Method for identifying mutton by near infrared spectroscopy
By combining near-infrared spectroscopy with baked, infrared carbon dot pretreatment and nu-SVC model training, the problem of low detection accuracy of mutton in the prior art is solved, and high accuracy detection of adulterated and cooked mutton is achieved.
Patent Information
- Application Number
- CN202510207611.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-25
- Publication Date
- 2025-04-15
AI Technical Summary
In the prior art, the accuracy of detecting mutton needs to be improved, especially the detection of adulterated mutton and cooked mutton is more difficult.
The near-infrared spectroscopy combined with baking, infrared carbon dot pretreatment and nu-SVC model training methods were used to bake mutton samples, infrared carbon dot pretreatment and spectral data analysis, and spectral parameters were extracted and the model was trained for identification.
It improves the accuracy of mutton detection, enhances the detection ability of cooked mutton, and improves the sensitivity and accuracy of the detection.
Smart Images
Figure CN119827456A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of mutton identification, and in particular to a method for identifying mutton by utilizing near infrared spectroscopy. Background Art
[0002] Traditional methods for detecting adulteration of meat and meat products, such as sensory inspection and ultraviolet, liquid and gas chromatography techniques, all have certain drawbacks, such as time-consuming, complicated sample pretreatment, expensive instruments and high requirements for operators. In recent years, emerging detection methods such as near-infrared, far-infrared spectroscopy and electronic nose technology in meat product quality detection have the characteristics of simplicity, efficiency and rapidity, and are becoming a research hotspot for meat product adulteration detection. Near-infrared light refers to electromagnetic waves with a wavelength range of 780 to 2526 nm, which mainly reflects the frequency doubling and combination frequency absorption of hydrogen-containing group vibrations, and almost covers all hydrogen-containing information in organic matter. Meat is rich in organic matter such as protein, fat, organic acid, carbohydrates, etc. Therefore, a large amount of information can be obtained through spectral analysis of meat and meat products.
[0003] In the prior art, raw mutton is tested directly by infrared spectroscopy, and analyzed and identified by modeling. However, there are no existing reports on new characteristic factors. The protein, fat, glucose, and water content of the meat are usually tested to determine the quality and authenticity of the mutton. However, for adulterated mutton, the accuracy of the direct detection method gradually decreases; and in actual applications, cooked mutton is difficult to detect. Summary of the invention
[0004] The embodiment of the present application solves the problem in the prior art that the detection accuracy needs to be improved by providing a method for identifying mutton using near-infrared spectroscopy, thereby achieving improved detection accuracy and having a certain detection capability for cooked mutton.
[0005] The present application embodiment provides a method for identifying mutton using near infrared spectroscopy, comprising the following steps:
[0006] S1. Prepare a mutton sample, from which fat tissue and connective tissue have been removed and the thickness is 1±0.05 cm;
[0007] S2, baking the sample at an oven temperature of 200°C for 15 minutes;
[0008] S3, homogenizing the baked sample and adding Coomassie Brilliant Blue G-250 solution;
[0009] S4, collecting near-infrared spectra of the samples, collecting each sample 3-5 times continuously, and taking the average as the original spectrum;
[0010] S5, preprocessing the original spectrum obtained by infrared detection of the roasted mutton sample, extracting key feature information in the spectrum, and obtaining spectrum parameters;
[0011] S6. By analyzing the roasted lamb samples, the nu-SVC model is trained based on the spectral parameters.
[0012] S7. Use the model to identify mutton, output result parameters, and identify whether it is pure mutton.
[0013] Furthermore, the preparation method of the Coomassie Brilliant Blue G-250 solution is as follows: 15 mg of Coomassie Brilliant Blue G-250 is dissolved in 50 ml of 95% ethanol, and 100 ml of phosphoric acid with a mass concentration of 0.85 g / ml is added to prepare the solution.
[0014] Furthermore, in step S3, the Coomassie Brilliant Blue G-250 solution accounts for 1% of the total mass of the sample.
[0015] Furthermore, in step S2, infrared carbon dot pretreatment is performed on the sample before baking.
