A method for detecting freshness of salmon fish meat by using a QCM gas sensor array combined with a physicochemical method

Abstract

Salmon meat is prone to spoilage during transportation and storage due to its rich protein and fatty acid content. During the spoilage process, the released volatile organic compounds (VOCs) can vary greatly, mainly including trimethylamine (TMA), dimethylamine (DMA), formaldehyde (HCHO), and ammonia (NH3). In this study, an array of quartz crystal microbalance (QCM) gas sensors was established, which was modified with graphene oxide (GO), Mxenes (Ti3C2T X ), hydroxylated multi-walled carbon nanotubes (MWCNTs), and graphdiyne oxide (GO P ), respectively, to detect TMA, DMA, HCHO, and NH3 in salmon meat, respectively. The results showed that the four kinds of sensor materials modified QCM sensors had good sensitivity, selectivity and repeatability for the corresponding target gas. Finally, the freshness of salmon meat was detected using the sensor array. The sensor response data was visualized by principal component analysis (PCA), and the first two principal components (PCs) explained 98.8% of the total contribution variance. A further discriminant model was established using quadratic support vector machine (QSVM), and the classification result was 100%. To further describe, the detection results were compared with the total volatile base nitrogen (TVB-N) content to judge the freshness of salmon meat.

Description

Technical Field

[0001] The invention relates to a method for preparing and applying a QCM gas sensor for detecting the freshness of salmon flesh, belonging to the fields of food and gas sensors. Background Technology

[0002] Salmon is rich in protein and fatty acids, making it very popular. However, salmon meat spoils easily, losing its freshness. Accidentally consuming spoiled salmon can lead to poisoning or even death, seriously affecting consumer health. As consumer demand for salmon increases, so too does the demand for its freshness. To protect consumer interests, many researchers have been searching for rapid and reliable methods to detect salmon freshness. Sensory evaluation is commonly used, but it relies on the analyst's experience, and subjective judgment can lead to erroneous results. Therefore, it is necessary to develop a simple and sensitive method for detecting salmon freshness. This study developed a QCM gas-sensitive sensor array to detect salmon freshness using four characteristic volatile organic compounds (VOCs) in the salmon spoilage process as target compounds.

[0003] With the rapid development of the aquatic product processing industry, new technologies for detecting the freshness of fish are constantly emerging. Currently, these mainly include high-performance liquid chromatography (HPLC), infrared spectroscopy, chemiluminescence immunoassay, enzyme-linked immunosorbent assay (ELISA), and gas sensor detection. Among these, gas sensor detection, with its advantages of speed, non-destructive testing, and accuracy, has become a research hotspot for fish freshness detection. The development of the Quartz Crystal Microbalance (QCM) began in the early 1960s. It is a highly sensitive mass measurement instrument with a measurement accuracy down to the nanogram level, 1000 times higher than the microgram-level sensitivity of electronic microbalances. Theoretically, it can measure mass changes equivalent to a fraction of a monolayer or atomic layer. The Quartz Crystal Microbalance utilizes the piezoelectric effect of quartz crystals, converting mass changes on the surface of the quartz crystal electrode into frequency changes in the electrical signal output by the quartz crystal oscillating circuit. High-precision data is then obtained through computers and other auxiliary equipment. We need a method that can achieve rapid and accurate detection, and the QCM gas sensor can well meet our requirements.

[0004] After salmon dies, the fermentation of intestinal microorganisms and its own enzymes gradually emits a putrid odor. The gases produced mainly include trimethylamine (TMA), dimethylamine (DMA), methylamine (HCHO), and ammonia (NH3). Therefore, the presence of these four key volatile substances can be detected to determine whether salmon has begun to spoil.

[0005] Quadrature-mass sensors (QCMs) possess advantages such as simple structure, low cost, high sensitivity, and measurement accuracy down to the nanogram level. They are widely used in fields such as chemistry, physics, biology, medicine, and surface science for gas and liquid composition analysis, micro-mass measurement, thin film thickness and viscoelastic structure detection, etc. In recent years, various nanomaterials have been widely used to improve the performance of QCM gold electrodes. Carbon-based materials have attracted widespread attention due to their large surface-to-volume ratio, high surface activity, and strong adsorption capacity. Therefore, four types of nano-carbon materials were selected to modify QCM gold electrodes, and the four modified gold electrodes were combined into a sensor array to detect four key volatiles. The results were then compared with TVB-N to determine the freshness of salmon flesh. Summary of the Invention

[0006] The purpose of this invention is to propose a QCM gas sensor array that can overcome the above-mentioned defects, and can be constructed with high sensitivity, high stability and good selectivity, and used for the detection of TMA, DMA, HCHO and NH3 to determine the freshness of salmon meat.

