This study aims to elucidate the influences of magnetic fields (MFs) on the production of Monascus pigments (MPs) and citrinin by modulating both the intracellular and extracellular iron content in Monascus purpureus . The organism was treated with varying concentrations of L-allylglycine (L-AG), along with MFs of different intensities for 2 days during the early fermentation phase, as well as combinations of 1 mM L-AG and the MFs. The primary indicators assessed were the biomass, MPs production, citrinin production, and intracellular and extracellular iron content. The results indicate that exposure to MFs of varying intensities had no significant effect on the biomass of M. purpureus , while it significantly inhibited citrinin production. Conversely, exposure to a 1.6-mT MF significantly enhanced MPs production, while exposure to a 1.2- or 1.6-mT MF promoted the extracellular excretion of intracellular iron. Further, concentrations of 3 and 5 mM of L-AG significantly inhibited the growth of M. purpureus , whereas different concentrations of L-AG significantly inhibited MPs and citrinin production. Moreover, following treatment with both L-AG and MFs, all combinations exhibited a significant increase in MPs and citrinin production compared to L-AG treatment alone. In contrast, citrinin production in the group treated by L-AG with either a 1.2-mT or a 1.6-mT MF was significantly lower than that in the control group. Furthermore, intracellular iron content in the group treated by L-AG and a 1.6-mT or a 2.0-mT MF was reduced compared to that in the L-AG treatment group, with no significant difference from the control group. These findings suggest that appropriate exposure to MFs can decrease intracellular iron content and mitigate reductions in MPs and citrinin production induced by L-AG. Therefore, the regulation of MPs and citrinin synthesis induced by MFs in M. purpureus may be related to the intracellular iron content.
Drying of Gracilaria verrucosa remains challenging because its high initial moisture content and moisture-binding matrix prolong drying, increase energy demand, and affect product quality. This study investigated the effect of zeolite-assisted air dehumidification in a fixed-bed dryer through an integrated evaluation of drying behavior, moisture adsorption, moisture transport, energy utilization, and product quality. Drying experiments were conducted at target air temperatures of 30 – 60 °C with and without zeolite dehumidification and were compared with open sun drying. Zeolite-assisted drying lowered the relative humidity of the drying air and enhanced moisture removal, with the 60-Z treatment reaching a final moisture content of 5.25% (wet basis) after 6h compared with 16.82% for the corresponding non-zeolite treatment. The Page model adequately described the drying kinetics, while the effective moisture diffusivity increased with drying temperature and remained consistently higher under zeolite-assisted drying. Correspondingly, the apparent activation energy decreased from 49.63 to 36.55kJ/mol. At 50 °C, zeolite-assisted drying reduced the final specific energy consumption and CO2 emissions by 22.3% and 21.9%, respectively. Lower drying temperatures better preserved the hydration properties of the dried seaweed, whereas FTIR analysis indicated no detectable changes in the major functional groups after drying up to 60 °C. Collectively, these results clarify the influence of air dehumidification on drying performance, moisture transport, energy utilization, and quality characteristics of G. verrucosa, providing an experimental basis for evaluating zeolite-assisted dehumidification in seaweed drying under the investigated conditions.
Visible and near-infrared (Vis/NIR) spectroscopy and hyperspectral imaging (HSI), combined with artificial intelligence, offer rapid and nondestructive approaches for assessing meat quality, freshness, composition, and authenticity. However, the reliability and industrial transferability of recently reported deep-learning (DL) models remain insufficiently evaluated. This review critically synthesizes different studies and compares their scope with directly relevant reviews, with emphasis on Vis/NIR device configurations, conventional chemometric and machine-learning methods, DL architectures, and their applications in meat-quality assessment. Particular attention is given to the fairness of DL–chemometric comparisons, biological sample independence, potential leakage arising from replicate spectra, pixels, or augmented observations, and the distinction between internal prediction sets and independently collected external validation. The reviewed evidence indicates that CNN-, LSTM-, attention-, and hybrid DL models can effectively exploit nonlinear and spectral–spatial information; however, their superiority over optimized conventional approaches is not universal. Several studies reported high internal predictive performance despite limited numbers of independent animals, carcasses, batches, or physical samples. Independently documented cross-site, cross-season, and cross-device validation was rare or could not be confirmed. Instrument variability, calibration transfer, environmental sensitivity, model interpretability, and prospective processing-line evaluation, therefore, remain major barriers to industrial adoption. Future research should prioritize group-aware data partitioning, transparent reporting of biological and derived sample numbers, representative multi-batch datasets, standardized DL–chemometric benchmarks, cross-device calibration transfer, stable biochemical interpretation of explainable-AI outputs, and prospective industrial validation. Overall, progress toward dependable AI-assisted meat spectroscopy will depend less on increasingly complex architectures than on rigorous validation, reproducibility, and demonstrated robustness under realistic operating conditions.
