Popovici, and H. Larochelle. 2006. Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems 19.
   Google Scholar
• Bhardwaj, S. K., K. Dwivedi, and D. Agarwal. 2016. A review: GC method development and validation. International Journal of Analytical and Bioanalytical Chemistry 6 (1):1–7.
   Google Scholar
• Bhatt, A. K, and D. Pant. 2015. Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI & Society 30 (1):45–56. doi: 10.1007/s00146-013-0516-5.
   Google Scholar
• Bi, H., C. Wang, X. Jiang, C. Jiang, B. Lin, and Q. Lin. 2020. Prediction of mass loss for sewage sludge-peanut shell blends in thermogravimetric experiments using artificial neural networks. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects (10):1–14.
   Google Scholar
• Bi, K., D. Zhang, T. Qiu, and Y. Huang. 2019. GC-MS fingerprints profiling using machine learning models for food flavor prediction. Processes 8 (1):23. doi: 10.3390/pr8010023.
   Web of Science ®Google Scholar
• Bossard, L., M. Guillaumin, and L. Van Gool. 2014. Food-101–mining discriminative components with random forests. European Conference on Computer Vision, 446–61.
   Google Scholar
• Caballero-Casero, N., L. Belova, P. Vervliet, J.-P. Antignac, A. Castaño, L. Debrauwer, M. E. López, C. Huber, J. Klanova, M. Krauss, et al. 2021. Towards harmonised criteria in quality assurance and quality control of suspect and non-target LC-HRMS analytical workflows for screening of emerging contaminants in human biomonitoring. TrAC Trends in Analytical Chemistry 136:116201. doi: 10.1016/j.trac.2021.116201.
   Web of Science ®Google Scholar
• Cao, G., K. Li, J. Guo, M. Lu, Y. Hong, and Z. Cai. 2020. Mass spectrometry for analysis of changes during food storage and processing. Journal of Agricultural and Food Chemistry 68 (26):6956–66.
   PubMed Web of Science ®Google Scholar
• Castelvecchi, D. 2016. Can we open the black box of AI? Nature 538 (7623):20–3. doi: 10.1038/538020a.
   PubMed Web of Science ®Google Scholar
• Cen, H. Y, and Y. He. 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 18 (2):72–83. doi: 10.1016/j.tifs.2006.09.003.
   Web of Science ®Google Scholar
• Chen, H., Z. Chen, F. Lin, and P. Zhuang. 2021. Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. IEEE Access 9:36008–18. doi: 10.1109/ACCESS.2021.3062410.
   Web of Science ®Google Scholar
• Cheng, P., W. Fan, and Y. Xu. 2014. Determination of Chinese liquors from different geographic origins by combination of mass spectrometry and chemometric technique. Food Control 35 (1):153–8. doi: 10.1016/j.foodcont.2013.07.003.
   Web of Science ®Google Scholar
• Chew, R., J. Rineer, R. Beach, M. O’Neil, N. Ujeneza, D. Lapidus, T. Miano, M. Hegarty-Craver, J. Polly, and D. S. Temple. 2020. Deep neural networks and transfer learning for food crop identification in UAV Images. Drones 4 (1):7. doi: 10.3390/drones4010007.
   Google Scholar
• Ciocca, G., P. Napoletano, and R. Schettini. 2018. CNN-based features for retrieval and classification of food images. Computer Vision and Image Understanding 176:70–7.
   Web of Science ®Google Scholar
• Ciresan, D. C., U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber. 2011. Flexible, high performance convolutional neural networks for image classification. Twenty-second international joint conference on artificial intelligence.
   Google Scholar
• Coskun, O. 2016. Separation techniques: Chromatography. Northern Clinics of Istanbul 3 (2):156–60.
   PubMed Web of Science ®Google Scholar
• Cosme, F., J. Milheiro, J. Pires, F. I. Guerra-Gomes, L. Filipe-Ribeiro, and F. M. Nunes. 2021. Authentication of Douro DO monovarietal red wines based on anthocyanin profile: Comparison of partial least squares: Discriminant analysis, decision trees and artificial neural networks. Food Control 125:107979. doi: 10.1016/j.foodcont.2021.107979.
   Web of Science ®Google Scholar
• Cozzolino, D. 2022. An overview of the successful application of vibrational spectroscopy techniques to quantify nutraceuticals in fruits and plants. Foods 11 (3):315. doi: 10.3390/foods11030315.
   PubMed Web of Science ®Google Scholar
• Cui, C, and T. Fearn. 2018. Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration. Chemometrics and Intelligent Laboratory Systems 182:9–20. doi: 10.1016/j.chemolab.2018.07.008.