[0007] The technical solution of the invention is as follows: a method for fabricating a QCM gas-sensitive sensor array, characterized in that: graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes, and graphyne oxide are respectively modified onto a QCM gold electrode by drop-coating, then placed into a gas chamber, and connected to an instrument to construct the sensor, including the following steps:

[0008] (1) Preparation of carbon-based materials

[0009] Weigh 5 mg of graphene oxide and dissolve it in 5 mL of ultrapure water. Stir at room temperature until the graphene oxide is completely dissolved to obtain a 1 mg / mL aqueous solution of graphene oxide. Similarly, prepare 1 mg / mL aqueous solutions of titanium carbide, carboxylated multi-walled carbon nanotubes, and graphyne oxide.

[0010] (2) Fabrication of QCM gas sensor array

[0011] Four materials were fixed onto the surface of a pretreated QCM gold electrode by drop coating and dried at room temperature to obtain a modified electrode.

[0012] QCM gas-sensitive sensor array sensing method includes the following steps

[0013] (1) Fabrication of QCM gas sensor array

[0014] Preparation of graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes, and graphyne oxide materials: 5 mg of each of the four materials were weighed and dissolved in 5 mL of ultrapure water. The solutions were stirred at room temperature for 30 minutes to ensure complete dissolution of all four materials. The four materials were then fixed onto the surface of a pretreated QCM gold electrode by drop coating and dried at room temperature to obtain the modified electrode.

[0015] (2) Detection of target substances

[0016] According to step (1), the QCM sensor array is installed in the gas chamber to detect the gases produced by the spoilage of salmon meat. The characteristic gases of salmon spoilage process, trimethylamine, dimethylamine, formaldehyde, and ammonia, are selected as target gases. Other gases in the process of fish meat spoilage are selected as interfering gases, namely ethyl acetate, glacial acetic acid, hexanal, ethanol, 1-octen-3-ol, and acetone. The gas concentration is calculated according to the static headspace method. A small amount of aqueous solution containing the corresponding analyte is dropped into a 1.158L headspace bottle and then evaporated at room temperature. All gases with a concentration of 20μmol / L are selected by static headspace method and selectively tested. Then, the sensitivity of the target gas is tested. The feature is that the mass change is recorded by frequency change. As the concentration of the target gas increases, the frequency change becomes more and more obvious. The standard curve obtained is used to calculate and judge the freshness of salmon.

[0017] (3) Data processing

[0018] The sensor response data was visualized using Principal Component Analysis (PCA), with the following characteristics: To visualize the clustering trend of salmon flesh samples at different storage times, PCA analysis was performed on the M120×4 feature matrix after normalization; Principal Component Analysis is an unsupervised method for visualizing data structures and eliminating redundancy in the original measurement data; this method attempts to transform the feature vectors of the original dataset into a set of linearly uncorrelated vectors through orthogonal transformation; To study the ability of the QCM sensor array to identify salmon flesh samples at different storage times, we adopted a quadratic support vector model. Qualitative microarray dynamics (QSVM) was used as the classification model; QSVM is used for object classification in n-dimensional hyperspace and can effectively prevent "overfitting"; the salmon flesh measured by TVB-N was divided into 18 samples (6 groups × 3 replicates); all samples were placed in a refrigerator at 4°C from day 0 to day 5; the semi-micro nitrogen determination method was used as the standard index of salmon flesh freshness, and the TVB-N of salmon samples and the detection of the gas sensor array were measured simultaneously; Pearson correlation analysis was used to evaluate the linear correlation between the salmon flesh spoilage process and the detection results of the gas sensor array.

[0019] The beneficial effects of this invention are:

[0020] (1) A QCM sensor array modified with nano-carbon material was combined with PCA to detect volatile organic compounds (TMA, DMA, HCHO and NH3) in the process of salmon meat spoilage.

[0021] (2). The QCM sensor array response data was compared with TVB-N to determine the freshness of refrigerated (4°C) salmon fillets;

[0022] (3) Sensor response data were visualized using principal component analysis. The first two principal components explained 98.8% of the total contribution variance. A discriminant model was further established using a quadratic support vector machine, and the classification result was 100%. Attached Figure Description

[0023] Figure 1 Assembly process of QCM gas sensor.

