CN114994025A - Quick beef freshness detection method based on olfactory visualization chip - Google Patents
Quick beef freshness detection method based on olfactory visualization chip Download PDFInfo
- Publication number
- CN114994025A CN114994025A CN202210509941.2A CN202210509941A CN114994025A CN 114994025 A CN114994025 A CN 114994025A CN 202210509941 A CN202210509941 A CN 202210509941A CN 114994025 A CN114994025 A CN 114994025A
- Authority
- CN
- China
- Prior art keywords
- beef
- chip
- olfactory
- freshness
- visualization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 235000015278 beef Nutrition 0.000 title claims abstract description 140
- 238000012800 visualization Methods 0.000 title claims abstract description 65
- 238000001514 detection method Methods 0.000 title claims abstract description 33
- RBTARNINKXHZNM-UHFFFAOYSA-K iron trichloride Chemical compound Cl[Fe](Cl)Cl RBTARNINKXHZNM-UHFFFAOYSA-K 0.000 claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 38
- 230000004044 response Effects 0.000 claims abstract description 37
- 239000011159 matrix material Substances 0.000 claims abstract description 26
- 230000000007 visual effect Effects 0.000 claims abstract description 25
- 238000003860 storage Methods 0.000 claims abstract description 21
- PJOJZHHAECOAFH-UHFFFAOYSA-N 5,10,15,20-tetrakis(4-methoxyphenyl)-21,23-dihydroporphyrin Chemical compound COc1ccc(cc1)-c1c2ccc(n2)c(-c2ccc(OC)cc2)c2ccc([nH]2)c(-c2ccc(OC)cc2)c2ccc(n2)c(-c2ccc(OC)cc2)c2ccc1[nH]2 PJOJZHHAECOAFH-UHFFFAOYSA-N 0.000 claims abstract description 19
- 229910021578 Iron(III) chloride Inorganic materials 0.000 claims abstract description 19
- BELBBZDIHDAJOR-UHFFFAOYSA-N Phenolsulfonephthalein Chemical compound C1=CC(O)=CC=C1C1(C=2C=CC(O)=CC=2)C2=CC=CC=C2S(=O)(=O)O1 BELBBZDIHDAJOR-UHFFFAOYSA-N 0.000 claims abstract description 19
- 229960003531 phenolsulfonphthalein Drugs 0.000 claims abstract description 13
- 239000000463 material Substances 0.000 claims abstract description 10
- 238000013528 artificial neural network Methods 0.000 claims abstract description 3
- 238000012549 training Methods 0.000 claims description 23
- 238000011156 evaluation Methods 0.000 claims description 13
- 239000002033 PVDF binder Substances 0.000 claims description 11
- 229920002981 polyvinylidene fluoride Polymers 0.000 claims description 11
- HEDRZPFGACZZDS-UHFFFAOYSA-N Chloroform Chemical compound ClC(Cl)Cl HEDRZPFGACZZDS-UHFFFAOYSA-N 0.000 claims description 6
- 239000012528 membrane Substances 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 2
- 230000007613 environmental effect Effects 0.000 abstract 1
- 210000002569 neuron Anatomy 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 5
- 239000003755 preservative agent Substances 0.000 description 5
- 230000002335 preservative effect Effects 0.000 description 5
- 238000010561 standard procedure Methods 0.000 description 5
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- 239000007789 gas Substances 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 235000013372 meat Nutrition 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 235000013305 food Nutrition 0.000 description 3
- 230000003321 amplification Effects 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000004587 chromatography analysis Methods 0.000 description 2
- 238000002790 cross-validation Methods 0.000 description 2
- 230000002209 hydrophobic effect Effects 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 238000004949 mass spectrometry Methods 0.000 description 2
- 238000002156 mixing Methods 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 238000003753 real-time PCR Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 238000009210 therapy by ultrasound Methods 0.000 description 2
- 238000004506 ultrasonic cleaning Methods 0.000 description 2
- 238000005303 weighing Methods 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 241000283690 Bos taurus Species 0.000 description 1
- 238000004566 IR spectroscopy Methods 0.000 description 1
- 235000011483 Ribes Nutrition 0.000 description 1
- 241000220483 Ribes Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013142 basic testing Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004090 dissolution Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000010794 food waste Substances 0.000 description 1
- 230000008014 freezing Effects 0.000 description 1
- 238000007710 freezing Methods 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 210000000614 rib Anatomy 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
- G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
- G01N21/78—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator producing a change of colour
- G01N21/783—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator producing a change of colour for analysing gases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0001—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00 by organoleptic means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/12—Meat; Fish
Landscapes
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Food Science & Technology (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Medicinal Chemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Plasma & Fusion (AREA)
- Investigating Or Analyzing Non-Biological Materials By The Use Of Chemical Means (AREA)
Abstract
The invention discloses a method for quickly detecting freshness of beef based on an olfactory visualization chip, which comprises the following steps: selecting tetramethoxyphenyl porphyrin iron (III) chloride and phenol red as gas-sensitive materials to prepare an olfactory visual chip with two sensors; and setting representative beef samples at different storage times, and combining the freshness index data with the response characteristic matrix obtained at the corresponding storage time, thereby establishing a backward propagation neural network quantitative model of beef freshness. Inputting the olfactory visualization chip response characteristic matrix of the beef sample to be tested into the established quantitative model in beef, thereby obtaining the predicted value of the TVB-N content and the predicted value of the TVC content of the beef sample to be tested. The invention can realize quick, simple, convenient and accurate quantitative detection of beef freshness; the method solves the defects of complex detection process, high detection cost, easy environmental interference, incapability of visual detection and the like in the prior art.
