To reveal potent ACE inhibitors, researchers screen various bioactive peptides from several sources, and more attention has been given to aquatic sources. This review summarizes the recent research achievements on marine peptides with ACE-inhibitory action and application. Marine peptides are considered excellent bioactives due to their large structural diversity and unusual bioactivities. The mechanisms by which these marine peptides inhibit ACE include competitive binding to ACEs’ active site, interfering with ACE conformational changes, and avoiding the identification of substrates. The unique 3D attributes of marine peptides confer inhibition advantages toward ACE activity. Because IC₅₀ values of marine peptides’ interaction with ACE are low, structure-based research assumes that the interaction between ACE and peptides increased the therapeutic application. Numerous studies on marine peptides focused on the sustainable extraction of ACE-inhibitory peptides produced from several fish, mollusks, algae, and sponges. Meanwhile, their potential applications and medical benefits are worth investigating and considering. Due to these peptides exhibiting antioxidant, antihypertensive, and even antimicrobial properties simultaneously, their therapeutic potential for cardiovascular disease and other illnesses only increases. In addition, as marine peptides show better pharmacological benefits, they have increased absorption rates and low toxicity and could perhaps be modified for better stability and bioefficacy. Biotechnological advances in peptide synthesis and formulation have greatly facilitated the generation of peptide-based ACE inhibitors from marine sources, which subsequently offer new treatment models. This article gives a complete assessment of the present state of knowledge about marine organism peptides as ACE inhibitors. In addition, it emphasizes the relevance of additional investigation into their mechanisms of action, the optimization of manufacturing processes, and assessment in in vivo, preclinical, and clinical settings, underlining the urgency and value of this study. Using marine peptides for ACE inhibition not only broadens the repertory of bioactive compounds but also shows promise for tackling the global health burden caused by cardiovascular diseases.

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900-1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp²) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.

This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.

In the present investigation, we have devised a hyperspectral imaging (HSI) apparatus to assess the chemical characteristics and freshness of the yellow croaker (Larimichthys polyactis) throughout its storage period. This system operates within the shortwave infrared spectrum, specifically ranging from 900 to 1700 nm. A variety of spectral pre-processing techniques, including standard normal variate (SNV), multiple scatter correction, and Savitzky–Golay (SG) derivatives, were employed to augment the predictive accuracy of total volatile basic nitrogen (TVB-N)—which serves as a critical freshness parameter. Among the assessed methodologies, SG-1 pre-processing demonstrated superior predictive accuracy (R_p² = 0.8166). Furthermore, this investigation visualized freshness indicators as concentration images to elucidate the spatial distribution of TVB-N across the samples. These results indicate that HSI, in conjunction with chemometric analysis, constitutes an efficacious instrument for the surveillance of quality and safety in yellow croakers during its storage phase. Moreover, this methodology guarantees the freshness and safety of seafood products within the aquatic food sector.

Seafood quality preservation remains a critical focus in the food industry, particularly as the freeze–thaw process significantly impacts the freshness and safety of aquatic products. This study investigated quality changes in frozen mackerel subjected to two thawing methods, room temperature (RT) and running water (WT), and assessed the potential of hyperspectral imaging (HSI) for classifying these methods. After thawing, mackerel samples were stored at 5 °C for 21 days, with physicochemical, textural, and spectroscopic analyses tracking quality changes and supporting the development of a spectroscopic classification model. Compared with the WT method, the RT method delayed changes in key quality indicators, including pH, total volatile basic nitrogen (TVB-N), and total viable count (TVC), by 1–2 days, suggesting it may better preserve initial quality. Texture profile analysis showed similar trends, supporting the benefit of RT in maintaining quality. A major focus was on using HSI to assess quality and classify thawing methods. HSI achieved high classification accuracy (R_c² = 0.9547) in distinguishing thawing methods up to three days post-thaw, with 1100, 1200, and 1400 nm wavelengths identified as key spectral markers. The HIS’s ability to detect differences between thawing methods, even when conventional analyses showed minimal variation, highlights its potential as a powerful tool for quality assessment and process control in the seafood industry, enabling detection of subtle quality changes that traditional methods may miss.