Method for detecting microbial transglutaminase in surimi by near infrared spectroscopy and pretreatment method

Abstract

The present application relates to a kind of near infrared spectroscopy technology detection fish paste in microorganism glutamine transaminase method and pretreatment method, wherein detection method includes step 1: after adding 0.1-0.3% casein to fish paste, it is chopped into paste by high speed, and semi-liquid slurry is formed, the semi-liquid slurry is sealed and then refrigerated, and fish paste pretreatment sample is obtained;Step 2: open near infrared spectrometer and carry out pretreatment, take fish paste pretreatment sample and place in sample cup, then carry out the spectrum scanning of fish paste pretreatment sample, and obtain spectrum information;Step 3: the spectrum information scanned in step 2 is introduced into fish paste in microorganism glutamine transaminase qualitative analysis model, and qualitative judgment is carried out.The present application has the advantages of fast analysis speed, high detection flux, pollution-free processing, low application cost, good analysis reproduction, etc., and can provide reference basis for the judgment of whether microorganism glutamine transaminase is added in fish paste.

Description

Technical Field

[0001] The invention belongs to the technical field of food detection, and particularly relates to a method for detecting microbial glutamine transaminase in fish paste using near-infrared spectroscopy technology and a pretreatment method. Background Art

[0002] Surimi is a muscle protein concentrate obtained by processing fresh fish through processes such as meat removal, rinsing, dehydration, and fine filtration. It is the primary raw material for surimi products. According to the national standard "GB / T 36187-2018 Frozen Surimi," gel strength is a key indicator for grading surimi. Some manufacturers add microbial transglutaminase during the production of frozen surimi. Due to the enzyme's high catalytic activity, generally only 1-10 units per gram of surimi are needed (the activity of domestically produced industrially pure microbial transglutaminase is 3,000-10,000 U / g, while the commercial enzyme has an activity of 120-140 U / g) to significantly improve the gel strength of the surimi during subsequent processing.

[0003] Transglutaminase (TGase, EC 2.3.2.13), produced by microbial fermentation, is a transferase that catalyzes acyl transfer reactions. This enzyme can cause cross-linking reactions within and between protein molecules, thereby altering protein structure and improving the gel strength of food systems. Due to its low cost and short production cycle, it has been widely used in the processing of foods such as surimi, dairy, meat, and baked goods.

[0004] Currently, research on the detection of microbial glutamine transaminase mainly focuses on methods based on the enzyme-linked immunosorbent assay principle. Chinese patent CN104090102B discloses an ELISA method for detecting microbial glutamine transaminase in frozen fish paste. This method is a detection method that combines the specific binding of antigen-antibody reactions with the enzyme's specific and efficient substrate catalytic ability. This method has the characteristics of high sensitivity and strong specificity. However, this method is cumbersome to operate, the detection process is time-consuming, and the detection cost is high.

[0005] Near-infrared spectroscopy (NIR) has been widely used in qualitative and quantitative analysis due to its fast analysis speed, high throughput, pollution-free processing, low application cost, and excellent analytical reproducibility. However, the detection limit of NIR spectroscopy requires the target compound to be greater than 0.1% by mass. Since microbial transglutaminase is only added to surimi in trace amounts, traditional NIR spectroscopy cannot be used to directly detect its presence in surimi. Due to the trace amounts of enzyme protein molecules in surimi, the spectral signals generated by the enzyme protein molecules themselves do not produce statistically significant differences in NIR spectroscopy. Summary of the Invention

[0006] In view of the problems existing in the prior art, the object of the present invention is to provide a method and a pretreatment method for detecting microbial glutamine transaminase in fish paste using near-infrared spectroscopy technology, which can realize the rapid detection of glutamine transaminase in fish paste.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] A pretreatment method for detecting microbial glutamine transaminase in surimi using near-infrared spectroscopy technology comprises: adding 0.1-0.3% casein to the surimi, chopping the surimi at a high speed of 3000-4000 r / min into a surimi, sealing the surimi semi-fluid slurry and placing it at 0-4°C for 5-6 hours to obtain a surimi pretreatment sample.