Description
Technical Field
The invention relates to a smell visual detection method for meat freshness, in particular to a quick beef freshness detection method based on a smell visual chip.
Background
The rapid and accurate detection of the freshness of the beef is a necessary condition for reducing food safety problems and food waste. At present, the detection method of the freshness of the beef mainly comprises a physicochemical index method, an organoleptic evaluation method, a chromatography method, a mass spectrometry method and the like. The physical and chemical index method mainly adopts methods in GB 5009.228-2016 and GB 4789.2-2010, and has complex operation and needs a large amount of chemical reagents; the sensory evaluation method needs professional training on the evaluation personnel and has strong subjectivity; the detection results of the chromatography and the mass spectrometry are objective and reliable, but the detection results are long in time consumption, complex in sample pretreatment and expensive in instruments.
Penyan and the like (patent application number CN201110376478.0) discloses a quick nondestructive evaluation method and system for freshness of fresh beef. The method combines a visible/near infrared spectrum measurement technology, a computer technology, a chemometrics technology and a basic test technology, establishes a mathematical prediction model between spectral information reflecting the components and the state information of the fresh beef and multiple indexes of freshness of the fresh beef, and builds the whole detection system. The system can simultaneously detect multiple indexes (volatile basic nitrogen (TVB-N), pH value, total bacteria (TVC), flesh color (L, a)) and the like of the freshness of the unknown fresh beef, can predict the storage time of the beef, comprehensively grades the freshness of the beef by combining the multiple indexes, and realizes the purpose of rapidly and nondestructively detecting and evaluating the freshness of the fresh beef. However, this method requires a dedicated spectrometer, the infrared spectroscopy detection is susceptible to moisture interference, and spectral noise and baseline drift are severe.
Liu Yong Feng et al (patent application No. CN201810301930.9) discloses a method for identifying the storage time of fresh beef by using DNA and PCR technology. The method comprises the steps of separating and extracting DNA of beef under different storage conditions (freezing at-20 ℃, refrigerating at 4 ℃, middle temperature of 10 ℃ and room temperature of 20 ℃), measuring the DNA content of the beef, and determining the freshness of the beef (first-level fresh meat, second-level fresh meat and deteriorated meat); and then, carrying out fluorescent quantitative PCR amplification by taking the DNA as a template, and accurately judging the storage and placement time of the beef according to a mathematical equation of the difference between the time of the beef at different storage temperatures and different freshness degrees and the ct value of the fluorescent quantitative PCR amplification, thereby providing a new method for identifying the placement time of the beef at different temperatures. However, this method is time consuming, requires specialized laboratory personnel, and consumes a large amount of chemical reagents.
All the above prior patents are qualitative analyses.
Disclosure of Invention
The invention aims to provide a quick beef freshness detection method based on an olfactory visualization chip.
In order to solve the technical problem, the invention provides a beef freshness rapid detection method based on an olfactory visualization chip, which comprises the following steps:
1) preparing a smell visualization chip:
selecting tetramethoxyphenyl porphyrin iron (III) chloride and phenol red as gas-sensitive materials, and preparing to obtain an olfactory visual chip with two sensors;
2) setting representative beef samples at different storage time, and respectively carrying out the following processing on the representative beef sample corresponding to each storage time:
dividing into two to form 2 small samples representing beef, wherein one small sample representing beef is used for the following step 2.1), and the other small sample representing beef is used for the following step 2.2):
2.1) placing the smell visualization chip containing the gas-sensitive material and a representative beef small sample in a closed container for reaction, obtaining RGB difference values (R, G, B color difference values) of the chip before and after reaction, and generating a response characteristic matrix of the smell visualization chip;
2.2) measuring the freshness index of another small sample representing beef, wherein the freshness index is TVB-N and TVC content;
namely, according to the national standard method, the TVB-N content and the TVC content in the representative beef small sample are respectively measured;
2.3) combining the freshness index data obtained in the step 2.2) with the response characteristic matrix obtained in the step 2.1) corresponding to the storage time, thereby establishing a Backward Propagation Neural Network (BPNN) quantitative model of beef freshness.