   Web of Science ®Google Scholar
• D’Orazio, G., C. Fanali, M. Asensio-Ramos, and S. Fanali. 2017. Chiral separations in food analysis. TrAC Trends in Analytical Chemistry 96:151–71. doi: 10.1016/j.trac.2017.05.013.
   Web of Science ®Google Scholar
• da Costa, N. L., M. S. da Costa, and R. Barbosa. 2021. A review on the application of chemometrics and machine learning algorithms to evaluate beer authentication. Food Analytical Methods 14 (1):136–55. doi: 10.1007/s12161-020-01864-7.
   Web of Science ®Google Scholar
• da Silva, C. E. T., V. L. Filardi, I. M. Pepe, M. A. Chaves, and C. M. S. Santos. 2015. Classification of food vegetable oils by fluorimetry and artificial neural networks. Food Control 47:86–91. doi: 10.1016/j.foodcont.2014.06.030.
   Web of Science ®Google Scholar
• Debska, B, and B. Guzowska-Swider. 2011. Application of artificial neural network in food classification. Analytica Chimica Acta 705 (1-2):283–91. doi: 10.1016/j.aca.2011.06.033.
   PubMed Web of Science ®Google Scholar
• Debus, B., H. Parastar, P. Harrington, and D. Kirsanov. 2021. Deep learning in analytical chemistry. TrAC Trends in Analytical Chemistry 145:116459. doi: 10.1016/j.trac.2021.116459.
   Web of Science ®Google Scholar
• Deng, J., W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition 2009:248–55.
   Google Scholar
• Deng, X., S. Cao, and A. L. Horn. 2021. Emerging applications of machine learning in food safety. Annual Review of Food Science and Technology 12:513–38.
   PubMed Web of Science ®Google Scholar
• Dooley, D. M., E. J. Griffiths, G. S. Gosal, P. L. Buttigieg, R. Hoehndorf, M. C. Lange, L. M. Schriml, F. S. L. Brinkman, and W. W. L. Hsiao. 2018. FoodOn: A harmonized food ontology to increase global food traceability, quality control and data integration. Npj Science of Food 2 (1):1–10. doi: 10.1038/s41538-018-0032-6.
   PubMedGoogle Scholar
• Esonye, C., O. D. Onukwuli, and A. U. Ofoefule. 2019. Optimization of methyl ester production from Prunus amygdalus seed oil using response surface methodology and artificial neural networks. Renewable Energy 130:61–72. doi: 10.1016/j.renene.2018.06.036.
   Web of Science ®Google Scholar
• Fang, S. B., Z. M. Shao, D. A. Kerr, C. J. Boushey, and F. Q. Zhu. 2019. An end-to-end image-based automatic food energy estimation technique based on learned energy distribution images: Protocol and methodology. Nutrients 11 (4):877. doi: 10.3390/nu11040877.
   PubMed Web of Science ®Google Scholar
• Gonzalez Viejo, C., S. Fuentes, A. Godbole, B. Widdicombe, and R. R. Unnithan. 2020. Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. Sensors and Actuators B: Chemical 308:127688. doi: 10.1016/j.snb.2020.127688.
   Web of Science ®Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, and Y. Bengio. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
   Google Scholar
• Guan, B., H. Ding, B. Chen, M. Zhou, and Z. Xue. 2021. Rapid qualitative and quantitative detection of formaldehyde in squids based on colorimetric sensor array. E3S Web of Conferences 233:02021. doi: 10.1051/e3sconf/202123302021.
   Google Scholar
• Gupta, H., A. Sharma, S. Kumar, and S. K. Roy. 2010. E-tongue: a tool for taste evaluation. Recent Patents on Drug Delivery & Formulation 4 (1):82–9. 10.2174/187221110789957309. 19807680
   PubMedGoogle Scholar
• Hai, A., G. Bharath, M. Daud, K. Rambabu, I. Ali, S. W. Hasan, P. Show, and F. Banat. 2021. Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling. Chemosphere 283:131162. doi: 10.1016/j.chemosphere.2021.131162.
   PubMed Web of Science ®Google Scholar
• Haiyan, W. 2020. China and central asian countries jointly building the digital silk road: Foundations, challenges and paths. China Int’l Stud 82:141.
   Google Scholar
• Hamilton, W., Z. Ying, and J. Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–8.
   Google Scholar
• Hochreiter, S, and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9 (8):1735–80.
   PubMed Web of Science ®Google Scholar
• Huang, G., L-m Yuan, W. Shi, X. Chen, and X. Chen. 2022. Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum. Food Chemistry 372:131219.
   PubMed Web of Science ®Google Scholar
• Hussain, N., D. W. Sun, and H. B. Pu. 2019. Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends in Food Science & Technology 91:598–608. doi: 10.1016/j.tifs.2019.07.018.
