CN114324233A - Near-infrared nondestructive online quality detection method and system for nutritional ingredients of agricultural products - Google Patents
Near-infrared nondestructive online quality detection method and system for nutritional ingredients of agricultural products Download PDFInfo
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Abstract
The application relates to a near-infrared nondestructive online quality detection method and system for nutritional ingredients of agricultural products, which are characterized in that sampling is carried out on a sample near-infrared spectrum acquisition point, and average spectrum data of the sampling point is acquired as predicted spectrum data; according to the predicted spectrum data, respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method to establish a prediction model, and acquiring component parameter data; and comparing the obtained component parameter data with the nutrient component prediction reference value, obtaining the variance and obtaining a quality detection result. The main parameters of the optimal models for different nutrient content by different spectrum pretreatment methods can be obtained, and nutrient analysis can be rapidly performed on agricultural products. In the embodiment of the application, sweet potatoes are taken as an example, different spectrum processing methods are utilized to simultaneously establish near infrared spectrum detection models of 6 nutrient components (moisture, reducing sugar, crude protein, cellulose, crude fat and mineral substances) in the sweet potatoes, and the detection regression coefficients of the optimal models of the nutrient components are all larger than 0.9.
Description
Technical Field
The disclosure relates to the technical field of intelligent agriculture, in particular to a near-infrared nondestructive online quality detection method and system for nutritional ingredients of agricultural products.
Background
With the development and progress of the agricultural product industry, the requirement on the quality of agricultural products is improved. How to rapidly and accurately obtain the nutrient component data of the agricultural products is a pipe fitting link for detecting the nutrient quality of the agricultural products.
The traditional method for detecting the nutrient components of the agricultural products mostly adopts a chemical measuring method to carry out chemical measurement on the components of the agricultural products, so as to calculate and obtain the nutrient component ratio, and further judge the quality of the agricultural products. The efficiency is low, the detection time is long, the sample can deteriorate due to overlong preservation time, the detection data and the identification effect are influenced, and the cost is increased. Therefore, it is necessary to provide a fast and accurate quality detection technique to solve this technical gap.
Disclosure of Invention
In view of the above, in order to solve the above technical problems, the present application discloses a near-infrared nondestructive online quality detection method and system for agricultural product nutritional components, which can realize rapid agricultural product nutritional component detection based on online near-infrared nondestructive detection and obtain a comparative analysis result.
According to one aspect of the application, a near-infrared nondestructive online quality detection method for nutritional ingredients of agricultural products is provided, and comprises the following steps:
s1, sample treatment: acquiring N samples, dividing each sample into two parts, and arranging a sample near infrared spectrum acquisition point on one part for near infrared spectrum acquisition of sample components;
s2, acquiring sample spectrum data: sampling near infrared spectrum collection points of the sample according to preset collection conditions, and obtaining average spectrum data of the sampling points as predicted spectrum data;
s3, establishing a prediction model: according to the predicted spectrum data, respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method to establish a prediction model, and acquiring component parameter data;
s4, quality comparison analysis: and comparing the obtained component parameter data with the nutrient component prediction reference value, obtaining the variance and obtaining a quality detection result.
In a possible implementation manner, optionally, in step S1, the method further includes:
measuring the chemical components of the other part of each sample to obtain a chemical measurement value;
arranging the chemical measurement values of the N samples from small to large, and screening the chemical measurement values into a verification set and a correction set according to preset screening conditions;
and processing the verification set and the correction set by using a two-dimensional principal component analysis chart and an analysis of variance chart to obtain a sample dispersion result.
In a possible implementation manner, optionally, in step S2, the preset acquisition condition includes:
presetting acquisition parameters: wave number range 12000cm-1-4000cm-1Number of scans 32, resolution 4cm-1(ii) a And
presetting acquisition frequency: background scanning was performed every 5 samples; and (4) parallelly measuring each sample near infrared spectrum acquisition point for three times, taking an average spectrum, and taking the average value of the average spectra of the two sample near infrared spectrum acquisition points as a final spectrum.
In a possible implementation manner, optionally, in step S3, after the building the prediction model by using the PCR modeling method and the PLS modeling method respectively, the method further includes:
obtaining RMSEP and R2Parameter values and according to the minimum of said RMSEP and R2Selecting a nutrient content optimal model from the prediction model according to the parameter values; wherein the content of the first and second substances,
in the formula:
is the predicted value of the ith sample in the prediction set sample; y ispiIs a chemical reference value for the ith sample in the prediction set of samples;
is a chemical reference average value of a prediction set sample; n is the number of prediction set samples;
establishing near-infrared nondestructive testing models of different nutrient components according to the nutrient component content optimal model; and the number of the first and second groups,
and calculating and obtaining measured values of different nutrient components according to the near-infrared nondestructive testing model.