[0016] Further, the infrared carbon dot pretreatment is specifically as follows:
[0017] The sample was immersed in a 0.03 g / ml o-phenylenediamine solution for 2-4 h.
[0018] Further, the pH of the samples was adjusted to 5.0 or 8.5 when immersed in a 0.03 g / ml o-phenylenediamine solution.
[0019] Furthermore, the pH is adjusted by soaking the sample in a 0.03 g / ml o-phenylenediamine solution, and then adjusting the pH to 5.0 using hydrochloric acid or adjusting the pH to 8.5 using sodium hydroxide, and then soaking for 2-4 hours.
[0020] Furthermore, a quencher is added into the o-phenylenediamine solution, and the quencher is glucose.
[0021] Furthermore, the concentration of glucose is: original amount 0g / ml, small amount 0.01g / ml, large amount 0.05g / ml.
[0022] Furthermore, the sample was tested a second time 24 hours after the test, and the result of the second test was subtracted from the result of the first test to obtain the fluorescence change parameter, and the model was established with the fluorescence change parameter and spectral parameter as input.
[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
[0024] 1. The roasting process will cause the protein in the mutton to denature, causing the conformation of the protein to change, thereby changing its near-infrared spectral characteristics. The water in the mutton will partially evaporate, resulting in a decrease in the water content, affecting the absorption characteristics of the spectrum and making the spectral characteristics more prominent. The fat in the mutton will undergo oxidation reaction to generate some new compounds. These newly generated compounds will affect the near-infrared spectrum and produce more prominent characteristic peaks, which will increase the accuracy of detection for other meats with different fat, protein and water contents.
[0025] 2. Lamb will produce fluorescent carbon dots after roasting, but the carbon dots prepared generally emit blue-green light. In addition to the characteristic peaks of the carbon dots themselves, the fluorescence has limited effect on the infrared detection range. However, after pretreatment with o-phenylenediamine, red fluorescence can be produced with a characteristic peak at around 600-900 nanometers, and the fluorescence has a greater impact on the characteristic peak, thus obtaining a higher sensitivity. In lamb, the raw materials of carbon dots are mainly protein and fat, so different meat qualities can be distinguished by analyzing the spectrum of carbon dots.
[0026] 3. Under the action of acid and high temperature, carbon-containing compounds are cut or corroded into nano-sized carbon dots through oxidation reactions, and oxygen-containing functional groups such as hydroxyl (-OH) and carboxyl (-COOH) are introduced. The presence of these functional groups gives carbon dots excellent hydrophilicity and fluorescence properties; under the action of alkali and high temperature, small molecular organic matter forms carbon nuclei through dehydration condensation reactions, and then carbonizes to form carbon dots. Although strong acids and strong bases have a stronger effect on the production of carbon dots, strong acids and strong bases can easily destroy the original structure of the meat in large quantities, which makes the uncertainty of the characteristic peaks greater; and the stability of fluorescent carbon dots is reduced under strong acid conditions, and they may be quenched;
[0027] 4. The degree of fluorescence quenching increases with the increase of glucose concentration. In mutton detection, glucose is an endogenous substance present in the sample, and its concentration change may be related to the freshness and maturity of mutton. Therefore, by introducing glucose as a quencher and observing its effect on fluorescence, the quality of mutton can be further identified. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 The infrared spectrum of raw mutton in the method of identifying mutton by using near infrared spectroscopy of the present invention;
[0029] Figure 2 The infrared spectrum of roasted mutton in the method of identifying mutton by using near infrared spectroscopy of the present invention;
[0030] Figure 3 The infrared spectrum of mutton roasted with o-phenylenediamine according to the method of identifying mutton by using near infrared spectroscopy of the present invention;
[0031] Figure 4 The infrared spectrum of roasted mutton at pH 5.0 according to the method for identifying mutton by using near infrared spectroscopy of the present invention;
[0032] Figure 5 The infrared spectrum of roasted mutton at pH 8.5 according to the method for identifying mutton by using near infrared spectroscopy of the present invention;
[0033] Figure 6 This is an infrared spectrum of lamb roasted at a pH of 5.0 with a small amount of glucose added in a method for identifying lamb using near infrared spectroscopy of the present invention;
[0034] Figure 7 The present invention discloses an infrared spectrum diagram of lamb roasted with a large amount of glucose added and a pH of 5.0 according to a method for identifying lamb using near infrared spectroscopy. DETAILED DESCRIPTION
[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.
