Umami is one of the five basic tastes, primarily represented by monosodium glutamate (MSG) and disodium 5′-inosinate (IMP). The current primary methods for detecting MSG and IMP are expensive and complex, limiting their widespread applications. Hence, there is a need to explore novel and more affordable methods to characterize umami taste substances. FT-IR was used to detect umami substances, MSG, and its mixture with IMP. Uniform spectral spacing method (USS) was combined separately with a continuous projection algorithm (SPA), competitive adaptive weighting algorithm (CARS), and uninformed variable elimination method (UVE) to simplify partial least squares regression (PLSR) and principal component regression (PCR) prediction models. The results demonstrated that the optimal model for MSG solution detection was the USS-CARS-PCR quantitative simplified model based on 17 feature wavelengths with $R_c^{\text{2}}$=0.97, ${\text{RMSE}}_c$=0.23 g/L, $R_p^{\text{2}}$ =0.96, and ${\text{RMSE}}_p$=0.27 g/L. For MSG and IMP mixture detection, the optimal model was the full wavelength model with $R_c^{\text{2}}$=0.97, ${\text{RMSE}}_c$=0.11 g/L, $R_p^{\text{2}}$=0.98, and ${\text{RMSE}}_p$=0.07 g/L. These findings indicate the feasibility of using FT-IR spectroscopy for rapid and quantitative detection of umami substances, providing a theoretical basis for detecting complex umami substances in food using FT-IR technology.

Umami substances play an important role in enhancing food flavor and ensuring human health, and biological sensing technology is an emerging method for detecting umami substances. Therefore, in this study, a highly sensitive detection method for monosodium glutamate (MSG) was developed based on competitive reactions that were mediated by polystyrene (PS) microspheres and MSG-BSA functionalized complexes. After washing and centrifugation, the polystyrene microspheres were decorated with MSG-BSA and competed with MSG to bind with magnetic beads (MB) loaded with MSG receptor T1R1 in the tested sample. The coupled complex was removed by magnetic separation, and the remaining polystyrene microspheres was counted by 15 s to achieve the detection of monosodium glutamate concentration. UV spectroscopy and Zeta potential analyzer were used to characterize the surface modification of polystyrene microspheres and magnetic beads. The characteristic absorption peaks of 250 nm and 270 nm were observed for the naked polystyrene microspheres labeled with MSG-BSA, while the characteristic absorption peaks of 600 nm and 210 nm were observed for T1R1-labeled magnetic beads. It was found that the particle size of both polystyrene microspheres and magnetic beads changed significantly, indicating the successful modification of their surface. To obtain the optimal detection performance, the concentration of polystyrene microspheres, magnetic beads, and incubation time were optimized. The optimal conditions for the experiment were identified as polystyrene microspheres diameter of 3 μm, magnetic beads diameter of 1 μm, concentration ratio of polystyrene microspheres and magnetic beads at 1:3, and incubation time of 10 min. Particle counter is highly sensitive and accurate in detecting the polystyrene microspheres state, and particles at different states present significant differences in resistance. This detection simplified the procedure and greatly improved the detection sensitivity. The response equation of the method to the tested monosodium glutamate solution was satisfactory with a fitting coefficient of R²=0.993 and a limit of detection of 5.17 pg/mL. Compared with other methods, this method yielded better linear range and detection limit.To verify the specificity of the detection system for taste substance detection, four interfering substances, namely citric acid, sucrose, benzalkonium chloride, and sodium chloride, were selected. The response value of the interference signal was negligible compared to that of monosodium glutamate, indicating the high specificity of this detection system.Three sets of monosodium glutamate (MSG) solutions with the same concentration in parallel configurations were tested, with each set tested 10 times. The intra-group and inter-group relative standard deviation (RSD) was calculated, demonstrating that this method provided good stability for MSG detection. Moreover, the sensitivity and applicability of the developed detection method for actual sample analysis were verified using commercially available crucian carp, carp, and grass carp as examples. After a series of pre-processing operations, the response values of different concentrations of supernatant from crucian carp, carp, and grass carp were determined. The results showed that as the concentration of the supernatant increased and within the linear response range of the method, the response value of the particle counter also increased, indicating that this method can be used to perceive the concentration of umami substances in fish samples. This study demonstrated that the developed method had good accuracy and sensitivity, showing great application potential in food preservation and quality evaluation.

