CN104990877A - Method for detecting irradiation dose of shrimp and shellfish peeled aquatic products on basis of multi-spectral imaging technology - Google Patents

Abstract

The invention discloses a method for measuring the irradiation dose of shelled shrimp and shellfish aquatic products based on multi-spectral imaging technology. In the method, the shelled shrimp and shellfish aquatic products are irradiated with different doses, and the shrimp and shellfish are collected by using a multi-spectral imaging system. The spectral images of shell-like aquatic products were combined with chemometrics to establish a model of the radiation dose of shelled shrimp and shellfish, so as to realize the rapid non-destructive detection of the radiation dose of shelled shrimp and shellfish. The method has the advantages of simple operation, fast analysis speed, good test reproducibility, no pretreatment of samples, etc., and can effectively determine the irradiation dose of shelled shrimp and shellfish aquatic products. This method helps to improve the quality control and authenticity identification level of irradiated shelled shrimp and shellfish aquatic products, and will further promote the international trade of irradiated shelled shrimp and shellfish aquatic products.

Description

A method for detecting radiation dose of shelled shrimp and shellfish based on multispectral imaging technology

technical field

The present invention relates to an analysis method for the quality and safety of agricultural products, in particular to a technical method for measuring the radiation dose of shelled shrimp and shellfish, specifically a method for measuring the radiation dose of shelled shrimp and shellfish based on multi-spectral imaging technology. Test method for radiation dose of shell-like aquatic products.

Background technique

In recent years, irradiation technology has been widely used in the food industry, and its main purpose is to sterilize, kill insects and inhibit plant germination. Due to the advantages of reducing post-production loss and prolonging product shelf life after irradiation treatment, it has been recognized by more and more countries. Some international authoritative organizations and relevant departments of various countries have set standards for food irradiation and require clear labeling of irradiated food. However, at present, there are many irradiated aquatic products in China that are not clearly marked, and some enterprises blindly pursue profits and increase the irradiation dose at will, which will not only destroy the nutritional content of the products, but may also affect the food safety; at the same time, the international Radiation technology is also regarded as an important means of international trade barriers. These problems have not been effectively resolved, which highlights the lack of a simple, systematic and feasible product radiation dose detection method, which seriously affects the international trade of irradiated food.

At present, some effective detection methods and means commonly used for radiation dose are mainly thermoluminescence analysis, electron spin resonance spectroscopy (ESR), high performance liquid chromatography, and chromatography-mass spectrometry (GC-MS and LC-MS). ), DNA cleavage product detection method, laser imaging detection method, etc. However, the above methods are time-consuming and complicated to operate, and are not suitable for fast online non-destructive testing. Therefore, there is an urgent need for a fast, accurate and simple method in the detection of radiation dose of shrimp and shellfish peeled aquatic products. The multi-spectral imaging technology has the characteristics of quantification, non-destructive and real-time monitoring, which is very suitable for the rapid detection of the radiation dose of aquatic products. At home and abroad, there are no related articles and reports on the application of multispectral imaging technology to quickly detect the radiation dose of shelled shrimp and shellfish.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a method for detecting the radiation dose of shelled shrimp and shellfish aquatic products based on multi-spectral imaging technology, and to provide radiation dose measurement for shelled shrimp and shellfish aquatic products. A fast non-destructive testing method was developed with accurate results.

The present invention is achieved through the following technical solutions:

(1) Selection and pretreatment of raw materials

Take the same batch of shrimp and shellfish peeled aquatic products, and carry out irradiation treatment with different doses, and use a part of the processed samples as a calibration set, and another part as a prediction set;

(2) Obtain spectral images: use the multi-spectral imaging system to scan the spectral information of all samples, and the scanning spectral range is 400-1000nm;

(3) Combining chemometric methods to establish a quantitative analysis model for radiation dose of shelled shrimp and shellfish;

Specifically:

1) Set the calibration set sample irradiation dose value as the actual value of the modeling;

2) The spectral wavelength of the sample in the calibration set and its actual value are analyzed through data analysis, and a model is established in combination with chemometric methods;

3) According to the established model, predict the radiation dose value of the prediction set samples, obtain the radiation dose value of the sample through spectral prediction, and analyze the difference between it and the actual radiation dose value of shelled shrimp and shellfish aquatic products, select the predicted value The model of irradiation dose of shelled shrimp and shellfish with the required precision can be used to realize rapid and non-destructive detection of radiation dose of shelled shrimp and shellfish.

