A colorimetric gas-sensitive array sensor using filter paper for the analysis of fish freshness

Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment

A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 ∼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms

This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t-distributed stochastic neighbour embedding (t-SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness.

Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets

Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.

Integration of lanthanide MOFs/methylcellulose-based fluorescent sensor arrays and deep learning for fish freshness monitoring

Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluorescein isothiocyanate (FITC) and methylcellulose (MC) to develop fluorescent sensor arrays for real-time monitoring the freshness of fish meat. The EuMOF-FITC/MC fluorescence films were characterized with excellent fluorescence response, ideal morphology, good mechanical properties, and improved hydrophobicity. The efficacy of the fluorescence sensor array was evaluated by testing various concentrations of spoilage gases (such as ammonia, dimethylamine, and trimethylamine) within a 20-min timeframe using a smartphone-based camera obscura device. This sensor array enables the real-time monitoring of fish freshness, with the ability to preliminarily identify the freshness status of mackerel meat with the naked eye. Furthermore, the study employed four convolutional neural network (CNN) models to enhance the performance of freshness assessment, all of which achieved accuracy levels exceeding 93 %. Notably, the ResNext-101 model demonstrated a particularly high accuracy of 98.97 %. These results highlight the potential of the EuMOF-based fluorescence sensor array, in conjunction with the CNN model, as a reliable and accurate method for real-time monitoring the freshness of fish meat.

Advanced technologies in biodegradable packaging using intelligent sensing to fight food waste

The limitation of conventional packaging in demonstrating accurate and real-time food expiration dates leads to food waste and foodborne diseases. Real-time food quality monitoring via intelligent packaging could be an effective solution to reduce food waste and foodborne illnesses. This review focuses on recent technological advances incorporated into food packaging for monitoring food spoilage, with a major focus on paper-based sensors and their combination with smartphone. This review paper offers a comprehensive exploration of advanced macromolecular technologies in biodegradable packaging, a general overview of paper-based probes and their incorporation into food packaging coupled with intelligent sensing mechanisms for monitoring food freshness. Given the escalating global concerns surrounding food waste, our manuscript serves as a pivotal resource, consolidating current research findings and highlighting the transformative potential of these innovative packaging solutions. We also highlight the current intelligent paper-based food freshness sensors and their various advantages and limitations. Examples of implementation of paper-based sensors/probes for food storage and their accuracy are presented. Finally, we examined how intelligent packaging can be an alternative to reduce food waste. Several technologies discussed here have good potential to be used in food packaging for real-time food monitoring, especially when combined with smartphone diagnosis.

Study on the Characteristics of Vacuum-Bagged Fermentation of Apo Pickle and Visualization Array Analysis of the Fermentation Process

Apo pickle is a fermented food with a long edible history in the Jiangnan region of China. Traditionally, plastic bottles are used as Apo pickle's fermentation containers, and artificial bottling costs are high. The goal of this study is to compare the fermentation effects of Apo pickle fermented under low pressure in a vacuum bag (VBA) and Apo pickle fermented under normal pressure in plastic bottles (TBA) to determine the feasibility of fermenting Apo pickle in a vacuum bag rather than a plastic bottle, thereby lowering production costs. At the same time, a gas-sensitive colorimetric sensor array (CSA) was developed to distinguish different fermentation stages of Apo pickle. The results revealed that the main genera in the initial and final phases of Apo pickle fermentation were Weissella and Lactobacillus, unaffected by fermentation containers. At the same fermentation time, the abundance of Lactobacillus and the content of flavor substances in VBA were higher, and the fermentation speed of VBA was faster at 0-15 d, so a vacuum bag could be used instead of a plastic bottle. The CSA could discriminate between different fermentation procedures of Apo pickles with an accuracy rate of 93.8%. Its principle is similar to that of an electronic nose. It has the advantages of convenience, rapidity, and no need for professional equipment, so it can be used as a new method to judge the fermentation degree of apo pickle.

Pattern-recognizing-assisted detection of mildewed wheat by Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array

In order to timely discriminate wheat with different mildew rates, a Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array was designed. Using array points to capture volatile gases of wheat with different mildew rates, and output RGB values. The correlation between ΔR/ΔG/ΔB values and odor components was established. The ΔG values of array points 2' and 3' showed the best correlation with mildew rate, with R² of 0.9816 and 0.9642. The ΔR value of 3 and the ΔG value of 2 correlate well with the mildew rate, with R² of 0.9625 and 0.9502, respectively. Then, the ΔRGB values are subjected to pattern recognition processing, and LDA achieves 100% correct discrimination for all samples, or divides high and low mildew areas. This method provides an odor-based monitoring tool for fast, visual and nondestructive evaluation of food safety and quality through visualization of odors produced by different mildew rates.