An accurate and rapid identification of species is one of the most important parts of freshwater fish pre-treatment. However, deep learning-based freshwater fish classification cannot quantitatively describe the feature parameters of fish, due to the complicated feature extraction. This study aims to propose a deep learning-based fish feature point detection and freshwater fish classification. Firstly, an image acquisition device was built to acquire the images of five freshwater fish species: bream, bighead carp, grass carp, silver carp, and common carp. Secondly, the data enhancement was performed on the original images. LabelMe software was also used to label 20 feature points of the fish body, in order to construct a freshwater fish feature point detection dataset. The feature point of freshwater fish was detected using an improved AlexNet feature point detection model with the convolutional neural networks (CNN). The improved AlexNet model also adjusted the network structure of the traditional model, in order to accommodate the feature point detection task by the modified convolutional kernel size, the removal of the local response normalization layer, the addition of batch normalization layer, and the replacement of the loss function. The Euclidean distance between feature points was extracted as the feature value using the coordinates of the feature points. Feature vectors were constructed. Fisher discriminant analysis was used to identify the species of freshwater fish. A series of ablation experiments were conducted on the improved AlexNet model to clarify the impact of different improvement schemes on model performance. The results showed that the improved schemes were beneficial to improve the training efficiency and performance of the model. The average value of normalized mean error (NME) of the improved AlexNet model on the test set was 0.0099, the failure rate (FR) at thresholds of 0.02 and 0.03 were 2.50% and 0.83%, and the average detection time was 0.037 s, indicating the better accuracy and error distribution. The number of parameters and floating-point operations per second (FLOPs) of the improved AlexNet model were smaller than those of the VGG16 model. The test set detection was also better than that of the VGG16 model, compared with different feature extraction models. Although the number of parameters and FLOPs of the improved AlexNet model was larger, the average NME and the FR were lower than that of the MobileNetV2 model. The recognition accuracy of 98.0% was achieved in the freshwater fish species recognition using the model and Fisher discriminant analysis for five freshwater fish species. The average recognition time of a single image was 0.368 s. The feature point detection model can be expected to detect the feature points of five species of freshwater fish with high accuracy. The finding can also facilitate freshwater fish species identification, size detection, and fish segmentation. The freshwater fish species identification using feature point detection and Fisher's discriminant analysis can lay the foundation for the development of an automated freshwater fish classification device.

Microchannel resistive biosensing can be expected to detect the concentration of polystyrene (PS) microspheres in solutions, thus quantifying the target analytes. The promising potential application can be introduced to test the agricultural products, due to its low-cost characteristics and high sensitivity. It is the high demand for automated and highly stable equipment. In this study, an automated detection device was developed to integrate the microchannels, a hardware control system, and a human-machine interface (HMI). Its feasibility was also explored in the detection of the umami substances. Firstly, the high-precision microchannels were fabricated for the high accuracy of the channel structure using 3D micro-nano printing. Secondly, the hardware control system was utilized to coordinate the various units during sample analysis, including automatic sampling, measurement display, cleaning and sample changing, and power management. The relative errors were reduced with the manual operations and detection efficiency. Additionally, a filter module was incorporated to minimize the impact of the external factors on the detection, such as electromagnetic interference and flow rate disturbances, particularly for the data reliability. Lastly, an HMI was designed to drive the entire control system, thus enabling data processing, display, and storage. While the real-time feedback was realized to monitor the detection. The coefficient of variation (CV) was employed to evaluate the effects of the microchannel inner diameter, microchannel length, and sample flow rate on the signal stability. A response surface method (RSM) was conducted to optimize these parameters. The optimal combination of the working parameters was identified as an inner diameter of 100 μm, a length of 2 mm, and a flow rate of 2 mL/min. The stability of the detection was enhanced after optimization. Furthermore, the phosphate-buffered