As an improvement of the quick beef freshness detection method based on the olfactory visualization chip, the quick beef freshness detection method further comprises the following steps:
operating the beef sample to be detected according to the 2.1) to obtain a response characteristic matrix of the olfactory visualization chip of the beef sample to be detected;
inputting the olfactory visual chip response characteristic matrix of the beef sample to be detected into the quantitative model in beef established in the step (2.3), thereby obtaining the predicted value of the TVB-N content of the beef sample to be detected
And prediction of TVC content
Therefore, the 2 freshness indexes of the beef sample to be detected can be quickly obtained by adopting the method; and then judging according to the 2 freshness indexes in a conventional manner.
The quick beef freshness detection method based on the olfactory visualization chip is further improved as follows: and (3) repeatedly carrying out the step 2) on the beef sample for verification, thereby verifying the accuracy and the robustness of the BPNN.
The quick beef freshness detection method based on the olfactory visualization chip is further improved as follows: the step 1) is as follows:
dissolving 8mg of tetramethoxyphenyl porphyrin iron (III) chloride in 4mL of chloroform to prepare a tetramethoxyphenyl porphyrin iron (III) chloride solution;
dissolving 8mg of phenol red in 4mL of absolute ethanol to prepare a phenol red solution;
and respectively dripping a tetramethoxyphenyl porphyrin iron (III) chloride solution and a phenol red solution into different positions on the same surface of a polyvinylidene fluoride (PVDF) membrane so as to prepare the olfactory visualization chip with two sensors.
Description of the drawings: the preparation of the 2 solutions described above is carried out in the absence of light (for example using a brown bottle) and is shaken ultrasonically to ensure complete dissolution. Each solution is respectively absorbed by a proper amount (about 1 mu L) of sample application capillary and dropped on a polyvinylidene fluoride (PVDF) membrane, and the distance between two color sensitive response points is about 5mm, so that a 1X 2 (one row and two columns) olfactory visual chip is manufactured.
As a further improvement of the quick beef freshness detection method based on the olfactory visualization chip, the step 2.1) is as follows:
placing a representative beef small sample in a closed container (such as a disposable plastic cup), enabling two sensors of the olfactory visualization chip to face the representative beef small sample (for example, the back of the olfactory visualization chip is adhered to a preservative film, and the plastic cup is sealed by the preservative film), standing at room temperature for reaction for 4-8 minutes, and then taking out the olfactory visualization chip; reading absolute values of RGB difference values (R, G, B color difference values) before and after reaction of two sensors in the olfactory visualization chip, and selecting the R value of tetramethoxyphenyl porphyrin iron (III) chloride and the R value of phenol red as response characteristic matrixes of the olfactory visualization chip.
As a further improvement of the quick beef freshness detection method based on the olfactory visualization chip, the step 2.3) is as follows:
the TVB-N value (y) obtained in the step 2.2) tra-TVBN ) Combining with the response characteristic matrix obtained corresponding to the storage time, adopting Matlab language programming to set the network structure of the BPNN model as 2-7-1 (the number of neurons in the input layer is 2, the number of neurons in the implicit layer is 27 neurons in an output layer are 1), and a prediction model of the TVB-N content in the beef is established;
the TVC value (y) obtained in the step 2.2) tra-TVC ) And combining with a response characteristic matrix obtained corresponding to storage time, setting the network structure of the BPNN model to be 2-3-1 (the number of input layer neurons is 2, the number of hidden layer neurons is 3, and the number of output layer neurons is 1) by adopting Matlab language programming, and establishing a prediction model of the TVC content in the beef.