In the step (1), the samples are respectively irradiated with electron beams of different intensities.

In the step (2), use the Videometer Lab multispectral imaging system to perform multispectral scanning on all samples, specifically: first use the calibration plate to calibrate the multispectral imaging system, and then perform multispectral scanning.

In said step 2), a partial least square method is used to establish a model by computer.

The detection method described can be used to design and establish a set of automatic detection and analysis device for non-destructive and rapid detection of radiation dose of shrimp and shellfish peeled aquatic products, and on this basis, the application of the device can be extended to shrimp and shellfish. An analysis device for the quality and safety of shellfish products.

Compared with the prior art, the present invention has the following advantages: the present invention provides a method for detecting the irradiation dose of shelled shrimp and shellfish aquatic products based on multispectral imaging technology. The rapid detection method of radiation dose of shelled aquatic products provides a fast, non-destructive and accurate method for determining the radiation dose of shelled shrimp and shellfish.

Description of drawings

Fig. 1 is the average reflectance spectrum figure of 400-1000nm wavelength range for different irradiation doses of peeled shrimp;

Figure 2 is a scatter diagram of the relationship between the actual value and the predicted value of the calibration set sample;

Figure 3 is a scatter diagram of the relationship between the actual value and the predicted value of the samples in the prediction set.

Detailed ways

The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

Example 1

This embodiment provides a method for detecting the radiation dose of shelled shrimp and shellfish aquatic products based on multispectral imaging technology, including the following steps:

(1) Selection and pretreatment of raw materials

Take 5 bags of frozen peeled shrimp from the same batch, each bag is 2.5kg; the selected samples are irradiated by electron beams at 0, 1, 4, 10 and 20kGy (usually the irradiation intensity for export to the United States is 3.5-4.5kGy) , the highest recognized safe radiation intensity is 20kGy); the processed samples are randomly divided into a calibration set and a prediction set. In this embodiment, the number of samples in the calibration set is 50, and the number of samples in the prediction set is 50.

(2) Obtain spectral image

The spectra were measured using the Videometer Lab multispectral imaging system (Videometer A/S, Denmark), its spectral range is 405‐970nm. The details are as follows: firstly use the calibration board (whiteboard, blackboard and geometric point board) to calibrate the multispectral imaging system, and then perform multispectral scanning on the calibration set and prediction set after step (1) thawed to obtain the original spectrum image;

(3) Using the partial least squares method PLS to establish a quantitative analysis model for the radiation dose of shelled shrimp and shellfish

1) Set the sample irradiation dose value as the actual modeled value;

2) Partial least squares PLS regression is performed on the spectral wavelength of the calibration set sample and the actual value of the sample modeling, and the analysis model is established, specifically:

Step 1: Decompose the spectral matrix X and concentration matrix Y of the processed spectral image, and its model is:

X=TP+E

Y=UQ+F

In the above formula: U and T are the scoring matrices of Y and X, respectively, Q and P are the load matrices of Y and X, respectively, and F and E are the errors brought in when the PLS model fits Y and X, respectively.

Step 2: Perform linear regression on T and U:

U=TB

B＝(T ^T T) ^-1 T ^T Y

Step 3: Calculate the score T _pre of the spectral matrix X _pre of the sample to be tested according to the decomposition model in the above step 1 and the obtained P value, and then obtain the predicted concentration value Y _pre , which is the radiation dose value of the predicted sample:

Y _pre =T _pre BQ

As shown in Figure 2, it is a scatter diagram of the relationship between the actual value and the predicted value of the calibration set sample for the establishment of the model.