[0009] A method for detecting microbial glutamine transaminase in surimi using near-infrared spectroscopy technology comprises the following steps:

[0010] Step 1: adding 0.1-0.3% casein to the surimi, and then chopping it at a high speed of 3000-4000 r / min into a surimi. The semi-fluid slurry after chopping is sealed and placed at 0-4°C for 5-6 hours to obtain a surimi pre-treated sample;

[0011] Step 2: Turn on the near-infrared spectrometer to preheat, scan the background, take the pre-treated fish paste sample and place it in the sample cup, smooth it evenly, close to the bottom glass of the sample cup, and there are no visible pores. Scan the spectrum of the pre-treated fish paste sample in the spectral range of 12800-4000cm -1 , resolution 16cm -1 , the number of scans was 96 to obtain spectral information;

[0012] Step 3: Import the spectral information scanned in step 2 into the qualitative analysis model of microbial glutamine transaminase in surimi to perform qualitative judgment of the microbial glutamine transaminase in the sample.

[0013] In step 3, the establishment of a qualitative analysis model for microbial glutamine transaminase in surimi comprises the following steps:

[0014] Step A: preparing two surimi control samples, one is a negative sample without microbial transglutaminase, and the other is a positive sample containing different amounts of microbial transglutaminase, wherein the mass fractions of microbial transglutaminase added to the positive samples are 0.01%, 0.02%, 0.04%, 0.08%, 0.1%, 0.2%, 0.5%, and 1%, respectively; chopping and mixing the samples to obtain surimi positive and negative control samples;

[0015] Step B: adding 0.1-0.3% casein to the surimi positive and negative control samples respectively, and then chopping them into a minced state at a high speed of 3000-4000 r / min. The semi-fluid slurry after chopping is sealed and placed at 0-4°C for 5-6 hours to obtain the surimi positive and negative control pre-treated samples;

[0016] Step C: After preheating the near-infrared spectrometer, scan the background. Take 100 samples of each concentration of the surimi negative and positive control pre-treatment samples, a total of 100 surimi negative control pre-treatment samples, and a total of 800 surimi positive control pre-treatment samples; take the surimi positive and negative control pre-treatment samples and place them in a sample cup, evenly smooth them, close to the bottom glass of the sample cup, and no pores are visible to the naked eye. Perform a spectral scan of the surimi positive and negative control pre-treatment samples in the spectral range of 12800-4000cm -1 , resolution 16cm -1 , the number of scans was 96 to obtain spectral information;

[0017] Step D: A qualitative analysis model is established using a discriminant analysis method. The near-infrared spectral data matrix of the surimi positive and negative control pre-treated samples obtained in step C is subjected to principal component analysis to establish a principal component analysis model for the surimi positive and negative control samples, and to obtain a qualitative analysis model for microbial glutamine transaminase in the surimi.

[0018] The thickness of the surimi pre-treated sample / surimi yin-yang control sample at the bottom of the sample cup is 1-1.5 cm.

[0019] After adopting the above scheme, the present invention is different from the existing enzyme-linked immunosorbent assay method, which has the characteristics of complicated detection steps and large reagent usage. It overcomes the defect of traditional near-infrared spectroscopy technology that the detection limit requires the content of the detection target to be higher than 0.1% by mass fraction. The present invention has the advantages of fast analysis speed, high detection throughput, pollution-free processing, low application cost, and good analysis reproducibility. It can provide a reference basis for judging whether microbial glutamine transaminase should be added to fish paste.

[0020] On one hand, the present invention adds casein to surimi as a catalytic substrate for microbial transglutaminase. This allows the spectral signal generated by the enzyme protein molecular structure itself to be converted into the spectral signal generated by the cross-linked structure of the surimi protein during near-infrared spectroscopy scanning of the surimi pre-treated sample. This extends the detection target from the trace addition of microbial transglutaminase to the cross-linked structure of the surimi protein, achieving a disguised effect of amplifying the differential signal between surimi molecules. Because this pre-treatment method changes the near-infrared spectral scanning target and spectral characteristics, the near-infrared spectrometer detector can capture the changes in optical information, and through the establishment of a mathematical model, predict whether microbial transglutaminase has been added to the surimi.

[0021] On the other hand, the present invention subjects the semi-fluid slurry obtained in step 1 to sealed refrigeration to avoid the changes in the non-cross-linked structure of the surimi protein caused by the high or low temperature environment during the cross-linking reaction catalyzed by the microbial transglutaminase, so that the myofibrillar protein and casein in the surimi only undergo a slow specific catalytic cross-linking reaction in the refrigerated environment, and other non-cross-linked structures in the surimi pre-treated sample do not undergo obvious changes, so that other protein structural changes will not interfere with the scanning spectral characteristic changes of the cross-linked structure of the surimi protein.