[0024] Figure 2 SEM images of four nanomaterials.

[0025] Figure 3 Sensor selectivity.

[0026] Figure 4 Standard curve graph.

[0027] Figure 5 PCA.

[0028] Figure 6 QSVM.

[0029] Figure 7 Actual sample testing

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, but the embodiments do not limit the present invention in any way.

[0031] (1) QCM gold electrode pretreatment: First, the bare QCM gold electrode is rinsed with ultrapure water and anhydrous ethanol. Then, the QCM gold electrode is installed on the QCM probe and the frequency change of the electrode within one minute does not exceed 1 Hz. It is then dried at room temperature for later use.

[0032] (2) Preparation of modified electrodes: First, graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes and graphyne oxide were respectively fixed on the electrode surface by drop coating and dried at room temperature for 12 hours to obtain modified electrodes.

[0033] (3) Trimethylamine, dimethylamine, formaldehyde, and ammonia were selected as target gases. Other gases that were present during the spoilage of fish were selected as interfering gases, namely ethyl acetate, glacial acetic acid, hexanal, ethanol, 1-octen-3-ol, and acetone. The gas concentrations were calculated based on static headspace analysis. A small amount of aqueous solution containing the corresponding analyte was added to a 1.158 L headspace vial and then evaporated at room temperature. Selectivity tests were performed on all gases for which the static headspace analysis method determined the concentration to be 20 μmol / L.

[0034] (4) Drop 1-12 μL of the target analyte into a 1.158 L headspace vial and record the mass change by frequency variation. As the concentration of the target gas increases, the frequency change becomes more and more obvious. The standard curve obtained is used to calculate and determine the freshness of the salmon.

[0035] (5) Salmon sample testing, characterized as follows: During the preparation of salmon samples, the salmon meat detected by the gas sensor array needs to be divided into 24 samples, each group 20 g; all samples were placed in a refrigerator at 4℃ from day 0 to day 5. Then, one sample was placed in a headspace vial with a volume of 1.158 L, and after headspace sampling for 2 hours, the gas was absorbed with a 50 mL syringe and injected into the laboratory; the half-micron-Krüger method was used as the standard index of salmon meat freshness, and the TVB-N of the salmon meat sample was determined at the same time;

[0036] (6) The sensor response data was visualized using Principal Component Analysis (PCA), and its characteristics are as follows: In order to visualize the clustering trend of salmon meat samples with different storage times, PCA analysis was performed on the M120×4 feature matrix after normalization; Principal Component Analysis is an unsupervised method for visualizing data structures and eliminating redundancy in the original measurement data; This method attempts to transform the feature vectors of the original dataset into a set of linearly uncorrelated vectors through orthogonal transformation; In order to study the recognition ability of the QCM sensor array for salmon meat samples with different storage times, we adopted a quadratic support vector machine (QSVM) as the classification model; QSVM is used for object classification in n-dimensional hyperspace and can effectively prevent "overfitting";

[0037] (7) The detection results were compared with the total volatile basic nitrogen (TVB-N) content to determine the freshness of salmon meat; the characteristics are as follows: the salmon meat for TVB-N determination was divided into 18 samples (6 groups × 3 replicates); all samples were placed in a refrigerator at 4°C from day 0 to day 5; the semi-micro nitrogen determination method was used as the standard index of salmon meat freshness, and the TVB-N of salmon samples and the detection of the gas sensor array were measured at the same time; the Pearson correlation analysis method was used to evaluate the linear correlation between the salmon meat spoilage process and the detection results of the gas sensor array.

Claims

1. A method for detecting the freshness of salmon flesh using a QCM gas sensor array combined with physicochemical methods, characterized in that: Four characteristic volatile gases produced during salmon spoilage were detected by drop-coating graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes, and graphyne oxide onto a QCM gold electrode. These characteristic volatile gases were trimethylamine, dimethylamine, formaldehyde, and ammonia. The freshness of the salmon flesh was determined by comparing the detection of these characteristic volatile gases with physicochemical analysis of the salmon flesh. The sensor response data was visualized using principal component analysis (PCA), with the first two principal components (PCs) explaining 98.8% of the total variance. A further discriminant model was established using quadratic support vector machine (QSVM). The detection results were compared with the total volatile basic nitrogen (TVB-N) content to determine the freshness of the salmon flesh. The experimental steps are as follows: (1) QCM gold electrode pretreatment: First, the bare QCM gold electrode is rinsed with ultrapure water and anhydrous ethanol. Then, the QCM gold electrode is installed on the QCM probe. The frequency change of the electrode within one minute does not exceed 1 Hz. It is then dried at room temperature for later use. (2) Preparation of modified electrodes: First, graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes and graphyne oxide were fixed on the electrode surface by drop coating and dried at room temperature for 12 hours to obtain modified electrodes.