[0036] Embodiment 1
[0037] The present application discloses a method for identifying mutton using near infrared spectroscopy, comprising the following steps:
[0038] S1. Prepare a mutton sample, from which fat tissue and connective tissue have been removed and the thickness is 1±0.05 cm;
[0039] S2, baking the sample at an oven temperature of 200°C for 15 minutes;
[0040] S3, homogenizing the baked sample and adding Coomassie Brilliant Blue G-250 solution, wherein the mass fraction of the Coomassie Brilliant Blue G-250 solution is 1%;
[0041] The Coomassie Brilliant Blue G-250 solution is prepared by dissolving 15 mg of Coomassie Brilliant Blue G-250 in 50 ml of 95% ethanol and adding 100 ml of phosphoric acid with a mass concentration of 0.85 g / ml;
[0042] S4, collecting near-infrared spectra of the samples, collecting each sample 3-5 times continuously, and taking the average as the original spectrum;
[0043] S5, performing area normalization and first-order derivative processing on the original spectrum obtained by infrared detection of the roasted mutton sample to pre-process the original spectrum, and using principal component analysis (PCA) and linear discriminant analysis (LDA) to extract key feature information in the spectrum to obtain spectral parameters;
[0044] S6. By performing sample analysis on roasted mutton, the nu-SVC model is trained using the training set data based on the spectral parameters. The training process of the nu-SVC model refers to the prior art and will not be described in detail in this article.
[0045] S7. Use the model to identify mutton, output result parameters, and identify whether it is pure mutton.
[0046] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:
[0047] The roasting process will cause the protein in the mutton to denature, causing the conformation of the protein to change, thereby changing its near-infrared spectral characteristics. The water in the mutton will partially evaporate, resulting in a decrease in the water content, affecting the absorption characteristics of the spectrum and making the spectral characteristics more prominent. The fat in the mutton will undergo oxidation reaction to generate some new compounds. These newly generated compounds will affect the near-infrared spectrum and produce more prominent characteristic peaks, which will increase the accuracy of detection for other meats with different fat, protein and water contents.
[0048] Raw meat contains more water and fat, and its spectral characteristics mask the spectral information of other components. After roasting, the water and fat content decreases, reducing interference factors. Since the spectral characteristics are more significant and the interference factors are reduced, the accuracy of mutton identification is improved. The roasting process makes the mutton samples more homogenous, reducing the differences between samples, thereby enhancing the robustness of the nu-SVC model established subsequently.
[0049] In actual applications, mutton is often eaten after being cooked. Therefore, using roasted mutton for infrared detection is more in line with actual application scenarios and helps to apply the detection results to actual production.
[0050] Embodiment 2
[0051] The above embodiment increases the accuracy and uniformity of the detection result by performing infrared detection on the roasted mutton. In order to further highlight the feature points and increase the accuracy of the detection, further improvements are made on the basis of the first embodiment.
[0052] In step S2, the sample is subjected to infrared carbon dot pretreatment before baking, specifically:
[0053] Soak the sample in 0.03 g / ml o-phenylenediamine solution for 2-4 h;
[0054] Then the samples pretreated with infrared carbon dots were baked.
[0055] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:
[0056] Lamb will produce fluorescent carbon dots after roasting, but the carbon dots prepared generally emit blue-green light. In addition to the characteristic peaks of the carbon dots themselves, the fluorescence has limited effect on the infrared detection range. However, after pretreatment with o-phenylenediamine, red fluorescence can be produced with a characteristic peak at around 600-900 nanometers, and the fluorescence has a greater impact on the characteristic peak, thus obtaining a higher sensitivity. In lamb, the raw materials of carbon dots are mainly protein and fat, so different meat qualities can be distinguished by analyzing the spectrum of carbon dots.
