CN112666038B - Method for representing moisture absorption process based on near infrared spectrum - Google Patents
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Abstract
The invention relates to a method for representing a moisture absorption process based on near infrared spectrum, which comprises the following steps: 1) Placing the dried sample in a constant temperature and humidity environment for moisture absorption, and drawing a moisture absorption curve; 2) Fitting a moisture absorption dynamic model of the sample according to moisture absorption data, and solving moisture absorption equilibrium time and equilibrium moisture absorption rate; 3) Collecting a near infrared spectrum of the sample at the time point in the step 1) of the moisture absorption process; 4) Preprocessing the near infrared spectrum to eliminate baseline drift; 5) Analyzing the spectral change of the characteristic wave band of the near infrared spectrum, and observing the characteristic peak state of the water by utilizing the second derivative; 6) Analyzing the characteristic wave band by adopting a principal component analysis method; 7) And (5) performing two-dimensional correlation spectral analysis in sections by using different stages of the moisture absorption process obtained in the step 6). The method discloses the adsorption mode and bonding action of water from the molecular level, explains the moisture absorption process more deeply, and provides reference for determining the production period, predicting the product stability and establishing a moisture-proof technology.
Description
Technical Field
The invention relates to a method for representing a moisture absorption process based on a near infrared spectrum.
Background
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art that is already known to a person of ordinary skill in the art.
Hygroscopicity is an expression of affinity between substance molecules and water vapor molecules, and is related to intrinsic factors such as hydrophilic groups, crystallinity, particle size, specific surface and the like of a substance, and is also influenced by environmental factors such as relative humidity, temperature, air flow rate and the like. When the hygroscopic substance is in contact with air, the problems of weight increase, blocking, color depth and the like are easily caused, and the product quality is seriously influenced.
At present, the analysis of hygroscopicity in the prior art is mainly a gravimetric analysis method, and the inventor finds that the existing hygroscopicity analysis method lacks understanding of the behavior mode of water molecules in the hygroscopicity process. The problem of moisture absorption is a common problem in production work, the process of analyzing moisture absorption of a substance is researched, and the method has important theoretical significance and application value for determining storage conditions, formulating a production process, selecting a packaging method, predicting product stability and the like.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a method for characterizing a moisture absorption process by using a near infrared spectrum, which reveals the adsorption mode and bonding action of water from a molecular level, explains the moisture absorption process more deeply and provides reference for determining a production period, predicting product stability and establishing a moisture-proof technology.
In order to achieve the purpose, the technical scheme of the invention is as follows:
in a first aspect of the present invention, there is provided a method for characterizing moisture absorption processes based on near infrared spectroscopy, comprising the steps of:
(1) Placing the dried sample in a constant temperature and humidity environment for moisture absorption, calculating the moisture absorption rate at different time points according to the mass change at different time points, and drawing a moisture absorption curve by taking the time as an abscissa and the moisture absorption rate as an ordinate;
(2) Fitting a moisture absorption dynamic model of the sample according to moisture absorption data, and solving moisture absorption equilibrium time and equilibrium moisture absorption rate;
(3) Collecting near infrared spectra of different time points in the sample moisture absorption process, wherein the time points correspond to the time points in the step (1);
(4) The near infrared spectrum is preprocessed, so that baseline drift is eliminated, and the influence caused by different particle sizes is eliminated, thereby improving the spectrum quality;
(5) Analyzing the spectral change of characteristic wave bands of water in the near infrared spectrum, wherein the spectral change comprises a first frequency doubling region of OH and a combined frequency region of OH, and observing the characteristic peak state of the water by utilizing a second derivative;
(6) Analyzing the selected characteristic wave band by adopting a Principal Component Analysis (PCA), and selecting principal components with the cumulative contribution rate of more than 99% for interpretation to obtain principal component information; according to the relation between the principal component score and the moisture absorption time, different stages of the moisture absorption process are distinguished by combining the load characteristics;
(7) Performing two-dimensional correlation spectrum analysis (2D-COS) in a segmented manner by using different stages of the moisture absorption process obtained in the step (6), and further exploring the slight change of water in the moisture absorption process; according to the change of peaks in the synchronous spectrum and the asynchronous spectrum under the moisture absorption disturbance, the adsorption behavior of water molecules in the moisture absorption process is revealed.
