CN110596038A - Method for rapidly determining starch content of sweet potatoes - Google Patents
Method for rapidly determining starch content of sweet potatoes Download PDFInfo
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- CN110596038A CN110596038A CN201910927606.2A CN201910927606A CN110596038A CN 110596038 A CN110596038 A CN 110596038A CN 201910927606 A CN201910927606 A CN 201910927606A CN 110596038 A CN110596038 A CN 110596038A
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- 244000017020 Ipomoea batatas Species 0.000 title claims abstract description 41
- 235000002678 Ipomoea batatas Nutrition 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 29
- 229920002472 Starch Polymers 0.000 title claims abstract description 14
- 235000019698 starch Nutrition 0.000 title claims abstract description 14
- 239000008107 starch Substances 0.000 title claims abstract description 14
- 229920000856 Amylose Polymers 0.000 claims abstract description 14
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 14
- 238000013178 mathematical model Methods 0.000 claims abstract description 11
- 238000002835 absorbance Methods 0.000 claims abstract description 7
- 238000000611 regression analysis Methods 0.000 claims abstract description 3
- 239000000126 substance Substances 0.000 claims description 10
- 238000002347 injection Methods 0.000 claims description 8
- 239000007924 injection Substances 0.000 claims description 8
- 238000012628 principal component regression Methods 0.000 claims description 6
- 238000001228 spectrum Methods 0.000 claims description 6
- 238000010238 partial least squares regression Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000001035 drying Methods 0.000 claims description 2
- 238000003333 near-infrared imaging Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 5
- 230000003595 spectral effect Effects 0.000 description 5
- 238000004497 NIR spectroscopy Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 238000009395 breeding Methods 0.000 description 3
- 230000001488 breeding effect Effects 0.000 description 3
- 238000000862 absorption spectrum Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 229920001592 potato starch Polymers 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 244000061456 Solanum tuberosum Species 0.000 description 1
- 235000002595 Solanum tuberosum Nutrition 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000012630 chemometric algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
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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
-
- 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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- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
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- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a method for rapidly determining starch content of sweet potatoes, which comprises the steps of pretreating a sweet potato sample, collecting near infrared spectrum data of the sweet potato sample by using a near infrared spectrometer, and determining amylose content of the corresponding sweet potato sample by using an absorbance method; inputting the near infrared spectrum data of the collected sweet potato block sample and the amylose content of the corresponding sweet potato block sample into chemometrics analysis software to perform regression analysis to evaluate the quality of various sample introduction modes and establish an optimal mathematical model; collecting the near infrared spectrum of an unknown sweet potato sample, and predicting the amylose content of the sample by utilizing an established mathematical model; and (5) optimizing a mathematical model. The near infrared method is adopted, the starch content of the sweet potato blocks can be rapidly identified, the identification time is shortened, the cost is reduced, professional knowledge is not required for testers, and the application is convenient; various data are collected for modeling, a mathematical model is continuously optimized, the robustness of the model is enhanced, and the data accuracy is higher.
Description
Technical Field
The invention relates to the technical field of sweet potato starch, in particular to a method for rapidly determining the content of sweet potato starch.
Background
Near Infrared (NIR) light has a wavelength range of about 780-2500 nm, and is electromagnetic waves between a visible region and a middle Infrared region, and forms a frequency doubling and frequency synthesis absorption spectrum of organic molecules through the action of X-H bonds with hydrogen-containing groups of the organic molecules in substances. According to the information characteristics of the near infrared absorption spectrum, such as the appearance position, the absorption intensity and the like, the composition is qualitatively and quantitatively analyzed by combining mathematical statistics. This technique requires more chemometric algorithms and software techniques than conventional analysis. With the development of computer technology, the deepening of chemometrics research and the increasing perfection of Near Infrared Spectroscopy instrument manufacturing technology, Near Infrared Spectroscopy (NIRS) analysis technology is developed dramatically. The product has the advantages of rapidness, no damage, environmental protection, etc., and can be widely used in the fields of agricultural products, food, chemistry, medicine, petroleum, etc.
