Nutritional and Sensory Quality of the Freshwater Prawn Macrobrachium rosenbergii and the Influence of Packaging Permeability on its Shelf Life

Developing colorimetric ammonia-sensing nanocomposite films based on potato starch/PVA and ZnCu-BTC nanorods for real-time monitoring food freshness

Smart packaging material capable of real-time monitoring of food freshness is essential for ensuring food safe. At present, colorimetric ammonia-sensing smart film often possesses issues with complicated production, high cost, and inferior long-term colour stability. Herein, Zinc‑copper bimetallic organic framework (ZnCu-BTC, BTC = 1,3,5-benzenetricarboxylate acid) nanorods with colorimetric ammonia-responsiveness were synthesized by adopting facile aqueous solution method, which were then explored as nano inclusions in potato starch/polyvinyl alcohol (PS/PVA) composite film towards developing high-performance smart packaging material. The results demonstrated that the introduction of ZnCu-BTC nanorods within PS/PVA brought about remarkable improvement in blend compatibility, accompanied by a boost in tensile strength to 47.2 MPa, as well as enhanced ultraviolet (UV) blocking efficacy (over 95.0 %). Additionally, the barrier properties of PS/PVA film against water vapor and oxygen were fortified due to the addition of ZnCu-BTC. More importantly, the developed PS/PVA/ZnCu-BTC nanocomposite film displayed satisfactory antibacterial activity (over 99 %) against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), favorable colorimetric ammonia-sensing ability, and long-term colour stability. The ZnCu-BTC incorporated PS/PVA nanocomposite film could grant real-time detection of prawn freshness decline via remarkable colour change, indicating vast promise for smart food packaging applications.

Applications of high-intensity ultrasound on shrimp: Potential, constraints, and prospects in the extraction and retrieval of bioactive compounds, safety, and quality

The global shrimp market holds substantial prominence within the food industry, registering a significant USD 24.7 billion in worldwide exportation in 2020. However, the production of a safe and high-quality product requires consideration of various factors, including the potential for allergenic reactions, occurrences of foodborne outbreaks, and risks of spoilage. Additionally, the exploration of the recovery of bioactive compounds (e.g., astaxanthin [AX], polyunsaturated fatty acids, and polysaccharides) from shrimp waste demands focused attention. Within this framework, this review seeks to comprehend and assess the utilization of high-intensity ultrasound (HIUS), both as a standalone method and combined with other technologies, within the shrimp industry. The objective is to evaluate its applications, limitations, and prospects, with a specific emphasis on delineating the impact of sonication parameters (e.g., power, time, and temperature) on various applications. This includes an examination of undesirable effects and identifying areas of interest for current and prospective research. HIUS has demonstrated promise in enhancing the extraction of bioactive compounds, such as AX, lipids, and chitin, while concurrently addressing concerns such as allergen reduction (e.g., tropomyosin), inactivation of pathogens (e.g., Vibrio parahaemolyticus), and quality improvement, manifesting in reduced melanosis scores and improved peelability. Nonetheless, potential impediments, particularly related to oxidation processes, especially those associated with lipids, pose a hindrance to its widespread implementation, potentially impacting texture properties. Consequently, further optimization studies remain imperative. Moreover, novel applications of sonication in shrimp processing, including brining, thawing, and drying, represent a promising avenue for expanding the utilization of HIUS in the shrimp industry.

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.