[0022] Based on the above two points, the present invention obtains a pre-treated fish paste sample through a specific pre-treatment method, and establishes a qualitative analysis model for microbial transglutaminase in fish paste using near-infrared spectroscopy scanning technology and a discriminant analysis method, thereby making a qualitative judgment on the microbial transglutaminase in fish paste. Therefore, the pre-treatment method and the method for detecting microbial transglutaminase in fish paste using near-infrared spectroscopy technology in the present invention together constitute the creative essence of the present invention, and the two are inseparable and indispensable.

[0023] The qualitative analysis model for microbial glutamine transaminase in surimi obtained in step 3 of the present invention has been verified to be reliable, with an internal validation accuracy of 99.9%. The qualitative analysis model is validated using the leave-one-out method. One sample is retained from the modeling set at a time, and the remaining samples are used to model and predict this sample. This sample is then added to the modeling set, and another sample is removed. This sample is then modeled and predicted using the remaining samples until all modeling samples have been predicted. The accuracy of the model is determined based on the prediction error, thereby evaluating the reliability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0025] like Figure 1 As shown, the present invention discloses a method for detecting microbial glutamine transaminase in fish paste using near-infrared spectroscopy technology, which comprises the following steps:

[0026] Step 1: adding 0.1-0.3% casein to the surimi, and then chopping it into a surimi at a high speed of 3000-4000 r / min. The semi-fluid slurry after chopping is sealed and placed in an environment of 0-4°C for 5-6 hours to obtain a surimi pretreatment sample; this step 1 corresponds to a pretreatment method for detecting microbial glutamine transaminase in surimi using near-infrared spectroscopy technology.

[0027] Step 2: Turn on the near-infrared spectrometer to preheat, scan the background, take the pre-treated fish paste sample and place it in the sample cup. Smooth it evenly and close to the bottom glass of the sample cup. There should be no visible pores (the thickness of the smoothed sample is 1-1.5 cm). Scan the spectrum of the pre-treated fish paste sample in the spectral range of 12800-4000 cm -1 , resolution 16cm -1 , the number of scans was 96 to obtain spectral information;

[0028] Step 3: Import the spectral information scanned in step 2 into the qualitative analysis model of microbial glutamine transaminase in surimi to perform qualitative judgment of the microbial glutamine transaminase in the sample.

[0029] In step 3, the establishment of a qualitative analysis model for microbial glutamine transaminase in surimi comprises the following steps:

[0030] Step A: preparing two surimi control samples, one is a negative sample without microbial transglutaminase, and the other is a positive sample containing different amounts of microbial transglutaminase, wherein the mass fractions of microbial transglutaminase added to the positive samples are 0.01%, 0.02%, 0.04%, 0.08%, 0.1%, 0.2%, 0.5%, and 1%, respectively; chopping and mixing the samples to obtain surimi positive and negative control samples;

[0031] Step B: adding 0.1-0.3% casein to the surimi positive and negative control samples respectively, and then chopping them into a minced state at a high speed of 3000-4000 r / min. The semi-fluid slurry after chopping is sealed and placed at 0-4°C for 5-6 hours to obtain the surimi positive and negative control pre-treated samples;

[0032] Step C: After preheating the near-infrared spectrometer, scan the background. Take 100 samples of each concentration of surimi negative and positive control pre-treatment samples, a total of 100 surimi negative control pre-treatment samples, and a total of 800 surimi positive control pre-treatment samples; place the surimi positive and negative control pre-treatment samples in a sample cup, smooth them evenly, close to the bottom glass of the sample cup, and no pores are visible to the naked eye (the thickness of the smoothed sample is 1-1.5 cm). Perform a spectral scan of the surimi positive and negative control pre-treatment samples in the spectral range of 12800-4000 cm -1 , resolution 16cm -1 , the number of scans was 96 to obtain spectral information;

[0033] Step D: A qualitative analysis model is established using a discriminant analysis method. The near-infrared spectral data matrix of the surimi positive and negative control pre-treated samples obtained in step C is subjected to principal component analysis to establish a principal component analysis model for the surimi positive and negative control samples, and to obtain a qualitative analysis model for microbial glutamine transaminase in the surimi.

[0034] The present invention is further described in detail below by way of specific examples. The following description of specific embodiments is only used to explain the present invention and does not limit the present invention. All the following examples and comparative examples are based on mass fractions.