The specific embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a method for characterizing a moisture absorption process of a substance, wherein a moisture absorption dynamic model of a sample is fitted according to moisture absorption data, characteristic parameters, namely moisture absorption equilibrium time and equilibrium moisture absorption rate, which can visually and quantitatively characterize the moisture absorption capacity are calculated, the moisture absorption characteristics of the substance are objectively described, and data support is provided for the characterization of subsequent absorption behaviors.
The characterization method of the embodiment of the invention explains the moisture absorption behavior from the molecular level based on the near infrared spectrum, reveals the absorption mode and the bonding effect of water molecules in the moisture absorption process, further understands the moisture absorption process and provides reference for further research and use of the moisture absorption process.
The characterization method provided by the embodiment of the invention is simple to operate, can rapidly and nondestructively obtain the binding state of water molecules, and provides a new visual angle for establishing an evaluation method of a moisture absorption process.
The invention represents the moisture absorption process by utilizing the near infrared spectrum for the first time, and has important theoretical significance and application value for determining storage conditions, formulating production process, selecting packaging method, predicting product stability and the like.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a moisture absorption curve of stevioside in example 1 of the present invention;
FIG. 2 is a chart of the near infrared spectrum of stevia sugar absorbing process in example 1;
FIG. 2 (a) is the raw spectrum;
FIG. 2 (b) is a spectrum of the original spectrum corrected for multiple scattering;
FIG. 2 (c) is a partial enlarged view of the OH combined frequency region;
FIG. 2 (d) is the combined frequency region second derivative spectrum of OH;
FIG. 3 shows the scores and loadings of the first two principal components of the combined frequency domain of OH in example 1 of the present invention;
FIG. 3 (a) shows the first trend of the score of the principal component;
FIG. 3 (b) is a first principal component loading signature;
FIG. 3 (c) is a second trend of the principal component score;
FIG. 3 (d) is a second principal component loading signature;
FIG. 4 shows two-dimensional correlation synchronous spectrum and asynchronous spectrum of moisture absorption of stevioside in example 1 of the invention;
FIG. 4 (a 1) is a synchronous spectrum 1h before moisture absorption;
FIG. 4 (a 2) is the asynchronous spectrum 1h before moisture absorption;
FIG. 4 (b 1) is a synchronous spectrum of the middle 2h of moisture absorption;
FIG. 4 (b 2) is an asynchronous spectrum of the middle 2h of moisture absorption;
FIG. 4 (c 1) is the synchronous spectrum of the last 1h of moisture absorption;
FIG. 4 (c 2) is the asynchronous spectrum for the last 1h of moisture absorption;
FIG. 5 is a schematic view of the process of moisture absorption of a sample of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Near infrared spectroscopy (NIRS) is mainly frequency doubling and frequency combining absorption of organic hydrogen-containing groups, is widely used for researching hydrogen bonding hydration of various molecules, but has not been researched for spectrum characterization of a moisture absorption process.
In one embodiment of the present invention, a method for characterizing a moisture absorption process based on near infrared spectroscopy is provided, which comprises the following steps:
(1) Placing the dried sample in a constant temperature and humidity environment for moisture absorption, calculating the moisture absorption rate at different time points according to the mass change at different time points, and drawing a moisture absorption curve by taking the time as an abscissa and the moisture absorption rate as an ordinate;
(2) Fitting a moisture absorption dynamic model of the sample according to moisture absorption data, and solving moisture absorption equilibrium time and equilibrium moisture absorption rate;
(3) Collecting near infrared spectra of different time points in the sample moisture absorption process, wherein the time points correspond to the time points in the step (1);
(4) The near infrared spectrum is preprocessed, so that baseline drift is eliminated, and the influence caused by different particle sizes is eliminated, thereby improving the spectrum quality;
(5) Analyzing the spectral change of the characteristic waveband of the water in the near infrared spectrum, wherein the spectral change comprises a first frequency doubling region of OH and a combined frequency region of OH, and observing the characteristic peak state of the water by using a second derivative;
(6) Analyzing the selected characteristic wave band by adopting a Principal Component Analysis (PCA), and selecting principal components with the cumulative contribution rate of more than 99% for interpretation to obtain principal component information; according to the relation between the principal component score and the moisture absorption time, different stages of the moisture absorption process are distinguished by combining the load characteristics;
(7) And (4) performing two-dimensional correlation spectrum analysis (2D-COS) in a segmented manner by using different stages of the moisture absorption process obtained in the step (6), and further exploring the slight change of water in the moisture absorption process. According to the change of peaks in the synchronous spectrum and the asynchronous spectrum under the moisture absorption disturbance, the absorption behavior of water molecules in the moisture absorption process is revealed.
