Strategic Short Note: Artificial Intelligence in Food Quality Assessments
Author:
Funder
Agricultural Research Organization
Japanese Society of Promotion Science
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1263-2_10
Reference13 articles.
1. Addanki, M., Patra, P., & Kandra, P. (2022). Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied Food Research, 2(2), 100126. https://doi.org/10.1016/j.afres.2022.100126
2. Anwar, H., Anwar, T., & Murtaza, S. (2023). Review on food quality assessment using machine learning and electronic nose system. Biosensors and Bioelectronics: X, 14(May), 100365. https://doi.org/10.1016/j.biosx.2023.100365
3. Arshad, F., Deliorman, M., Sukumar, P., Qasaimeh, M. A., Olarve, J. S., Santos, G. N., Bansal, V., & Ahmed, M. U. (2023). Recent developments and applications of nanomaterial-based lab-on-a-chip devices for sustainable agri-food industries. Trends in Food Science and Technology, 136(April), 145–158. https://doi.org/10.1016/j.tifs.2023.04.010
4. Chakravartula, S. S. N., Moscetti, R., Bedini, G., Nardella, M., & Massantini, R. (2022). Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee. Food Control, 135(December 2021), 108816. https://doi.org/10.1016/j.foodcont.2022.108816
5. Cui, D. D., Liu, Y., Chen, Y. P., Feng, X., Lu, Y., & Yu, B. (2020). Application of SPME-GC-TOFMS, E-nose, and sensory evaluation to investigate the flavor characteristics of Chinese Yunnan coffee at three different conditions (beans, ground powder, and brewed coffee). Flavour and Fragrance Journal, 35(5), 541–560. https://doi.org/10.1002/ffj.3597
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