[0035] Example 1

[0036] This embodiment includes the following steps:

[0037] Step 1: Add 0.2% casein to the surimi and chop it into a surimi at a high speed of 3000 r / min. Seal the semi-fluid slurry after chopping and place it at 4°C for 6 hours to obtain a surimi pretreatment sample.

[0038] Step 2: After preheating the NIR spectrometer for 30 minutes, scan the background. Place the pre-treated surimi sample in a sample cup and smooth it evenly, close to the bottom glass of the sample cup. There should be no visible pores (the thickness of the smoothed sample is 1.5 cm). Scan the spectrum of the pre-treated surimi sample in the spectral range of 12800-4000 cm -1 (780-2500nm), resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0039] Step 3: Import the spectral information scanned in step 2 into the qualitative analysis model of microbial glutamine transaminase in surimi to perform qualitative judgment of the microbial glutamine transaminase in the sample.

[0040] In step 3, the establishment of a qualitative analysis model for microbial glutamine transaminase in surimi comprises the following steps:

[0041] Step A: Prepare two surimi control samples, one is a negative sample without microbial glutamine transaminase, and the other is a positive sample containing different amounts of microbial glutamine transaminase. The added mass fractions of microbial glutamine transaminase in the positive samples are 0.01%, 0.02%, 0.04%, 0.08%, 0.1%, 0.2%, 0.5%, and 1%, respectively. Chop and mix evenly to obtain surimi positive and negative control samples.

[0042] Step B: Add 0.2% casein to the surimi positive and negative control samples respectively, and then chop them into a minced state at a high speed of 3000 r / min. After chopping, the semi-fluid slurry after chopping is sealed and placed at 4°C for 6 hours to obtain the surimi positive and negative control pre-treated samples.

[0043] Step C: After preheating the near-infrared spectrometer for 30 minutes, scan the background. Take 100 samples of each concentration of the surimi negative and positive control pre-treatment samples, a total of 100 surimi negative control pre-treatment samples, and a total of 800 surimi positive control pre-treatment samples. Place the surimi positive and negative control pre-treatment samples in a sample cup, smooth them evenly, close to the bottom glass of the sample cup, and without visible pores (the thickness of the smoothed sample is 1.5 cm). Perform a spectral scan of the surimi positive and negative control pre-treatment samples in the spectral range of 12800-4000 cm -1 (780-2500nm), resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0044] Step D: A qualitative analysis model is established using a discriminant analysis method. The near-infrared spectral data matrix of the surimi positive and negative control pre-treated samples obtained in step C is subjected to principal component analysis to establish a principal component analysis model for the surimi positive and negative control samples, and to obtain a qualitative analysis model for microbial glutamine transaminase in the surimi.

[0045] In this embodiment, the model prediction effect, the model internal verification accuracy is 99.9%, and the prediction accuracy of fish paste is 95.5%, which can be used for the qualitative detection of microbial glutamine transaminase in fish paste.

[0046] Example 2

[0047] In this example, step 1 of Example 1 was adjusted as follows: 0.1% casein was added to the surimi, followed by high-speed chopping at 3000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at 0°C for 5 hours to obtain a surimi pretreatment sample.

[0048] Step B in Example 1 was adjusted as follows: 0.1% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 3000 r / min. The semi-fluid slurry after chopping was sealed and placed at 0°C for 5 hours to obtain the surimi yin and yang control pretreatment samples.

[0049] The remaining steps are the same as in Example 1.

[0050] In this example, the model prediction effect, the model internal verification accuracy is 99.5%, and the prediction accuracy of fish paste is 95.1%, which can be used for the qualitative detection of microbial glutamine transaminase in fish paste, but the prediction accuracy is slightly lower than that of Example 1.

[0051] Example 3

[0052] In this example, step 1 of Example 1 was adjusted as follows: 0.3% casein was added to the surimi, followed by high-speed chopping at 4000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at 2° C. for 5.5 hours to obtain a surimi pretreatment sample.

[0053] Step B in Example 1 was adjusted as follows: 0.3% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 4000 r / min. The semi-fluid slurry after chopping was sealed and placed at 2°C for 5.5 hours to obtain the surimi yin and yang control pretreatment samples.

[0054] The remaining steps are the same as in Example 1.

[0055] In this example, the model prediction effect, the model internal verification accuracy is 99.8%, and the prediction accuracy of fish paste is 95.3%, which can be used for the qualitative detection of microbial glutamine transaminase in fish paste, but the prediction accuracy is slightly lower than that of Example 1.

