CN111965135A - Method for rapidly determining content of potato whole flour in noodles based on near infrared spectrum - Google Patents
Method for rapidly determining content of potato whole flour in noodles based on near infrared spectrum Download PDFInfo
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- 244000061456 Solanum tuberosum Species 0.000 title claims abstract description 52
- 235000002595 Solanum tuberosum Nutrition 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 28
- 235000012149 noodles Nutrition 0.000 title claims abstract description 28
- 235000013312 flour Nutrition 0.000 title claims abstract description 27
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 24
- 238000001228 spectrum Methods 0.000 claims abstract description 29
- 229920001592 potato starch Polymers 0.000 claims abstract description 9
- 239000000843 powder Substances 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 53
- 238000012937 correction Methods 0.000 claims description 17
- 238000012549 training Methods 0.000 claims description 16
- 230000009466 transformation Effects 0.000 claims description 11
- 238000012795 verification Methods 0.000 claims description 7
- 238000009499 grossing Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 6
- 230000002452 interceptive effect Effects 0.000 claims description 5
- 238000002790 cross-validation Methods 0.000 claims description 3
- 238000005303 weighing Methods 0.000 claims description 3
- 238000001816 cooling Methods 0.000 claims description 2
- 238000002156 mixing Methods 0.000 claims description 2
- 238000004497 NIR spectroscopy Methods 0.000 claims 4
- 235000013305 food Nutrition 0.000 description 14
- 235000012015 potatoes Nutrition 0.000 description 13
- 238000007781 pre-processing Methods 0.000 description 7
- 238000001514 detection method Methods 0.000 description 6
- 238000012628 principal component regression Methods 0.000 description 5
- 230000003595 spectral effect Effects 0.000 description 5
- 240000007594 Oryza sativa Species 0.000 description 3
- 235000007164 Oryza sativa Nutrition 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 235000009566 rice Nutrition 0.000 description 3
- 108010068370 Glutens Proteins 0.000 description 2
- 244000017020 Ipomoea batatas Species 0.000 description 2
- 235000002678 Ipomoea batatas Nutrition 0.000 description 2
- 235000013339 cereals Nutrition 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 235000021312 gluten Nutrition 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 235000013606 potato chips Nutrition 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000003756 stirring Methods 0.000 description 2
- 238000012356 Product development Methods 0.000 description 1
- 240000004808 Saccharomyces cerevisiae Species 0.000 description 1
- 229920002472 Starch Polymers 0.000 description 1
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 240000008042 Zea mays Species 0.000 description 1
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 1
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 235000008429 bread Nutrition 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 235000005822 corn Nutrition 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000011868 grain product Nutrition 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 235000012054 meals Nutrition 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000011812 mixed powder Substances 0.000 description 1
- 238000000643 oven drying Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 239000003755 preservative agent Substances 0.000 description 1
- 230000002335 preservative effect Effects 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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Abstract
The invention discloses a method for rapidly determining the content of whole potato flour in noodles by using near infrared spectrum, which comprises the following steps: collecting near infrared spectra of noodles containing different whole flour proportions; carrying out spectrum pretreatment; establishing a prediction model; predicting the content of the whole potato powder in the unknown sample through the model; the method for rapidly measuring the potato whole flour in the noodles specifically comprises the steps of establishing a prediction model by a partial least square method or a principal component method, and selecting a spectrum interval of 10000-4000 cm‑1(ii) a The method is simple to operate, and can be used for rapidly and nondestructively detecting the content of the potato whole powder in the flour product.
Description
Technical Field
The invention relates to the field of food detection, and in particular relates to a method for rapidly determining the content of whole potato powder in noodles based on near infrared spectrum.
