Design of A Portable Electronic Nose system and Application in K Value Prediction for Large Yellow Croaker (Pseudosciaena crocea)

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Assessment and Prediction of Fish Freshness Using Mathematical Modelling: A Review

Fish freshness can be considered as the combination of different nutritional and organoleptic attributes that rapidly deteriorate after fish capture, i.e., during processing (cutting, gutting, packaging), storage, transport, distribution, and retail. The rate at which this degradation occurs is affected by several stress variables such as temperature, water activity, or pH, among others. The food industry is aware that fish freshness is a key feature influencing consumers’ willingness to pay for the product. Therefore, tools that allow rapid and reliable assessment and prediction of the attributes related to freshness are gaining relevance. The main objective of this work is to provide a comprehensive review of the mathematical models used to describe and predict the changes in the key quality indicators in fresh fish and shellfish during storage. The work also briefly describes such indicators, discusses the most relevant stress factors affecting the quality of fresh fish, and presents a bibliometric analysis of the results obtained from a systematic literature search on the subject.

Detection of decomposition in mahi-mahi, croaker, red snapper, and weakfish using an electronic-nose sensor and chemometric modeling

This study evaluated an electronic-nose (e-nose) sensor in combination with support vector machine (SVM) modeling for predicting the decomposition state of four types of fish fillets: mahi-mahi, croaker, red snapper, and weakfish. The National Seafood Sensory Expert scored fillets were thawed, 10-g portions were weighed into glass jars which were then sealed, and the jars were held at approximately 30°C to allow volatile components to be trapped and available for analysis. The measurement of the sample vial headspace was performed with an e-nose device consisting of nanocomposite, metal oxide semiconductor (MOS), electrochemical, and photoionization sensors. Classification models were then trained based on the sensory grade of each fillet, and the e-nose companion chemometric software identified that eight MOS were the most informative for determining a sensory pass from sensory fail sample. For SVM, the cross-validation (CV) correct classification rates for mahi-mahi, croaker, red snapper, and weakfish were 100%, 100%, 97%, and 97%, respectively. When the SVM prediction performances of the eight MOS were evaluated using a calibration-independent test set of samples, correct classification rates of 93-100% were observed. Based on these results, the e-nose measurements coupled with SVM models were found to be potentially promising for predicting the spoilage of these four fish species. PRACTICAL APPLICATION: This report describes the application of an electronic-nose sensor as a potential rapid and low-cost screening method for fish spoilage. It could provide regulators and stakeholders with a practical tool to rapidly and accurately assess fish decomposition.

Aroma dynamic characteristics during the process of variable-temperature final firing of Congou black tea by electronic nose and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

The drying technology is crucial to the quality of Congou black tea. In this study, the aroma dynamic characteristics during the variable-temperature final firing of Congou black tea was investigated by electronic nose (e-nose) and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). Varying drying temperatures and time obtained distinctly different types of aroma characteristics such as faint scent, floral aroma, and sweet fragrance. GC × GC-TOFMS identified a total of 243 volatile compounds. Clear discrimination among different variable-temperature final firing samples was achieved by using partial least squares discriminant analysis (R2Y = 0.95, Q2 = 0.727). Based on a dual criterion of variable importance in the projection value (VIP > 1.0) and one-way ANOVA (p < 0.05), ninety-one specific volatile biomarkers were identified, including 2,6-dimethyl-2,6-octadiene and 2,5-diethylpyrazine with VIP > 1.5. In addition, for the overall odor perception, e-nose was able to distinguish the subtle difference during the variable-temperature final firing process.

Evaluating aroma quality of black tea by an olfactory visualization system: Selection of feature sensor using particle swarm optimization

Aroma is an important index to evaluate the quality and grade of black tea. This work innovatively proposed the sensory evaluation of black tea aroma quality based on an olfactory visual sensor system. Firstly, the olfactory visualization system, which can visually represent the aroma quality of black tea, was assembled using a lab-made color sensitive sensor array including eleven porphyrins and one pH indicator for data acquisition and color components extraction. Then, the color components from different color sensitive spots were optimized using the particle swarm optimization (PSO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized characteristic color components for the sensory evaluation of black tea aroma quality. Results demonstrated that the BPNN models, which were developed using three color components from FTPPFeCl (component G), MTPPTE (component B) and BTB (component B), can get better results based on comprehensive consideration of the generalization performance of the model and the fabrication cost of the sensor. In the validation set, the average of correlation coefficient (R_P) value was 0.8843 and the variance was 0.0362. The average of root mean square error of prediction (RMSEP) was 0.3811 and the variance was 0.0525. The overall results sufficiently reveal that the optimized sensor array has promising applications for the sensory evaluation of black tea products in the process of practical production.

Combining near-infrared hyperspectral imaging with elemental and isotopic analysis to discriminate farm-raised pacific white shrimp from high-salinity and low-salinity environments

White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near-infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model. The results showed that sequential forward selection combined with partial least squares discriminant analysis (SFS-PLS-DA) generated the best discrimination performance with an overall accuracy of 99.2%. The elemental and isotopic analysis indicated a high correlation between 918 and 925 nm region (which was selected by SFS) and ¹³C concentration. This agrees with the fact that there is more ¹³C in shrimp of salty water compared to those of freshwater. The results demonstrated (hyperspectral imaging) HSI is promising to discriminate L. vannamei raised in fresh and seawater environments.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

Evaluation of Smart Portable Device for Food Diagnostics: A Preliminary Study on Cape Hake Fillets (M. capensis and M. paradoxus)

The new smartphone-based food diagnostic technologies offer significant advantages over traditional methods as they can be easily applied in various steps of the agrifood supply chain including household use and also in the food recovery field for charitable purposes, aimed at helping to reduce food waste. Further advantages include the low cost, the minimal equipment, and nonspecialized personnel required. This study evaluated the performance of two instrumental measurements of the sensors: an electronic nose (PEN3; WinMuster Airsense Analytics) and a smart portable device (FOODsniffer; ARS LAB US). The preliminary study was conducted on cape hake fillets. In order to test the performance of PEN3 and FOODsniffer, total volatile basic nitrogen (TVB-N) values were considered as the reference. Principal component analysis (PCA) and Pearson’s correlation were performed in order to compare PEN3 with TVB-N, and for the FOODsniffer evaluation, a one-way ANOVA was carried out. A significant correlation was shown between PEN3, first component, and TVB-N (r = 0.92, P=0.01). The ANOVA results also confirmed a good agreement between FOODsniffer, TVB-N (F = 519.9, P=0.01), and PEN3 (F = 143.17, P=0.01). Our simulation results confirmed good performance in both methods.