[0056] Comparative Example 1

[0057] Step 1 in Example 1 was adjusted as follows: the surimi was chopped into a surimi at a high speed of 3000 r / min to obtain a surimi pre-treated sample.

[0058] The step B in Example 1 was adjusted as follows: the surimi yin-yang control sample was chopped into a surimi at a high speed of 3000 r / min to obtain the surimi yin-yang control pre-treated sample.

[0059] The remaining steps were the same as in Example 1. The internal validation accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi obtained by this comparative example was only 78.3%. The positive and negative control pretreatment samples of surimi partially overlapped and were difficult to distinguish, resulting in a decrease in the accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi.

[0060] Comparative Example 2

[0061] Step 1 in Example 1 was adjusted as follows: the surimi was chopped into a surimi at a high speed of 3000 r / min, and the semi-fluid slurry after chopped was sealed and placed at 4° C. for 6 hours to obtain a surimi pretreatment sample.

[0062] Step B in Example 1 was adjusted as follows: the surimi yin-yang control sample was chopped into a surimi at a high speed of 3000 r / min, and the semi-fluid slurry after chopping was sealed and placed at 4° C. for 6 hours to obtain the surimi yin-yang control pre-treated sample.

[0063] The remaining steps were the same as in Example 1. The internal validation accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi obtained by this comparative example was only 90.3%. The positive and negative control pretreatment samples of surimi partially overlapped and were difficult to distinguish, resulting in a decrease in the accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi.

[0064] Comparative Example 3

[0065] Step 1 in Example 1 was adjusted as follows: 1% casein was added to the surimi, followed by high-speed chopping at 3000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at 4° C. for 6 hours to obtain a surimi pretreatment sample.

[0066] Step B in Example 1 was adjusted as follows: 1% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 3000 r / min. The semi-fluid slurry after chopping was sealed and placed at 4°C for 6 hours to obtain the surimi yin and yang control pre-treated samples.

[0067] The remaining steps were the same as in Example 1. The surimi pretreatment sample and the surimi yin-yang control pretreatment sample obtained in this comparative example had become a gel state and could not be completely filled in the solid sample cup of the near-infrared spectrometer. There was a gap between the glass at the bottom of the sample cup and the sample. After slicing, bubbles were found in the gel, and complete spectral data of the scanned object could not be obtained.

[0068] Comparative Example 4

[0069] Step 1 in Example 1 was adjusted as follows: 0.2% casein was added to the surimi and then placed at 4° C. for 6 hours to obtain a surimi pre-treated sample.

[0070] Step B in Example 1 was adjusted as follows: 0.2% casein was added to the surimi positive and negative control samples respectively, and then placed at 4° C. for 6 hours to obtain the surimi positive and negative control pre-treated samples.

[0071] The remaining steps were the same as in Example 1. The surimi pretreatment sample and the surimi yin-yang control pretreatment sample obtained in this comparative example were not uniform, and the casein was not fully homogenized with the surimi, which did not meet the sample uniformity requirements of the near-infrared spectrometer.

[0072] Comparative Example 5

[0073] Step 1 in Example 1 was adjusted as follows: 0.2% casein was added to the surimi, and the mixture was chopped at a high speed of 3000 r / min into a surimi to obtain a surimi pre-treated sample.

[0074] Step B in Example 1 was adjusted as follows: 0.2% casein was added to the surimi yin and yang control samples respectively, and then the mixture was chopped into a minced state at a high speed of 3000 r / min to obtain the surimi yin and yang control pre-treated samples.

[0075] The remaining steps were the same as those in Example 1. The surimi yin-yang control pre-treated samples obtained in this comparative example could not be distinguished into two categories after scanning the spectrum, resulting in the inability to establish a qualitative analysis model for microbial glutamine transaminase in surimi.

[0076] Comparative Example 6

[0077] Step 1 in Example 1 was adjusted as follows: 0.2% casein was added to the surimi, followed by high-speed chopping at 3000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at 45° C. for 6 hours to obtain a surimi pretreatment sample.

[0078] Step B in Example 1 was adjusted as follows: 0.2% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 3000 r / min. The semi-fluid slurry after chopping was sealed and placed at 45°C for 6 hours to obtain the surimi yin and yang control pretreatment samples.