Background
Potatoes are planted in almost all latitudes as the third major food crop in the world, and currently, the number of countries planting potatoes in the world reaches 161. Since 1986, the potato planting area in China has rapidly increased, and in 2008, the potato planting area reaches 7000 ten thousand acres, the yield exceeds 7000 ten thousand t, which accounts for more than 20% of the total yield of potatoes in the world, and the potato planting area and the total yield both live at the first place in the world. The annual fresh sweet potato production reaches more than 7000 ten thousand t, most of fresh sweet potatoes depend on domestic market digestion, and the export proportion only accounts for about 0.5 percent. The export product contains more than 80% of fresh potatoes, and the main export countries are the surrounding countries and the southeast Asia region. In China, 30% of fresh potatoes in the consumption structure of the potatoes are directly eaten, 38% of the fresh potatoes are reserved for seeds and feed, 22% of the fresh potatoes are used as processing raw materials, and 10% of the fresh potatoes are used for other purposes in links of storage, processing, transportation and sale and the like. More than 60 percent of the 22 percent of the processing proportion belongs to small-scale processing and self-use processing of farmers, and the total yield of the industrial processed products such as potato fine starch, whole flour, frozen potato chips, potato chips and the like which are really in a certain scale is less than 10 percent. It can be seen that the current weakness in the potato industry is the processing and consumption link. Since fresh potatoes are not conducive to preservation, they are processed to produce whole potatoes which can be preserved for a long period of time. Meanwhile, the potato whole flour can be made into noodles, steamed stuffed buns, noodles, bread and other foods, and becomes a staple food for common people with three meals a day. The strategy of making potato staple food with the initiative of the research and study on the strategy of developing potato staple food and the strategy of making potato staple food actively promoted by the department of agriculture. So far, the potatoes enter an innovative stage of processing and researching staple grain products, and the aim is to enable the potatoes to gradually become the fourth staple grain crop in China after rice, wheat and corn.
In the research and development products of potato staple food grain, the products comprise noodles, rice flour, compound rice and the like, and the proportion of the whole potato flour can be improved by measures such as formula optimization and the like. The price of the high-proportion potato whole powder staple food is far higher than that of the low-proportion staple food product, and a method and a system for rapidly detecting the content of the potato whole powder in the potato staple food product are lacked in the market. At present, no report is found on a detection method for the content of the whole potato powder in a potato staple food product. This undoubtedly poses challenges to market regulation and correct guidance of human consumption in the relevant sector. Therefore, a rapid and nondestructive determination method for the content of the whole potato flour in the potato staple food product is urgently needed in both product development and market supervision.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for rapidly determining the content of the whole potato flour in the noodles based on the near infrared spectrum is simple and easy to implement, has good accuracy and does not damage a sample.
One of the purposes of the invention is realized by the following technical scheme: the method for rapidly determining the content of the whole potato flour in the noodles based on the near infrared spectrum comprises the following steps:
1) collecting a near-infrared spectrogram of a model sample;
2) performing spectrum pretreatment on the near infrared spectrum of the sample;
3) establishing a data processing model by using the obtained preprocessed near infrared spectrum;
4) setting a spectrum processing interval and a main factor number of a data processing model;
5) and processing the pre-processed near infrared spectrum to be detected through a data processing model to obtain a measured value of the whole potato powder content.
The data processing model of step 4) and step 5) is determined by the following steps:
A) collecting the near infrared spectrum of the correction set sample, and performing spectrum pretreatment on the near infrared spectrum;
B) obtaining a data processing model by setting a training spectrum processing interval and a training main factor number of a training data processing model;
C) obtaining interactive verification correlation coefficient R of preset correction set modelC 2Predicting the correlation coefficient RCV 2Interactive verification error root mean square and prediction error root mean square;
D) selecting interactive verification correlation coefficient RC 2And a predictive correlation coefficient RCV 2When the maximum error is maximum and the cross validation error root mean square and the prediction error root mean square are minimum, the corresponding training data processing model is used as a data processing model for processing the sample to be tested; taking a training spectrum processing interval of the corresponding training data processing model as a spectrum processing interval; the number of training main factors of the corresponding training data processing model is used as the number of main factors.