In the breeding process of the quality of the sweet potatoes, the screening work of a large number of samples can be reduced by utilizing the near infrared technology for quantitative analysis, the breeding material and time are saved, the sweet potatoes with higher starch content are directly selected, and the requirement of modern breeding is met.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the technical defects and provide a method for rapidly determining the starch content of sweet potatoes, which is simple to use and rapid in measurement.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: comprises the following steps of (a) carrying out,
the method comprises the following steps: preprocessing a sweet potato sample, collecting near infrared spectrum data of the sweet potato sample by using a near infrared spectrometer after preprocessing, and simultaneously measuring the amylose content of the corresponding sweet potato sample by using an absorbance method;
step two: inputting the near infrared spectrum data of the collected sweet potato block sample and the amylose content (measured value by an absorbance method) of the corresponding sweet potato block sample into chemometrics analysis software Unscamblebler v10.1 to perform regression analysis to evaluate the quality of various sample introduction modes and establish an optimal mathematical model;
step three: collecting the near infrared spectrum of an unknown sweet potato sample, and predicting the amylose content of the sample by utilizing an established mathematical model;
step four: determination of amylose content for each prediction sample the established mathematical model was evaluated and refined.
Further, the pretreatment comprises manual shredding, shredding by a shredder (the section is 3mm multiplied by 3mm or 3mm multiplied by 7 mm), disc crosscutting, drying and sample injection, crushing and sample injection.
Further, in the second step, the near infrared spectrum data is processed, a relation is established between methods such as Multivariate Linear Regression (MLR), Principal Component Regression (PCR) and partial least squares regression (PLS) and a chemical value (AC), and an optimal scaling modeling method is selected by comprehensively evaluating the simplicity of a sample injection mode and the linear correlation between the spectrum data and the chemical value.
The invention has the beneficial effects that: the near infrared method is adopted, the starch content of the sweet potato blocks can be rapidly identified, the identification time is shortened, the cost is reduced, professional knowledge is not required for testers, and the application is convenient; various data are collected for modeling, a mathematical model is continuously optimized, the robustness of the model is enhanced, and the data accuracy is higher.
Drawings
FIG. 1 is an operation flow of a first step of the method for rapidly determining the starch content of sweet potatoes.
FIG. 2 is a near infrared spectrum of a fresh sample (upper) and a dried sample (lower) of sweet potato according to the method for rapidly determining the starch content of sweet potato.
FIG. 3 is a first derivative spectrogram of a fresh sweet potato sample obtained by the method for rapidly determining the starch content of sweet potatoes.
FIG. 4 is a diagram of the relevant parameters of the model after optimization of the method for rapidly determining the starch content of sweet potatoes.
Detailed Description
In order to make the content of the present invention more clearly understood, the technical solutions in the embodiments of the present invention are clearly and completely described below.
The first embodiment is as follows: in order to realize the rapid determination and analysis of the amylose content, the research firstly harvests the expanded potato blocks of 60 different strains under the greenhouse cultivation condition, and collects the near-infrared region spectral data and determines the amylose content according to the flow shown in figure 1. After various pretreatments are carried out on the spectral data (figure 2) collected by different pretreatment sample injection modes, a relation is established between a chemical value (AC) and methods such as Multivariate Linear Regression (MLR), Principal Component Regression (PCR) and partial least squares regression (PLS), and the optimal scaling modeling method is selected by comprehensively evaluating the simplicity of the sample injection modes and the linear correlation between the spectral data and the chemical value. Finally, after the spectral data directly fed after the manual shredding is subjected to first order derivation and interference signal filtering (fig. 3), an optimal primary calibration model can be established by using a Partial Least Squares (PLS), so that the method is used for analyzing subsequent samples and establishing a further optimized model.
FIG. 2 is a near infrared spectrum of fresh sweet potato sample (upper) and oven-dried sweet potato sample (lower): the ordinate is relative absorbance and the abscissa is near infrared wavelength, and the presence of a large number of water molecules in the fresh sample shows higher relative absorbance (stretching vibration of O — H bond is the main cause of occurrence of peak at 1440 nm).
FIG. 3 is a first derivative spectrogram of fresh sweet potato sample: the ordinate is the first derivative of the original near infrared spectrum (calculating the slope of the spectrum curve at each wavelength point), the abscissa is the wavelength of the near infrared light, and the slope of the curve is irrelevant to the baseline, so the first derivative spectrum data effectively eliminates the influence of the baseline translation in the measurement process.