[0079] The remaining steps were the same as in Example 1. The obtained surimi pretreatment sample and the surimi yin-yang control pretreatment sample had become a gel state and could not be completely filled in the solid sample cup of the near-infrared spectrometer. There was a gap between the glass at the bottom of the sample cup and the sample, and complete spectral data of the sample could not be obtained. In addition, the sample had an odor, which resulted in the inability to establish a qualitative analysis model for microbial glutamine transaminase in surimi.

[0080] Comparative Example 7

[0081] Step 1 in Example 1 was adjusted as follows: 0.2% casein was added to the surimi, followed by high-speed chopping at 3000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at 15° C. for 6 hours to obtain a surimi pretreatment sample.

[0082] Step B in Example 1 was adjusted as follows: 0.2% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 3000 r / min, and the semi-fluid slurry after chopped was sealed and placed at 15°C for 6 hours to obtain the surimi yin and yang control pre-treated samples.

[0083] The remaining steps were the same as in Example 1. The surimi pretreatment sample and the surimi yin-yang control pretreatment sample were already in a gel state and could not be completely filled in the solid sample cup of the near-infrared spectrometer. There was a gap between the glass at the bottom of the sample cup and the sample, and complete spectral data of the scanned object could not be obtained. In addition, the sample had a certain odor.

[0084] Comparative Example 8

[0085] Step 1 in Example 1 was adjusted as follows: 0.2% casein was added to the surimi, followed by high-speed chopping at 3000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at -4°C for 6 hours to obtain a surimi pretreatment sample.

[0086] Step B in Example 1 was adjusted as follows: 0.2% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 3000 r / min. The semi-fluid slurry after chopped was sealed and placed at -4°C for 6 hours to obtain the surimi yin and yang control pre-treated samples.

[0087] The remaining steps were the same as in Example 1. The internal validation accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi obtained by this comparative example was only 78.6%. The positive and negative control pretreatment samples of surimi partially overlapped and were difficult to distinguish, resulting in a decrease in the accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi.

[0088] Comparative Example 9

[0089] Step 1 in Example 1 was adjusted as follows: 0.2% casein was added to the surimi, followed by high-speed chopping at 3000 r / min into a surimi-like state, and the semi-fluid slurry after chopping was sealed and placed at 4° C. for 12 hours to obtain a surimi pretreatment sample.

[0090] Step B in Example 1 was adjusted as follows: 0.2% casein was added to the surimi yin and yang control samples respectively, and then the surimi was chopped into a morsel at a high speed of 3000 r / min. The semi-fluid slurry after chopping was sealed and placed at 4°C for 12 hours to obtain the surimi yin and yang control pretreatment samples.

[0091] The remaining steps were the same as those in Example 1. Liquid oozed out of the sample surface, indicating corruption. The sample lost its uniformity and could not be detected by near-infrared spectroscopy scanning.

[0092] Comparative Example 10

[0093] Step 2 in Example 1 was adjusted as follows: After preheating the near-infrared spectrometer for 30 minutes, the background was scanned. The pre-treated surimi sample was placed in a sample cup and evenly smoothed, close to the bottom glass of the sample cup, with visible pores (the thickness of the smoothed sample was 1.5 cm). The spectral scan of the pre-treated surimi sample was performed in the spectral range of 12800-4000 cm -1 , resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0094] Step C in Example 1 was adjusted as follows: After preheating the near-infrared spectrometer for 30 minutes, a background scan was performed. 100 samples of each surimi negative control pretreatment sample were taken, a total of 100 surimi negative control pretreatment samples, and a total of 800 surimi positive control pretreatment samples were taken. The surimi positive control pretreatment samples were placed in a sample cup and evenly smoothed, close to the bottom glass of the sample cup, with visible pores (the smoothed sample thickness was 1.5 cm). Spectral scans of the surimi positive control pretreatment samples were performed in the spectral range of 12800-4000 cm -1 , resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0095] The remaining steps were the same as in Example 1. The internal validation accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi obtained by this comparative example was only 74.6%. The positive and negative control pretreatment samples of surimi partially overlapped and were difficult to distinguish, resulting in a decrease in the accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi.