In a possible implementation manner, optionally, after obtaining the optimal model of the nutrient content, the method further includes:
presetting a plurality of spectrum pretreatment methods;
obtaining the influence parameter values R of different spectrum preprocessing methods on the nutrient content optimal model based on the nutrient content optimal model respectively established by PCR and PLS2And RMSEP;
according to the smallest value of said influencing parameter R2And RMSEP, obtaining the optimal spectrum pretreatment method.
In a possible implementation manner, optionally, after obtaining the optimal spectrum preprocessing method, the method further includes:
obtaining the optimal spectrum pretreatment method of different nutrient components;
acquiring influence parameter values RMSEP of different nutrient contents on the nutrient content optimal model based on the nutrient content optimal models respectively established by PCR and PLS;
and acquiring optimal near-infrared nondestructive testing models corresponding to different nutrient components according to the minimum influence parameter value RMSEP.
In a possible implementation manner, optionally, the method further includes:
according to the optimal near-infrared nondestructive testing model, acquiring measured values of different nutrient components;
comparing the measured value with the prediction reference value, and calculating to obtain a variance;
and according to the variance calculation result, obtaining a quality detection result, and verifying the reliability of the optimal near-infrared nondestructive detection model.
According to another aspect of the present disclosure, a detection system for implementing the above near-infrared nondestructive online quality detection method for agricultural product nutrient components is provided, which includes a sample spectrum data acquisition module, a chemical determination module, a prediction model establishment module, a near-infrared nondestructive detection model and a quality comparison analysis module, wherein:
a sample spectral data acquisition module: the device is used for sampling the near infrared spectrum acquisition points of the sample according to preset acquisition conditions, and acquiring average spectrum data of the sampling points as predicted spectrum data; wherein, the preset collection condition comprises: presetting acquisition parameters: wave number range 12000cm-1-4000cm-1Number of scans 32, resolution 4cm-1(ii) a And presetting acquisition frequency: background scanning was performed every 5 samples; measuring each sample near infrared spectrum acquisition point in parallel for three times, taking an average spectrum, and taking the average value of the average spectra of the two sample near infrared spectrum acquisition points as a final spectrum;
a chemical assay module: the chemical composition measurement is carried out on the sample to obtain a chemical measurement value; arranging the chemical measurement values of the N samples from small to large, and screening the chemical measurement values into a verification set and a correction set according to preset screening conditions; processing the verification set and the correction set by using a two-dimensional principal component analysis chart and an analysis of variance chart to obtain a sample dispersion result;
a prediction model building module: the device is used for establishing a prediction model by respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method according to the predicted spectrum data and acquiring component parameter data;
near-infrared nondestructive testing model: for obtaining RMSEP and R2Parameter values and according to the minimum of said RMSEP and R2Selecting a nutrient content optimal model from the prediction model according to the parameter values; wherein the content of the first and second substances,
in the formula:
is the predicted value of the ith sample in the prediction set sample; y ispiIs a chemical reference value for the ith sample in the prediction set of samples;
is a chemical reference average value of a prediction set sample; n is the number of prediction set samples; establishing near-infrared nondestructive testing models of different nutrient components according to the nutrient component content optimal model; calculating and obtaining measured values of different nutrient components according to the near-infrared nondestructive testing model;
a quality comparison analysis module: and the device is used for comparing the obtained measured value with the nutrient component prediction reference value, obtaining the variance and obtaining the quality detection result.
In a possible implementation manner, optionally, the system further includes a spectrum preprocessing module, configured to:
presetting a plurality of spectrum pretreatment methods; obtaining the influence parameter values R of different spectrum preprocessing methods on the nutrient content optimal model based on the nutrient content optimal model respectively established by PCR and PLS2And RMSEP; according to the smallest value of said influencing parameter R2And RMSEP, obtaining the optimal spectrum pretreatment method; and the number of the first and second groups,
obtaining the optimal spectrum pretreatment method of different nutrient components; acquiring influence parameter values RMSEP of different nutrient contents on the nutrient content optimal model based on the nutrient content optimal models respectively established by PCR and PLS; and acquiring optimal near-infrared nondestructive testing models corresponding to different nutrient components according to the minimum influence parameter value RMSEP.