As a further improvement of the quick beef freshness detection method based on the olfactory visualization chip,
respectively collecting response characteristic matrixes, TVB-N (total volatile content) and TVC (total volatile content) contents of olfactory visualization chips of beef samples (which do not belong to the same batch as 'representative beef samples') for verification during storage, and respectively introducing the response characteristic matrixes, the TVB-N, the response characteristic matrixes and the TVC into corresponding BPNN models for verifying the precision and the robustness of pre-established BPNN quantitative models;
the evaluation indexes of the BPNN quantitative model are respectively a training Root Mean Square Error (RMSEC) and a decision coefficient (R) of a training set c ) Prediction Root Mean Square Error (RMSEP), prediction decision coefficient (R) p ) And relative analytical error (RPD), in particular:
wherein y is pre And
respectively representing the true value and prediction of the j sample in the prediction setA value;
represents the average of all the true value contents in the prediction set; n is a radical of pre Representing the number of samples in the prediction set; training set RMSEC, R c Is calculated by the formula and in the prediction set RMSEP, R p The calculation formula of (2) is similar; SD represents the standard deviation of all true values in the prediction set.
According to the method, 2 gas sensitive materials are made into the olfactory visual chip and react with the odor given out by the beef, so that different color changes are presented, the freshness of the beef is judged in a visual mode, and BPNN quantitative models of freshness indexes TVB-N and TVC are established, so that the freshness of the beef is quantitatively detected.
The invention has the beneficial effects that:
(1) the olfactory visualization chip of the beef freshness is prepared by only two color sensitive materials, and the olfactory visualization chip is simple to prepare, low in cost and good in portability;
(2) the olfactory visualization chip is not interfered by moisture and temperature during detection, has high response speed and better stability, and is a simple portable beef freshness detection device;
(3) the beef freshness quantitative model established based on the BPNN can be directly input without data preprocessing, and the model is simple in structure and high in speed;
(4) the BPNN model can predict the content of TVB-N and TVC in beef end to end without establishing a mathematical model, and can establish a nonlinear mapping relation between a smell visual chip response value and a beef freshness index, and the established BPNN model has high precision and good robustness.
The invention can realize quick, simple and accurate quantitative detection of beef freshness; the method and the device solve the defects that the detection process is complicated, the detection cost is high, the environment interference is easy to cause, the visual detection cannot be realized and the like in the prior art.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for quickly detecting freshness of beef based on an olfactory visual chip.
FIG. 2 is a schematic diagram of a smell visualization chip for detecting freshness of beef according to an embodiment of the present invention;
the left is a tetramethoxyphenyl porphyrin iron (III) chloride color sensitive response point, and the right is a phenol red sensitive response point.
FIG. 3 is a color change chart before and after the olfactory visualization chip used in the embodiment of the invention detects the smell released from the stored beef;
the left is the color difference characteristic before and after the reaction of tetramethoxyphenyl porphyrin iron (III) chloride, and the right is the color difference characteristic before and after the reaction of phenol red.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
Embodiment 1, a method for quickly detecting freshness of beef based on an olfactory visualization chip, which sequentially comprises the following steps:
step 1: tetramethoxyphenyl porphyrin iron (III) chloride and phenol red are selected as gas-sensitive materials to be made into an olfactory visualization chip shown in figure 2, and the olfactory visualization chip is used for quickly detecting the freshness of beef:
(1.1) weighing 8mg of tetramethoxyphenyl porphyrin iron (III) chloride, filling the weighed 8mg of tetramethoxyphenyl porphyrin iron (III) chloride into a 5mL brown bottle, adding 4mL of chloroform into the brown bottle, covering the brown bottle with a brown bottle cover, putting the brown bottle into an ultrasonic cleaning chamber, performing ultrasonic treatment for 20 minutes, and taking out the bottle to obtain a tetramethoxyphenyl porphyrin iron (III) chloride solution;
(1.2) weighing 8mg of phenol red, filling the phenol red into a 5mL brown bottle, adding 4mL of absolute ethyl alcohol into the brown bottle, covering a brown bottle cover, putting the brown bottle cover into an ultrasonic cleaning chamber, performing ultrasonic treatment for 20 minutes, and taking out the brown bottle cover as a phenol red solution;
the ultrasonic frequency in the steps (1.1) and (1.2) is 35kHz, and the power density is 100W/L.
(1.3) selecting the hydrophobic material having good hydrophobic propertyThe PVDF film is used as a substrate material of the olfactory visualization chip, and the PVDF film is cut into 2 multiplied by 3cm 2 And (3) respectively absorbing the two prepared solutions with a proper amount by using a sample application capillary tube, dripping the two solutions on a PVDF film to prepare a 1 x 2-arranged olfactory visual chip for detecting the freshness of the beef subsequently.
The method specifically comprises the following steps: dropping 1 μ L of the iron (III) chloride solution of tetramethoxyphenylporphyrin prepared in step (1.1) on the cut PVDF membrane, and then dropping 1 μ L of the phenol red solution prepared in step (1.2), wherein the dropping points of the two solutions are on the same straight line and are about 5mm apart, so that the thickness of the PVDF membrane is 2 x 3cm 2 The PVDF film of (1X 2) array olfactory visualization chip, the sensing surface of the olfactory visualization chip is the surface on which the solution is dropped, and the sensing surface is the front surface of the olfactory visualization chip.