[0096] Comparative Example 11

[0097] Step 2 in Example 1 was adjusted as follows: After preheating the near-infrared spectrometer for 30 minutes, the background was scanned, and the pre-treated surimi sample was placed in a sample cup and evenly smoothed to fit snugly against the bottom glass of the sample cup, leaving no visible pores (the thickness of the smoothed sample was 1.5 cm). The spectral scan of the pre-treated surimi sample was performed in the spectral range of 12800-6000 cm -1 , resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0098] Step C in Example 1 was adjusted as follows: After preheating the near-infrared spectrometer for 30 minutes, a background scan was performed. 100 samples of each surimi yin and yang control pretreatment sample were taken, a total of 100 surimi negative control pretreatment samples, and a total of 800 surimi positive control pretreatment samples were taken. The surimi yin and yang control pretreatment samples were placed in a sample cup and evenly smoothed, close to the bottom glass of the sample cup, with no visible pores (the smoothed sample thickness was 1.5 cm). Spectral scans of the surimi yin and yang control pretreatment samples were performed in the spectral range of 12800-6000 cm -1 , resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0099] The remaining steps were the same as in Example 1. The internal verification accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi obtained by this comparative example was only 70.8%. The narrowing of the near-infrared spectrum range resulted in partial overlap and difficulty in distinguishing the positive and negative control pretreatment samples of surimi, and the accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi decreased.

[0100] Comparative Example 12

[0101] Step A in Example 1 was adjusted as follows: two surimi control samples were prepared, one being a negative sample without microbial transglutaminase, and the other being a positive sample containing different amounts of microbial transglutaminase, wherein the added mass fractions of microbial transglutaminase in the positive samples were 0.01%, 0.02%, 0.04%, 0.08%, 0.1%, 0.2%, 0.5%, 1%, and 5%, respectively. The samples were chopped and mixed evenly to obtain surimi positive and negative control samples.

[0102] The remaining steps were the same as those in Example 1. The positive control sample with an added mass fraction of 5% of microbial glutamine transaminase obtained in this comparative example was subjected to step B to obtain the surimi positive control pretreatment sample, which had become a gel state and could not be completely filled in the solid sample cup of the near-infrared spectrometer. There was a gap between the glass at the bottom of the sample cup and the sample, and the complete spectral data of the scanned object could not be obtained.

[0103] Comparative Example 13

[0104] Step C in Example 1 was adjusted as follows: After preheating the near-infrared spectrometer for 30 minutes, a background scan was performed. 50 samples of each surimi negative control pretreatment sample were taken, for a total of 50 surimi negative control pretreatment samples and a total of 400 surimi positive control pretreatment samples. The surimi positive control pretreatment samples were placed in a sample cup and evenly smoothed, close to the bottom glass of the sample cup, with no visible pores (the smoothed sample thickness was 1.5 cm). Spectral scans of the surimi positive control pretreatment samples were performed in the spectral range of 12800-4000 cm -1 , resolution 16cm -1 , scanning times 96 times, and spectrum information was obtained.

[0105] The remaining steps were the same as in Example 1. The internal validation accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi obtained by this comparative example was only 91.6%. The positive and negative control pretreatment samples of surimi partially overlapped and were difficult to distinguish, resulting in a decrease in the accuracy of the qualitative analysis model for microbial glutamine transaminase in surimi.

[0106] In summary, the present invention is different from the existing enzyme-linked immunosorbent assay methods, which have the characteristics of complicated detection steps and large reagent usage. It overcomes the defect of traditional near-infrared spectroscopy technology that the detection limit requires the content of the detection target to be higher than 0.1% by mass fraction. The present invention has the advantages of fast analysis speed, high detection throughput, pollution-free processing, low application cost, and good analysis reproducibility. It can provide a reference basis for judging whether microbial glutamine transaminase should be added to fish paste.

[0107] On one hand, the present invention adds casein to surimi as a catalytic substrate for microbial transglutaminase. This allows the spectral signal generated by the enzyme protein molecular structure itself to be converted into the spectral signal generated by the cross-linked structure of the surimi protein during near-infrared spectroscopy scanning of the surimi pre-treated sample. This extends the detection target from the trace addition of microbial transglutaminase to the cross-linked structure of the surimi protein, achieving a disguised effect of amplifying the differential signal between surimi molecules. Because this pre-treatment method changes the near-infrared spectral scanning target and spectral characteristics, the near-infrared spectrometer detector can capture the changes in optical information, and through the establishment of a mathematical model, predict whether microbial transglutaminase has been added to the surimi.

[0108] On the other hand, the present invention subjects the semi-fluid slurry obtained in step 1 to sealed refrigeration to avoid the changes in the non-cross-linked structure of the surimi protein caused by the high or low temperature environment during the cross-linking reaction catalyzed by the microbial transglutaminase, so that the myofibrillar protein and casein in the surimi only undergo a slow specific catalytic cross-linking reaction in the refrigerated environment, and other non-cross-linked structures in the surimi pre-treated sample do not undergo obvious changes, so that other protein structural changes will not interfere with the scanning spectral characteristic changes of the cross-linked structure of the surimi protein.