Preferably, in the step (1), the moisture absorption rate is the total mass M of the weighing bottle and the sample at different time points i Subtract the total mass M of the weighing flask and the initial sample 1 The obtained difference value and the precisely weighed initial drying weight loss sample mass M 0 The ratio of (a) to (b);
wherein, i =0, t, 2t, 3t \8230, nt min, the interval of i is t min;
preferably, in the step (1), the operation steps for obtaining the dried sample are as follows: drying the sample in a vacuum drying oven at 105 +/-5 ℃ for 5-7h, placing in a dryer for 30min, and cooling to constant weight;
the moisture absorption conditions are as follows: temperature 26 ± 1 ℃, humidity 40 ± 1%, time interval t =10min, lasting 4h.
Preferably, in step (2), the model fitting method includes: bi-exponential model, quadratic equation, higuchi model, zero order process, first order process, and Weibull model to determine the coefficient R 2 Evaluating the fitting effect of the model for the index, and screening and determining the optimal model;
further preferably, the hygroscopic kinetic model is obtained by fitting a quadratic curve equation.
Preferably, in step (3), the spectrum collection method is as follows: collecting near infrared diffuse reflection spectrum of sample by near infrared spectrometer using integrating sphere module, repeatedly collecting each sample for 3 times, and taking average lightThe spectrum is used as the original spectrum of a sample, air is used as reference, a background spectrum is collected once per hour, and the scanning range is 10000-4000cm -1 Resolution 8cm -1 Scan number 32 times, gain 2x;
further preferably, the near-infrared spectrometer in the step (3) adopts an Antaris II Fourier transform near-infrared spectrometer;
preferably, in the step (4), the preprocessing method is a Multiple scattering correction preprocessing (MSC).
Preferably, in the step (5), the characteristic wave band of the spectrum is a combined frequency region (1850-2050 nm) of OH, and characteristic peaks of surface water and bound water are extracted by using a second derivative;
the surface water is water molecules directly adsorbed on a substance, and the combined water is water molecules adsorbed on the surface water;
preferably, in the step (6), the cumulative contribution rate of the first two principal components is 99.94%, and the whole moisture absorption process can be divided into three areas, namely the first 1h, the middle 2h and the last 1h according to the score change of the second principal component and the result of the absorption equation;
preferably, in the step (7), the whole moisture absorption process is analyzed in three stages, the position of an autocorrelation peak in a synchronous spectrum is firstly red shifted and then unchanged, and the absorbed water is continuously increased and then reaches saturation; the surface water in the asynchronous spectrum always changes before the bound water, and the acting force of directly adsorbing water is stronger than that of indirectly adsorbing water.
In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific embodiments.
Example 1
The method disclosed by the invention is applied to the moisture absorption process characterization of the stevioside. In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific embodiments.
Firstly, putting a stevioside sample in a vacuum drying oven for drying for 6h at 105 ℃, taking out, putting the stevioside sample in a dryer for 30min, and cooling to constant weight. Precisely weigh 1g (M) 0 ) Putting the mixture into a flat weighing bottle, opening a weighing bottle cap, and placing the weighing bottle cap in a constant-temperature and constant-humidity environment to simulate an actual storage environment for absorbing moisture, wherein the temperature is 26 +/-1 ℃ and the humidity is 40 +/-1 percent, and the process lasts for 4 hours. The sample is precisely weighed every 10min (M) by weighing the bottle and the sample i ) And calculating the moisture absorption rate at different time points according to the mass change at the different time points. And drawing a moisture absorption curve by taking the time as an abscissa and the moisture absorption rate as an ordinate.
