CN103424374B - A kind of near-infrared spectrum technique detects the method for hairtail freshness fast - Google Patents
A kind of near-infrared spectrum technique detects the method for hairtail freshness fast Download PDFInfo
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Abstract
The invention discloses a kind of method that near-infrared spectrum technique detects hairtail freshness fast, be intended to overcome the procedure complexity of existing detection hairtail freshness, there is the deficiency of chemical reagent pollution, the method is by near-infrared spectrum technique and mathematical modeling, set up the detection model of TVB-N content in a hairtail body, then calculated the TVB-N content in sample by the near infrared spectrum of model analysis hairtail sample.The last grade of freshness evaluating hairtail according to TVB-N content.The present invention has the advantage that analysis speed is fast, can not cause chemical contamination.
Description
Technical field
The present invention relates to food freshness and detect analysis field, especially relate to a kind of method that near-infrared spectrum technique detects hairtail freshness fast.
Background technology
Hairtail has another name called hairtail, dental strip fish etc., net-rope Perciformes Trichiuridae animal, is one of important marine economy fingerling of China, the tender body of its meat is fertile, delicious flavour, has very high commercial value.But hairtail, owing to being rich in unsaturated fatty acid, being vulnerable to the effect of biochemical reaction, microbial reproduction and enzyme and occurring putrid and deteriorated in storage.Wherein, total volatile basic nitrogen (TVBN) is the important indicator judging aquatic products degree of spoilage, is often used to detect aquatic products freshness.TVB-N content is higher, and aquatic products freshness is poorer.As Chinese patent Authorization Notice No.: CN101892288A, in the patent document in authorized announcement date on November 24th, 2010, disclose the assay method of the corrupt ability of fish spoilage organisms, comprise the pure bacterium solution preparation of spoilage organisms, aseptic fish block preparation, inoculation and spoilage organisms growth rate and corrupt metabolic product generating rate are analyzed, the aseptic fish block of preparation is it is characterized in that to immerse in the pure bacterium liquid of spoilage organisms to be determined, fish block after inoculation is preserved at 5 DEG C of temperature, get inoculation fish block every 24 hours and do sensory evaluation respectively, total volatile basic nitrogen (TVBN) measures, spoilage organisms counting to be determined, the amount of the corrupt metabolic product (TVBN) that the corrupt ability of spoilage organisms produces with the unit spoilage organisms be inoculated in aseptic fish block represents, using the TVBN yield factor (YTVBN/CFU) of sense organ corruption point as the quantitative target of the corrupt ability of spoilage organisms.
And the method for direct-detection total volatile basic nitrogen is as Micro-kjoldahl method, sample need be taken back, carry out complicated chemical experiment process, in more difficult satisfied modern aquatic products industry, directly detect demand fast.
Summary of the invention
The present invention be the procedure in order to overcome existing detection hairtail freshness complicated, there is the deficiency that chemical reagent pollutes, provide a kind of method that analysis speed is fast, detect hairtail freshness fast without the near-infrared spectrum technique of chemical contamination.
To achieve these goals, the present invention is by the following technical solutions:
Near-infrared spectrum technique detects a method for hairtail freshness fast, it is characterized in that, step is as follows:
1) Modling model:
A gets the refrigeration of fresh hairtail, regularly takes out part hairtail as sample with cold preservation time change;
The sample that b takes out divides two parts, and a Micro-kjoldahl method that adopts measures TVB-N content in sample, detects, detect the near infrared light spectrogram obtaining sample after another increment product carry out pre-service with near infrared spectrometer;
The near infrared light spectrogram of c to sample is for data processing, and obtains the data after processing, data processing method: first carry out first order derivative process to spectrum, then does following computing:
1. averaged spectrum is calculated:
2. one-variable linear regression:
3. calculate and correct spectrum:
In formula: A
ifor single sample spectrum vector,
for the averaged spectrum vector of all samples, n is sample number, m
iand b
ithe deviation ratio obtained after being respectively each sample one-variable linear regression and translational movement;
D is by the data after process, and the TVB-N content data recorded with Micro-kjoldahl method are as modeling data, set up TVB-N content detection model by partial least square method;
2) verify: the verification sample getting known TVB-N content, detect with near infrared spectrometer after verification sample is carried out pre-service, obtain near infrared light spectrogram, with the near infrared light spectrogram of the model analysis verification sample established, calculate the model inspection value of TVB-N content in verification sample, the model inspection value of TVB-N content and given value in comparatively validate sample, both requirements relative deviation is less than 5%;
3) testing sample analysis: get testing sample and carry out, detect with near infrared spectrometer after carrying out pre-service, obtain near infrared light spectrogram, with the near infrared light spectrogram of the model analysis testing sample established, calculate TVB-N content in testing sample, evaluate hairtail freshness.