[0109] Based on the above two points, the present invention obtains a pre-treated fish paste sample through a specific pre-treatment method, and establishes a qualitative analysis model for microbial transglutaminase in fish paste using near-infrared spectroscopy scanning technology and a discriminant analysis method, thereby making a qualitative judgment on the microbial transglutaminase in fish paste. Therefore, the pre-treatment method and the method for detecting microbial transglutaminase in fish paste using near-infrared spectroscopy technology in the present invention together constitute the creative essence of the present invention, and the two are inseparable and indispensable.

[0110] The qualitative analysis model for microbial glutamine transaminase in surimi obtained in step 3 of the present invention has been verified to be reliable, with an internal validation accuracy of 99.9%. The qualitative analysis model is validated using the leave-one-out method. One sample is retained from the modeling set at a time, and the remaining samples are used to model and predict this sample. This sample is then added to the modeling set, and another sample is removed. This sample is then modeled and predicted using the remaining samples until all modeling samples have been predicted. The accuracy of the model is determined based on the prediction error, thereby evaluating the reliability of the model.

[0111] The above description is only a preferred embodiment of the present invention and does not limit the technical scope of the present invention. Therefore, any changes and modifications made according to the claims and description of this invention should fall within the scope of the patent of this invention.

Claims

1. A method for detecting microbial transglutaminase in surimi using near-infrared spectroscopy, characterized in that: The following steps are involved: Step 1: adding 0.1-0.3% casein to the surimi, and then chopping it at a high speed of 3000-4000 r / min into a surimi. The semi-fluid slurry after chopping is sealed and placed at 0-4°C for 5-6 hours to obtain a surimi pre-treated sample; Step 2: Turn on the near-infrared spectrometer to preheat, scan the background, take the pre-treated fish paste sample and place it in the sample cup, smooth it evenly, close to the bottom glass of the sample cup, and there are no visible pores. Scan the spectrum of the pre-treated fish paste sample in the spectral range of 12800-4000cm -1 , resolution 16cm -1 , the number of scans was 96 to obtain spectral information; Step 3: Importing the spectral information scanned in step 2 into the qualitative analysis model of microbial transglutaminase in surimi to perform qualitative judgment of the microbial transglutaminase in the sample; In step 3, the establishment of a qualitative analysis model for microbial glutamine transaminase in surimi comprises the following steps: Step A: preparing two surimi control samples, one is a negative sample without microbial transglutaminase, and the other is a positive sample containing different amounts of microbial transglutaminase, wherein the mass fractions of microbial transglutaminase added to the positive samples are 0.01%, 0.02%, 0.04%, 0.08%, 0.1%, 0.2%, 0.5%, and 1%, respectively; chopping and mixing the samples to obtain surimi positive and negative control samples; Step B: adding 0.1-0.3% casein to the surimi positive and negative control samples respectively, and then chopping them into a minced state at a high speed of 3000-4000 r / min. The semi-fluid slurry after chopping is sealed and placed at 0-4°C for 5-6 hours to obtain the surimi positive and negative control pre-treated samples; Step C: After preheating the near-infrared spectrometer, scan the background. Take 100 samples of each concentration of the surimi negative and positive control pre-treatment samples, a total of 100 surimi negative control pre-treatment samples, and a total of 800 surimi positive control pre-treatment samples; take the surimi positive and negative control pre-treatment samples and place them in a sample cup, evenly smooth them, close to the bottom glass of the sample cup, and no pores are visible to the naked eye. Perform a spectral scan of the surimi positive and negative control pre-treatment samples in the spectral range of 12800-4000cm -1 , resolution 16cm -1 , the number of scans was 96 to obtain spectral information; Step D: A qualitative analysis model is established using a discriminant analysis method. The near-infrared spectral data matrix of the surimi positive and negative control pre-treated samples obtained in step C is subjected to principal component analysis to establish a principal component analysis model for the surimi positive and negative control samples, and to obtain a qualitative analysis model for microbial glutamine transaminase in the surimi.

2. The method for detecting microbial transglutaminase in surimi using near infrared spectroscopy according to claim 1, characterized in that: The thickness of the surimi pre-treated sample / surimi yin-yang control sample at the bottom of the sample cup is 1-1.5 cm.

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