Recently, due to the enhancement in consumer awareness of food safety, considerable attention has been paid to intelligent packaging that displays the quality status of food through color changes. Natural food colorants show useful functionalities (antibacterial and antioxidant activities) and obvious color changes due to their structural changes in different acid and alkali environments, which could be applied to detect these acid and alkali environments, especially in the preparation of intelligent packaging. This review introduces the latest research on the progress of pH-responsive freshness indicators based on natural food colorants and biodegradable polymers for monitoring packaged food quality. Additionally, the current methods of detecting food freshness, the preparation methods for pH-responsive freshness indicators, and their applications for detecting the freshness of perishable food are highlighted. Subsequently, this review addresses the challenges and prospects of pH-responsive freshness indicators in food packaging, to assist in promoting their commercial application.

Food safety is a pressing concern for human society, as it directly impacts people’s lives, while food freshness serves as one of the most crucial indicators in ensuring food safety. There exist diverse techniques for monitoring food freshness, among which intelligent packaging based on artificial intelligence technology boasts the advantages of low cost, high efficiency, fast speed and wide applicability; however, it is currently underutilized. By analyzing the current research status of intelligent packaging both domestically and internationally, this paper provides a clear classification of intelligent packaging technology. Additionally, it outlines the advantages and disadvantages of using intelligent packaging technology for food freshness detection methods, while summarizing the latest research progress in applying artificial intelligence-based technologies to food freshness detection through intelligent packaging. Finally, the author points out the limitations of the current research, and anticipates future developments in artificial intelligence technology for assisting freshness detection in intelligent packaging. This will provide valuable insights for the future development of intelligent packaging in the field of food freshness detection.

This study reported the production of pH-responsive films based on 8 wt% polyvinyl alcohol solution/0.2 wt% agar solution incorporated with cochineal-loaded starch particles (CSN) (2, 4, 6 and 8 wt% on agar basis) by a casting process. Results revealed that CSN presented obvious color changes over the pH range of 2–12. FTIR, XRD spectra and SEM micrographs presented that the incorporation of CSN formed new hydrogen bonds with a matrix and a tighter network structure. A certain improvement was observed in the color stability, swelling index and functional properties (antimicrobial and antioxidant activities) but water solubility, water vapor permeability and water contact angle of the pH-responsive films were decreased by the addition of CSN. The release of cochineal was a rate-limiting step following the Korsmeyer-Peppas model. The agar/polyvinyl alcohol film containing 6% CSN (PVA/GG-6) exhibited the best sensitivity for ammonia detection and its limit of detection was 35.4 ppm (part per million) for ammonia. The application trials showed that the PVA/GG-6 film presented different color changes for pork freshness. Hence, these pH-responsive films can be used as potential packaging materials for tracking the freshness of protein-rich fresh food in a non-destructive way.

With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.

Layout analysis of elements is indispensable in graphic design, and layout design can make the audience receive visual information as well as more attractive. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved deeplabv3+ model. The method reduces the number of model parameters and training time by replacing the backbone network, and in order to improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model.