saline (PBS) solution was taken as the blank control group in order to evaluate the performance of the developed device. Polystyrene (PS) microsphere solutions were mixed uniformly with the concentrations ranging from 1 pg/mL to 1μg/mL. The current values were recorded for the different concentrations. As the concentration of the microsphere solution increased gradually, a high correlation was observed between the current and the concentration of PS microspheres in the range of 1 to 1×106 pg/mL, with a detection limit of 0.76 pg/mL. Furthermore, the maximum coefficient was just 1.13% over seven consecutive days, where the maximum coefficient was only 1.8% among three microchannels. These results indicated that the device also exhibited high stability and repeatability after detection. Additionally, the monosodium glutamate (MSG) samples were prepared to verify the feasibility of the microchannel resistive biosensor. The developed device was then used to detect the umami substance, according to the competitive immunoassay reactions. The results showed that the current difference also increased after detection, as the MSG concentration increased gradually. There was a positive correlation between the current difference and the MSG concentration. Taking the monosodium glutamate (MSG) as a representative substance, the linear range of 1 to 1×104 pg/mL was determined with a limit of detection (LOD) of 0.24 pg/mL in the practical testing. The better performance was achieved, including the particle counters, green spectrophotometric approaches, and high-performance liquid chromatography (HPLC). The practical utility of the equipment was validated after comparison. The findings can provide technical support to rapidly and accurately detect quality and safety monitoring of agricultural products.

A homogeneous and stable flow field is required to promote the healthy growth of the fish in the recirculating aquaculture systems. Among them, the structure of the inlet pipes can dominate the water inflow patterns and the distribution of the flow field in the aquaculture ponds. At the same time, there is also a great influence on the growth of the fish and the removal of suspended particulate matter, such as the uneaten feed and the feces. This study aims to investigate the impacts of the different structures of the inlet pipes on the flow field distribution in circular aquaculture ponds. The optimal structural parameters of the inlet pipes were determined for the uniform distribution of the flow field. A numerical model was established for the circular aquaculture pond using computational fluid dynamics (CFD) software. A series of simulations were then conducted to verify the structural parameters, including the number of the inlet pipes, the ejection angle of each inlet pipe, and the number of the jet holes on the inlet pipes. The uniformity of the flow field was evaluated, according to the uniformity coefficient of velocity distribution U50 as an assessment indicator. Orthogonal experiments were designed to obtain the optimal combination for the structural parameters of the inlet pipes in the circular aquaculture pond. Simultaneously, a recirculating aquaculture system was constructed in accordance with the optimal structure parameters of the inlet pipes. An aquaculture experiment was carried out on Micropterus salmoides, in order to verify the optimization. The flow field was then evaluated to monitor the water quality and the growth indicators of Micropterus salmoides samples in the aquaculture system. The experimental results demonstrate that the most uniform distribution of the flow field was achieved, when the circular aquaculture pond was equipped with three inlet pipes arranged at 120°, with six jet holes on each single inlet pipe and a jet angle of 45° for the inlet pipe. Three groups of Micropterus salmoides aquaculture experiments were conducted with different densities at the water flow velocity of 0.1 m/s. The concentration values of the nitrite and ammonia nitrogen were less than or equal to 0.2 and 0.3mg/L, respectively in the aquaculture pond. The turbidity values of the water were all lower than 0.3 NTU (nephelometric turbidity unit), indicating the high water quality and the low level of the suspended particles. The lowest feed coefficient of Micropterus salmoides samples with the three different density groups was 1.06, while the highest weight gain rate was 161.03%, and the highest survival rate was 100%. The flow field contributed to the weight gain rate of fish for the favorable survival rate of the fish population. The feed coefficient of fish was also reduced to facilitate the healthy growth of fish. The optimal structure of the inlet pipes was conducive to the uniform and stable flow field in a circular aquaculture pond. The suspended particulate matter was effectively discharged to maintain the water quality of the aquaculture water body. The finding can provide theoretical guidance to optimize the structures of the inlet pipes for the uniform flow field in a recirculating aquaculture system.