Step 2: 120 beef samples of Ribes nivalis positions obtained from 10 cattle, each beef sample having a size of 6 × 3 × 2cm 3 And (5) the size of the model is used for establishing a BPNN quantitative model for beef freshness detection. Placing beef samples in a 4 deg.C refrigerator, storing for 0, 2, 3, 4, 6, 8, 10 and 12 days respectively, wherein different storage days represent different sampling points, and total of 8 sampling points, randomly taking out 15 samples from the refrigerator each time, dividing the samples into 3 × 3 × 2cm in a sterile room 3 Selecting a small block at random for the induction experiment of the olfactory visualization chip; and the other small block is used for measuring the beef freshness indexes TVB-N and TVC.
The induction experiment process of the olfactory visualization chip comprises the following steps:
(2.1) acquiring an olfactory visual chip image before reaction by using a scanner;
(2.2) cutting the preservative film into a proper size at room temperature, adhering the back of the olfactory visual chip on the preservative film, and sticking the chip with the size of 3 multiplied by 2cm 3 Putting the stored beef sample with the size into a disposable plastic cup, sealing the disposable plastic cup by using a preservative film adhered with the smell visualization chip, enabling the sensing surface of the smell visualization chip to face the beef sample, reacting for 4-8 minutes, and taking out the smell visualization chip;
(2.3) obtaining the olfactory visualization chip image after reaction by using the scanner again;
(2.4) processing chip images before and after reaction by using an image processing tool kit in Matlab software and adopting a median filtering and threshold segmentation method, wherein a color change graph before and after the smell release of the storage beef is detected by an olfactory visualization chip, an absolute value of R, G, B color difference before and after the reaction of two sensor arrays in the olfactory visualization chip is obtained from the color change graph shown in FIG. 3, and an R value of tetramethoxyphenylporphyrin iron (III) chloride and an R value of phenol red are further selected from 6 color characteristic variables as response characteristics of the olfactory visualization chip, and the size of a response characteristic matrix is 120 multiplied by 2(120 samples, 2 color characteristic variables) because 120 samples are shared.
And step 3: measuring 3X 2cm 3 And (3) storing the TVB-N and TVC contents of the beef samples in the large and small sizes, and establishing a BPNN quantitative model of beef freshness by combining the response characteristic matrix obtained in the step (2):
(3.1) detecting the TVB-N content in the beef by adopting a method in the national standard GB 5009.228-2016 (determination of volatile basic nitrogen in food);
(3.2) detecting the TVC content in the beef by adopting a method in the national standard GB 4789.2-2010 food microbiology test colony total number determination;
(3.3) 120 beef samples (N) tra ) TVB-N value (y) of tra-TVBN ) Combining the olfactory visual chip response characteristic matrix obtained in the step (2.4) to serve as a training set, training a BPNN quantitative model by adopting a 7-fold cross validation method, and optimizing a BPNN network structure to be 2-7-1 (the number of neurons in an input layer is 2, the number of neurons in a hidden layer is 7, and the number of neurons in an output layer is 1), so that the BPNN quantitative model for predicting the TVB-N content in the beef is established;
(3.4) 120 beef samples (N) tra ) TVC value (y) of tra-TVC ) Combining the olfactory visual chip response characteristic matrix obtained in the step (2.4) to serve as a training set, training a BPNN quantitative model by adopting a 7-fold cross validation method, and optimizing a BPNN network structure to be 2-3-1 (the number of neurons in an input layer is 2, the number of neurons in a hidden layer is 3, and the number of neurons in an output layer is 1), so that beef is builtBPNN quantitative model for medium TVC content prediction.
(3.5) evaluation indexes of the BPNN quantitative model are a training Root Mean Square Error (RMSEC) and a training decision coefficient (R) c ) The method specifically comprises the following steps:
wherein y is tra Representing the true measurement of the ith sample in the training set measured according to the national standard method,
representing a training value of an ith sample obtained from a training set according to a BPNN model;
represents the average of the content of all the real measurements in the training set. N is a radical of tra Indicating the number of samples in the prediction set.
Table 1 summarizes the fitting evaluation results of the BPNN model on the olfactory visualization chip and the TVB-N content under the training set and the contents of the olfactory visualization chip and the TVC in the embodiment of the invention. The smaller the RMSEC, the R c The larger the BPNN model is, the better the BPNN model is trained, the TVB-N and TVC contents in the beef can be better and quantitatively trained, and further the freshness of the beef can be rapidly judged (the TVB-N is less than 20mg/100g, and the TVC is less than 6log CFU/g, the beef is judged to be fresh, otherwise, the beef is judged to be not fresh when any condition is not met).