   Web of Science ®Google Scholar
• Hwang, H., H. K. Jeong, H. K. Lee, G. W. Park, J. Y. Lee, S. Y. Lee, Y.-M. Kang, H. J. An, J. G. Kang, J.-H. Ko, et al. 2020. Machine learning classifies core and outer fucosylation of N-glycoproteins using mass spectrometry. Scientific Reports 10 (1):1–10. doi: 10.1038/s41598-019-57274-1.
   PubMed Web of Science ®Google Scholar
• Jha, K., A. Doshi, P. Patel, and M. Shah. 2019. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2:1–12. doi: 10.1016/j.aiia.2019.05.004.
   Google Scholar
• Jiang, S., W. Min, L. Liu, and Z. Luo. 2020. Multi-scale multi-view deep feature aggregation for food recognition. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 29:265–76.
   PubMed Web of Science ®Google Scholar
• Kalogiouri, N. P., R. Aalizadeh, M. E. Dasenaki, and N. S. Thomaidis. 2020. Application of high resolution mass spectrometric methods coupled with chemometric techniques in olive oil authenticity studies: A review. Analytica Chimica Acta 1134:150–73. doi: 10.1016/j.aca.2020.07.029.
   PubMed Web of Science ®Google Scholar
• Kantz, E. D., S. Tiwari, J. D. Watrous, S. Cheng, and M. Jain. 2019. Deep neural networks for classification of LC-MS spectral peaks. Analytical Chemistry 91 (19):12407–13.
   PubMed Web of Science ®Google Scholar
• Karami, H., M. Rasekh, and E. Mirzaee‐Ghaleh. 2020. Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation 44 (9):e14696. doi: 10.1111/jfpp.14696.
   Web of Science ®Google Scholar
• Karoui, R, and C. Blecker. 2011. Fluorescence spectroscopy measurement for quality assessment of food systems—A review. Food and Bioprocess Technology 4 (3):364–86. doi: 10.1007/s11947-010-0370-0.
   Web of Science ®Google Scholar
• Kawano, Y, and K. Yanai. 2014. Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. European Conference on Computer Vision, 3–17.
   Google Scholar
• Kensert, A., R. Bouwmeester, K. Efthymiadis, P. Van Broeck, G. Desmet, and D. Cabooter. 2021. Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data. Analytical Chemistry 93 (47):15633–41.
   PubMed Web of Science ®Google Scholar
• Kipf, T. N, and M. Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv Preprint arXiv:1609.02907.
   Google Scholar
• Kress, G. 2009. Multimodality: A social semiotic approach to contemporary communication. London, UK: Routledge.
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 60 (6):84–90.
   Google Scholar
• LeCun, Y, and Y. Bengio. 1995. Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks 3361 (10):1995.
   Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11):2278–324. doi: 10.1109/5.726791.
   Web of Science ®Google Scholar
• Li, C., Y. Cui, J. Lu, L. Meng, C. Ma, Z. Liu, Y. Zhang, and W. Kang. 2021a. Spectrum-effect relationship of immunologic activity of Ganoderma lucidum by UPLC-MS/MS and component knock-out method. Food Science and Human Wellness 10 (3):278–88. doi: 10.1016/j.fshw.2021.02.019.
   Google Scholar
• Li, S., Y. Tian, P. Jiang, Y. Lin, X. Liu, and H. Yang. 2021. Recent advances in the application of metabolomics for food safety control and food quality analyses. Critical Reviews in Food Science and Nutrition 61 (9):1448–69. doi: 10.1080/10408398.2020.1761287.
   PubMed Web of Science ®Google Scholar
• Liang, N., S. S. Sun, C. Zhang, Y. He, and Z. J. Qiu. 2022. Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food. Critical Reviews in Food Science and Nutrition 62 (11):2963–84. doi: 10.1080/10408398.2020.1862045.
   PubMed Web of Science ®Google Scholar
• Liang, Z., A. Mahmoud Abdelshafy, Z. Luo, T. Belwal, X. Lin, Y. Xu, L. Wang, M. Yang, M. Qi, Y. Dong, et al. 2022. Occurrence, detection, and dissipation of pesticide residue in plant-derived foodstuff: A state-of-the-art review. Food Chemistry 384:384, Article 132494. doi: 10.1016/j.foodchem.2022.132494.
   Web of Science ®Google Scholar
• Lin, H., F. Wang, Y. Duan, W. Kang, Q. Chen, and Z. Xue. 2022. Early detection of wheat Aspergillus infection based on nanocomposite colorimetric sensor and multivariable models. Sensors and Actuators B: Chemical 351:130910. doi: 10.1016/j.snb.2021.130910.
