CN113030009A - Green radish quality detection method based on near infrared spectrum - Google Patents
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Abstract
The invention relates to a green radish quality detection method based on near infrared spectrum, which comprises the following specific operation steps: (1) pretreating a sample to be detected; (2) collecting spectral information; (3) and (4) arranging data and establishing a model. The method disclosed by the invention is scientific and reasonable in design, convenient and efficient to operate, the nondestructive detection of the internal quality of the green radish can be realized by utilizing the spectrum technology on the detection platform, the sugar content and the moisture in the green radish can be rapidly analyzed, the quality of the green radish can be predicted, the quality predicted value can be directly displayed, the predicted precision meets the actual production requirement, the operation method of the test platform is simple, easy to control and high in detection precision, and the test platform can be used for on-line detection and classification of the quality of fruits and vegetables, is convenient to operate and high in accuracy.
Description
Technical Field
The invention belongs to the field of agricultural product detection, relates to a non-damage fruit and vegetable detection technology, and particularly relates to a green radish quality detection method based on near infrared spectrum.
Background
The green radish is a green-peel radish, the part embedded in the soil is white, the other parts are emerald green, the green radish is suitable for being eaten raw, the taste is crisp and sweet, the nutritive value is rich, and the green radish is a common vegetable. The green radish is rich in various nutrient substances, Ca, P, Fe and other elements, and has higher economic value and dietary therapy and health care value. According to related experimental researches, the green radish not only has the effects of guiding qi downward, helping digestion, eliminating phlegm, stopping diarrhea, promoting urination and the like, but also has good cancer prevention and anticancer effects. The edible range is very wide, and the radish can be eaten as fruits, fried vegetables or pickled radish pickles, aired and dried radish, and the like.
With the improvement of living standard of people, consumers pay more and more attention to the quality of fruits and vegetables, the development of related industries of green radishes in China is more original at present, the detection means of the quality of the green radishes in the storage and transportation process is more traditional, the operation flow of the traditional analysis method of the quality and safety index of agricultural products is complex, the analysis cost is high, the structure of the green radishes is damaged in the analysis process, and secondary sale is influenced. Therefore, a method for detecting the quality of green radish without damaging the structure of green radish is needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the green radish quality detection method based on the near infrared spectrum, which is simple and efficient to operate, accurate to detect and free of damage.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a green radish quality detection method based on near infrared spectrum comprises the following specific operation steps:
(1) pretreatment of a sample to be detected:
(1.1) fresh-keeping treatment of a sample to be detected: all samples to be detected need to be placed into a natural cold source fresh-keeping warehouse for storage before detection after picking, and the samples to be detected are placed into PE fresh-keeping bags for fresh-keeping storage;
(1.2) before detection, taking out samples to be detected from a pretreatment fresh-keeping library, and dividing 60 samples into 40 modeling sets and 20 testing sets;
(2) collecting spectral information:
(2.1) preparation of the experiment: preheating a light source of a near-infrared spectrometer for 30min, and preparing for later use after the temperature of the light source is stable and the light intensity of the emitted near-infrared light band is gentle and the jitter is small;
(2.2) selection of measurement point regions: marking two sampling points on the flat surface of each green radish sample by using a marker pen, wherein the first sampling point is 3 cm away from the top end of the radish, the second sampling point is 1/2 in the middle of the longitudinal axis of the radish, and the two sampling points are on a vertical line;
and (2.3) collecting spectral information: aligning the central part of a spectrometer to sampling points, tightly attaching the central part to the surface of the radish, starting a switch to obtain a sample spectrum curve and spectrum data, and parallelly collecting each sampling point for 3 times;
(3) and (4) arranging data and establishing a model.
2. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: the processing conditions of the fresh-keeping treatment of the sample to be detected are as follows: the relative humidity in the fresh-keeping storehouse with a natural cold source is 90-95%, the storage and fresh-keeping temperature is 0 ℃, and the environment sterilization time in the ultraviolet lamp irradiation time range in the fresh-keeping storehouse every day is 0-30 min.
3. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: in the step of collecting the spectral information, the arrangement of the spectral data comprises the steps of uniformly arranging the obtained spectral text data into a table, opening chemometric software Unscamblebler v9.7, leading the arranged spectral data into the software, and finding out the modeling method with the highest comprehensive judgment coefficient of the model by preprocessing the spectral information.
4. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: in the step of collecting the spectral information, the preprocessing of the spectral data comprises preprocessing of the spectral data of the sugar degree and the moisture of the green radish, and the preprocessing method is Moving Average smoothing processing.
5. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: in the step of acquiring the spectral information, the establishment of the regression model comprises the steps of establishing the PC number of the model by comparing and analyzing various preprocessing data, determining a coefficient R2, correcting a root mean square error RMSEC, selecting an optimal prediction model, and finally correcting the model by using an algorithm tool of partial least square regression in software.
The invention has the advantages and positive effects that:
the method disclosed by the invention is scientific and reasonable in design, convenient and efficient to operate, the nondestructive detection of the internal quality of the green radish can be realized by utilizing the spectrum technology on the detection platform, the sugar content and the moisture in the green radish can be rapidly analyzed, the quality of the green radish can be predicted, the quality predicted value can be directly displayed, the predicted precision meets the actual production requirement, the operation method of the test platform is simple, easy to control and high in detection precision, and the test platform can be used for on-line detection and classification of the quality of fruits and vegetables, is convenient to operate and high in accuracy.
Drawings
FIG. 1 is a graph of raw spectral data for the near infrared spectral absorbance of 60 green radish plants in accordance with the present invention;
FIG. 2 is a graph of the potential variation of the selected brix value of the cross-validation method of the present invention;
FIG. 3 is a scatter diagram of true and predicted values of sample brix in a prediction set according to the method of the present invention;
FIG. 4 is a scatter diagram of predicted values and actual values of sugar degrees of green radish according to an embodiment of the method of the present invention.
Detailed Description
The present invention is further illustrated by the following specific examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
The invention provides a method for carrying out nondestructive testing on the internal quality of green radish by using a near infrared light source, which is used for carrying out spectrum acquisition on a sample in a characteristic waveband range of 700-950 nm by using an H100F type portable near infrared spectrometer and Unscamblebler v9.7 spectrum acquisition software. Each green radish sample flat surface is marked with 2 sampling points, the first sampling point is 3 cm away from the top end of the radish, the second sampling point is arranged in the middle (1/2) of the longitudinal axis of the radish, the 2 sampling points are arranged on a vertical line, each sampling point parallelly collects 3 times of spectral data, and the obtained green radish reflectivity information is complete and representative. The nondestructive testing of the internal quality of the green radish can be realized by utilizing the spectrum technology on the detection platform, the sugar content, the moisture and the bran center of the green radish can be rapidly analyzed, the platform operation method is simple, easy to control and high in detection precision, and the method can be used for online detection and classification of vegetable quality.
A green radish quality nondestructive testing method based on near infrared spectrum is a method for performing nondestructive testing on the sugar content and the water content of green radish by using a visible near infrared light source; the method comprises the following steps of fresh-keeping treatment of a sample to be detected, pretreatment before detection, acquisition of spectral information, sorting of spectral data, establishment of a regression model, establishment and correction of a prediction model, and analysis and display of a predicted value, and specifically comprises the following operation steps:
(1) pretreatment of a sample to be detected:
(1.1) fresh-keeping treatment of a sample to be detected: all samples to be detected need to be stored in a natural cold source fresh-keeping warehouse before being detected after being picked, and the samples to be detected are placed in PE fresh-keeping bags for fresh-keeping storage.
The relative humidity in the natural cold source fresh-keeping warehouse is that the storage and fresh-keeping temperature is 0 ℃, and the environment sterilization time in the ultraviolet lamp irradiation time range in the fresh-keeping warehouse every day is 0-30 min.
(1.2) after the green radish sample is taken out from the fresh-keeping storehouse by pretreatment before detection, manual inspection needs to be carried out on the sample before actual measurement, so that the surface of the sample is ensured to have no obvious deterioration conditions such as defects, decay, mildew and the like, and 60 samples are divided into 40 modeling sets and 20 inspection sets.