In hairtail, TVB-N content is the important indicator judging hairtail degree of spoilage, is often used to the freshness detecting hairtail.Along with the prolongation of hairtail holding time, the protein in hairtail, under the effect of enzyme and bacterium, occurs decompose and produce the alkaline nitrogenous thing such as ammonia (NH) and amine (R-NH :), is total volatile basic nitrogen.By detecting TVB-N content in hairtail, hairtail freshness can be evaluated.TVB-N content is higher, and hairtail freshness is poorer.Cleaning Principle of the present invention is: fresh hairtail is with the prolongation of cold preservation time, and quality changes, and in body, TVB-N content increases gradually.And frequency multiplication, sum of fundamental frequencies and difference frequency absorption band that the spectral absorbance bands of near-infrared region is chemical bond (mainly CH, OH, NH) that in organic substance, energy is higher to be absorbed at middle infrared spectral region fundamental frequency are formed by stacking.Near infrared spectrum detection is carried out to hairtail, from the near infrared light spectrogram obtained, protein and amino acid composition information thereof in hairtail can be reflected.So carry out data analysis and process to the near infrared light spectrogram of multiple sample, add the TVB-N content data of these samples, use partial least square method can set up a TVB-N content detection model.After model establishes, only need the near infrared spectrum detecting unknown sample, just calculate the TVB-N content of unknown sample by model analysis near infrared spectrum, thus evaluate the freshness of hairtail.Compared to traditional chemical measure, as prior art: Micro-kjoldahl method, near infrared spectrum detect detection time short, analysis speed fast, without chemical contamination.
Different sample near infrared spectrum curves shift is more serious, the absorption peak of frequency multiplication, sum of fundamental frequencies and difference frequency is wider and overlapped simultaneously, understand spectrogram more difficult, need to carry out data processing to spectrum, derivative processing is carried out near infrared original spectrum and can eliminate the impact that the factors such as baseline wander bring, improve the resolution of spectrum.Linear regression is carried out near infrared original spectrum and corrects the noise can removed the mirror-reflection of sample in near-infrared diffuse reflection spectrum and unevenness and cause, eliminate baseline translation and the shift phenomenon of diffuse reflection spectrum.
As preferably, the evaluation criterion of hairtail freshness is: when the TVB-N content measured is less than or equal to 13mg/100g, be one-level freshness; When the TVB-N content measured is 13 ~ 25mg/100g, it is secondary freshness; When the TVB-N content measured is more than or equal to 25mg/100g, be three grades of freshnesss.According to Ministry of Agriculture, Animal Husbandry and Fisheries of People's Republic of China (PRC) ministerial standard SC-T3102-1984 " fresh hairtail " standard, TVB-N content is the physical and chemical index always evaluating hairtail grade, primes≤13mg/100g, seconds≤25mg/100g.
As preferably, near infrared spectrometer adopts vehicle mounted portable near infrared detection instrument, and detection mode is directly contact pretreated sample and compacting post-sampling with the fibre-optical probe of vehicle mounted portable near infrared detection instrument.Vehicle mounted portable near infrared detection instrument can carry with car, facilitates testing staff to go out Site Detection.It is sampled by fibre-optical probe, and during for preventing from detecting, air interference, need use fibre-optical probe compacting sample.
