Ultrasound (US) has become one of the most important techniques in green chemistry and emerging technologies. Many research investigations documented the usefulness of US in a wide range of applications in food science, nanotechnology, and complementary medicine, where effective extraction of natural products is important. However, as with all novel technologies, US has advantages and limitations that require clarification for full adaptation at an industrial scale. The present review discusses recent applications of US in herbal phytochemistry with the emphasis on US effects on chemical structures of bioactive compounds extracted from herbs and their bioactivities. The impact of different US processing conditions such as frequency, intensity, duration, temperature, and pressure on the effectiveness of the extraction process and the properties of the extracted materials are also discussed. Different frequencies and intensities of US have demonstrated its potential applications in modifying, determining, and predicting the physicochemical properties of herbs and their extracts. US has important applications in nanotechnology where it supports the fabrication of inexpensive and eco-friendly herbal nanostructures, as well as acoustic-based biosensors for chemical imaging of the herbal tissues. The application of US enhances the rates of chemical processes such as hydrolysis of herbal fibers, which reduces the time and energy consumed without affecting the quality of the final products. Overall, the use of US in herbal science has great potential to create novel chemical constructions and to be used as an innovative diagnostic system in various biomedical, food, and analytical applications.

Vanillin, a plant-derived antimicrobial volatile substance, has potential microbial control applications in the food industry. However, the effect of vanillin on the food-borne pathogen Escherichia coli (E. coli) O157:H7 has not been well studied. This study aims to explore the antibacterial mechanism of vanillin against E. coli O157:H7. The minimum inhibitory concentration (MIC) and antibacterial effect of vanillin were determined by microdilution. Scanning electron microscopy (SEM) was used to observe the damage of vanillin to the cell membrane, while cell membrane potential and the leakage of nucleic acid protein were measured to explore the effect of vanillin on the membrane system. Confocal laser scanning and intracellular adenosine triphosphate (ATP) concentration determination were utilized to investigate the effects of vanillin on the energy, life, and death of E. coli. Finally, transcriptome sequencing was conducted to investigate the gene expression differences induced by vanillin treatment. The results showed that vanillin treatment effectively controlled E. coli O157:H7 with an MIC of 2 mg/mL. After treatment, damage to the membrane system, depolarization of the membrane, and leakage of nucleic acid and protein were observed. Meanwhile, vanillin treatment caused decreased ATP content and cell death. Transcriptome analysis showed that vanillin treatment significantly affected the expression of genes involved in cell membrane formation, tricarboxylic acid (TCA) cycling pathway, and oxidative phosphorylation pathway in E. coli O157:H7. In conclusion, membrane damage and energy metabolism disruption are important mechanisms of vanillin's inhibitory effect on E. coli O157:H7. This study provides new insights into the molecular reaction mechanism of vanillin against E. coli O157:H7, highlighting its potential as an antibacterial substance for preventing E. coli contamination in the food industry.

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L⁻¹) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.