Chinese Quince (Cydonia oblonga Miller) Freshness Rapid Determination Method Using Surface Acoustic Wave Resonator Combined with Electronic Nose

Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model

The relationship between freshness changes and visual images of Litopenaeus vannamei was established based on Sensory Evaluation, Total Volatile Base Nitrogen (TVB-N), Total Viable Count (TVC), and Gray Value during storage at 4 °C. A non-destructive detection system using the advanced YOLO(You Only Look Once)-Shrimp model was developed to evaluate shrimp freshness. The results revealed a gradual increase in freshness indices over time, with the gray value showing strong positive correlations with TVB-N and TVC (0.88 and 0.81). The advanced YOLO-Shrimp model demonstrated notable performance enhancements over the YOLOv8 model, as evidenced by a precision increase of 5.07 %, a recall improvement of 1.58 %, a 3.25 % rise in the F1 score, and a 2.84 % elevation in mAP50. This innovative approach offers substantial potential for enhancing food safety and quality control in the seafood industry.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Recent innovations of ultrasound green technology in herbal phytochemistry: A review

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.

Quality retention and shelf life extension of fresh beef using Lepidium sativum seed mucilage-based edible coating containing Heracleum lasiopetalum essential oil: an experimental and modeling study

The instability and strong flavor or odor of essential oils (EO) limit their direct incorporation into food products. In this study, the antioxidant and antimicrobial Heracleum lasiopetalum essential oil (HLEO) was added to Lepidium sativum seed mucilage (LSSM) solution at four concentrations (0, 0.5, 1, and 1.5%) to develop a novel edible coating and expand its food application. HLEO-loaded LSSM coating was then used to improve the shelf life and quality of beef as a model food system. The coated and control beef samples were periodically analyzed for physicochemical analysis, microbiological, and sensory characteristics over a period of 9 days at 4 °C. The HLEO-enriched LSSM coating, particularly 1.5% loaded one resulted in a significant (p < 0.05) increase in oxidative and microbiological stability and overall acceptance of the beef samples, compared to the control counterpart. HLEO-loaded LSSM coating, therefore, provides a promising alternative to preserve the meat products under cold storage.

Combining near-infrared hyperspectral imaging with elemental and isotopic analysis to discriminate farm-raised pacific white shrimp from high-salinity and low-salinity environments

White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near-infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model. The results showed that sequential forward selection combined with partial least squares discriminant analysis (SFS-PLS-DA) generated the best discrimination performance with an overall accuracy of 99.2%. The elemental and isotopic analysis indicated a high correlation between 918 and 925 nm region (which was selected by SFS) and ¹³C concentration. This agrees with the fact that there is more ¹³C in shrimp of salty water compared to those of freshwater. The results demonstrated (hyperspectral imaging) HSI is promising to discriminate L. vannamei raised in fresh and seawater environments.