As preferably, the preprocess method of sample, for sample is carried out freeze drying, then grinds, and forms dry powder, dry powder is crossed 60 mesh sieves.Freeze drying, for removing most moisture in sample, is beneficial near infrared spectrum and detects.
As preferably, the sweep limit that near infrared spectrometer detects is 4000 ~ 12000 cm
-1, resolution is 4cm
-1, scanning times is 32.At 4000 ~ 12000cm
-1in scope, its dominant absorption material is protein, Fat and moisture.
As preferably, the modeling data described in steps d adopts wavelength band to be 7502.3 ~ 4246.8cm
-1data.The spectrum range selected during Modling model, comprise sample for detecting the best information amount of principal ingredient, this wavelength band mainly reflects protein and amino acid composition information thereof in hairtail.The accuracy of institute's established model is improved with this data modeling.
As preferably, the TVB-N content detection model preference pattern dimension set up in steps d is 8.The precision of model depends on selected number of principal components, i.e. model dimension.Dimension is too small, spectral information effectively can not be utilized to cause matching not enough, and make modeling effect undesirable, cannot explain its complete characteristic; Dimension is excessive, will cause the increase of the overfitting of model, background interference and noise, the reduction of model calculation speed, thus not reach the requirement improving model.Experimentally data compare, and preference pattern dimension is 8.
As preferably, sample near infrared spectrometer detects, replication three times, gets the final near infrared light spectrogram of averaged spectrum as sample of three scannings.Eliminate accidental error, improve detection accuracy.
Accompanying drawing explanation
Fig. 1 is the near infrared light spectrogram that in the present invention, detection 0 DEG C of hairtail sample (0 ~ 8d) obtains.
Fig. 2 is the near infrared light spectrogram of 0 DEG C of hairtail sample (0 ~ 8d) in the present invention after data processing.
Fig. 3 adopts modeler model dimension of the present invention to the impact effect figure of model.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Fresh hairtail in the present embodiment is purchased from Zhejiang Prov Xingye Group Co., Ltd.Select body surface be rich in gloss, the cheek be scarlet, mucus is transparent, eyeball is full, cornea fish body clearly.
In the present embodiment near infrared spectrometer adopt Mat rix-F type near infrared spectrometer (German Bruker company), be vehicle mounted portable near infrared detection instrument.
In the present embodiment, the evaluation criterion of hairtail freshness is with reference to Ministry of Agriculture, Animal Husbandry and Fisheries of People's Republic of China (PRC) ministerial standard SC-T3102-1984 " fresh hairtail " standard, specific as follows: when the TVB-N content measured is less than or equal to 13mg/100g, to be one-level freshness; When the TVB-N content measured is 13 ~ 25mg/100g, it is secondary freshness; When the TVB-N content measured is more than or equal to 25mg/100g, be three grades of freshnesss.
Get fresh hairtail stored refrigerated 0,1,2,3,4,5,6,7,8 day at 0 DEG C.Every day, interval took out hairtail 1 ~ 2 for 5 hours as sample, and each sample divides A, B two parts.
A sample, after taking-up, measures TVB-N content in sample according to Micro-kjoldahl method, obtains sample TVB-N content, and replicate determination three times, as given value.