[0057] Infrared carbon dots are a type of zero-dimensional carbon nanomaterial with excellent optical properties and chemical stability, and can produce characteristic absorption peaks in the near-infrared spectral region. During the roasting process, infrared carbon dots are embedded or attached to the surface and interior of mutton, making the near-infrared spectral characteristics of mutton more significant, thereby improving the sensitivity of spectral detection. Even at lower concentrations, mutton samples containing infrared carbon dots can produce strong spectral signals, which is convenient for subsequent data analysis and processing.
[0058] The carbon dots themselves can also increase the intensity of characteristic spectral peaks such as hydroxyl and carbonyl, making spectral analysis more sensitive and accurate. Infrared carbon dots have good chemical stability and can maintain the stability of their structure and optical properties during the baking process, reduce the fluctuation of spectral signals caused by environmental changes or improper sample handling, and improve the stability and reliability of spectral data. Infrared carbon dots provide more characteristic information for the nu-SVC model, optimizing the model's identification ability and accuracy.
[0059] Embodiment 3
[0060] The above embodiment uses pretreatment to make the carbon dots emit red light after the mutton is roasted, thereby increasing the ability to identify the mutton. In order to increase the amount of carbon dots, further improvements are made on the basis of the second embodiment.
[0061] In step S2, the sample is subjected to infrared carbon dot pretreatment before baking, and the pH is adjusted to 5.0 or 8.5, specifically:
[0062] The sample is soaked in a 0.03 g / ml o-phenylenediamine solution, and then the pH is adjusted to 5.0 using hydrochloric acid or adjusted to 8.5 using sodium hydroxide, and then soaked for 2-4 hours; the sample pretreated with infrared carbon dots is then baked.
[0063] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:
[0064] Under the action of acid and high temperature, carbon-containing compounds are cut or corroded into nano-sized carbon dots through oxidation reactions, introducing oxygen-containing functional groups such as hydroxyl (-OH) and carboxyl (-COOH). The presence of these functional groups gives the carbon dots excellent hydrophilicity and fluorescence properties.
[0065] Under the action of alkali and high temperature, small molecular organic matter forms carbon cores through dehydration condensation reaction, and then carbonizes to form carbon dots;
[0066] Although strong acids and strong bases have stronger effects on the production of carbon dots, they can easily destroy the original structure of the meat, which leads to a greater uncertainty in the characteristic peaks. In addition, the stability of fluorescent carbon dots is reduced under strong acid conditions and they may be quenched.
[0067] pH value is one of the important factors affecting chemical reactions. During the generation of infrared carbon dots, changes in pH value can affect the solubility, ionization state and reaction rate of the reactants, thereby affecting the generation efficiency of carbon dots. In addition, changes in pH value can also change the surface properties of carbon dots, thereby increasing the stability and dispersibility of carbon dots.
[0068] Embodiment 4
[0069] In Example 3, the amount of carbon dots was increased by adjusting the pH value. In order to further improve and optimize the establishment of the nu-SVC model, further improvements were made on the basis of Example 3.
[0070] The effect of infrared carbon dots' fluorescence on infrared detection depends on the carbon dots' fluorescence. The fluorescence yield increases with the increase of temperature, while the degree of fluorescence quenching increases with the increase of glucose concentration.
[0071] Therefore, based on the original model, further input parameters are added, specifically:
[0072] Glucose was also added as a quencher when the sample was immersed in a 0.03 g / ml o-phenylenediamine solution, and was divided into an original amount of 0 g / ml, a small amount of 0.01 g / ml, and a large amount of 0.05 g / ml;
[0073] The sample is tested a second time after 24 hours, and the result of the second test is subtracted from the result of the first test to obtain the fluorescence change parameter, and a model is established using the fluorescence change parameter and spectral parameter as input.
[0074] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:
[0075] The degree of fluorescence quenching increases with the increase of glucose concentration. In the detection of mutton, glucose is an endogenous substance present in the sample, and its concentration change may be related to the freshness and maturity of mutton. Therefore, by introducing glucose as a quencher and observing its effect on fluorescence, the quality of mutton can be further identified.