   Web of Science ®Google Scholar
• Lin, H., Z. Wang, W. Ahmad, Z. Man, and Y. Duan. 2020. Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology. Journal of Stored Products Research 85:101523. doi: 10.1016/j.jspr.2019.101523.
   Web of Science ®Google Scholar
• Liu, G., M. Lu, X. Huang, T. Li, and D. Xu. 2018. Application of gold-nanoparticle colorimetric sensing to rapid food safety screening. Sensors 18 (12):4166. doi: 10.3390/s18124166.
   PubMed Web of Science ®Google Scholar
• Liu, J., L. Liu, W. Guo, M. Fu, M. Yang, S. Huang, F. Zhang, and Y. Liu. 2019. A new methodology for sensory quality assessment of garlic based on metabolomics and an artificial neural network. RSC Advances 9 (31):17754–65. doi: 10.1039/c9ra01978b.
   PubMed Web of Science ®Google Scholar
• Liu, J., J. Sun, and S. Wang. 2006. Pattern recognition: An overview. IJCSNS International Journal of Computer Science and Network Security, 6 (6):57–61.
   Google Scholar
• Liu, S., H. Jiang, S. Chen, J. Ye, R. He, and Z. Sun. 2020. Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning. Transportation Research Part E: Logistics and Transportation Review 142:102070. doi: 10.1016/j.tre.2020.102070.
   Web of Science ®Google Scholar
• Liu, Y., H. B. Pu, and D. W. Sun. 2021. Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends in Food Science & Technology 113:193–204. doi: 10.1016/j.tifs.2021.04.042.
   Web of Science ®Google Scholar
• Liu, Y., S. Zhou, W. Han, W. Liu, Z. Qiu, and C. Li. 2019b. Convolutional neural network for hyperspectral data analysis and effective wavelengths selection. Analytica Chimica Acta 1086:46–54. doi: 10.1016/j.aca.2019.08.026.
   PubMed Web of Science ®Google Scholar
• Liu, Z., J. Shi, J. Wan, Q. Pham, Z. Zhang, J. Sun, L. Yu, Y. Luo, T. T. Wang, and P. Chen. 2021. Profiling of polyphenols and glucosinolates in Kale and Broccoli microgreens grown under chamber and Windowsill conditions by ultrahigh-performance liquid chromatography high-resolution mass spectrometry. ACS Food Science & Technology 2 (1):101–13. doi: 10.1021/acsfoodscitech.1c00355.
   Google Scholar
• Lohumi, S., S. Lee, H. Lee, and B.-K. Cho. 2015. A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science & Technology 46 (1):85–98. doi: 10.1016/j.tifs.2015.08.003.
   Web of Science ®Google Scholar
• Lv, J., J. Wei, Z. Wang, and J. Cao. 2019. Multiple compounds recognition from the tandem mass spectral data using convolutional neural network. Molecules, 24 (24):4590. doi: 10.3390/molecules24244590.
   Web of Science ®Google Scholar
• Ma, P., C. P. Lau, N. Yu, A. Li, P. Liu, Q. Wang, and J. Sheng. 2021a. Image-based nutrient estimation for Chinese dishes using deep learning. Food Research International 147:110437. doi: 10.1016/j.foodres.2021.110437.
   PubMed Web of Science ®Google Scholar
• Ma, P., C. P. Lau, N. Yu, A. Li, and J. Sheng. 2022. Application of deep learning for image-based Chinese market food nutrients estimation. Food Chemistry 373 (Pt B):130994. doi: 10.1016/j.foodchem.2021.130994.
   PubMedGoogle Scholar
• Ma, P., A. Li, N. Yu, Y. Li, R. Bahadur, Q. Wang, and J. K. Ahuja. 2021. Application of machine learning for estimating label nutrients using USDA Global Branded Food Products Database (BFPD). Journal of Food Composition and Analysis 100:103857. doi: 10.1016/j.jfca.2021.103857.
   Web of Science ®Google Scholar
• Ma, P., J. Zhang, P. Liu, Q. Wang, Y. Zhang, K. Song, R. Li, and L. Shen. 2020. Computer-assisted design for stable and porous metal-organic framework (MOF) as a carrier for curcumin delivery. LWT 120:108949. doi: 10.1016/j.lwt.2019.108949.
   Web of Science ®Google Scholar
• Ma, P., J. Zhang, Z. Teng, Y. Zhang, G. R. Bauchan, Y. Luo, D. Liu, and Q. Wang. 2021. Metal–organic framework-stabilized high internal phase pickering emulsions based on computer simulation for curcumin encapsulation: Comprehensive characterization and stability mechanism. ACS Omega 6 (40):26556–65. doi: 10.1021/acsomega.1c03932.