B sample takes out 5.0 grams, puts into freeze drier, dry 10 hours, becomes flake, removes moisture in sample.Then thin slice is ground, in dry powdered form, after crossing 60 mesh sieves, with the fibre-optical probe of near infrared detection instrument, contact sample and compacting, select sweep limit to be 4000 ~ 12000 cm
-1, resolution is 4cm
-1, scanning times is 32, each sample replication three times and each all scanning backgrounds, gets the near infrared light spectrogram of averaged spectrum as sample of three scannings.The near infrared light spectrogram of all samples in 0 ~ 8 day is collected in after on a spectrogram, the results are shown in Figure 1.Visible in Fig. 1,8418cm
-1with 6603 cm
-1there is stronger absorption peak, represent C-H sum of fundamental frequencies and the absorption of N-H frequency multiplication respectively; 4595cm
-1be the sum of fundamental frequencies peak of primary amine and tertiary amine stretching vibration, mainly reflect protein and amino acid composition information thereof in hairtail; 5772cm
-1the first overtone peak of methylene C-H stretching vibration, 5138cm
-1for the secondary frequency multiplication peak of C=O stretching vibration, 4354cm
-1for the sum of fundamental frequencies peak of methylene C-H stretching vibration and flexural vibrations, be mainly the absorption peak of fatty material.
Meanwhile, observe Fig. 1 and also find, the curves shift of spectrogram is comparatively serious, and the absorption peak of frequency multiplication, sum of fundamental frequencies and difference frequency is wider and overlapped simultaneously.Therefore, with OPUS/QUANT-2 software, data processing is carried out to spectrum, first first order derivative process is carried out to spectrum, then do following computing:
1. averaged spectrum is calculated:
2. one-variable linear regression:
3. calculate and correct spectrum:
In formula: A
ifor single sample spectrum vector,
for the averaged spectrum vector of all samples, n is sample number, m
iand b
ithe deviation ratio obtained after being respectively each sample one-variable linear regression and translational movement;
After data processing, obtain the data after a series of process.By the data convert after process near infrared light spectrogram, obtain Fig. 2.Curves shift phenomenon in spectrogram obviously improves.Derivative processing is carried out near infrared original spectrum and can eliminate the impact that the factors such as baseline wander bring, improve the resolution of spectrum.Linear regression is carried out near infrared original spectrum and corrects the noise can removed the mirror-reflection of sample in near-infrared diffuse reflection spectrum and unevenness and cause, eliminate baseline translation and the shift phenomenon of diffuse reflection spectrum.
Then by the data after process, and TVB-N content in the sample measured with Micro-kjoldahl method, namely given value is as modeling data, uses partial least square method Modling model, with coefficients R in model
2, cross-validation root-mean-square error RMSECV is the overall target of evaluation model accuracy.R
2more illustrate that the predicted value of model is more accurate close to 100, RMSECV is less shows that model prediction accuracy is higher.And with coefficients R
2, cross-validation root-mean-square error RMSECV is that evaluation index carrys out Optimized model, improves model accuracy.
Optimization Modeling one, selects best band scope.
Four different spectral band intervals are selected to be respectively 11996 ~ 4246.8cm during Modling model
-1, 7502.3 ~ 4246.8cm
-1, 7502.3 ~ 5446.4cm
-1, 6102.1 ~ 4246.8cm
-1, carry out modeling.Result table 1:
Select modeling scope 7502.3 ~ 4246.8cm
-1, coefficient of determination R
2maximum, cross-validation root-mean-square error RMSECV is minimum.
Optimization Modeling two, selects best model dimension.
Selection Model dimension 1 ~ 10 is analyzed, and the results are shown in Figure 3, and predicted root mean square error RMSECV reflects the stability of model to dimension (Rank) mapping, coefficient of determination R
2dimension (Rank) mapping is complemented each other with RMSECV, along with the increase of dimension, coefficient of determination R
2improve, predicted root mean square error RMSECV reduces simultaneously.Visible dimension has impact to model quality, selects coefficient of determination R respectively
2the dimension that maximum and root-mean-square error RMSECV is minimum.As can be seen from Figure 3, selected model dimension is 8.
Model is verified model after setting up.Get the verification sample of 4 known TVB-N contents, measure the near infrared spectrum of verification sample, go out the TVB-N content of sample according to the model inspection set up.Comparison model detected value and given value, as shown in table 2,
In table, 4 groups of verification samples, relative deviation is all less than 5%, illustrates that model inspection value is accurate.