3) Calculate the radiation dose value of the prediction set according to the analysis model established by the calibration set samples:

Use the established discriminant model to predict the samples in the prediction set, and obtain the comparison results between the actual value and the predicted value as shown in Table 1 below and the relationship scatter diagram shown in Figure 3:

Table 1: Comparison results of the actual and predicted values of samples in the prediction set

The correlation coefficient R, the root mean square error SEC and the predicted root mean square error SEP are used as effective indicators for evaluating the accuracy of the model. The higher the R, the smaller the SEC and SEP, and the higher the accuracy of the model:

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span>\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$

In the formula, R is the correlation coefficient, n is the number of samples, y _i and are the measured value and predicted value of the i-th sample in the sample set, including the calibration set and prediction set; is the average value of the measured values of the i-th sample in the sample set;

$\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

$\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; =</span>\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; no</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

In the formula: y _i and are the measured value and predicted value of the i-th sample in the sample set, respectively; N is the number of samples in the calibration set, n is the number of samples in the prediction set, and P is the number of principal components;

Through the prediction of 50 calibration set samples and comparing with their actual values, it was found that in the prediction set, the R value was 0.9778 and the SEP was 1.7845, which indicated that the prediction effect of the verification model was good.

(4) Detection of the irradiation dose of the sample to be tested

Take the sample to be tested, and use the Videometer Lab multispectral imaging system to perform multispectral scanning after thawing to obtain the original spectral image;

According to the model established above, the score T _pre of the spectral matrix X _pre of the sample to be tested is calculated, and then the predicted concentration value Y _pre is obtained, which is the radiation dose value of the sample to be tested.

Claims (5)

1. to shell based on the shrimp shellfish of multi-optical spectrum imaging technology the detection method of aquatic products irradiation dose, it is characterized in that, comprise the following steps:

(1), the choosing and pre-service of raw material

The shrimp shellfish of getting same batch is shelled aquatic products, carry out the radiation treatment of various dose, and using the sample part after process as being calibration set, another part is as forecast set;

(2), obtain spectrum picture: utilize multi-optical spectrum imaging system all samples to be scanned to the spectrogram information obtained, its scanning optical spectrum scope be 400 ?1000nm;

(3), set up shrimp shellfish in conjunction with chemometrics method to shell aquatic products irradiation dose Quantitative Analysis Model;

Be specially:

1) setting calibration set sample irradiation dose value is modeling actual value;

2) spectral wavelength of calibration set sample and its actual value are through data analysis, in conjunction with chemometrics method Modling model;

3) according to the model set up, the irradiation dose value of forecast set sample is predicted, Forecast of Spectra obtains the irradiation dose value of sample, and analyze itself and shrimp shellfish and to shell the difference of aquatic products irradiation dose actual value, choose precision of prediction to meet the requirements of shrimp shellfish and to shell the model of aquatic products irradiation dose, utilize this model can realize prawn shellfish and to shell aquatic products irradiation dose Fast nondestructive evaluation.

2. a kind of shrimp shellfish based on multi-optical spectrum imaging technology according to claim 1 is shelled the detection method of aquatic products irradiation dose, it is characterized in that, in described step (1), sample is respectively through varying strength irradiating electron beams irradiation.

3. a kind of shrimp shellfish based on multi-optical spectrum imaging technology according to claim 1 is shelled the detection method of aquatic products irradiation dose, it is characterized in that, in described step (2), Videometer Lab multi-optical spectrum imaging system is utilized to carry out multispectral scanner to all samples, be specially: first utilize calibration plate to calibrate multi-optical spectrum imaging system, then carry out multispectral scanner.

4. a kind of shrimp shellfish based on multi-optical spectrum imaging technology according to claim 1 is shelled the detection method of aquatic products irradiation dose, it is characterized in that, described step 2) in, adopt partial least square method, by computing machine Modling model.

5. a kind of shrimp shellfish based on multi-optical spectrum imaging technology according to claim 1 is shelled the detection method of aquatic products irradiation dose, it is characterized in that: described detection method may be used for designing, set up a set of spectrum can't harm and detect fast shrimp shellfish and to shell the automatic determination and analysis device of aquatic products irradiation dose, and on this basis this application of installation can be expanded to shrimp shellfish and shell in fish quality analytical equipment.