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.

Advances in the Preparation, Stability, Metabolism, and Physiological Roles of Anthocyanins: A Review.

Anthocyanins are natural flavonoid polyphenolic compounds widely found in fruits and vegetables. They exhibit antioxidant properties and prophylactic effects in the immune and cardiovascular systems, confer protection against cancer, and contribute to the prevention of cardiovascular diseases. Thus, their incorporation into functional foods, pharmaceuticals, supplements, and cosmetic formulations aims at promoting human well-being. This review comprehensively outlined the structural attributes of anthocyanins, expanding upon diverse methodologies employed for their extraction and production. Additionally, the stability, metabolic pathways, and manifold physiological functions of anthocyanins were discussed. However, their constrained fat solubility, susceptibility to instability, and restricted bioavailability collectively curtail their applicability and therapeutic efficacy. Consequently, a multidimensional approach was imperative, necessitating the exploration of innovative pathways to surmount these limitations, thereby amplifying the utilitarian significance of anthocyanins and furnishing pivotal support for their continual advancement and broader application.

Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay.

Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to µg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.

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.

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection.

The development of a multitarget ultrasensitive immunoassay is significant to fields such as medical research, clinical diagnosis, and food safety inspection. In this study, an artificial intelligence (AI)-assisted programmable-particle-decoding technique (APT)-based digital immunoassay system was developed to perform multitarget ultrasensitive detection. Multitarget was encoded by programmable polystyrene (PS) microspheres with different characteristics (particle size and number), and subsequent visible signals were recorded under an optical microscope after the immune reaction. The resultant images were further analyzed using a customized, AI-based computer vision technique to decode the intrinsic properties of polystyrene microspheres and to reveal the types and concentrations of targets. Our strategy has successfully detected multiple inflammatory markers in clinical serum and antibiotics with a broad detection range from pg/mL to µg/mL without extra signal amplification and conversion. An AI-based digital immunoassay system exhibits great potential to be used for the next generation of multitarget detection in disease screening for candidate patients.

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.

Non-Contact Evaluation of Pigs' Body Temperature Incorporating Environmental Factors.

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs' internal body temperature.

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.

Establishment of validated models for non-invasive prediction of rectal temperature of sows using infrared thermography and chemometrics.

Rectal temperature is an important physiological indicator used to characterize the reproductive and health status of sows. Infrared thermography, a surface temperature measurement technology, was investigated in this study to explore its feasibility in non-invasive detection of rectal temperature in sows. A total of 124 records of rectal temperature and surface temperature in various body regions of 99 Landrace × Yorkshire crossbred sows were collected. These surface temperatures together with ambient temperature, ambient humidity, and wind speed in pig pens were correlated with the real rectal temperature of sows to establish rectal temperature prediction models by introducing chemometrics algorithms. Two types of models, i.e., full feature models and selected feature models, were established by applying the partial least squares regression (PLSR) method. The optimal model was attained when 7 important features were selected by LARS-Lasso, where correlation coefficients and root mean squared errors of calibration were 0.80 and 0.30 °C, respectively. Particularly, the validity and stability of established simplified models were further evaluated by applying the model to an independent prediction set, where correlation coefficients and root mean squared errors for prediction were 0.80 and 0.35 °C, respectively. The validation of established models is scarce in previous similar studies. Above all, this study demonstrated that introduction of chemometrics methodologies would lead to more reliable and accurate model for predicting sow rectal temperature, thus the potential for ensuring animal welfare in a broader view if extended to more applications.

Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging.

Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate statistical analysis methods including partial least squares regression (PLSR), support vector machine (SVM), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were carried out in developing full wavelength models. Good prediction was obtained by applying LS-SVM in the spectral range of 496-1000 nm with coefficients of determination (R2) of 0.94 and 0.94 as well as root-mean-squared errors (RMSEs) of 5.39% and 6.29% for calibration and prediction, respectively. To reduce the high dimensionality of hyperspectral data and to establish simplified models, a novel method named invasive weed optimization (IWO) was developed to select key wavelengths and it was compared with competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Among the four multivariate analysis models based on important wavelengths determined by IWO, the LS-SVM simplified model performed best where R2 of 0.97 and 0.95 as well as RMSEs of 4.74% and 5.67% were attained for calibration and prediction, respectively. The optimum simplified model was applied to hyperspectral images in pixel-wise to visualize the distribution of spoiled beef adulterant in fresh minced beef. The current study demonstrated that it was feasible to use Vis-NIR hyperspectral imaging to detect homologous adulterant in beef.