With the hairtail of the unknown freshness of the model inspection established.Get 5.0g as testing sample, put into freeze drier, dry 10 hours, grind in dry powdered form, then cross 60 mesh sieves, with the fibre-optical probe of near infrared detection instrument, contact sample and compacting, select sweep limit to be 4000 ~ 12000 cm
-1, resolution is 4cm
-1scanning times is 32, each sample replication three times and each all scanning backgrounds, get the near infrared light spectrogram of averaged spectrum as sample of three scannings, with the near infrared light spectrogram of the model analysis testing sample established, calculating TVB-N content in testing sample is 16.93 mg/100g, is within the scope of 13 ~ 25mg/100g, is evaluated as secondary freshness.
Claims (6)
1. near-infrared spectrum technique detects a method for hairtail freshness fast, it is characterized in that, step is as follows:
1) Modling model:
A gets the refrigeration of fresh hairtail, regularly takes out part hairtail as sample with cold preservation time change;
The sample that b takes out divides two parts, and a Micro-kjoldahl method that adopts measures TVB-N content in sample, detects, detect the near infrared light spectrogram obtaining sample after another increment product carry out pre-service with near infrared spectrometer;
The near infrared light spectrogram of c to sample is for data processing, and obtains the data after processing, data processing method: first carry out first order derivative process to spectrum, then does following computing:
1. averaged spectrum is calculated:
2. one-variable linear regression:
3. calculate and correct spectrum:
In formula: A
ifor single sample spectrum vector,
for the averaged spectrum vector of all samples, n is sample number, m
iand b
ithe deviation ratio obtained after being respectively each sample one-variable linear regression and translational movement;
D is by the data after process, and the TVB-N content data recorded with Micro-kjoldahl method are as modeling data, set up TVB-N content detection model by partial least square method;
2) verify: the verification sample getting known TVB-N content, detect with near infrared spectrometer after verification sample is carried out pre-service, obtain near infrared light spectrogram, with the near infrared light spectrogram of the model analysis verification sample established, calculate the model inspection value of TVB-N content in verification sample, the model inspection value of TVB-N content and given value in comparatively validate sample, both requirements relative deviation is less than 5%, wherein the preprocess method of sample is for carry out freeze drying by sample, then grind, form dry powder, dry powder is crossed 60 mesh sieves,
3) testing sample analysis: get testing sample and carry out, detect with near infrared spectrometer after carrying out pre-service, obtain near infrared light spectrogram, with the near infrared light spectrogram of the model analysis testing sample established, calculate TVB-N content in testing sample, evaluate hairtail freshness.
2. a kind of near-infrared spectrum technique according to claim 1 detects the method for hairtail freshness fast, it is characterized in that, near infrared spectrometer adopts vehicle mounted portable near infrared detection instrument, and detection mode is directly contact pretreated sample and compacting post-sampling with the fibre-optical probe of vehicle mounted portable near infrared detection instrument.
3. a kind of near-infrared spectrum technique according to claim 1 and 2 detects the method for hairtail freshness fast, it is characterized in that, the sweep limit that near infrared spectrometer detects is 4000 ~ 12000 cm
-1, resolution is 4cm
-1, scanning times is 32.
4. a kind of near-infrared spectrum technique according to claim 1 and 2 detects the method for hairtail freshness fast, it is characterized in that, the modeling data described in steps d adopts wavelength band to be 7502.3 ~ 4246.8cm
-1data.
5. a kind of near-infrared spectrum technique according to claim 1 and 2 detects the method for hairtail freshness fast, it is characterized in that, the TVB-N content detection model preference pattern dimension set up in steps d is 8.
6. a kind of near-infrared spectrum technique according to claim 1 and 2 detects the method for hairtail freshness fast, it is characterized in that, sample near infrared spectrometer detects, replication three times, gets the final near infrared light spectrogram of averaged spectrum as sample of three scannings.
