Marsh, K. & Bugusu, B. Food packaging—roles, materials, and environmental issues. J. Food Sci. 72, R39–R55 (2007).
Choudalakis, G. & Gotsis, A. Permeability of polymer/clay nanocomposites: a review. Eur. Polym. J. 45, 967–984 (2009).
Tyagi, P., Salem, K. S., Hubbe, M. A. & Pal, L. Advances in barrier coatings and film technologies for achieving sustainable packaging of food products–a review. Trends Food Sci. Technol. 115, 461–485 (2021).
Chen, Y. & Li, Y. Determination of water vapor transmission rate (WVTR) of HDPE bottles for pharmaceutical products. Int. J. Pharmaceutics 358, 137–143 (2008).
Jouppila, K. & Roos, Y. Water sorption and time-dependent phenomena of milk powders. J. Dairy Sci. 77, 1798–1808 (1994).
Geijer, T. Plastic packaging in the food sector. Six ways to tackle the plastic puzzle. ING THINK Economic and Financial Analysis, (ING Group, Amsterdam, 2019) Available at: https://think.ing.com/reports/plastic-packaging-in-the-food-sector-sixways-to-tackle-the-plastic-puzzle/.
Defruyt, S. Towards a new plastics economy. Field actions Science reports. The journal of field actions, 78-81 (2019).
European Parliament and Council. Regulation (EU) 2025/40 of the European Parliament and of the Council of 19 December 2024 on packaging and packaging waste, amending Regulation (EU) 2019/1020 and Directive (EU) 2019/904, and repealing Directive 94/62/EC. Official Journal of the European Union, L, 2025/40 (2025).
Ncube, L. K., Ude, A. U., Ogunmuyiwa, E. N., Zulkifli, R. & Beas, I. N. Environmental impact of food packaging materials: a review of contemporary development from conventional plastics to polylactic acid based materials. Materials 13, 4994 (2020).
Sangroniz, A. et al. Packaging materials with desired mechanical and barrier properties and full chemical recyclability. Nat. Commun. 10, 3559 (2019).
Lentschat, M., Buche, P., Dibie-Barthelemy, J., Menut, L. & Roche, M. Food packaging permeability and composition dataset dedicated to text-mining. Data Brief. 36, 107135 (2021).
Guillard, V. et al. The next generation of sustainable food packaging to preserve our environment in a circular economy context. Front. Nutr. 5, 121 (2018).
Schaarsberg, N. G. & van de Stadt, K. Toward sustainable packaging practices in the soap and detergents industry: a sector-specific tool. In Proc. IAPRI World Packaging Conference 2024. International Association of Packaging Research Institutes (IAPRI, 2024).
Rankin, J., Wolff, I., Davis, H. & Rist, C. Permeability of amylose film to moisture vapor, selected organic vapors, and the common gases. Ind. Eng. Chem. Chem. Eng. Data Ser. 3, 120–123 (1958).
Stocchetti, G. Technology that bridges the gap. Packag. Films 3, 16–18 (2012).
Makino, Y. & Hirata, T. Modified atmosphere packaging of fresh produce with a biodegradable laminate of chitosan-cellulose and polycaprolactone. Postharvest Biol. Technol. 10, 247–254 (1997).
Strantz, A. & Zottola, E. Bacterial survival on lean beef and Bologna wrapped with cornstarch-containing polyethylene film. J. Food Prot. 55, 782–786 (1992).
Ester, M., Kriegel, H. P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd 226-231 (1996).
Pezer, D. Application of cluster analysis for polymer classification according to mechanical properties. Technium 3, 67–75 (2021).
Stadlthanner, D., Steinkellner, H., Landschützer, C. & Kaever, D. A hierarchical density-based clustering method applied to mixed-mail in Austria. Logist. Res. 17, 1–17 (2024).
Wessling, M. et al. Modelling the permeability of polymers: a neural network approach. J. Membr. Sci. 86, 193–198 (1994).
Phan, B. K. et al. Gas permeability, diffusivity, and solubility in polymers: simulation-experiment data fusion and multi-task machine learning. npj Comput. Mater. 10, 186 (2024).
Wu, F., Misra, M. & Mohanty, A. K. Challenges and new opportunities on barrier performance of biodegradable polymers for sustainable packaging. Prog. Polym. Sci. 117, 101395 (2021).
Bae, H. J. et al. Effect of clay content, homogenization RPM, pH, and ultrasonication on mechanical and barrier properties of fish gelatin/montmorillonite nanocomposite films. LWT Food Sci. Technol. 42, 1179–1186 (2009).
