Multienzyme-Mediated Dual-Channel Magnetic Relaxation Switching Taste Biosensor (D-MRSTB) for Simultaneous Detection of Umami Compounds and Synergistic Enhancement in Food.

Umami substances play a significant role in the evaluation of food quality, and their synergistic enhancement is of great importance in improving and intensifying food flavors and tastes. Current biosensors available for umami detection still confront challenges in simultaneous quantification of multiple umami substances and umami intensities. In this study, an innovative dual-channel magnetic relaxation switching taste biosensor (D-MRSTB) was developed for the quantitative detection of representative umami substances. The multienzyme signal of D-MRSTB specifically catalyzes the umami substances of interest to generate hydrogen peroxide (H2O2), which is then used to oxidate Fe2+ to Fe3+. Such a valence-state transition of paramagnetic ions was utilized as a magnetic relaxation signaling switch to influence the transverse magnetic relaxation time (T2) within the reaction milieu, thus achieving simultaneous detection of monosodium glutamate (MSG) and inosine 5'-monophosphate (IMP). The biosensor showed good linearity (R2 > 0.99) in the concentration range of 50-1000 and 10-1000 µmol/L, with limits of detection (LOD) of 0.61 and 0.09 µmol/L for MSG and IMP, respectively. Furthermore, the biosensor accurately characterized the synergistic effect of the mixed solution of IMP and MSG, where ΔT2 showed a good linear relationship with the equivalent umami concentration (EUC) of the mixed solution (R2 = 0.998). Moreover, the D-MRSTB successfully achieved the quantitative detection of umami compounds in real samples. This sensing technology provides a powerful tool for achieving the detection of synergistic enhancement among umami compounds and demonstrates its potential for application in the food industry.

Novel competitive electrochemical impedance biosensor for the ultrasensitive detection of umami substances based on Pd/Cu-TCPP(Fe).

The development of biosensors capable of assessing umami intensity has elicited significant attention. However, the detection range of these biosensors is constrained by the sensing components and strategies used. In this study, we introduce a novel competitive, ultra-high-sensitivity impedance biosensor by utilizing composite nanomaterials and T1R1 as a composite signal probe. Pd/Cu-TCPP(Fe) had a substantial surface area, effectively enhancing the loading capacity of the T1R1 and thus augmenting the biosensor's recognition precision. Furthermore, the Pd/Cu-TCPP(Fe) elevated peroxidase-like activity catalyzed the formation of insoluble precipitates of 4-chloro-1-naphthol (4-CN), resulting in cascaded amplification of the impedance signal. The remarkable catalytic activity of the composite signal probe endowed the biosensor with exceptional analytical performance, featuring a limit of detection (LOD) of 0.86 pg/mL and a linear detection range spanning from 10 to 10,000 pg/mL. Successful application of the biosensor for umami detection in fish was demonstrated, signifying its substantial potential in food-flavor evaluation.

Development of sensitive and stable electrochemical impedimetric biosensor based on T1R1 receptor and its application to detection of umami substances.

Umami substances can provide a palatable flavour for food. In this study, an electrochemical impedimetric biosensor was developed for detecting umami substances. This biosensor was fabricated by immobilising T1R1 onto AuNPs/reduced graphene oxide/chitosan which was in advance electro-deposited onto a glassy carbon electrode. The evaluation by the electrochemical impedance spectrum method showed that the T1R1 biosensor performed well with low detection limits and wide linear ranges. Under the optimised incubation time (60 s), the electrochemical response was linearly related to the concentrations of the detected targets monosodium glutamate and inosine-5'-monophosphate within their respective linear range of 10-14 to 10-9 M and 10-16 to 10-13 M. The low detection limit of monosodium glutamate and inosine-5'-monophosphate was 10-15 M and 10-16 M, respectively. Moreover, the T1R1 biosensor exhibited high specificity to umami substances even in the real food sample. The developed biosensor still retained 89.24% signal intensity after 6-day storage, exhibiting a desirable storability.

Kinetics of a new porcine taste-bud tissue biosensor for the detection of umami substances and their synergistic effect.

Umami substances are nutrients to humans, and their synergistic effect is associated with food acceptance. In this study, a new biosensor was developed to detect umami substances, their synergistic effect, and detection kinetics. Porcine taste-bud tissues were used as the sensing element, and the umami substance signals were characterized using an electrochemical workstation. The responses of taste-bud tissue sensors to monosodium L-glutamate (MSG) were compared based on different tongue sites. The interaction law between MSG and receptors in the taste-bud tissues of the three sensors conforms to enzymatic-reaction kinetics, where rectangular hyperbola curves in the Michaelis-Menten equation were followed with fitting coefficients (>0.91). However, the taste-bud sensors respond differently to MSG stimuli, with those based on a tip and mediolateral tongue, producing the lowest detection limit of 10-16 mol/L. The number of receptors required for a single cell to achieve maximum output signal is 3.68, 30.42, and 7.27, respectively. Moreover, the taste-bud tissue sensors identified the synergistic effect of umami substances. In addition, they were sensitive to umami variations in soy sauce and mandarin fish. The developed porcine taste-bud tissue biosensor revealed the interaction law between umami substances and receptors, providing a new idea for umami evaluation.

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning.

A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria-Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.