   PubMed Web of Science ®Google Scholar
• Ma, P., Z. Zhang, Y. Li, N. Yu, J. Sheng, H. Küçük McGinty, Q. Wang, and J. K. Ahuja. 2022. Deep learning accurately predicts food categories and nutrients based on ingredient statements. Food Chemistry 391:133243. doi: 10.1016/j.foodchem.2022.133243.
   PubMed Web of Science ®Google Scholar
• Ma, P., Z. Zhang, W. Xu, Z. Teng, Y. Luo, C. Gong, and Q. Wang. 2021. Integrated portable shrimp-freshness prediction platform based on ice-templated metal–organic framework colorimetric combinatorics and deep convolutional neural networks. ACS Sustainable Chemistry & Engineering 9 (50):16926–36. doi: 10.1021/acssuschemeng.1c04704.
   Web of Science ®Google Scholar
• Mandal, B., N. B. Puhan, and A. Verma. 2019. Deep convolutional generative adversarial network-based food recognition using partially labeled data. IEEE Sensors Letters 3 (2):1–4. doi: 10.1109/LSENS.2018.2886427.
   Google Scholar
• Marvin, H. J., Y. Bouzembrak, H. J. van der Fels-Klerx, C. Kempenaar, R. Veerkamp, A. Chauhan, S. Stroosnijder, J. Top, G. Simsek-Senel, H. Vrolijk, et al. 2022. Digitalisation and artificial Intelligence for sustainable food systems. Trends in Food Science & Technology 120:344–8. doi: 10.1016/j.tifs.2022.01.020.
   Web of Science ®Google Scholar
• Masteghin, M. G., D. R. Godoi, and M. O. Orlandi. 2019. Heating method effect on SnO micro-disks as NO2 gas sensor. Frontiers in Materials 6:171. doi: 10.3389/fmats.2019.00171.
   Web of Science ®Google Scholar
• Matindoust, S., M. Baghaei-Nejad, M. H. S. Abadi, Z. Zou, and L.-R. Zheng. 2016. Food quality and safety monitoring using gas sensor array in intelligent packaging. Sensor Review 36 (2):169–83. doi: 10.1108/SR-07-2015-0115.
   Web of Science ®Google Scholar
• Matsuda, Y., H. Hoashi, and K. Yanai. 2012. Recognition of multiple-food images by detecting candidate regions. 2012 IEEE International Conference on Multimedia and Expo, 25–30.
   Google Scholar
• Melnikov, A. D., Y. P. Tsentalovich, and V. V. Yanshole. 2020. Deep learning for the precise peak detection in high-resolution LC–MS data. Analytical Chemistry 92 (1):588–92.
   PubMed Web of Science ®Google Scholar
• Mendes, E, and N. Duarte. 2021. Mid-infrared spectroscopy as a valuable tool to tackle food analysis: A literature review on coffee, dairies, honey, olive oil and wine. Foods 10 (2):477. doi: 10.3390/foods10020477.
   PubMed Web of Science ®Google Scholar
• Min, W., Z. Wang, Y. Liu, M. Luo, L. Kang, X. Wei, X. Wei, and S. Jiang. 2021. Large scale visual food recognition. arXiv Preprint arXiv:2103.16107.
   Google Scholar
• Moros, J., S. Garrigues, and M. de la Guardia. 2010. Vibrational spectroscopy provides a green tool for multi-component analysis. TrAC Trends in Analytical Chemistry 29 (7):578–91. doi: 10.1016/j.trac.2009.12.012.
   Web of Science ®Google Scholar
• Nallan Chakravartula, S. S., R. Moscetti, G. Bedini, M. Nardella, and R. Massantini. 2022. Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee. Food Control 135:108816. doi: 10.1016/j.foodcont.2022.108816.
   Web of Science ®Google Scholar
• National Academies of Sciences, E., and Medicine. 2019. Science breakthroughs to advance food and agricultural research by 2030, 10–30. Washington, DC, USA: National Academies Press.
   Google Scholar
• Neto, H. A., W. L. F. Tavares, D. Ribeiro, R. C. O. Alves, L. M. Fonseca, and S. V. A. Campos. 2019. On the utilization of deep and ensemble learning to detect milk adulteration. BioData Mining 12:Article 13. doi: 10.1186/s13040-019-0200-5.
   PubMed Web of Science ®Google Scholar
• Ng, W., B. Minasny, M. Montazerolghaem, J. Padarian, R. Ferguson, S. Bailey, and A. B. McBratney. 2019. Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma 352:251–67. doi: 10.1016/j.geoderma.2019.06.016.