[0076] The sample was retested after 24 hours and subtracted from the initial test result to obtain a fluorescence change spectrum. This step obtained a pure fluorescence influence spectrum, which improved the accuracy and sensitivity of the test. By introducing the fluorescence characteristics of infrared carbon dots and glucose as a quencher, a more comprehensive and accurate assessment of mutton quality was achieved, the accuracy and sensitivity of the test was improved, and the misjudgment rate was reduced. By monitoring the changes in fluorescence output and the degree of fluorescence quenching, rapid and non-destructive testing of mutton quality can be achieved, providing strong support for quality control in mutton production and processing.
[0077] The detection methods used in Examples 1 to 4 were tested by using infrared detection, wherein the mutton samples were selected from the front leg meat of adult 3-year-old male Daolang sheep for the experiment, and the infrared detection wavelength was 500-2500nm; the results are as follows Figure 1-7 .
[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A method for identifying mutton using near infrared spectroscopy, characterized in that: The steps include: S1. Prepare a mutton sample, from which fat tissue and connective tissue have been removed and the thickness is 1±0.05 cm; S2, baking the sample at an oven temperature of 200°C for 15 minutes; S3, homogenizing the baked sample and adding Coomassie Brilliant Blue G-250 solution; S4, collecting near-infrared spectra of the samples, collecting each sample 3-5 times continuously, and taking the average as the original spectrum; S5, preprocessing the original spectrum obtained by infrared detection of the roasted mutton sample, extracting key feature information in the spectrum, and obtaining spectrum parameters; S6. By analyzing the roasted lamb samples, the nu-SVC model is trained based on the spectral parameters. S7. Use the model to identify mutton, output result parameters, and identify whether it is pure mutton.
2. The method for identifying mutton using near infrared spectroscopy as claimed in claim 1, characterized in that: The preparation method of the Coomassie Brilliant Blue G-250 solution is as follows: 15 mg of Coomassie Brilliant Blue G-250 is dissolved in 50 ml of 95% ethanol, and 100 ml of phosphoric acid with a mass concentration of 0.85 g / ml is added to prepare the solution.
3. The method for identifying mutton using near infrared spectroscopy as claimed in claim 1, characterized in that: In step S3, the Coomassie Brilliant Blue G-250 solution accounts for 1% of the total mass of the sample.
4. The method for identifying mutton using near infrared spectroscopy as claimed in claim 1, characterized in that: In step S2, infrared carbon dot pretreatment is performed on the sample before baking.
5. The method for identifying mutton using near infrared spectroscopy as claimed in claim 4, characterized in that: The infrared carbon dot pretreatment is specifically as follows: The sample was immersed in a 0.03 g / ml o-phenylenediamine solution for 2-4 h.
6. The method for identifying mutton using near infrared spectroscopy as claimed in claim 5, characterized in that: The samples were immersed in a 0.03 g / ml o-phenylenediamine solution and the pH was adjusted to 5.0 or 8.
5.
7. The method for identifying mutton using near infrared spectroscopy as claimed in claim 6, characterized in that: The specific method of adjusting the pH is as follows: soak the sample in a 0.03 g / ml o-phenylenediamine solution, then adjust the pH to 5.0 using hydrochloric acid or adjust the pH to 8.5 using sodium hydroxide, and then soak for 2-4 hours.
8. The method for identifying mutton using near infrared spectroscopy as claimed in claim 7, characterized in that: A quencher is also added into the o-phenylenediamine solution, and the quencher is glucose.
9. The method for identifying mutton using near infrared spectroscopy as claimed in claim 8, characterized in that: The concentration of glucose is: original amount 0g / ml, small amount 0.01g / ml, large amount 0.05g / ml.
10. The method for identifying mutton using near infrared spectroscopy as claimed in claim 9, characterized in that: The sample was tested for the second time 24 hours after the test, and the result of the second test was subtracted from the result of the first test to obtain the fluorescence change parameter, and the model was established with the fluorescence change parameter and spectral parameter as input.