Abstract
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.
Highlights
Oysters are marine organisms known as “ocean milk”, which represent an important part of global seafood consumption
The principal component analysis (PCA) was implemented in Matlab R2021.6.bM, thuletiBvaPr-iAateNSNtataislgtiocarlitAhnmalywsiass implemented in NeuroShell 2, and the paired sample t-test waMs uimltipvlaermiaetenatendaliynsiSsPmSeSthSotadtsisptilcasye1d7 a(mkeaynurofalectiunrceh,aCrahcitceargizoi,nIgLT, UVBS-AN).in oyster samples based on the colorimetric sensor array
This paper presents a novel method for total volatile basic nitrogen (TVB-N) value detection in oysters which is based on color-sensitive material doped with mesoporous silica nanospheres (MSNs)
Summary
Oysters are marine organisms known as “ocean milk”, which represent an important part of global seafood consumption. The gas sensor converts the chemical input of a certain gas into an electrical signal which would be processed using suitable pattern-recognition analysis methods after appropriate preprocessing It has the advantages of a short response time and fast detection speed. The colorimetric sensor array, invented by Kenneth Suslick, is a novel electronic nose which consists of chemical dyes that are sensitive to specific VOCs. Quantitative or qualitative analysis of the VOCs would be detected by the unique color changes in the colorimetric sensor array after reaction with VOCs [16,17]. Current sensors are mostly unmodified raw commercial color-sensitive materials, and these sensors are difficult to be used for the detection and analysis of one or more specific gases of food that reflect its quality characteristics. A paired sample t-test was used to verify the difference between the predicted values of the model and measured values
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