In a possible implementation manner, optionally, the quality comparison analysis module includes:
an actual measurement value acquisition module: the system is used for acquiring measured values of different nutrient components according to the optimal near-infrared nondestructive testing model;
a variance calculation module: the device is used for comparing the measured value with the prediction reference value and calculating and acquiring variance;
a verification module: and the method is used for calculating a result according to the variance to obtain a quality detection result and verifying the reliability of the optimal near-infrared nondestructive detection model.
The technical effects of this application:
in view of the technical implementation of the embodiment, the near infrared spectrum acquisition points of the sample are sampled through sample processing according to preset acquisition conditions, and average spectrum data of the sampling points are acquired as predicted spectrum data; according to the predicted spectrum data, respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method to establish a prediction model, and acquiring component parameter data; and comparing the obtained component parameter data with the nutrient component prediction reference value, obtaining the variance and obtaining a quality detection result. Can obtain main parameters (RMSEP and R) of optimal models of different nutrient contents by different spectrum pretreatment methods2) Therefore, the optimal spectrum pretreatment method and the optimal calculation model matched with different nutritional ingredients are established, the nutritional ingredients of the agricultural products can be rapidly analyzed, and the quality detection time and cost of the agricultural products are reduced.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a schematic diagram of the implementation flow of the near-infrared nondestructive online quality detection method for the nutrient content of agricultural products;
FIG. 2 shows the near infrared original spectrum of fresh sweet potato.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
In the embodiment, the sweet potatoes are selected for the agricultural products to be implemented, near-infrared nondestructive detection is carried out on 6 basic nutritional ingredients of the sweet potatoes, and the online quality monitoring of the sweet potatoes is carried out.
Example 1
As shown in fig. 1, according to an aspect of the present disclosure, a near-infrared nondestructive online quality detection method for agricultural product nutrient components is provided, which includes the following steps:
s1, sample treatment: acquiring N samples, dividing each sample into two parts, and arranging a sample near infrared spectrum acquisition point on one part for near infrared spectrum acquisition of sample components;
firstly, transversely cutting a sweet potato sample into two halves from the center, wherein one half is used for component determination by a sweet potato chemical method, and the other half is used for near infrared spectrum collection, wherein the center of a tangent plane is taken as a midpoint, and the two points from the center to the midpoint of the edge are selected as near infrared spectrum collection points of the sample.
The component data of chemical component measurement can be used as a verification set and a correction set to carry out comparative analysis and verification on the model and the detection result of the model.
S2, acquiring sample spectrum data: sampling near infrared spectrum collection points of the sample according to preset collection conditions, and obtaining average spectrum data of the sampling points as predicted spectrum data;
in this embodiment, the sweet potato to be studied is a solid sample, so an AntarisII Fourier near-infrared spectrometer and an integrating sphere diffuse reflection accessory of Therno Fisher Scientific company are selected to collect a sweet potato sample near-infrared spectrum instrument, the instrument is preheated for 30min when the instrument is started, and the instrument is tested before formal sampling, so that the instrument can work stably. After the test passed, a background scan was performed. The instrument parameters were as follows: wave number range 12000cm-1-4000cm-1(i.e., spectral wavelength range 831-2500nm), scanning frequency 32, resolution 4cm-1The InGaAs detector collects the signal and a background scan is performed every 5 samples to eliminate noise interference. And (4) parallelly measuring each sampling point for three times, taking an average spectrum, and taking the final spectrum as the average value of the average spectra of the two sampling points, wherein the average value is used as predicted spectrum data and is used as later-stage modeling basic data.
S3, establishing a prediction model: according to the predicted spectrum data, respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method to establish a prediction model, and acquiring component parameter data;
this example compares the effect of different spectral preprocessing methods on the prediction model. Preprocessing a spectrum by using The Unscrambler software, wherein The preprocessing mainly comprises SG, 1st, 2nd, SNV, MSC, combinations thereof and The like, establishing a prediction model according to The influence of different spectrum preprocessing methods on main parameters (RMSEP and R2) of a nutrient content optimal model (PCR and PLS), and obtaining RMSEP and R obtained by modeling PCR and PLS2Selecting an optimal spectrum preprocessing method and selecting a nutrient content optimal model matched with different nutrient components from the prediction model according to the parameter values.
In this embodiment, near-infrared nondestructive testing is performed on 6 basic nutritional components of sweet potato, so that 6 near-infrared nondestructive testing models corresponding to the basic nutritional components of sweet potato are established to respectively obtain component parameter data of the 6 basic nutritional components, that is, the measurement value finally measured by the nutritional component content optimization model
S4, quality comparison analysis: and comparing the obtained component parameter data with the nutrient component prediction reference value, obtaining the variance and obtaining a quality detection result.