The data processing model in the steps 4) and 5) is as follows: deflectionLeast square PLS or principal component PCR correction model; the spectrum processing interval in the step 4) is 10000-4000 cm-1。
The calibration set sample in the step A) is realized by the following steps:
I) weighing flour and potato flour in a preset proportion;
II) wherein the mixing mass ratio of the flour to the potato flour is 100: 0, 95: 5, 90: 10, 85: 15, 80: 20, 75: 25 and 70: 30 respectively;
III) in the flour, the mass ratio of the medium gluten flour to the high gluten flour is 1: 1;
IV) adding yeast accounting for 1 percent of the mass of the flour, 0.4 percent of improver and 8.8 to 11 percent of water;
v) cooling the flour and the noodles containing the potato whole flour at room temperature to be tested.
The spectrum pretreatment of the near infrared spectrum of the sample in the step 2) is specifically as follows: one or more of baseline correction, smoothing processing, vector normalization, multivariate scattering correction, standard normal variable transformation, first derivative transformation or second derivative transformation derivative processing is adopted.
The invention has the advantages that: the method takes the noodles as a research object, applies the near infrared spectrum technology and combines the chemometrics method to rapidly detect the content of the potato whole flour in the noodles, has simple operation, no damage to samples and short detection time, and combines the near infrared portable equipment, so that the model can be competent for the online detection task of the production end and the consumption end of the noodles containing the potato whole flour.
Detailed Description
The following will describe in detail preferred embodiments of the present invention; it should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
Example 1 of the invention: a method for quickly measuring the content of potato flakes in noodles based on near infrared spectrum is described in detail by taking noodles without potato flakes and with different addition amounts (10%, 15%, 20%, 25%, 30%) as samples, and specifically comprises the following steps:
materials: flour, inner mongolia hengfeng food industry; potato Whole flour, Yanbei potato industry development Co., Ltd, Zhang Jiakou.
Preparing noodles: weighing 500g of potato whole flour-wheat flour mixed powder at a certain ratio (10-30% of potato whole flour), adding appropriate amount of drinking water, stirring at low speed for 2-5min, and stirring at high speed for 10-15 min; sealing in aluminum basin with preservative film, fermenting in constant temperature and humidity incubator (20-25 deg.C) for 10-15min, rolling, adjusting the roller spacing of noodle machine to 3.5-1.2mm, rolling for 2 times, pressing into noodle strip with thickness of 1mm, cutting into 2mm wide noodles with cutter, cutting into 180mm long noodles, and oven drying at 60-70 deg.C.
And (3) spectral determination: adopting an Antaris near-infrared spectrometer of the Saimei Fei company (Thermo Fisher), and scanning the spectrum of the spectrometer within 10000-4000 cm-1Resolution of 4cm-1And the scanning times are 16 times, and a PbS detector adopts a quartz sample cup and a sample rotating platform to collect signals in a diffuse reflection mode.
Data processing and model building: and measuring by an instrument to obtain the near infrared spectrum of the calibration set sample. The near infrared spectrum is derived into JAMCP-DX format, and then introduced into The Unscamblebler 9.0, and The map is processed.
Model optimization: the spectrum preprocessing can effectively eliminate the noise introduced into the spectrum measurement data due to system errors and random errors, high-frequency noise is mixed in the high-dimensional spectrum data, and simultaneously, the spectrum signal baseline drift and baseline rotation caused by various random factors can also exist. In order to extract a spectral useful signal from a complex, overlapped and fluctuating background and obtain an analysis signal with high signal-to-noise ratio and low background interference, the following processing modes are compared: preprocessing (baseline correction, smoothing, vector normalization), Multivariate Scatter Correction (MSC), Standard normal transformation (SNV), first derivative transformation, and second derivative transformation. The optimization process includes the selection of a proper spectrum interval and a spectrum preprocessing mode, and the number of main factors used by a quantitative detection model. The model is established by Principal Component Regression (PCR) or Partial Least Squares (PLS).
And (3) evaluating a model: and evaluating the model in an interactive verification mode. Using the corrected standard deviation (RMSEC) and the coefficient of determination (R)C 2) A cross-validation standard deviation (RMSECV) and a coefficient of determination (R)CV 2) To assess the effectiveness of the model detection.
TABLE 1 influence of different spectral processing methods on the PCR modeling Effect
Note: the preprocessing comprises baseline correction, smoothing processing and vector normalization.