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| CN111521580A (en) * | 2020-06-16 | 2020-08-11 | 海南大学 | Fillet freshness detection method based on portable near-infrared spectrometer |
| CN112213281A (en) * | 2020-11-16 | 2021-01-12 | 湖北省农业科学院农产品加工与核农技术研究所 | Comprehensive evaluation method for rapidly determining freshness of freshwater fish based on transmission near infrared spectrum |
| CN113791049B (en) * | 2021-08-30 | 2023-02-28 | 华中农业大学 | Method for rapidly detecting freshness of chilled duck meat by combining NIRS and CV |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4980294A (en) * | 1989-09-01 | 1990-12-25 | National Research Council Of Canada/Conseil National De Recherches Du Canada | Method for testing the freshness of fish |
| CN101059424A (en) * | 2007-05-22 | 2007-10-24 | 浙江大学 | Multiple spectrum meat freshness artificial intelligence measurement method and system |
| JP2008089529A (en) * | 2006-10-05 | 2008-04-17 | Fisheries Research Agency | Nondestructive measurement method of frozen ground fish meat component by near infrared analysis |
| CN102353644A (en) * | 2011-06-30 | 2012-02-15 | 上海海洋大学 | Rapid near infrared spectroscopy method for simultaneously detecting moisture content and protein content of Trichiurus japonicus surimi |
| CN102507459A (en) * | 2011-11-23 | 2012-06-20 | 中国农业大学 | Method and system for quick lossless evaluation on freshness of fresh beef |
| CN102636451A (en) * | 2012-04-24 | 2012-08-15 | 上海海洋大学 | Method for fast determination of phosphate content in hairtail surimi and fish paste |
| CN103063600A (en) * | 2012-09-19 | 2013-04-24 | 浙江省海洋开发研究院 | Fourier transform infrared spectroscopy-based method for detecting quality of trichiurus haumela |
-
2013
- 2013-06-19 CN CN201310243424.6A patent/CN103424374B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4980294A (en) * | 1989-09-01 | 1990-12-25 | National Research Council Of Canada/Conseil National De Recherches Du Canada | Method for testing the freshness of fish |
| JP2008089529A (en) * | 2006-10-05 | 2008-04-17 | Fisheries Research Agency | Nondestructive measurement method of frozen ground fish meat component by near infrared analysis |
| CN101059424A (en) * | 2007-05-22 | 2007-10-24 | 浙江大学 | Multiple spectrum meat freshness artificial intelligence measurement method and system |
| CN102353644A (en) * | 2011-06-30 | 2012-02-15 | 上海海洋大学 | Rapid near infrared spectroscopy method for simultaneously detecting moisture content and protein content of Trichiurus japonicus surimi |
| CN102507459A (en) * | 2011-11-23 | 2012-06-20 | 中国农业大学 | Method and system for quick lossless evaluation on freshness of fresh beef |
| CN102636451A (en) * | 2012-04-24 | 2012-08-15 | 上海海洋大学 | Method for fast determination of phosphate content in hairtail surimi and fish paste |
| CN103063600A (en) * | 2012-09-19 | 2013-04-24 | 浙江省海洋开发研究院 | Fourier transform infrared spectroscopy-based method for detecting quality of trichiurus haumela |
Non-Patent Citations (5)
| Title |
|---|
| Automatic freshness assessment of cod (Gadus morhua) fillets by Vis/Nir spectroscopy;Agnar H. Sivertsen et al.;《Journal of Food Engineering》;20111111;第103卷(第3期);317-323 * |
| 基于近红外光谱技术的带鱼新鲜度检测研究;张玉华等;《食品工业科技》;20131001;第34卷(第19期);281-286 * |
| 基于近红外光谱的大黄鱼新鲜度评价模型;徐富斌等;《食品安全质量检测学报》;20121231;第3卷(第6期);644-648 * |
| 近红外光谱技术快速检测带鱼新鲜度的研究;孟志娟等;《食品科技》;20131231;第38卷(第12期);294-298 * |
| 鱼体新鲜度近红外光谱检测方法的比较研究;程旎等;《食品安全质量检测学报》;20130430;第4卷(第2期);427-432 * |
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