2. The method for detecting the freshness of salmon flesh using a QCM gas sensor array combined with physicochemical methods according to claim 1, characterized in that, Includes the following steps: (1) Preparation of graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes and graphdiyne oxide: Weigh 5 mg of graphene oxide, titanium carbide, carboxylated multi-walled carbon nanotubes and graphdiyne oxide respectively, dissolve them in 5 mL of ultrapure water, and stir at room temperature for 30 minutes to dissolve all four materials. (2) Preparation of QCM gas sensor: Four materials were respectively drop-coated onto four QCM gold electrodes and dried overnight at room temperature.

3. The method for detecting the freshness of salmon flesh using a QCM gas sensor array combined with physicochemical methods according to claim 1, characterized in that: Trimethylamine, dimethylamine, formaldehyde, and ammonia, characteristic volatile gases emitted during salmon spoilage, were selected as target gases. Other gases emitted during the spoilage process were selected as interfering gases: ethyl acetate, glacial acetic acid, hexanal, ethanol, 1-octen-3-ol, and acetone. The gas concentrations were calculated using the static headspace method. A small amount of aqueous solution containing the corresponding analyte was added dropwise into a 1.158 L headspace vial, and then evaporated at room temperature. The static headspace method yielded a concentration of 20 μmol / L for all gases, and all gases were selectively tested.

4. A method for detecting the freshness of salmon flesh using a QCM gas sensor array combined with physicochemical methods according to claim 3, wherein sensitivity testing is performed, characterized in that: The change in mass is recorded by frequency variation. As the concentration of the target gas increases, the frequency variation becomes more and more obvious, thus obtaining a standard curve and calculating the freshness of salmon.

5. A method for detecting the freshness of salmon meat using a QCM gas sensor array combined with physicochemical methods according to claim 1, characterized as follows: During the salmon sample preparation process, the salmon meat detected by the gas sensor array needs to be divided into 24 samples, each group containing 20 g; all samples are placed in a refrigerator at 4°C from day 0 to day 5; then, one sample is placed in a headspace vial with a volume of 1.158 L, and after headspace sampling for 2 hours, gas is absorbed using a 50 mL syringe and injected into the gas chamber; the TVB-N is detected using a semi-micro nitrogen determination method as a standard indicator of salmon meat freshness, and the TVB-N of the salmon meat sample is measured simultaneously.

6. The method for detecting the freshness of salmon flesh using a QCM gas sensor array combined with physicochemical methods according to claim 1, wherein the sensor response data is visualized using PCA, characterized as follows: to visualize the clustering trend of salmon flesh samples at different storage times, after normalization, M... 120×4 PCA analysis was performed on the feature matrix; PCA analysis is an unsupervised method for visualizing data structures and eliminating redundancy in raw measurement data; this method attempts to transform the feature vectors of the original dataset into a set of linearly uncorrelated vectors through orthogonal transformation; in order to study the ability of the QCM sensor array to identify salmon meat samples stored for different times, QSVM was used as the classification model; QSVM is used for object classification in n-dimensional hyperspace and can effectively prevent "overfitting".

7. A method for detecting the freshness of salmon meat using a QCM gas sensor array combined with physicochemical methods according to claim 1, wherein the detection results are compared with TVB-N content to determine the freshness of the salmon meat, characterized as follows: 18 samples of salmon meat for TVB-N determination are collected, and all samples are placed in a 4°C refrigerator from day 0 to day 5; a semi-micro nitrogen determination method is used to detect TVB-N as a standard indicator of salmon meat freshness, and the TVB-N of the salmon samples and the detection results of the gas sensor array are measured simultaneously; Pearson correlation analysis is used to evaluate the linear correlation between the salmon meat spoilage process and the detection results of the gas sensor array.

Images