M 0 The initial loss on drying sample mass is precisely weighed;
M 1 the total mass of the weighing bottle and the initial sample is taken;
M i the total mass of the vial and the sample was weighed at different time points, i =0, 10, 20 \ 823030, 240min, with an i interval of 10min, lasting for 4h.
Moisture absorption curve of stevioside is shown in fig. 1, and the water content gradually increases with time and finally remains unchanged due to saturated adsorption. The moisture absorption curve rises steeply at the beginning, and the moisture absorption rate is high; then the curve rises gradually and smoothly, and the moisture absorption rate is slow; and finally gradually reach an equilibrium state.
In order to enable the moisture absorption data to be more visual and accurate, quadratic curve equation fitting is carried out on the moisture absorption data, and a moisture absorption dynamic model is determined. The resulting moisture absorption equation was F = at 2 +bt+c(a<0) Determining the coefficient R 2 The closer to 1, the better the model fitting effect is, and the stronger the moisture absorption curve adaptability is. The moisture absorption speed equation v = dF/dt =2at + b and the moisture absorption acceleration equation v' = dv/vt =2a are obtained by performing first derivation twice on the moisture absorption speed equation v = dF/dt =2at +. T =0,v at the beginning of moisture absorption 0 And the moisture absorption equilibrium time t' = -b/2a, and the moisture absorption kinetic model of the stevioside in the table 1 shows that the moisture absorption effect of the stevioside reaches equilibrium in 191min, and the equilibrium moisture content is 7.538%.
TABLE 1 moisture absorption kinetics model of stevia sugar
Then, an Antaris II Fourier transform near-infrared spectrometer is used, an integrating sphere module is used for collecting the near-infrared diffuse reflection spectrum of the moisture absorption sample, the sample is collected once every 10min (corresponding to a gravimetric method time point), 3 times of collection are repeated each time, the average spectrum is taken as the original spectrum of the sample, and spectrum collection parameters are set as follows: collecting background spectrum once per hour by using air as reference, wherein the scanning range is 10000-4000cm -1 (wavelength representation was used in this study) with a resolution of 8cm -1 Scan number 32 times, gain 2x.
In the near infrared region, OH has two characteristic absorption bands with peaks around 1420nm (first frequency doubling) and 1920nm (combined frequency), respectively, and these absorption bands are strong, especially in the combined frequency region of OH, whose exact position and width will vary slightly depending on the chemical and physical environment. Fig. 2 (a) is the original spectrum of the stevia sample during the moisture absorption process, and it can be seen that the spectral characteristics of the sample have significant changes under different moisture levels. Fig. 2 (b) shows the original spectrum after Multivariate Scatter Correction (MSC), which can effectively eliminate baseline drift and influence caused by different particle sizes, and the following data are analyzed based on the baseline drift and influence. Fig. 2 (c) is a partial enlarged view of the OH combination frequency region, the peak value of which increases with the moisture absorption time, showing the increase of moisture during the moisture absorption process. FIG. 2 (d) is the corresponding second derivative spectrum, 1911nm is mainly related to surface water, 1944nm is related to bound water, the inset is the graph of the change trend of the absorption intensity at 1911nm, the surface water peak value (absolute value) at 1h and 1911nm before moisture absorption is rapidly increased, and the change trend is not obvious later, which may mean that water molecules are rapidly adsorbed on the surface of stevioside at the beginning of moisture absorption; and then, the surface adsorption sites are reduced, the moisture absorption rate is obviously slowed, the adsorbed water molecules also have adsorption capacity, additional water molecules are additionally adsorbed on the surface at the same time to form bound water, and the total adsorption of the stevioside reaches saturation after the process lasts for 2h and 190min according to a moisture absorption curve, so that the moisture absorption amount is not increased any more.