TABLE 2 influence of different spectral processing methods on PLS modeling Effect
Note: the preprocessing comprises baseline correction, smoothing processing and vector normalization.
Spectrum preprocessing and correction model establishment: the spectrum processing mode adopts preprocessing (baseline correction, smoothing and normalization), derivative transformation, multivariate scattering correction and standard normal variable transformation to improve the obtained original spectrum. The modeling method of regression analysis adopts two methods of PCR and PLS. Verifying the correlation coefficient R by comparisonC 2And the Root Mean Square Error (RMSEC) of mutual authentication, and the correlation R between the predicted value and the actual valueCV 2And root mean square error prediction (RMSECV) to determine the effectiveness of the spectral processing methods and modeling methods. As can be seen from tables 1 and 2, the original spectrum modeling was less effective. Performing second derivative processing, modeling by PLS, and verifying correlation coefficient Rv 2The maximum and prediction error Root Mean Square (RMSECV) are minimal, indicating that the model prediction is most accurate.
Claims (5)
1. A method for rapidly determining the content of potato flakes in noodles based on near infrared spectrum is characterized by comprising the following steps: the method comprises the following steps:
1) collecting a near-infrared spectrogram of a model sample;
2) performing spectrum pretreatment on the near infrared spectrum of the sample;
3) establishing a data processing model by using the obtained preprocessed near infrared spectrum;
4) setting a spectrum processing interval and a main factor number of a data processing model;
5) and processing the pre-processed near infrared spectrum to be detected through a data processing model to obtain a measured value of the whole potato powder content.
2. The method for rapidly determining the content of potato flakes in noodles based on near infrared spectroscopy according to claim 1, wherein the method comprises the following steps: the data processing model of step 4) and step 5) is determined by the following steps:
A) collecting the near infrared spectrum of the correction set sample, and performing spectrum pretreatment on the near infrared spectrum;
B) obtaining a data processing model by setting a training spectrum processing interval and a training main factor number of a training data processing model;
C) obtaining interactive verification correlation coefficient R of preset correction set modelC 2Predicting the correlation coefficient RCV 2Interactive verification error root mean square and prediction error root mean square;
D) selecting interactive verification correlation coefficient RC 2And a predictive correlation coefficient RCV 2When the maximum error is maximum and the cross validation error root mean square and the prediction error root mean square are minimum, the corresponding training data processing model is used as a data processing model for processing the sample to be tested; taking a training spectrum processing interval of the corresponding training data processing model as a spectrum processing interval; the number of training main factors of the corresponding training data processing model is used as the number of main factors.
3. The near infrared spectroscopy-based according to claim 1 or 2The method for rapidly measuring the content of the potato whole flour in the noodles is characterized by comprising the following steps: the data processing model in the steps 4) and 5) is as follows: partial least squares PLS or a principal component PCR correction model; the spectrum processing interval in the step 4) is 10000-4000 cm-1。
4. The method for rapidly determining the content of potato flakes in noodles based on near infrared spectroscopy according to claim 2, wherein the method comprises the following steps: the calibration set sample in the step A) is realized by the following steps:
I) weighing flour and potato flour in a preset proportion;
II) wherein the mixing mass ratio of the flour to the potato flour is 100: 0, 95: 5, 90: 10, 85: 15, 80: 20, 75: 25 and 70: 30 respectively;
III) cooling the flour and the noodles containing the potato whole flour at room temperature to be tested.
5. The method for rapidly determining the content of potato flakes in noodles based on near infrared spectroscopy according to claim 1, wherein the method comprises the following steps: the spectrum pretreatment of the near infrared spectrum of the sample in the step 2) is specifically as follows: one or more of baseline correction, smoothing processing, vector normalization, multivariate scattering correction, standard normal variable transformation, first derivative transformation or second derivative transformation derivative processing is adopted.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113030010A (en) * | 2021-03-11 | 2021-06-25 | 贵州省生物技术研究所(贵州省生物技术重点实验室、贵州省马铃薯研究所、贵州省食品加工研究所) | Near infrared spectrum characteristic wave number screening method based on step-by-step shortening of step length optimization |
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