The nutrient component prediction reference value is selected by adopting the following content determination reference standards: the sweet potato moisture content is determined by reference GB 5009.1-2016; the content of reducing sugar is determined by reference GB 5009.7-2016; the content of crude protein is determined by reference GB 5009.5-2016; the crude fiber content determination is referred to GB/T5009.10-2003; crude fat content determination reference GB 5009.6-2016: mineral determination references GB 5009.91-2017 (potassium) and GB 5009.92-2016 (calcium).
And comparing the measured value with the nutrient component prediction reference value to obtain the variance and obtain a quality detection result, so that the quality of different agricultural products can be rapidly measured.
In a possible implementation manner, optionally, in step S1, the method further includes:
measuring the chemical components of the other part of each sample to obtain a chemical measurement value;
arranging the chemical measurement values of the N samples from small to large, and screening the chemical measurement values into a verification set and a correction set according to preset screening conditions;
and processing the verification set and the correction set by using a two-dimensional principal component analysis chart and an analysis of variance chart to obtain a sample dispersion result.
In order to ensure that the divided subset samples are representative and reduce deviation, all samples are arranged from small to large according to chemical measurement values (reducing sugar or moisture content), then one of every four samples is selected as a verification sample, 37 samples are continuously selected as a verification set, and the remaining 109 samples are used as a correction set. By utilizing a two-dimensional principal component analysis chart and an analysis of variance, the dispersion condition of the sample can be visually seen.
In a possible implementation manner, optionally, in step S2, the preset acquisition condition includes:
presetting acquisition parameters: wave number range 12000cm-1-4000cm-1Number of scans 32, resolution 4cm-1(ii) a And
presetting acquisition frequency: background scanning was performed every 5 samples; and (4) parallelly measuring each sample near infrared spectrum acquisition point for three times, taking an average spectrum, and taking the average value of the average spectra of the two sample near infrared spectrum acquisition points as a final spectrum.
In order to eliminate the high-frequency random noise, baseline shift, light scattering and other influence factors generated by instruments, sample backgrounds and the like, improve spectral characteristics and improve spectral information effectiveness, the embodiment researches and compares the influence of different spectral preprocessing methods on a prediction model. The Unscrambler software is used for preprocessing spectra, and mainly comprises SG, 1st, 2nd, SNV, MSC, combinations thereof and The like. Among the NIR quantitative analysis models, the most commonly used multivariate calibration algorithms are mainly Multivariate Linear Regression (MLR), Stepwise Multivariate Linear Regression (SMLR), PCR, PLS, etc. The research selects two modeling methods of PCR and PLS in The Unscamblebler 9.8 for research, and finally selects an optimal model.
After the model is built, in order to confirm the accuracy of the built model, the model needs to be verified and evaluated, and the main parameters are RMSEP and R2And a principal component number (FC). Generally, the smaller the RMSEP value, the better the model, and in this study, the best model was chosen for the model with the smallest RMSEP value.
In a possible implementation manner, optionally, in step S3, after the building the prediction model by using the PCR modeling method and the PLS modeling method respectively, the method further includes:
obtaining RMSEP and R2Parameter values and according to the minimum of said RMSEP and R2Selecting a nutrient content optimal model from the prediction model according to the parameter values; wherein the content of the first and second substances,
in the formula:
is the predicted value of the ith sample in the prediction set sample; y ispiIs a chemical reference value for the ith sample in the prediction set of samples;
is a chemical reference average value of a prediction set sample; n is the number of prediction set samples; establishing near-infrared nondestructive testing models of different nutrient components according to the nutrient component content optimal model; and calculating and obtaining measured values of different nutrient components according to the near-infrared nondestructive testing model.
Different compositions, having different RMSEP and R2Parameter value, and according to RMSEP and R2And selecting a model with optimal nutrient content corresponding to the component as a parameter value, and taking the model as a near-infrared nondestructive testing model for testing the component.
In this example, The Unscrambler 9.8, Matlab R2015b and SPSS 19.0 software were used for data analysis, and Origin 8.0 software was used for mapping.