Table 1, fitting evaluation results of BPNN model on olfactory visualization chip and TVB-N content and olfactory visualization chip and TVC content under training set
In general, RMSEC requires an index unit error of < 5% (5 mg/100g for TVB-N; 0.5log CFU/g for TVC), while R c More than 0.9 is needed to indicate the effectiveness of model training; therefore, the data in table 1 can prove that the beef freshness rapid detection model established by the invention has the technical advantages of accuracy and robustness on a training set.
Example 2: repurchase 40 beef samples as beef samples to be tested for validation (i.e., as a prediction set); the method of the step 2 in the embodiment 1 is adopted to obtain a response characteristic matrix of 40 × 2, the method of the step 3 in the embodiment 1 is adopted to obtain the TVB-N and TVC contents of the beef sample, and the TVB-N and TVC contents are used for verifying the accuracy and robustness of the two BPNN quantitative models established in the step 3 in the embodiment 1, and the specific process is as follows:
(1) mixing 40 beef samples (N) pre ) TVB-N value (y) of pre-TVBN ) And the response characteristic matrix of the 40 multiplied by 2 olfactory visual chip obtained in the step (2.4) of the reference example 1 are combined to be used as a prediction set for verifying the BPNN quantitative model for predicting the TVB-N content in beef established in the step (3.3) of the example 1;
(2) mixing 40 beef samples (N) pre ) TVC value (y) of pre-TVC ) And the response characteristic matrix of the 40 × 2 olfactory visualization chip obtained in the step (2.4) of the reference example 1 are combined to serve as a prediction set to verify the BPNN quantitative model for predicting the TVC content in the beef established in the step (3.4) of the example 1;
(3) inputting the olfactory visualization chip response characteristic matrix obtained in the step (1) in the embodiment 2 into the BPNN quantitative model for predicting the TVB-N content in beef established in the step (3.3) in the embodiment 1, and further rapidly obtaining the predicted value of the TVB-N content in beef
Inputting the olfactory visual chip response characteristic matrix obtained in the step (2) in the embodiment 2 into the BPNN quantitative model for predicting the TVC content in beef established in the step (3.4) in the embodiment 1, and further quickly obtaining the predicted value of the TVC content in beef
(4) In order to verify the accuracy, robustness and generalization capability of the method for detecting the content of the beef freshness indexes (TVB-N and TVC), a BPNN predicted value (A), (B) and (C) are used
And
) And the actual value measured according to the national Standard method (the actual value (y) of TVB-N measured at step (1) of example 2) pre-TVBN ) Example 2 true value (y) of TVC measured in step (2) pre-TVC ) Carrying out statistical analysis and comparison, namely judging according to performance evaluation indexes of the BPNN model on a prediction set. The performance evaluation indexes of the BPNN quantitative model are respectively a prediction Root Mean Square Error (RMSEP) and a prediction determination coefficient (R) p ) And relative analytical error (RPD), in particular:
wherein y is pre Representing the true measurement of the jth sample in the prediction set measured according to the national standard method,
representing the predicted value of the j sample obtained from the BPNN model in the prediction set;
represents the average of all the real values in the prediction set. N is a radical of pre Representing the number of samples in the prediction set. SD represents the standard deviation of all true values in the prediction setAnd (4) poor.
Table 2 summarizes the model evaluation results of the BPNN quantitative model for predicting the contents of the TVB-N and TVC of the beef freshness index under the prediction set in the embodiment of the invention. The smaller the RMSEP, the R p The larger the prediction result is, the better the prediction result of the BPNN model is, the more accurate quantitative prediction of the TVB-N and TVC contents in the beef can be realized, and the freshness of the beef can be further rapidly judged; an RPD greater than 3 indicates that the established BPNN model may be used in real life. The established BPNN model was found to be excellent in robustness based on RMSEC in example 1 and RMSEP in example 2, according to RMSEP, R p And RPD can find that the established BPNN model can accurately predict the freshness of the beef.
Table 2, model evaluation results of BPNN quantitative model for beef freshness index TVB-N and TVC content prediction under prediction set
When a completely new sample was used to verify the performance of the BPNN model built in example 1, it was found that: RMSEP in the prediction set is similar to an index unit error of < 5% (5 mg/100g for TVB-N; 0.5log CFU/g for TVC), R p The same is larger than 0.9, and the RPD is larger than 3, which shows that the BPNN model established by the invention has better precision and robustness, and can be used for rapidly obtaining the content of the beef freshness index (TVB-N, TVC) so as to judge the beef freshness.
