David, J., Edwards, D. & Wright, P. Revitalize audit: Erectile dysfunction and testosterone review in primary care. Diabetes Primary Care 1967–72 (2017).
Debnath, S., Rastogi, NK, Gopala Krishna, A. & Lokesh, B. Effect of frying cycles on physical, chemical and heat transfer quality of rice bran oil during deep-fat frying of poori: An indian traditional fried food. Food Bioprod. Process. 90, 249–256. https://doi.org/10.1016/j.fbp.2011.05.001 (2012).
Yang, J., Zhao, K. & He, Y. Quality evaluation of frying oil deterioration by dielectric spectroscopy. J. Food Eng. 180, 69–76. https://doi.org/10.1016/j.jfoodeng.2016.02.012 (2016).
Karimi, S., Wawire, M. & Mathooko, FM Impact of frying practices and frying conditions on the quality and safety of frying oils used by street vendors and restaurants in nairobi, kenya. J. Food Compos. Anal. 62, 239–244. https://doi.org/10.1016/j.jfca.2017.07.004 (2017).
Nayak, PK, Dash, U., Rayaguru, K. & Krishnan, KR Physio-chemical changes during repeated frying of cooked oil: A review. J. Food Biochem. 40, 371–390. https://doi.org/10.1111/jfbc.12215 (2016).
Choe, E. & Min, D. Chemistry of deep-fat frying oils. J. Food Sci. 72, R77–R86. https://doi.org/10.1111/j.1750-3841.2007.00352.x (2007).
Zhang, Q., Saleh, AS, Chen, J. & Shen, Q. Chemical alterations taken place during deep-fat frying based on certain reaction products: A review. Chem. Phys. Lipid. 165, 662–681. https://doi.org/10.1016/j.chemphyslip.2012.07.002 (2012).
Vorria, E., Giannou, V. & Tzia, C. Hazard analysis and critical control point of frying-safety assurance of fried foods. Eur. J. Lipid Sci. Technol. 106, 759–765. https://doi.org/10.1002/ejlt.200401033 (2004).
Gertz, C. Chemical and physical parameters as quality indicators of used frying fats. Eur. J. Lipid Sci. Technol. 102566–572 (2000).
Hosseini, H., Ghorbani, M., Meshginfar, N. & Mahoonak, AS A review on frying: procedure, fat, deterioration progress and health hazards. J. Am. Oil. Chem. Soc. 93445–466 (2016).
Dana, D., Blumenthal, MM & Saguy, IS The protective role of water injection on oil quality in deep fat frying conditions. Eur. Food Res. Technol. 217104–109 (2003).
Li, J., Cai, W., Sun, D. & Liu, Y. A quick method for determining total polar compounds of frying oils using electric conductivity. Food Anal. methods 91444–1450 (2016).
Chen, Y. et al. The analysis of trans fatty acid profiles in deep frying palm oil and chicken fillets with an improved gas chromatography method. food control 44191–197 (2014).
Tsuzuki, W., Matsuoka, A. & Ushida, K. Formation of trans fatty acids in edible oils during the frying and heating process. Food Chem. 123976–982 (2010).
Brühl, L. Fatty acid alterations in oils and fats during heating and frying. Eur. J. Lipid Sci. Technol. 116707–715 (2014).
Hammouda, IB et al. Comparative study of polymers and total polar compounds as indicators of refined oil degradation during frying. Eur. Food Res. Technol. 245967–976 (2019).
Liu, Y., Sun, L., Du, C. & Wang, X. Near-infrared prediction of edible oil frying times based on Bayesian ridge regression. Optik 218164950 (2020).
Tarmizi, AHA, Hishamuddin, E. & Abd Razak, RA Impartial assessment of oil degradation through partitioning of polar compounds in vegetable oils under simulated frying practice of fast food restaurants. food control 96445–455 (2019).
Ng, CL, Wehling, RL & Cuppett, SL Method for determining frying oil degradation by near-infrared spectroscopy. J. Agric. Food Chem. 55593–597 (2007).
Villringer, A., Planck, J., Hock, C., Schleinkofer, L. & Dirnagl, U. Near infrared spectroscopy (nirs): a new tool to study hemodynamic changes during activation of brain function in human adults. Neurosci. Lett. 154101–104 (1993).
Cascant, MM, Garrigues, S. & de la Guardia, M. Comparison of near and mid infrared spectroscopy as green analytical tools for the determination of total polar materials in fried oils. Microchem. J. 13555–59 (2017).
Kuligowski, J., Carrión, D., Quintás, G., Garrigues, S. & de la Guardia, M. Direct determination of polymerized triacylglycerides in deep-frying vegetable oil by near infrared spectroscopy using partial least squares regression. Food Chem. 131353–359 (2012).
Gertz, C., Fiebig, H.-J. & Hancock, JN Ft-near infrared (nir) spectroscopy-screening analysis of used frying fats and oils for rapid determination of polar compounds, polymerized triacylglycerols, acid value and anisidine value [dgf c-vi 21a (13)]. Eur. J. Lipid Sci. Technol. 1151193–1197 (2013).
Liu, X. et al. Model for prediction of the carbonyl value of frying oil from the initial composition. LWT 117108660 (2020).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521436–444 (2015).
Goodfellow, I. et al. Generative adversarial nets. Advances in neural information processing systems 27 (2014).
Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
Holt, CC Forecasting seasonals and trends by exponentially weighted moving averages. Int. J. Forecast. twenty5–10 (2004).
Stock, JH & Watson, MW Vector autoregressions. J. Econ. perspective fifteen101–115 (2001).
Hyndman, RJ & Koehler, AB Another look at measures of forecast accuracy. Int. J. Forecast. 22679–688 (2006).
Lehmann, EL & Casella, G. Theory of Point Estimation (Springer, 2006).
Khaled, AY, Abd Aziz, S. & Rokhani, FZ Capacitive sensor probe to assess frying oil degradation. Info.Process. agriculture two142–148 (2015).
Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition1125–1134 (2017).
Ho, J. & Ermon, S. Generative adversarial imitation learning. Adv. Neural. Info.Process. Syst. 294565–4573 (2016).
Zhang, H. et al. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision5907–5915 (2017).
Li, W., Ding, W., Sadasivam, R., Cui, X. & Chen, P. His-gan: A histogram-based gan model to improve data generation quality. Neural Netw. 11931–45 (2019).
Li, W. et al. Hausdorff GAN: Improving GAN generation quality with Hausdorff metric. IEEETrans. Cybern. PP, 1–13. https://doi.org/10.1109/tcyb.2021.3062396 (2021).
Li, W., Fan, L., Wang, Z., Ma, C. & Cui, X. Tackling mode collapse in multi-generator gans with orthogonal vectors. Pattern Recognition 110107646 (2021).