Preparation of Nanopaper for Colorimetric Food Spoilage Indication

In this study, we are reporting the fabrication of a nanocellulose (NFC) paper-based food indicator for chicken breast spoilage detection by both visual color change observation and smartphone image analysis. The indicator consists of a nanocellulose paper (nanopaper) substrate and a pH-responsive dye, bromocresol green (BCG), that adsorbs on the nanopaper. The nanopaper is prepared through vacuum filtration and high-pressure compression. The nanopaper exhibits good optical transparency and strong mechanical strength. The color change from yellow to blue in the nanopaper indicator corresponding to an increase in the solution pH and chicken breast meat storage data were observed and analyzed, respectively. Further, we were able to use color differences determined by the RGB values from smartphone images to analyze the results, which indicates a simple, sensitive, and readily deployable approach toward the development of future smartphone-based food spoilage tests.

Cyanostilbene-based fluorescent paper array for monitoring fish and meat freshness via amino content detection

The detection of biogenic amines released from degraded meats is an effective method for evaluating meat freshness. However, existing traditional methods like titration are deemed tedious, while the use of sophisticated analytical instruments is not amenable to field testing. Herein, a cyanostilbene-based fluorescent array was rapidly fabricated using macroarray synthesis on a cellulose paper surface to detect amines liberated from spoiled beef, fish, and chicken. The fluorescence changes of immobilized molecules from the interaction with gaseous amines were used to monitor changes in freshness levels. Thanks to the high-throughput nature of macroarray synthesis, a set of highly responsive molecules such as pyridinium and dicyanovinyl moieties were quickly revealed. Importantly, this method offers flexibility in sensing applications including (1) sensing by individual sensor molecules, where the fluorescence response correlated well with established titration methods, and (2) collective sensing whereby chemometric analysis was used to provide a cutoff of freshness with 73-100% accuracy depending on meat types. Overall, this study paves the way for a robust and cost-effective tool for monitoring meat freshness.

Fish Freshness Indicator for Sensing Fish Quality during Storage

This study aims to develop a freshness indicator for fish products that changes color to indicate ammonia among volatile base compounds (TVB-N) generated during storage. Through an optimization experiment, we observed the indicator's color change relative to the ammonia concentration standard, finally selecting cresol red and bromocresol purple for the indicator mixture. In addition, eco-DEHCH and Breathron film were applied to the freshness indicator, considering environmental and economic values. For the storage experiment, Chub mackerel (Scomber japonicus), Spanish mackerel (Scomberomorus niphonius), and Largehead hairtail (Trichiurus lepturus) samples were stored at three different temperatures (4, 10, and 20 °C) for seven days, and we measured pH, TVB-N, total bacterial count, and ammonia content every 24 h. The pH-sensitive sensors' color changes monitor amine release, especially ammonia, from decomposing fish. The chromatic parameter ∆E value increased significantly with fish product storage periods. We confirmed that when the freshness limit and bacterial spoilage level were reached, the color of the indicator changed from yellow to black and sequentially changed to purple as the storage period increased. Therefore, a developed freshness indicator can be used for determining the quality of fish products quickly and non-destructively by reflecting the freshness and spoilage degree of fish products during storage.

Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method

Apo pickle is a traditional Chinese fermented vegetable. However, the traditional fermentation process of Apo pickle is slow, easy to ruin, and cannot be judged with regard to time. To improve fermentation, LP-165 (L. Plantarum), which has a high salt tolerance, acidification, and growth capacity, was chosen as the starter culture. Meanwhile, a colorimetric sensor array (CSA) sensitive to pickle volatile compounds was developed to differentiate Apo pickles at varying degrees of fermentation. The color components were extracted from each dye in the color change profiles and were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). The fermentation process of the Apo pickle was classified into four phases by LDA. The accuracy of backward substitution verification was 99% and the accuracy of cross validation was 92.7%. Furthermore, the partial least squares regression (PLSR) showed that data from the CSA were correlated with pH total acid, lactic acid, and volatile acids of the Apo pickle. These results illustrate that the CSA reacts quickly to inoculated Apo pickle and could be used to detect fermentation.

Bio-inspired polymer array vapor sensor with dual signals of fluorescence intensity and wavelength shift

Organic vapor sensors based on polymer owing to their tunable molecular structures and designable functions have attracted considerable research interest. However, detecting multiple organic vapors with high accuracy and a low detection limit is still challenging. Herein, inspired by the mammalian olfactory recognition system, organic vapor sensors based on one-dimensional microfilament array structures with a wide range of sensing gases are demonstrated. By introducing aggregation-induced emission (AIE) molecules, sensors possess dual-optical sensing mechanisms of variation in fluorescence intensity and wavelength. By virtue of the synergistic effects of dual signals, superb accuracy and incredibly low detection limit are achieved for identifying analytes. In particular, the polymer/AIE microfilament array can detect acetone vapor down to 0.03% of saturated vapor pressure. In the saturated vapor of acetone, the fluorescence intensity of the sensor arrays was reduced by 53.7%, while the fluorescence wavelength was red-shifted by 21 nm. Combined with the principal component analysis (PCA) algorithm, the polymer/AIE molecular sensor arrays accomplished the classification and identification of acetone, ethanol, methylene chloride, toluene, and benzene. This bioinspired approach with dual sensing signals may broaden practical applications to high-performance gas sensors for precise molecular detection.