In summary, the indexes of the training set of example 1 are mainly used to illustrate: the content of the beef freshness physicochemical index (TVB-N, TVC) can be rapidly obtained according to the model, namely the established model can replace a fussy physicochemical experiment; the physicochemical index content (TVB-N, TVC) obtained according to the model and the content measured by the national standard method have no big difference (for TVB-N, the error mean value is 2.821mg/100 g; for TVC, the error mean value is 0.357log CFU/g), and Rc is more than 0.9, which indicates that the model established according to the training set data is good. Example 2 verifies that the model has equally good performance in the face of strange samples, namely general adaptation, robustness and model generalization ability.
The method for quickly detecting the freshness of the beef based on the olfactory visual chip can quickly, simply, conveniently and accurately detect the contents of the freshness indexes TVB-N and TVC of the beef, and the olfactory visual chip is simple to manufacture, low in cost and good in stability.
Finally, it is also noted that the above-mentioned lists merely illustrate a few specific embodiments of the invention. It is obvious that the invention is not limited to the above embodiments, but that many variations are possible. All modifications which can be derived or suggested by a person skilled in the art from the disclosure of the present invention are to be considered within the scope of the invention.
Claims (7)
1. A method for quickly detecting freshness of beef based on an olfactory visualization chip is characterized by comprising the following steps:
1) preparing a smell visualization chip:
selecting tetramethoxyphenyl porphyrin iron (III) chloride and phenol red as gas-sensitive materials to prepare an olfactory visual chip with two sensors;
2) setting representative beef samples in different storage time, and respectively carrying out the following processing on the representative beef sample corresponding to each storage time:
dividing into two to form 2 small representative beef samples, wherein one small representative beef sample is used for the following step 2.1), and the other small representative beef sample is used for the following step 2.2):
2.1) placing an olfactory visualization chip containing a gas-sensitive material and a representative beef small sample in a closed container for reaction, obtaining RGB difference values of the chip before and after reaction, and generating a response characteristic matrix of the olfactory visualization chip;
2.2) measuring the freshness index of another representative beef small sample, wherein the freshness index is TVB-N and TVC content;
2.3) combining the freshness index data obtained in the step 2.2) with the response characteristic matrix obtained by the corresponding storage time in the step 2.1), thereby establishing a backward propagation neural network quantitative model of beef freshness.
2. The method for quickly detecting freshness of beef based on the olfactory visualization chip as claimed in claim 1, further comprising the following steps:
operating the beef sample to be detected according to the 2.1) to obtain a response characteristic matrix of the olfactory visualization chip of the beef sample to be detected;
inputting the olfactory visual chip response characteristic matrix of the beef sample to be detected into the quantitative model in beef established in the step 2.3), thereby obtaining the predicted value of the TVB-N content of the beef sample to be detected
And prediction of TVC content
3. The quick beef freshness detection method based on the olfactory visual chip as claimed in claim 2, further comprising: and (3) repeatedly carrying out the step 2) on the beef sample for verification, thereby verifying the accuracy and the robustness of the BPNN.
4. The method for rapidly detecting freshness of beef based on the olfactory visualization chip according to any one of claims 1 to 3, wherein the step 1) is as follows:
dissolving 8mg of tetramethoxyphenyl porphyrin iron (III) chloride in 4mL of chloroform to prepare a tetramethoxyphenyl porphyrin iron (III) chloride solution;
dissolving 8mg of phenol red in 4mL of absolute ethanol to prepare a phenol red solution;
and respectively dripping the tetramethoxyphenyl porphyrin iron (III) chloride solution and the phenol red solution to different positions on the same surface of the polyvinylidene fluoride membrane to prepare the olfactory visualization chip with two sensors.
5. The method for quickly detecting freshness of beef based on the olfactory visualization chip as claimed in claim 4, wherein the step 2.1) is:
placing a small representative beef sample in a closed container, enabling two sensors of the olfactory visualization chip to be opposite to the small representative beef sample, standing at room temperature for reaction for 4-8 minutes, and taking out the olfactory visualization chip; reading absolute values of RGB difference values before and after reaction of two sensors in the olfactory visualization chip, and selecting the R value of tetramethoxyphenyl porphyrin iron (III) chloride and the R value of phenol red as response characteristic matrixes of the olfactory visualization chip.