(2) Collecting spectral information:
(2.1) preparation of the experiment: and connecting the equipment, switching on a power supply, preheating the light source for 30min, and preparing for standby application after the temperature of the light source is stable and the light intensity of the emitted near-infrared light band is gentle and small in jump.
(2.2) selection of measurement point regions: two samples were marked with a marker on the flat surface of each green radish sample, the first sample being 3 cm from the top of the radish, the second sample being in the middle of the longitudinal axis of the radish (1/2), and the two samples being on a vertical line.
And (2.3) collecting spectral information: aligning the central part of the spectrometer to the sampling points, attaching the central part to the surface of the radish as close as possible, starting a switch to obtain a sample spectrum curve and spectrum data, and parallelly collecting each sampling point for 3 times.
(3) Data sorting and model building:
(3.1) sorting of spectral data: the obtained spectrum text data are uniformly arranged in a table, chemometric software Unscamblebler v9.7 is opened, the arranged spectrum data are led into the software, a modeling method with the highest comprehensive judgment coefficient of a model is found through pretreatment of spectrum information, an original spectrum data graph of the near infrared spectrum absorption values of 60 green radish is shown in figure 1, the abscissa in the graph is a wavelength value (nm), and the ordinate is absorbance.
(3.2) preprocessing of spectral data: the optimal spectrum data preprocessing method for the sugar degree and the water content of the green radish is Moving Average smoothing processing.
(3.3) establishing a regression model: PC number and determination coefficient R of model established by comparative analysis of various preprocessing data2Correcting the root mean square error RMSEC, selecting an optimal prediction model, and finally correcting the model by using an algorithm tool of partial least square regression in software, wherein specific results are shown in tables 1 and 2.
TABLE 1 comparison of the PLS brix prediction model effects under different treatment methods
TABLE 2 statistical table of optimal pretreatment method for each quality spectrum of sample under different spectra
The cross verification method selects a sugar degree value potential variable diagram as shown in figure 2, a true value and a predicted value scatter diagram of the sugar degree of the prediction set sample are shown in figure 3, and a predicted value and an actual value scatter diagram of the sugar degree are shown in figure 4.
Firstly, the physical and chemical indexes of the green radish treated by the method are measured:
(1) sampling:
selecting a section of green radish 1.5cm above and below each sampling point, and dividing the green radish into two parts: cutting a part of the fresh radish, wrapping the cut part of the fresh radish with gauze, squeezing and juicing to obtain green radish juice, and taking the green radish juice to measure the sugar content; each sample is parallelly measured for 3 times, and the average value is taken as the sugar degree value of the green radish at the sampling point and is used as the observed value of the analysis and prediction model; and cutting the other part into 2-3 mm slices, measuring the moisture by adopting a constant weight method, measuring each sample in parallel for 3 times, and calculating the moisture content to be used as an observed value of an analysis and prediction model.
(2) Preprocessing the spectral data of the green radish sample:
inputting the obtained spectral data, the observed values of sugar degree and water content into Unscamblebler v9.7 software (self-contained and portable) of a portable fruit sugar degree nondestructive detector, finding a modeling method with the highest comprehensive judgment coefficient of the model by preprocessing spectral information, establishing a prediction model, and finally correcting the model by using an algorithm tool of partial least square regression in the Unscamblebler v9.7 software to obtain a corrected prediction model;
secondly, the green radish treated by the method is subjected to quality measurement:
and selecting a batch of green radish samples, and collecting spectra to obtain spectral data. And measuring the physical and chemical indexes according to the steps to obtain the measured values of sugar degree and water content. And inputting the spectral data into the corrected prediction model to obtain the predicted values of the sugar degree and the water content. And respectively calculating correlation coefficients of the predicted values and the measured values of the sugar degree and the moisture, and analyzing and predicting effects.
Selecting a spectrum acquisition instrument: model H100F of Beijing sunshine Yishidao Technical Co Ltd; portable fruit nondestructive detection equipment; the apparatus used for measuring sweetness was a pal-l sugar meter manufactured by the company Atago, Japan; the apparatus used for measuring sweetness was a pal-l sugar meter manufactured by the company Atago, Japan; the prism of the glucometer was first cleaned with distilled water and wiped clean of water, correcting for zero.
