Electronic nose system fabrication and application in large yellow croaker (Pseudosciaena crocea) fressness prediction

Freshness analysis based on lipidomics for farmed Atlantic salmon (Salmo salar L.) stored at different times

Liquid chromatography-mass spectrometry was used to study the changes of lipids in salmon muscle stored at 4 °C for different storage times to explore the relationship between lipid composition and salmon freshness. Ninety-two kinds of lipid changes were observed at three different storage times (5, 10, and 15 days) compared with the fresh control group (0 day). Bioinformatics analysis revealed that the contents of four lipids were significantly increased from the tenth day, namely, lysophosphatidylcholine (LPC) (17:0), LPC (18:0), LPC (22:2), and phosphatidylcholine (PC) (18:4/16:1). LPC (17:0) and LPC (18:0) are produced by PC (18:4/16:1) hydrolysis. The traditional freshness index also showed that the salmon slices were in the initial state of spoilage on the tenth day. Therefore, they may be indicators of raw salmon freshness.

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.

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.

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.