   Web of Science ®Google Scholar
• Nie, P., J. Zhang, X. Feng, C. Yu, and Y. He. 2019. Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sensors and Actuators B: Chemical 296:126630. doi: 10.1016/j.snb.2019.126630.
   Web of Science ®Google Scholar
• Okur, S., M. Sarheed, R. Huber, Z. Zhang, L. Heinke, A. Kanbar, C. Wöll, P. Nick, and U. Lemmer. 2021. Identification of mint scents using a QCM based e-nose. Chemosensors 9 (2):31. doi: 10.3390/chemosensors9020031.
   Web of Science ®Google Scholar
• World Health Organization. 2015. Guidance document for the integration of data in foscollab.
   Google Scholar
• Pradana-Lopez, S., A. M. Perez-Calabuig, J. C. Cancilla, M. A. Lozano, C. Rodrigo, M. L. Mena, and J. S. Torrecilla. 2021. Deep transfer learning to verify quality and safety of ground coffee. Food Control. 122:107801. doi: 10.1016/j.foodcont.2020.107801.
   Web of Science ®Google Scholar
• Pranoto, W. J., S. G. Al-Shawi, P. Chetthamrongchai, T.-C. Chen, E. Petukhova, N. Nikolaeva, and S. Aravindhan. 2021. Employing artificial neural networks and fluorescence spectrum for food vegetable oils identification. Food Science and Technology 42, 1.
   Web of Science ®Google Scholar
• Pu, Z.-J., S.-J. Yue, H. Yan, Y.-P. Tang, Y.-Y. Chen, Y.-J. Tan, X.-Q. Shi, Z.-H. Zhu, H.-J. Tao, J.-Q. Chen, et al. 2020. Analysis and evaluation of nucleosides, nucleobases, and amino acids in safflower from different regions based on ultra high performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry. Journal of Separation Science 43 (16):3170–82. doi: 10.1002/jssc.202000180.
   PubMed Web of Science ®Google Scholar
• Qin, J. Y., Y. B. Ying, and L. J. Xie. 2013. The detection of agricultural products and food using terahertz spectroscopy: A review. Applied Spectroscopy Reviews 48 (6):439–57. doi: 10.1080/05704928.2012.745418.
   Web of Science ®Google Scholar
• Qiu, Z., J. Chen, Y. Zhao, S. Zhu, Y. He, and C. Zhang. 2018. Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Applied Sciences, 8 (2):212. doi: 10.3390/app8020212.
   Google Scholar
• Rasekh, M, and H. Karami. 2021. E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices. International Journal of Food Properties 24 (1):592–602. doi: 10.1080/10942912.2021.1908354.
   Web of Science ®Google Scholar
• Reich, G. 2005. Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications. Advanced Drug Delivery Reviews 57 (8):1109–43. doi: 10.1016/j.addr.2005.01.020.
   PubMed Web of Science ®Google Scholar
• Ren, G., T. Li, Y. Wei, J. Ning, and Z. Zhang. 2021. Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis. Microchemical Journal 163:105899. doi: 10.1016/j.microc.2020.105899.
   Web of Science ®Google Scholar
• Rifna, E. J., R. Pandiselvam, A. Kothakota, K. V. Subba Rao, M. Dwivedi, M. Kumar, R. Thirumdas, and S. V. Ramesh. 2022. Advanced process analytical tools for identification of adulterants in edible oils-A review. Food Chemistry 369: 130898. doi: 10.1016/j.foodchem.2021.130898.
   PubMed Web of Science ®Google Scholar
• Ruder, S. 2017. An overview of multi-task learning in deep neural networks. arXiv Preprint arXiv:1706.05098.
   Google Scholar
• Sanchez-Lengeling, B., C. Outeiral, G. L. Guimaraes, and A. Aspuru-Guzik. 2017. Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC).
   Google Scholar
• Semenov, V., S. Volkov, M. Khaydukova, A. Fedorov, I. Lisitsyna, D. Kirsanov, and A. Legin. 2019. Determination of three quality parameters in vegetable oils using potentiometric e-tongue. Journal of Food Composition and Analysis, 75:75–80. doi: 10.1016/j.jfca.2018.09.015.
   Web of Science ®Google Scholar
• Simonyan, K, and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556.
   Google Scholar
• Smith, J. S., B. T. Nebgen, R. Zubatyuk, N. Lubbers, C. Devereux, K. Barros, S. Tretiak, O. Isayev, and A. Roitberg. 2019. Outsmarting quantum chemistry through transfer learning.
   Google Scholar
• Su, W. H, and D. W. Sun. 2018. Fourier transform infrared and Raman and hyperspectral imaging techniques for quality determinations of powdery foods: A review. Comprehensive Reviews in Food Science and Food Safety 17 (1):104–22. doi: 10.1111/1541-4337.12314.