As shown in FIG. 2, it is a near-infrared original spectrum of fresh sweet potato. In the parameter setting of the near infrared spectrometer, the spectral range is 12000-4000cm-1, and the resolution is 4cm-1, but the actual wavelength range used by the invention is 11998.92-3999.64cm-1, and the resolution is 3.855 cm-1. The distribution of the typical vibration band in the near infrared region in fresh sweet potatoes is shown in FIGS. 1-5: 1) second order frequency doubling of-OH and-NH; 2) -second order frequency doubling of CH; 3) first order doubling of-OH and-NH; 4) -first order frequency doubling of CH; 5) -CH, -OH and-NH. As can be seen from FIGS. 1-5, the absorption spectrum characteristics of the 146 parts of fresh sweet potato tuber samples in the near infrared spectrum region are basically consistent, which indicates that the components contained in the samples are similar in category; meanwhile, due to the difference of the content of each component in the sample, the absorption spectrum intensities of the sample are different, and the absorption spectra of the sample are not completely overlapped, so that the possibility of predicting the content of different components in the sample is provided. However, the NIR absorption bands are relatively wide and overlap seriously, so that a specific spectral band is difficult to find by the method, and the method is difficult to model and identify the moisture, reducing sugar, crude protein, crude fiber, crude fat and mineral content of sweet potatoes directly on the basis of an original spectral diagram. Before the calibration model is usually established, in order to eliminate baseline drift in the original spectrum and reduce noise and other unnecessary interference signals, the original spectrum needs to be preprocessed first to improve the stability and reliability of the model.
In the present embodiment, 8 spectrum preprocessing methods, i.e., SNV, MS, 1st, 2nd, SNV +1st, SNV +2nd, MSC +1st, and MSC +2nd, are used for the study.
Tables 1-1, 1-2, 1-3, 1-4, 1-5, and 1-6 below summarize 8 spectral pretreatment methods for modeling sweet potato moisture, reducing sugars, crude protein, cellulose, crude fat, and mineral content (PCR and PLS) major parameters (RMSEP and R), respectively2) The influence of (a):
TABLE 1-1
Tables 1 to 2
Tables 1 to 3
Tables 1 to 4
Tables 1 to 5
Tables 1 to 6
As shown in tables 1-1 to 1-6 above, different spectral preprocessing methods for modeling PCR and PLS at R can be obtained2And RMSEP parameter values.
In a possible implementation manner, optionally, after obtaining the optimal model of the nutrient content, the method further includes:
presetting a plurality of spectrum pretreatment methods;
obtaining the influence parameter values R of different spectrum preprocessing methods on the nutrient content optimal model based on the nutrient content optimal model respectively established by PCR and PLS2And RMSEP;
according to the smallest value of said influencing parameter R2And RMSEP, obtaining the optimal spectrum pretreatment method.
As can be seen from Table 1-1, R of sweet potato moisture correction model created by PCR method based on original spectrum2And RMSEP of 0.940, 1.763, respectively, indicating that the model is acceptable; based on the original spectrum, the R2 and the RMSEP of the sweet potato moisture correction model established by the PLS method are respectively 0.940 and 1.759, which shows that the model is equally acceptable and indicates the existence of the sweet potato moisture content and the near infrared spectrum absorbanceIn good relation. Compared with the correction model of the original unprocessed spectrum, the spectrum pretreatment of 1st, 2nd, SNV +1st, SNV +2nd, MSC +1st, MSC +2nd and the like improves the performance of the sweet potato moisture correction model, wherein the optimal spectrum pretreatment method is MSC +1st, and RMSEP corresponding to the optimal PLS and PCR model is minimum and is 1.474 and 1.498 respectively.
In a possible implementation manner, optionally, after obtaining the optimal spectrum preprocessing method, the method further includes:
obtaining the optimal spectrum pretreatment method of different nutrient components;
acquiring influence parameter values RMSEP of different nutrient contents on the nutrient content optimal model based on the nutrient content optimal models respectively established by PCR and PLS;
and acquiring optimal near-infrared nondestructive testing models corresponding to different nutrient components according to the minimum influence parameter value RMSEP.
As can be seen from Table 1-2, the R of the sweet potato reducing sugar content correction model established by the PCR method under the original spectrum2And RMSEP of 0.610 and 0.500, respectively; r of sweet potato reducing sugar content correction model established under original spectrum by PLS method2And RMSEP is 0.786 and 0.369, respectively. R of correction model established by comparing PCR and PLS modeling methods2And RMSEP, finding that the correction model created by PCR is ubiquitous R2The RMSEP is a big problem, and no pretreatment method capable of improving the model performance is found, so that the PCR method modeling is not ideal for sweet potato reducing sugar, and the PLS model is acceptable. Comparing the PLS correction models of sweet potato reducing sugar under different spectrum pretreatment methods, finding that the SNV and MSC treatment can improve the model performance, wherein the MCS treatment can obtain an optimal model, and the RMSEP is the minimum and is 0.328.