Sharma, S. et al. Active film packaging based on bio-nanocomposite TiO2 and cinnamon essential oil for enhanced preservation of cheese quality. Food Chem. 405, 134798 (2023).
Peter, A. et al. Chemical and organoleptic changes of curd cheese stored in new and reused active packaging systems made of Ag-graphene-TiO2-PLA. Food Chem. 363, 130341 (2021).
Ligaj, M., Tichoniuk, M., Cierpiszewski, R. & Foltynowicz, Z. Efficiency of novel antimicrobial coating based on iron nanoparticles for dairy products’ packaging. Coatings 10, 156 (2020).
European Commission. (2009).
Park, H. M. et al. Preparation and properties of biodegradable thermoplastic starch/clay hybrids. Macromol. Mater. Eng. 287, 553–558 (2002).
Picard, E., Espuche, E. & Fulchiron, R. Effect of an organo-modified montmorillonite on PLA crystallization and gas barrier properties. Appl. Clay Sci. 53, 58–65 (2011).
Fortunati, E., Peltzer, M., Armentano, I., Jiménez, A. & Kenny, J. M. Combined effects of cellulose nanocrystals and silver nanoparticles on the barrier and migration properties of PLA nano-biocomposites. J. Food Eng. 118, 117–124 (2013).
Rhim, J.-W., Hong, S.-I. & Ha, C.-S. Tensile, water vapor barrier and antimicrobial properties of PLA/nanoclay composite films. LWT Food Sci. Technol. 42, 612–617 (2009).
Peelman, N. et al. Application of bioplastics for food packaging. Trends Food Sci. Technol. 32, 128–141 (2013).
Rhim, J.-W., Lee, J. H. & Ng, P. K. Mechanical and barrier properties of biodegradable soy protein isolate-based films coated with polylactic acid. LWT Food Sci. Technol. 40, 232–238 (2007).
Zong, Y. et al. Fabrication of antimicrobial and high-toughness poly (lactic acid) composite films using tung oil derivatives. Int. J. Biol. Macromol. 254, 127792 (2024).
Brandão, R. M. et al. Active packaging of poly (lactic acid) nanofibers and essential oils with antifungal action on table grapes. FEMS Microbiol. Lett. 369, fnac116 (2022).
Bascón-Villegas, I. et al. A new eco-friendly packaging system incorporating lignocellulose nanofibres from agri-food residues applied to fresh-cut lettuce. J. Clean. Prod. 372, 133597 (2022).
Wang, L., Zhang, Y., Xing, Q., Xu, J. & Li, L. Quality and microbial diversity of homemade bread packaged in cinnamaldehyde loaded poly (lactic acid)/konjac glucomannan/wheat gluten bilayer film during storage. Food Chem. 402, 134259 (2023).
Nivre, J. Dependency parsing. Lang. Linguist. Compass 4, 138–152 (2010).
Mehta, V., Bawa, S. & Singh, J. Analytical review of clustering techniques and proximity measures. Artif. Intell. Rev. 53, 5995–6023 (2020).
Nanjundan, S., Sankaran, S., Arjun, C. & Anand, G. P. Identifying the number of clusters for K-Means: a hypersphere density based approach. arXiv preprint https://doi.org/10.48550/arXiv.1912.00643 (2019).
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B. & Heming, J. K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. 622, 178–210 (2023).
Quiñones-Grueiro, M., Prieto-Moreno, A., Verde, C. & Llanes-Santiago, O. Data-driven monitoring of multimode continuous processes: a review. Chemometrics Intell. Lab. Syst. 189, 56–71 (2019).
Xuan, G., Zhang, W. & Chai, P. Filter pruning via expectation-maximization. In Proc. International Conference on Image Processing (Cat. No. 01CH37205). 145–148 (IEEE, 2001).
Cui, M. Introduction to the k-means clustering algorithm based on the elbow method. Account. Audit. Financ. 1, 5–8 (2020).
Shahapure, K. R. & Nicholas, C. Cluster Quality Analysis Using Silhouette Score. In Proc. IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). 747–748 (IEEE, 2020).
Vila, J. P. & Schniter, P. Expectation-maximization Gaussian-mixture approximate message passing. IEEE Trans. Signal Process. 61, 4658–4672 (2013).
Giri, K., Biswas, T. K. & Sarkar, P. ECR-DBSCAN: an improved DBSCAN based on computational geometry. Mach. Learn. Appl. 6, 100148 (2021).
Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
Davies, D. L. & Bouldin, D. W. A cluster separation measure. In Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, 224–227 (IEEE, 2009).