   PubMed Web of Science ®Google Scholar
• Surareungchai, S., C. Borompichaichartkul, C. Rachtanapun, N. Pongprasert, P. Jitareerat, and V. Srilaong. 2021. Simplify product safety and quality risk analysis of raw materials for conventional, soilless culture and organic salads. Food Control 130:108359. doi: 10.1016/j.foodcont.2021.108359.
   Web of Science ®Google Scholar
• Sutton, R. S, and A. G. Barto. 2018. Reinforcement learning: An introduction. Cambridge, Massachusetts, USA: MIT press.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, and A. Rabinovich. 2015. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.
   Google Scholar
• Tan, J, and W. L. Kerr. 2018. Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS). Journal of the Science of Food and Agriculture 98 (10):3851–9. doi: 10.1002/jsfa.8901.
   PubMed Web of Science ®Google Scholar
• Tan, J, and J. Xu. 2020. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture 4:104–15. doi: 10.1016/j.aiia.2020.06.003.
   Google Scholar
• Tsai, T.-H., M. Wang, and H. W. Ressom. 2016. Preprocessing and analysis of LC-MS-based proteomic data. In Statistical analysis in proteomics, 63–76. Berlin/Heidelberg, Germany: Springer.
   Google Scholar
• Unluturk, M. S., S. Kucukyasar, and F. Pazir. 2021. Classification of organic and conventional olives using convolutional neural networks. Neural Computing and Applications 33 (23):16733–44. doi: 10.1007/s00521-021-06269-z.
   Web of Science ®Google Scholar
• Vasafi, P. S., O. Paquet-Durand, K. Brettschneider, J. Hinrichs, and B. Hitzmann. 2021. Anomaly detection during milk processing by autoencoder neural network based on near-infrared spectroscopy. Journal of Food Engineering 299:110510. doi: 10.1016/j.jfoodeng.2021.110510.
   Web of Science ®Google Scholar
• Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, and I. Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems, 30.
   Google Scholar
• Velickovic, P., G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2017. Graph attention networks. Stat 1050:20.
   Google Scholar
• Vinyals, O., C. Blundell, T. Lillicrap, and D. Wierstra. 2016. Matching networks for one shot learning. Advances in Neural Information Processing Systems, 29.
   Google Scholar
• Wang, G., S. Huang, H. He, J. Cheng, T. Zhang, Z. Fu, S. Zhang, Y. Zhou, H. Li, and X. Liu. 2022. Fabrication of a “progress bar” colorimetric strip sensor array by dye-mixing method as a potential food freshness indicator. Food Chemistry 373 (Pt B):131434. doi: 10.1016/j.foodchem.2021.131434.
   PubMedGoogle Scholar
• Wei, J., X. Wang, Z. Wang, and J. Cao. 2021. Qualitative detection of pesticide residues using mass spectral data based on convolutional neural network. SN Applied Sciences 3 (7):1–13. doi: 10.1007/s42452-021-04661-x.
   Web of Science ®Google Scholar
• Wen, Y., H. Liu, P. Han, Y. Gao, F. Luan, and X. Li. 2010. Determination of melamine in milk powder, milk and fish feed by capillary electrophoresis: A good alternative to HPLC. [Article]Journal of the Science of Food and Agriculture 90 (13):2178–82. doi: 10.1002/jsfa.4066.
   PubMed Web of Science ®Google Scholar
• West, J., D. Ventura, and S. Warnick. 2007. Spring research presentation: A theoretical foundation for inductive transfer. Brigham Young University. College of Physical and Mathematical Sciences, 1.
   Google Scholar
• Woldegebriel, M, and E. Derks. 2017. Artificial neural network for probabilistic feature recognition in liquid chromatography coupled to high-resolution mass spectrometry. Analytical Chemistry 89 (2):1212–21.
   PubMed Web of Science ®Google Scholar
• Wu, D, and D. W. Sun. 2013. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review – Part II: Applications. Innovative Food Science & Emerging Technologies 19:15–28. doi: 10.1016/j.ifset.2013.04.016.
   Web of Science ®Google Scholar
• Wu, N., Y. Zhang, R. Na, C. Mi, S. Zhu, Y. He, and C. Zhang. 2019. Variety identification of oat seeds using hyperspectral imaging: Investigating the representation ability of deep convolutional neural network. RSC Advances 9 (22):12635–44. doi: 10.1039/c8ra10335f.
   PubMed Web of Science ®Google Scholar
• Wu, X., X. Fu, Y. Liu, E.-P. Lim, S. C. Hoi, and Q. Sun. 2021. A large-scale benchmark for food image segmentation. Proceedings of the 29th ACM International Conference on Multimedia, 506–15.