In order to clarify the change of water molecules in the moisture absorption process of stevioside, PCA is carried out on the combined frequency region of OH, and the scores and the load characteristics of the first two main components of the spectrum are respectively shown in FIG. 3. The first principal component score trend is gradually increased and then becomes stable as shown in fig. 3 (a), and coincides with the moisture absorption curve, and the load characteristic thereof is a broad peak centered at 1921nm as shown in fig. 3 (b). The first principal component contribution rate is 99.28%, and the first principal component can be considered to be interpreted as a change in moisture content during moisture absorption. The second principal component score, as shown in FIG. 3 (c), has a tendency to decrease and then increase, and then to stabilize, and its load characteristic FIG. 3 (d) is dominated by a negative peak at 1905nm and a positive peak at 1944nm, and is characterized by surface water and bound water, respectively. The second main component contribution rate is 0.66%, the whole moisture absorption process can be divided into three areas, namely the first 1h, the middle 2h and the last 1h, the surface water at the beginning of moisture absorption is considered to be more, the bound water is increased along with the progress of moisture absorption, and finally the whole adsorption reaches the saturated structure and tends to be stable.
In order to further explore the water slight change in the moisture absorption process of stevioside, 2D-COS analysis is carried out on the basis of three stages of the moisture absorption process obtained by PCA. Fig. 4 shows the synchronous and asynchronous spectra of the hygroscopic 1h, the middle 2h and the last 1h, respectively, with the solid colored lines indicating positive signs and the dashed lines indicating negative signs. In FIG. 4 (a 1), the sync spectrum shows an autocorrelation peak around 1911nm, indicating the formation of surface water; in FIG. 4 (b 1), the autocorrelation peak appears at 1936nm, and the red shift of the autocorrelation peak indicates that more hydrogen bonds are formed, which represents more bound water is working; in fig. 4 (c 1), the autocorrelation peak position is unchanged, indicating saturation of adsorption. Meanwhile, negative cross peaks appear at 1885nm and 1936nm, which shows that the change trend of free water at 1885nm is opposite to that of bound water at 1936nm, the free water is continuously reduced along with the adsorption process, and the bound water is increased.
The sequence of spectral changes shown for the asynchronous spectra in fig. 4 (a 2) and (b 2) according to Noda's rule is as follows: 1911nm >; moisture absorption middle 2h,1944nm are formed by the cloth with the size of 1905nm, although the process has bound water, the water can form surface water with stevioside more easily, and the hydrogen bonding force of the surface water is stronger than that of the bound water. As adsorption proceeds, the surface adsorption sites become fewer and the adsorption rate becomes significantly slower. The asynchronous spectrum 4 (c 2) of the last 1h shows zero correlation intensity near the noise spectrum, which saturates the stevia whole adsorption.
The results show that the moisture absorption process of the substance can be represented by utilizing the near infrared spectrum, and the hydrogen bond effect of water in the process can be explained. For the stevioside sample, a moisture absorption process is just started, water molecules are rapidly adsorbed on the surface of the stevioside powder to form a monomolecular layer, and water at the stage is surface water; after the surface adsorption tends to be saturated, the adsorbed water molecules also have adsorption capacity, and extra water molecules are synchronously additionally adsorbed on the monomolecular layer to form the stabilizing effect of the combined water on a hydrogen bond network; and then, the whole moisture absorption of the stevioside reaches a saturated state, and the water content is kept balanced.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for characterizing a moisture absorption process based on near infrared spectroscopy is characterized by comprising the following steps:
(1) Placing the dried sample in a constant temperature and humidity environment for moisture absorption, calculating the moisture absorption rate at different time points according to the mass change at different time points, and drawing a moisture absorption curve by taking the time as an abscissa and the moisture absorption rate as an ordinate;
(2) Fitting a moisture absorption dynamic model of the sample according to moisture absorption data, and solving moisture absorption equilibrium time and equilibrium moisture absorption rate;
(3) Collecting near infrared spectra of different time points in the sample moisture absorption process, wherein the time points correspond to the time points in the step (1);
(4) The near infrared spectrum is preprocessed, so that baseline drift is eliminated, and the influence caused by different particle sizes is eliminated, thereby improving the spectrum quality;
