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.

Hyperspectral technology has been widely used in many fields due to its excellent performance characteristics such as high sensitivity, fine resolution, multi-channel data acquisition and processing capabilities, and non-destructive testing. However, despite its outstanding performance in data acquisition and information extraction, it also faces the challenges of high requirements and costs of hardware equipment. Hardware equipment such as high-precision optics, high-quality detectors, and complex data acquisition devices required for hyperspectral imaging have become one of the key factors limiting its popularity and application. To overcome this dilemma, researchers have begun to explore the reconstruction of RGB images into hyperspectral images by extracting information from traditional RGB images through spectral reconstruction techniques at relatively low cost. This innovative approach significantly reduces the complexity and cost of spectral data acquisition and provides a brand new way for wider application of hyperspectral technology. In the study of this paper, we use surimi adulteration detection as an example to explore the performance of different spectral reconstruction algorithms in depth. By mixing the dorsal muscle of silver carp with commercially available starch in different ratios and simulating the real state of surimi when it is sold, we successfully acquired RGB images and corresponding spectral data of a series of samples. In order to ensure the scientific validity and reliability of the experiment, we adopted the spectral-physical-chemical value covariance distance method (SPXY) for the sample set and divided the data set into calibration and validation sets according to the ratio of 5:3. In the process of spectral reconstruction, we explored two mainstream methods, the multivariate polynomial least squares regression algorithm (PMLR) and the deep learning hierarchical regression network (HRNet). Comparative experimental results show that the HRNet-based reconstruction technique has relative and root-mean-square errors of 0.010 4 between the spectra and the actual spectra, respectively, while the PMLR-based algorithm corresponds to relative and root-mean-square errors of 0.012 6. These results indicate that both methods are within acceptable error ranges, which provides a subsequent experimental results provide a solid foundation. In order to verify the reliability of the reconstructed spectra in practical applications, we established models of extreme learning machine regression (ELMR), support vector machine regression (SVR), and partial least squares regression (PLSR) based on the reconstructed spectra, and combined S-G convolutional smoothing (SG), mean centering (MC), standard normal variable transformation (SNV), derivative smoothing (DE), and normalization (NOR) methods were used to preprocess the spectra with a view to improving the model performance. By using corrected correlation coefficient (R_C) corrected root mean square error (RMSE_C) predicted correlation coefficient (R_P) and root mean square error of prediction (RMSE_P) as evaluation metrics, we successfully detected the adulteration ratio of adulterated surimi. The experimental results show that the support vector machine regression model with standard normal transform (SNV) preprocessing achieves the best results with a prediction correlation coefficient (R_P) of 0.983 0 and a root mean square error of prediction (RMSE_P) of 3.954 4% in the model based on the reconstruction of spectra by the PMLR algorithm. In the model based on the reconstructed spectrum of deep learning HRNet network, the support vector machine regression model, also using SNV preprocessing, achieved the best performance, with a prediction correlation coefficient of 0.9987 and a prediction root-mean-square error of 4.080 8%. Therefore, the PMLR algorithm and HRNet network reconstruction technique based on RGB images not only realized efficient spectral reconstruction, but also provided a reliable solution for surimi adulteration detection. This research result provides new ideas and methods for quality and safety detection of food and agricultural products using low-cost hardware devices.

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.

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 freezing mode has a major impact on the quality of the product in the long-distance cold chain. In this study, a series of tests were carried out on samples of Micropterus salmoides to reduce the deterioration of largemouth perch during the freezing process. A systematic investigation was also implemented to explore the effects of freezing at -20, -50, and -80 ℃ and freezing at -80 and -50 ℃ combined with storage at -20 ℃ on the quality of perch samples. These quality indicators included color, pH, ice crystals, texture, water-holding capacity, and moisture distribution. The freezing temperatures of both -50 and -80 °C were found to be effective in suppressing the deterioration of the quality of the perch during freezing. The test results showed that the phase transition time of the perch samples in the -80 °C group was 68.8% less than that of the bass samples in the -20 °C group; The phase transition time of the bass samples in the -80 °C group was reduced by 37.5%, compared with the perch samples in the -50°C group. All perch samples showed significant changes (P<0.05) in total color and chromatic aberration scores, compared with the control. However, the ΔE values were all less than 12 in the perch samples treated with the different freezing modes, which were the same color as that of fresh perch. Among them, the -80 °C treated samples showed the smallest change in ΔE values. Except for the perch samples frozen at -80°C, there were significant changes (P < 0.05) in pH in all test groups, compared with the control. Meanwhile, the ice crystal equivalent diameter and cross-sectional area of the perch samples frozen at -80 °C were significantly lower than those of the rest (P < 0.05). The perch samples treated with five freezing modes showed decreasing trends in color, pH, water-holding capacity, and textural properties. The smallest variation was found in the perch samples frozen at -80 °C, similar to the control group. In contrast, the freezing at -50 °C combined with -20 °C storage and freezing at -80 °C combined with -20 °C failed to improve the quality of perch during freezing storage. Correlation analysis between freezing rate and ice crystals showed that the equivalent diameter and cross-sectional area of ice crystals decreased with the increasing freezing rate and duration during the freezing of perch samples. At the same time, correlation analysis between the indicators showed that there were significant correlations between ice crystals, water distribution and color, texture, and water holding capacity. Low freezing temperatures reduced the size of ice crystals and water loss, thus better maintaining the quality characteristics of perch, such as color, pH, texture and water holding capacity. Freezing modes at -50 and -80 ℃better maintained the freezing quality of perch, in terms of the color of perch samples during freezing, pH change, the decreasing size of ice crystals, water migration, hardness, elasticity and water holding capacity. Ultra-low temperature freezing can be expected to reduce the quality deterioration of sea bass. The finding can provide a theoretical basis for reducing the quality deterioration of largemouth perch during cryopreservation and cold chain transport.