As can be seen from tables 1-3 to tables 1-6, R of the correction model for crude protein and minerals of sweet potato based on the original spectrum using PCR method2And RMSEP is 0.952, 0.936 and 0.122, 0.321, respectively. R of sweet potato crude fiber and crude fat correction model established by PLS method based on original spectrum2And RMSEP of 0.926, 0.935 and 0.018, 0.653, respectively.
In this embodiment, reliability verification is also performed on the detection model.
In a possible implementation manner, optionally, the method further includes:
according to the optimal near-infrared nondestructive testing model, acquiring measured values of different nutrient components;
comparing the measured value with the prediction reference value, and calculating to obtain a variance;
and according to the variance calculation result, obtaining a quality detection result, and verifying the reliability of the optimal near-infrared nondestructive detection model.
As shown in the following tables 1-7, the data statistics and variance analysis table of the measured values and the prediction reference values in the verification set for the model with the optimal content of 6 nutrient components of the fresh sweet potatoes is as follows:
tables 1 to 7
It can be seen that, under the same index, there is no significant difference between the measured value and the predicted value data. Through analysis and comparison of variance, no significant difference is found between the measured value and the predicted value of the nutrient content, which shows that the optimal model can be accepted, and the model is verified to be reliable as the result is consistent with the previous conclusion.
It should be noted that, although the near-infrared nondestructive online quality detection of the nutrient components is described above by taking sweet potatoes as an example, those skilled in the art will understand that the disclosure should not be limited thereto. In fact, the user can set the detection object flexibly according to personal preference and/or actual application scene, and only need to establish the model for measurement in the above manner.
Therefore, the near infrared spectrum acquisition points of the sample are sampled through sample processing according to the preset acquisition conditions, and the average spectrum data of the sampling points are acquired as the predicted spectrum data; according to the abovePredicting spectral data, establishing a prediction model by respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method, and acquiring component parameter data; and comparing the obtained component parameter data with the nutrient component prediction reference value, obtaining the variance and obtaining a quality detection result. Can obtain main parameters (RMSEP and R) of optimal models of different nutrient contents by different spectrum pretreatment methods2) Therefore, the optimal spectrum pretreatment method and the optimal calculation model matched with different nutritional ingredients are established, the nutritional ingredients of the agricultural products can be rapidly analyzed, and the quality detection time and cost of the agricultural products are reduced. The constructed quality detection model is applicable and reliable.
Example 2
Based on the technical implementation principle of embodiment 1, this embodiment provides an online quality detection system to implement the detection method of embodiment 1. The specific principle is described in embodiment 1, and this embodiment is not described in detail.
According to another aspect of the present disclosure, a detection system for implementing the above near-infrared nondestructive online quality detection method for agricultural product nutrient components is provided, which includes a sample spectrum data acquisition module, a chemical determination module, a prediction model establishment module, a near-infrared nondestructive detection model and a quality comparison analysis module, wherein:
a sample spectral data acquisition module: the device is used for sampling the near infrared spectrum acquisition points of the sample according to preset acquisition conditions, and acquiring average spectrum data of the sampling points as predicted spectrum data; wherein, the preset collection condition comprises: presetting acquisition parameters: wave number range 12000cm-1-4000cm-1Number of scans 32, resolution 4cm-1(ii) a And presetting acquisition frequency: background scanning was performed every 5 samples; measuring each sample near infrared spectrum acquisition point in parallel for three times, taking an average spectrum, and taking the average value of the average spectra of the two sample near infrared spectrum acquisition points as a final spectrum;
and the sample spectral data acquisition module comprises an Antaris II Fourier near-infrared spectrometer and an integrating sphere diffuse reflection accessory for acquiring the sweet potato sample near-infrared spectrometer and is used for measuring the parameters.
A chemical assay module: the chemical composition measurement is carried out on the sample to obtain a chemical measurement value; arranging the chemical measurement values of the N samples from small to large, and screening the chemical measurement values into a verification set and a correction set according to preset screening conditions; processing the verification set and the correction set by using a two-dimensional principal component analysis chart and an analysis of variance chart to obtain a sample dispersion result;
a prediction model building module: the device is used for establishing a prediction model by respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method according to the predicted spectrum data and acquiring component parameter data;
near-infrared nondestructive testing model: for obtaining RMSEP and R2Parameter values and according to the minimum of said RMSEP and R2Selecting a nutrient content optimal model from the prediction model according to the parameter values; wherein the content of the first and second substances,
in the formula:
is the predicted value of the ith sample in the prediction set sample; y ispiIs a chemical reference value for the ith sample in the prediction set of samples;
is a chemical reference average value of a prediction set sample; n is the number of prediction set samples; establishing near-infrared nondestructive testing models of different nutrient components according to the nutrient component content optimal model; calculating and obtaining measured values of different nutrient components according to the near-infrared nondestructive testing model;
a quality comparison analysis module: and the device is used for comparing the obtained measured value with the nutrient component prediction reference value, obtaining the variance and obtaining the quality detection result.