When the model is used for analyzing 186 strains planted in the experimental field of Taian in Shandong, a part of supernormal samples exceeding the prediction range of the model are found, and the prediction precision is not enough and the error is too large. The chemical value (AC) of the part of the sample is measured according to the maintenance and optimization principle of the near infrared model, and the part of the sample is added into a calibration sample set to update the prediction model. And performing partial least squares regression on the first-order derivative spectrum according to the original model, wherein the updated calibration sample set comprises spectra and chemical value information of 49 greenhouse cultivation samples and 38 field cultivation samples for 87 samples in total, the optimized prediction model finally utilizes 83 sample information, the results of verification by taking samples not contained in 115 models as a test set are shown in the attached figure 4, and the paired two-tail T test value of the predicted value and the measured value is 0.002. The optimized model can realize the rapid qualitative and quantitative screening of a large number of samples, and lays a foundation for solving the problem of raw material quality control in the large-scale planting and industrialization of sweet potatoes by utilizing the near infrared technology.
FIG. 4 is a diagram of the correlation parameters of the optimized model: the calibration sample set comprises 83 sample spectral data and chemical measured values, the correlation coefficient between the amylose content and the regression principal component reaches 0.96, the mean square error of the calibration set is 5.75, and the fact that the system deviation (Bias) is small indicates that the model after calibration is stable.
The present invention and the embodiments thereof have been described above, but the description is not limited to the embodiments, but only one of the embodiments of the present invention, and the actual embodiments are not limited thereto. In conclusion, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A method for rapidly measuring the starch content of sweet potatoes is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
the method comprises the following steps: preprocessing a sweet potato sample, collecting near infrared spectrum data of the sweet potato sample by using a near infrared spectrometer after preprocessing, and simultaneously measuring the amylose content of the corresponding sweet potato sample by using an absorbance method;
step two: inputting the near infrared spectrum data of the collected sweet potato block sample and the amylose content (measured value by an absorbance method) of the corresponding sweet potato block sample into chemometrics analysis software Unscamblebler v10.1 to perform regression analysis to evaluate the quality of various sample introduction modes and establish an optimal mathematical model;
step three: collecting the near infrared spectrum of an unknown sweet potato sample, and predicting the amylose content of the sample by utilizing an established mathematical model;
step four: determination of amylose content for each prediction sample the established mathematical model was evaluated and refined.
2. The method for rapidly measuring the starch content of sweet potatoes according to claim 1, which is characterized in that: the pretreatment comprises manual shredding, shredding by a shredding machine (the section is 3mm multiplied by 3mm or 3mm multiplied by 7 mm), disc crosscutting, drying and sample injection, crushing and sample injection.
3. The method for rapidly measuring the starch content of sweet potatoes according to claim 1, which is characterized in that: and step two, processing the near infrared spectrum data, establishing a relation between methods such as Multivariate Linear Regression (MLR), Principal Component Regression (PCR), partial least squares regression (PLS) and the like and a chemical value (AC), and selecting an optimal scaling modeling method by comprehensively evaluating the simplicity of a sample injection mode and the linear correlation between the spectrum data and the chemical value.
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| CN201910927606.2A CN110596038A (en) | 2019-09-27 | 2019-09-27 | Method for rapidly determining starch content of sweet potatoes |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111024647A (en) * | 2020-01-08 | 2020-04-17 | 山东金璋隆祥智能科技有限责任公司 | Method for detecting moisture and protein content in dry starch based on GSA near infrared technology |
| CN111965135A (en) * | 2020-06-05 | 2020-11-20 | 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) | Method for rapidly determining content of potato whole flour in noodles based on near infrared spectrum |
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2019
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111024647A (en) * | 2020-01-08 | 2020-04-17 | 山东金璋隆祥智能科技有限责任公司 | Method for detecting moisture and protein content in dry starch based on GSA near infrared technology |
| CN111965135A (en) * | 2020-06-05 | 2020-11-20 | 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) | Method for rapidly determining content of potato whole flour in noodles based on near infrared spectrum |
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Application publication date: 20191220 |