Recent applications of hyperspectral imaging in microbiology.

Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed.

Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review.

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms.

Near infrared (NIR) hyperspectral imaging (HSI) and different spectroscopic transforms were investigated for their potential in detecting total viable counts in raw chicken fillets. A laboratory-based pushbroom hyperspectral imaging system was utilized to acquire images of raw chicken breast fillets and the resulting reflectance images were corrected and transformed into hypercubes in absorbance and Kubelka-Munck (K-M) units. Full wavelength partial least regression models were established to correlate the three spectral profiles with measured bacterial counts, and the best calibration model was based on absorbance spectra, where the correlation coefficients (R) were 0.97 and 0.93, and the root mean squared errors (RMSEs) were 0.37 and 0.57 log10 colony forming units (CFU) per gram for calibration and cross validation, respectively. To simplify the models, several wavelengths were selected by stepwise regression. More robustness was found in the resulting simplified models and the model based on K-M spectra was found to be excellent with an indicative high ratio of performance to deviation (RPD) value of 3.02. The correlation coefficients and RMSEs for this model were 0.96 and 0.40 log10 CFU per gram as well as 0.94 and 0.50 log10 CFU per gram for calibration and cross validation, respectively. Visualization maps produced by applying the developed models to the images could be an alternative to test the adaptability of a calibration model. Moreover, multi-spectral imaging systems were suggested to be developed for online applications.

Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets.

Hyperspectral imaging was exploited for its potential in direct and fast determination of Pseudomonas loads in raw chicken breast fillets. A line-scan hyperspectral imaging system (900-1700 nm) was employed to obtain sample images, which were then further corrected, modified and processed. The prepared images were correlated with the true Pseudomonas counts of these samples using partial least squares (PLS) regression. To enhance model performance, different spectral extraction approaches, spectral preprocessing methods as well as wavelength selection schemes based on genetic algorithm were investigated. The results revealed that extraction of mean spectra is more efficient for representation of sample spectra than computation of median spectra. The best full wavelength model was attained based on spectral images preprocessed with standard normal variate, and the correlation coefficients (R) and root mean squared errors (RMSEs) for the model were above 0.81 and below 0.80 log10 CFU g(-1), respectively. In development of simplified models, wavelengths were selected by using a proposed two-step method based on genetic algorithm. The best model utilized only 14 bands in five segments and produced R and RMSEs of 0.91 and 0.55 log10 CFU g(-1), 0.87 and 0.65 log10 CFU g(-1) as well as 0.88 and 0.64 log10 CFU g(-1) for calibration, cross-validation and prediction, respectively. Moreover, the prediction maps offered a novel way for visualizing the gradient of Pseudomonas loads on meat surface. Hyperspectral imaging is demonstrated to be an effective tool for nondestructive measurement of Pseudomonas in raw chicken breast fillets.

Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets.

Bacterial pathogens are the main culprits for outbreaks of food-borne illnesses. This study aimed to use the hyperspectral imaging technique as a non-destructive tool for quantitative and direct determination of Enterobacteriaceae loads on chicken fillets. Partial least squares regression (PLSR) models were established and the best model using full wavelengths was obtained in the spectral range 930-1450 nm with coefficients of determination R(2)≥ 0.82 and root mean squared errors (RMSEs) ≤ 0.47 log(10)CFUg(-1). In further development of simplified models, second derivative spectra and weighted PLS regression coefficients (BW) were utilised to select important wavelengths. However, the three wavelengths (930, 1121 and 1345 nm) selected from BW were competent and more preferred for predicting Enterobacteriaceae loads with R(2) of 0.89, 0.86 and 0.87 and RMSEs of 0.33, 0.40 and 0.45 log(10)CFUg(-1) for calibration, cross-validation and prediction, respectively. Besides, the constructed prediction map provided the distribution of Enterobacteriaceae bacteria on chicken fillets, which cannot be achieved by conventional methods. It was demonstrated that hyperspectral imaging is a potential tool for determining food sanitation and detecting bacterial pathogens on food matrix without using complicated laboratory regimes.

Application of hyperspectral imaging in food safety inspection and control: a review.

Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.