In a possible implementation manner, optionally, the system further includes a spectrum preprocessing module, configured to:
presetting a plurality of spectrum pretreatment methods; obtaining the influence parameter values R of different spectrum preprocessing methods on the nutrient content optimal model based on the nutrient content optimal model respectively established by PCR and PLS2And RMSEP; according to the smallest value of said influencing parameter R2And RMSEP, obtaining the optimal spectrum pretreatment method; and the number of the first and second groups,
obtaining the optimal spectrum pretreatment method of different nutrient components; acquiring influence parameter values RMSEP of different nutrient contents on the nutrient content optimal model based on the nutrient content optimal models respectively established by PCR and PLS; and acquiring optimal near-infrared nondestructive testing models corresponding to different nutrient components according to the minimum influence parameter value RMSEP.
In a possible implementation manner, optionally, the quality comparison analysis module includes:
an actual measurement value acquisition module: the system is used for acquiring measured values of different nutrient components according to the optimal near-infrared nondestructive testing model;
a variance calculation module: the device is used for comparing the measured value with the prediction reference value and calculating and acquiring variance;
a verification module: and the method is used for calculating a result according to the variance to obtain a quality detection result and verifying the reliability of the optimal near-infrared nondestructive detection model.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A near-infrared nondestructive online quality detection method for nutritional ingredients of agricultural products is characterized by comprising the following steps:
s1, sample treatment: acquiring N samples, dividing each sample into two parts, and arranging a sample near infrared spectrum acquisition point on one part for near infrared spectrum acquisition of sample components;
s2, acquiring sample spectrum data: sampling near infrared spectrum collection points of the sample according to preset collection conditions, and obtaining average spectrum data of the sampling points as predicted spectrum data;
s3, establishing a prediction model: according to the predicted spectrum data, respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method to establish a prediction model, and acquiring component parameter data;
s4, quality comparison analysis: and comparing the obtained component parameter data with the nutrient component prediction reference value, obtaining the variance and obtaining a quality detection result.
2. The near-infrared nondestructive online quality detection method for the nutrient content of agricultural products of claim 1, wherein in step S1, the method further comprises:
measuring the chemical components of the other part of each sample to obtain a chemical measurement value;
arranging the chemical measurement values of the N samples from small to large, and screening the chemical measurement values into a verification set and a correction set according to preset screening conditions;
and processing the verification set and the correction set by using a two-dimensional principal component analysis chart and an analysis of variance chart to obtain a sample dispersion result.
3. The near-infrared nondestructive online quality detection method for the nutrient content of agricultural products of claim 1, wherein in step S2, the preset collection conditions comprise:
presetting acquisition parameters: wave number range 12000cm-1-4000cm-1Number of scans 32, resolution 4cm-1(ii) a And
presetting acquisition frequency: background scanning was performed every 5 samples; and (4) parallelly measuring each sample near infrared spectrum acquisition point for three times, taking an average spectrum, and taking the average value of the average spectra of the two sample near infrared spectrum acquisition points as a final spectrum.
4. The near-infrared nondestructive online quality detection method for the nutrient content of agricultural products of claim 1, wherein in step S3, after the establishing the prediction models by using the PCR modeling method and the PLS modeling method respectively, the method further comprises:
obtaining RMSEP and R2Parameter values and according to the minimum of said RMSEP and R2Selecting a nutrient content optimal model from the prediction model according to the parameter values; wherein the content of the first and second substances,
in the formula:
is the predicted value of the ith sample in the prediction set sample; y ispiIs a chemical reference value for the ith sample in the prediction set of samples;
is a chemical reference average value of a prediction set sample; n is the number of prediction set samples;
establishing near-infrared nondestructive testing models of different nutrient components according to the nutrient component content optimal model; and the number of the first and second groups,
and calculating and obtaining measured values of different nutrient components according to the near-infrared nondestructive testing model.
5. The near-infrared nondestructive online quality detection method for the nutrient content of the agricultural product according to claim 4, after obtaining the optimal model for the nutrient content, further comprising:
presetting a plurality of spectrum pretreatment methods;
obtaining the influence parameter values R of different spectrum preprocessing methods on the nutrient content optimal model based on the nutrient content optimal model respectively established by PCR and PLS2And RMSEP;
according to the smallest value of said influencing parameter R2And RMSEP, obtaining the optimal spectrum pretreatment method.