(5) Analyzing the spectral change of characteristic wave bands of water in the near infrared spectrum, wherein the spectral change comprises a first frequency doubling region of OH and a combined frequency region of OH, and observing the characteristic peak state of the water by utilizing a second derivative;
(6) Analyzing the selected characteristic wave band by adopting a principal component analysis method, and selecting principal components with the accumulated contribution rate of more than 99% for interpretation to obtain principal component information; according to the relation between the principal component score and the moisture absorption time, different stages of the moisture absorption process are distinguished by combining the load characteristics;
(7) Performing two-dimensional correlation spectrum analysis in sections by using different stages of the moisture absorption process obtained in the step (6), and further exploring the slight change of water in the moisture absorption process; according to the change of peaks in the synchronous spectrum and the asynchronous spectrum under the moisture absorption disturbance, the absorption behavior of water molecules in the moisture absorption process is revealed;
in the step (1), the operation steps of obtaining the dry sample are as follows: drying the sample in a vacuum drying oven at 105 +/-5 ℃ for 5-7h, and placing the sample in a dryer for 30min to cool the sample to constant weight; the moisture absorption conditions are as follows: the temperature is 26 +/-1 ℃, the humidity is 40 +/-1%, the time interval t =10min, and the time interval lasts for 4h;
in the step (4), the pretreatment method is multivariate scattering correction pretreatment;
in the step (5), the characteristic wave band of the spectrum comprises a first frequency doubling region of OH and a combined frequency region of OH, and characteristic peaks of the surface water and the combined water are extracted by utilizing a second derivative.
2. The method for characterizing moisture absorption processes based on near infrared spectroscopy according to claim 1, wherein in step (1), the moisture absorption rate is a ratio of a difference obtained by subtracting the total mass M1 of the weighing bottle and the initial sample from the total mass Mi of the weighing bottle and the sample at different time points to a precisely-measured initial loss on drying sample mass M0;
wherein, i =0, t, 2t, 3t \8230, nt min, the interval of i is t min.
3. The method for characterizing moisture absorption processes based on near infrared spectroscopy as claimed in claim 1 wherein in step (2) the model fitting method is selected from the group consisting of: the method comprises the following steps of evaluating the fitting effect of a model by using a determination coefficient R2 as an index through a double exponential model, a quadratic curve equation, a Higuchi model, a zero-order process, a first-order process and a Weibull model, and screening and determining the optimal model.
4. The method for near infrared spectroscopy-based characterization of moisture absorption processes according to claim 3 wherein the moisture absorption kinetic model is a quadratic curve equation fit.
5. The method for characterizing moisture absorption process based on near infrared spectroscopy as claimed in claim 1, wherein in step (3), the spectrum is collected by the following method: collecting near-infrared diffuse reflection spectrum of sample by near-infrared spectrometer with integrating sphere module, repeatedly collecting each sample for 3 times, taking average spectrum as original spectrum of sample, collecting background spectrum once per hour with air as reference, and scanning range of 10000-4000cm -1 Resolution 8cm -1 Scan number 32 times, gain 2x.
6. The method for characterizing moisture absorption processes based on near infrared spectroscopy as claimed in claim 5 wherein the near infrared spectrometer of step (3) employs an Antaris II Fourier transform near infrared spectrometer.
7. A method for characterizing an hygroscopic process based on the near infrared spectrum as claimed in claim 1 characterized in that in step (5) the combined frequency region of OH is 1850-2050nm.
8. The method for characterizing moisture absorption process based on near infrared spectrum according to claim 1, wherein in the step (6), the cumulative contribution rate of the first two principal components is 99.94%, and the whole moisture absorption process can be divided into three regions, the first 1h, the middle 2h and the last 1h according to the score change of the second principal component and the result of the absorption equation.
9. The method for characterizing moisture absorption process based on near infrared spectrum according to claim 1, wherein in the step (7), the whole moisture absorption process is analyzed in three stages, the position of the autocorrelation peak in the synchronous spectrum is firstly red shifted and then unchanged, and the absorbed water is increased continuously and then reaches saturation; the surface water in the asynchronous spectrum always changes before the bound water, and the acting force of directly adsorbing water is stronger than that of indirectly adsorbing water.
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