   Google Scholar
• Xiao-Wei, H., Z. Xiao-Bo, S. Ji-Yong, L. Zhi-Hua, and Z. Jie-Wen. 2018. Colorimetric sensor arrays based on chemo-responsive dyes for food odor visualization. Trends in Food Science & Technology 81:90–107. 10.1016/j.tifs.2018.09.001.
   Web of Science ®Google Scholar
• Xin, Z., S. Jun, T. Yan, C. Quansheng, W. Xiaohong, and H. Yingying. 2020. A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves. Chemometrics and Intelligent Laboratory Systems 200:103996. doi: 10.1016/j.chemolab.2020.103996.
   Web of Science ®Google Scholar
• Xu, K., W. Hu, J. Leskovec, and S. Jegelka. 2018. How powerful are graph neural networks? arXiv Preprint arXiv:1810.00826.
   Google Scholar
• Yang, Q., H. Ji, H. Lu, and Z. Zhang. 2021. Prediction of liquid chromatographic retention time with graph neural networks to assist in small molecule identification. Analytical Chemistry 93 (4):2200–6.
   PubMed Web of Science ®Google Scholar
• Yu, D., X. Wang, H. Liu, and Y. Gu. 2019. A multitask learning framework for multi-property detection of wine. IEEE Access 7:123151–7. doi: 10.1109/ACCESS.2019.2937599.
   Google Scholar
• Yu, T.-K., Y.-T. Chang, S.-P. Hung, J.-M. Lu, J.-H. Peng, and S.-F. Chen. 2021. Development of convolutional neural network based models for the prediction of specialty coffee aroma using gas chromatography-mass spectrometry. 2021 ASABE Annual International Virtual Meeting, 1.
   Google Scholar
• Yu, X., H. Lu, and Q. Liu. 2018. Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf. Chemometrics and Intelligent Laboratory Systems 172:188–93. doi: 10.1016/j.chemolab.2017.12.010.
   Web of Science ®Google Scholar
• Yuanyuan, C, and W. Zhibin. 2018. Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks. Chemometrics and Intelligent Laboratory Systems 181:1–10. doi: 10.1016/j.chemolab.2018.08.001.
   Web of Science ®Google Scholar
• Zaremba, W., I. Sutskever, and O. Vinyals. 2014. Recurrent neural network regularization. arXiv Preprint arXiv:1409.2329.
   Google Scholar
• Zhang, C., W. Wu, L. Zhou, H. Cheng, X. Ye, and Y. He. 2020. Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging. Food Chemistry 319:126536. doi: 10.1016/j.foodchem.2020.126536.
   PubMed Web of Science ®Google Scholar
• Zhang, F., P. Zhan, H. Tian, Z. Wei, and P. Wang. 2018. Optimization of HS-SPME using artificial neural network and response surface methodology in combination with experimental design for determination of volatile components by gas chromatography-mass spectrometry in Korla Pear Juice. Food Analytical Methods 11 (8):2218–28. doi: 10.1007/s12161-018-1173-6.
   Web of Science ®Google Scholar
• Zhang, X., J. Xu, J. Yang, L. Chen, H. Zhou, X. Liu, H. Li, T. Lin, and Y. Ying. 2020. Understanding the learning mechanism of convolutional neural networks in spectral analysis. Analytica Chimica Acta 1119:41–51. doi: 10.1016/j.aca.2020.03.055.
   PubMed Web of Science ®Google Scholar
• Zhang, X. L., J. Yang, T. Lin, and Y. B. Ying. 2021. Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends in Food Science & Technology 112:431–41. doi: 10.1016/j.tifs.2021.04.008.
   Web of Science ®Google Scholar
• Zhou, L., C. Zhang, F. Liu, Z. J. Qiu, and Y. He. 2019. Application of deep learning in food: A review. Comprehensive Reviews in Food Science and Food Safety 18 (6):1793–811. doi: 10.1111/1541-4337.12492.
   PubMed Web of Science ®Google Scholar
• Zhou, X., J. Sun, Y. Tian, B. Lu, Y. Hang, and Q. Chen. 2020. Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce. Food Chemistry 321:126503. doi: 10.1016/j.foodchem.2020.126503.
   PubMed Web of Science ®Google Scholar
• Zhou, Z., X. Li, and R. N. Zare. 2017. Optimizing chemical reactions with deep reinforcement learning. ACS Central Science 3 (12):1337–44.
   PubMed Web of Science ®Google Scholar
• Zhu, B., C.-W. Ngo, J. Chen, and Y. Hao. 2019. R2gan: Cross-modal recipe retrieval with generative adversarial network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11477–86.
   Google Scholar