6. The near-infrared nondestructive online quality detection method for the nutrient content of the agricultural products according to claim 5, characterized by further comprising the following steps after the optimal spectrum pretreatment method is obtained:
obtaining the optimal spectrum pretreatment method of different nutrient components;
acquiring influence parameter values RMSEP of different nutrient contents on the nutrient content optimal model based on the nutrient content optimal models respectively established by PCR and PLS;
and acquiring optimal near-infrared nondestructive testing models corresponding to different nutrient components according to the minimum influence parameter value RMSEP.
7. The near-infrared nondestructive online quality detection method for the nutrient content of the agricultural product according to claim 6, characterized by further comprising:
according to the optimal near-infrared nondestructive testing model, acquiring measured values of different nutrient components;
comparing the measured value with the prediction reference value, and calculating to obtain a variance;
and according to the variance calculation result, obtaining a quality detection result, and verifying the reliability of the optimal near-infrared nondestructive detection model.
8. A detection system for realizing the near-infrared nondestructive online quality detection method for the nutrient components of agricultural products of any one of claims 1 to 7, which is characterized by comprising a sample spectrum data acquisition module, a chemical determination module, a prediction model establishment module, a near-infrared nondestructive detection model and a quality contrast analysis module, wherein:
a sample spectral data acquisition module: the device is used for sampling the near infrared spectrum acquisition points of the sample according to preset acquisition conditions, and acquiring average spectrum data of the sampling points as predicted spectrum data; wherein, the preset collection condition comprises: presetting acquisition parameters: wave number range 12000cm-1-4000cm-1Number of scans 32, resolution 4cm-1(ii) a And presetting acquisition frequency: background scanning was performed every 5 samples; measuring each sample near infrared spectrum acquisition point in parallel for three times, taking an average spectrum, and taking the average value of the average spectra of the two sample near infrared spectrum acquisition points as a final spectrum;
a chemical assay module: the chemical composition measurement is carried out on the sample to obtain a chemical measurement value; arranging the chemical measurement values of the N samples from small to large, and screening the chemical measurement values into a verification set and a correction set according to preset screening conditions; processing the verification set and the correction set by using a two-dimensional principal component analysis chart and an analysis of variance chart to obtain a sample dispersion result;
a prediction model building module: the device is used for establishing a prediction model by respectively adopting a PCR (polymerase chain reaction) modeling method and a PLS (partial least squares) modeling method according to the predicted spectrum data and acquiring component parameter data;
near-infrared nondestructive testing model: for obtaining RMSEP and R2Parameter values and according to the minimum of said RMSEP and R2Selecting a nutrient content optimal model from the prediction model according to the parameter values; wherein the content of the first and second substances,
in the formula:
is the predicted value of the ith sample in the prediction set sample; y ispiIs a chemical reference value for the ith sample in the prediction set of samples;
is a chemical reference average value of a prediction set sample; n is the number of prediction set samples; establishing near-infrared nondestructive testing models of different nutrient components according to the nutrient component content optimal model; calculating and obtaining measured values of different nutrient components according to the near-infrared nondestructive testing model;
a quality comparison analysis module: and the device is used for comparing the obtained measured value with the nutrient component prediction reference value, obtaining the variance and obtaining the quality detection result.
9. The detection system of claim 8, further comprising a spectral pre-processing module to:
presetting a plurality of spectrum pretreatment methods; obtaining the influence parameter values R of different spectrum preprocessing methods on the nutrient content optimal model based on the nutrient content optimal model respectively established by PCR and PLS2And RMSEP; according to the smallest value of said influencing parameter R2And RMSEP, obtaining the optimal spectrum pretreatment method; and the number of the first and second groups,
obtaining the optimal spectrum pretreatment method of different nutrient components; acquiring influence parameter values RMSEP of different nutrient contents on the nutrient content optimal model based on the nutrient content optimal models respectively established by PCR and PLS; and acquiring optimal near-infrared nondestructive testing models corresponding to different nutrient components according to the minimum influence parameter value RMSEP.
10. The inspection system of claim 9, wherein the quality contrast analysis module comprises:
an actual measurement value acquisition module: the system is used for acquiring measured values of different nutrient components according to the optimal near-infrared nondestructive testing model;
a variance calculation module: the device is used for comparing the measured value with the prediction reference value and calculating and acquiring variance;
a verification module: and the method is used for calculating a result according to the variance to obtain a quality detection result and verifying the reliability of the optimal near-infrared nondestructive detection model.
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