6. The method for quickly detecting freshness of beef based on the olfactory visualization chip as claimed in claim 5, wherein the step 2.3) is:
the TVB-N value (y) obtained in the step 2.2) tra-TVBN ) Combining with a response characteristic matrix obtained in corresponding storage time, adopting Matlab language programming, setting the network structure of the BPNN model to be 2-7-1, and establishing a prediction model of the TVB-N content in the beef;
the TVC value (y) obtained in the step 2.2) tra-TVC ) And combining the model with a response characteristic matrix obtained in corresponding storage time, setting the network structure of the BPNN model to be 2-3-1 by adopting Matlab language programming, and establishing a prediction model of the TVC content in the beef.
7. The quick beef freshness detection method based on the olfactory visualization chip as claimed in claim 6, wherein:
respectively collecting the response characteristic matrix, TVB-N and TVC content of the olfactory visual chip of the beef sample for verification during storage, and respectively importing the response characteristic matrix, the TVB-N, the response characteristic matrix and the TVC into corresponding BPNN models for verifying the precision and robustness of the pre-established BPNN quantitative model;
the evaluation indexes of the BPNN quantitative model are respectively a training Root Mean Square Error (RMSEC) and a decision coefficient (R) of a training set c ) Prediction Root Mean Square Error (RMSEP), prediction decision coefficient (R) p ) And relative analytical error (RPD), in particular:
wherein y is pre And
respectively representing the true value and the predicted value of the jth sample in the prediction set;
representing the average value of all the actual values in the prediction set; n is a radical of pre Representing the number of samples in the prediction set; training set RMSEC, R c Is calculated by the formula and in the prediction set RMSEP, R p The calculation formula of (2) is similar; SD represents the standard deviation of all true values in the prediction set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210509941.2A CN114994025A (en) | 2022-05-11 | 2022-05-11 | Quick beef freshness detection method based on olfactory visualization chip |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210509941.2A CN114994025A (en) | 2022-05-11 | 2022-05-11 | Quick beef freshness detection method based on olfactory visualization chip |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114994025A true CN114994025A (en) | 2022-09-02 |
Family
ID=83027348
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210509941.2A Pending CN114994025A (en) | 2022-05-11 | 2022-05-11 | Quick beef freshness detection method based on olfactory visualization chip |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114994025A (en) |
-
2022
- 2022-05-11 CN CN202210509941.2A patent/CN114994025A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array | |
| El Barbri et al. | Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage | |
| Legin et al. | Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception | |
| CN101382531B (en) | Method for detecting fresh degree of shrimp by electronic nose | |
| CN101493431A (en) | Method for detecting fresh degree of chicken meat by electronic nose | |
| Li et al. | Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques | |
| CN102621192B (en) | Method for detecting freshness of mangos by aid of electronic nose | |
| Zhang et al. | Electronic nose with an air sensor matrix for detecting beef freshness | |
| Li et al. | Non-destructive evaluation of pork freshness using a portable electronic nose (E-nose) based on a colorimetric sensor array | |
| Huang et al. | Non-destructive evaluation of total volatile basic nitrogen (TVB-N) and K-values in fish using colorimetric sensor array | |
| CN108760647A (en) | A kind of wheat content of molds line detecting method based on Vis/NIR technology | |
| CN106770004A (en) | Method based on the species and content of microorganism in near-infrared spectrum technique detection dairy products | |
| CN110702815A (en) | Olfactory sensing detection method for flour storage quality | |
| CN106556680A (en) | It is a kind of based on Electronic Nose Technology fast prediction meat and its method for product fat oxidation degree | |
| Shirai et al. | Detection of fluorescence signals from ATP in the second derivative excitation–emission matrix of a pork meat surface for cleanliness evaluation | |
| Guo et al. | Method and system for nondestructive detection of freshness in Penaeus vannamei based on hyperspectral technology | |
| Grassi et al. | E-sensing systems for shelf life evaluation: A review on applications to fresh food of animal origin | |
| CN104792686A (en) | Method for detecting microbe quantity and drug content of semisolid preparation through near infrared spectroscopy | |
| Tan et al. | E-nose screening of pesticide residue on chilli and double-checked analysis through different data-recognition algorithms | |
| CN114994025A (en) | Quick beef freshness detection method based on olfactory visualization chip | |
| CN115809427B (en) | Mixed gas identification method based on mixed strategy optimization BP neural network | |
| CN113176353B (en) | Aromatic vinegar flavor smell-taste interaction characterization method based on visualization technology | |
| CN110426366A (en) | Meat adulteration ratio detection method based on visualization olfactory sensor and near-infrared | |
| CN114354564A (en) | Mutton freshness detection method based on fluorescence quenching technology | |
| CN104483460A (en) | Sensor array optimization method for meat detection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |