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The schematic diagram of colorimetric sensor array system (a), visible near-infrared spectroscopy system (b) and difference map acquisition of oysters (c).
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Volatile organic compounds (VOCs) could be used as an indicator of the freshness of oysters. However, traditional characterization methods for VOCs have some disadvantages, such as having a high instrument cost, cumbersome pretreatment, and being time consuming. In this work, a fast and non-destructive method based on colorimetric sensor array (CSA...
Contexts in source publication
Context 1
... color-sensitive materials were dissolved in N,N-Dimethylformamide (DMF) with a concentration of 2.0 mg/mL, as shown in Figure 1a, and then poured into a silica gel plate (Merck, Germany) through a capillary tube (0.5 mm × 100 mm), structing a 4 × 5 sensor array, as shown in Figure 1b. The schematic diagram of the colorimetric sensor array is shown in Figure 2a. The CCD camera recorded the "before image" of the colorimetric sensor array before it was exposed to the VOCs of the oysters. ...
Context 2
... and 8-(6-methoxy-2-naphthyl)-4,4-difluoroboron dipyrromethane (NaiOCH3BDP) were selected as the color-sensitive materials. The schematic diagram of the colorimetric sensor array is shown in Figure 2a. The CCD camera recorded the "before image" of the colorimetric sensor array before it was exposed to the VOCs of the oysters. ...
Context 3
... seen from Figure 2c, a 2 × 2 sensor array was constructed to determine the VOCs of oysters with different storage times (stored at 4 • C for 0 day, 2 days, 4 days, 6 days, 8 days, and 10 days, with 30 samples in each group for a total of 180 samples). After being recorded as the "before image", the colorimetric sensor array was fixed on the cover of the gas collecting chamber, and the oyster samples were placed in the gas collecting chamber. ...
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... schematic diagram of a visible near-infrared spectroscopy system was shown as Figure 2b. After the VOCs of the oysters fully react with the colorimetric sensor array for 10 min, the colorimetric sensor array was taken out and placed in the visible near-infrared spectroscopy acquisition device, and the reflected spectrum data of the colorimetric sensor array after reaction was collected. ...
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... schematic diagram of a visible near-infrared spectroscopy system was shown as Figure 2b. After the VOCs of the oysters fully react with the colorimetric sensor array for 10 min, the colorimetric sensor array was taken out and placed in the visible near-infrared spectroscopy acquisition device, and the reflected spectrum data of the colorimetric sensor array after reaction was collected. ...
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The shell-closing strength (SCS) of oysters is the main parameter for physiological activities. The aim of this study was to evaluate the applicability of SCS as an indicator of live oyster health. This study developed a flexible pressure sensor system with polydimethylsiloxane (PDMS) as the substrate and reduced graphene oxide (rGO) as the sensitive layer to monitor SCS in live oysters (rGO-PDMS). In the experiment, oysters of superior, medium and inferior grades were selected as research objects, and the change characteristics of SCS were monitored at 4 °C and 25 °C. At the same time, the time series model was used to predict the survival rate of live oyster on the basis of changes in their SCS characteristics. The survival times of superior, medium and inferior oysters at 4 °C and 25 °C were 31/25/18 days and 12/10/7 days, respectively, and the best prediction accuracies for survival rate were 89.32%/82.17%/79.19%. The results indicate that SCS is a key physiological indicator of oyster survival. The dynamic monitoring of oyster vitality by means of flexible pressure sensors is an important means of improving oyster survival rate. Superior oysters have a higher survival rate in low-temperature environments, and our method can provide effective and reliable survival prediction and management for the oyster industry.
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In this work, a new colorimetric sensor based on mesoporous silica nanosphere-modified color-sensitive materials was established for application in monitoring the total volatile basic nitrogen (TVB-N) of oysters. Firstly, mesoporous silica nanospheres (MSNs) were synthesized based on the improved Stober method, then the color-sensitive materials were doped with MSNs. The “before image” and the “after image” of the colorimetric senor array, which was composed of nanocolorimetric-sensitive materials by a charge-coupled device (CCD) camera were then collected. The different values of the before and after image were analyzed by principal component analysis (PCA). Moreover, the error back-propagation artificial neural network (BP-ANN) was used to quantitatively predict the TVB-N values of the oysters. The correlation coefficient of the colorimetric sensor array after being doped with MSNs was greatly improved; the Rc and Rp of BP-ANN were 0.9971 and 0.9628, respectively when the principal components (PCs) were 10. Finally, a paired sample t-test was used to verify the accuracy and applicability of the BP-ANN model. The result shows that the colorimetric-sensitive materials doped with MSNs could improve the sensitivity of the colorimetric sensor array, and this research provides a fast and accurate method to detect the TVB-N values in oysters.
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Total volatile basic nitrogen (TVB-N) content is one of the critical safety evaluation indexes in seafood. This study investigated the colorimetric sensing array (CSA) technique with two data collection blocks, imaging and visible near-infrared spectroscopy, for non-destructive prediction of TVB-N content in Fujian oysters. Three data fusion strategies coupled with variable selection approaches including variable combination population analysis (VCPA), ant colony optimization, and competitive adaptive reweighted sampling were applied to establish the partial least squares (PLS) model for predicting the TVB-N content of oysters. Better prediction performance was obtained using data fusion of two information blocks than using individual block data. The results showed that the high-level-VCPA strategy showed the best performance with the correlation coefficient of the prediction set (Rp) at 0.9128. This work demonstrates that the use of CSA has the potential for non-destructive monitoring of TVB-N content in Fujian oysters.