The moisture determination adopts a constant weight method, and the weighing bottle is placed in a drying oven at 101-105 ℃ for repeated drying until the weight is constant; uniformly placing a proper amount of samples into a weighing bottle, precisely weighing and recording data; placing the sample in a drying oven for 2-4 h, taking out, cooling, weighing, repeatedly measuring each sample for three times, taking an average value, and calculating the moisture content;
the test results are given in the following table:
TABLE 3 prediction of the model for the estimation of the sugar content value (. degree Brix) of a green radish sample
Standard deviation 0.4654 correlation coefficient 0.9075
Table 4 prediction result of green radish sample prediction model on moisture content (%)
Standard deviation 0.7307 correlation coefficient 0.8458
The evaluation results are shown in the following table:
TABLE 5 determination of soluble solids content of green radish
TABLE 6 determination of the Water content of green radish
Although the embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the embodiments disclosed.
Claims (5)
1. A green radish quality detection method based on near infrared spectrum is characterized by comprising the following steps: the specific operation steps are as follows:
(1) pretreatment of a sample to be detected:
(1.1) fresh-keeping treatment of a sample to be detected: all samples to be detected need to be put into PE fresh-keeping bags to be stored in a natural cold source fresh-keeping warehouse for fresh keeping before being detected after being picked;
(1.2) before detection, taking out samples to be detected from a pretreatment fresh-keeping library, and dividing 60 samples into 40 modeling sets and 20 testing sets;
(2) collecting spectral information:
(2.1) preparation of the experiment: preheating a light source of a near-infrared spectrometer for 30min, and preparing for later use after the temperature of the light source is stable and the light intensity of the emitted near-infrared light band is gentle and the jitter is small;
(2.2) selection of measurement point regions: marking two sampling points on the flat surface of each green radish sample by using a marker pen, wherein the first sampling point is 3 cm away from the top end of the radish, the second sampling point is 1/2 in the middle of the longitudinal axis of the radish, and the two sampling points are on a vertical line;
and (2.3) collecting spectral information: aligning the central part of a spectrometer to sampling points, tightly attaching the central part to the surface of the radish, starting a switch to obtain a sample spectrum curve and spectrum data, and parallelly collecting each sampling point for 3 times;
(3) and (4) arranging data and establishing a model.
2. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: the processing conditions of the fresh-keeping treatment of the sample to be detected are as follows: the relative humidity in the fresh-keeping storehouse with a natural cold source is 90-95%, the storage and fresh-keeping temperature is 0 ℃, and the environment sterilization time in the ultraviolet lamp irradiation time range in the fresh-keeping storehouse every day is 0-30 min.
3. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: in the step of collecting the spectral information, the arrangement of the spectral data comprises the steps of uniformly arranging the obtained spectral text data into a table, opening chemometric software Unscamblebler v9.7, leading the arranged spectral data into the software, and finding out the modeling method with the highest comprehensive judgment coefficient of the model by preprocessing the spectral information.
4. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: in the step of collecting the spectral information, the preprocessing of the spectral data comprises preprocessing of the spectral data of the sugar degree and the moisture of the green radish, and the preprocessing method is Moving Average smoothing processing.
5. The green radish quality detection method based on the near infrared spectrum of claim 1, characterized in that: in the step of acquiring the spectral information, the establishment of the regression model comprises the steps of establishing the PC number of the model by comparing and analyzing various preprocessing data, determining a coefficient R2, correcting a root mean square error RMSEC, selecting an optimal prediction model, and finally correcting the model by using an algorithm tool of partial least square regression in software.
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|---|---|---|---|---|
| US5324945A (en) * | 1991-10-04 | 1994-06-28 | Director Of National Food Research Institute, Ministry Of Agriculture, Forestry And Fisheries | Method of nondestructively measuring sugar content of fruit by using near infrared transmittance spectrum |
| CN105044021A (en) * | 2015-07-08 | 2015-11-11 | 湖南环境生物职业技术学院 | Mid-autumn crispy jujube sugar degree nondestructive test method |
| CN106018320A (en) * | 2015-10-26 | 2016-10-12 | 沈阳农业大学 | Carotenoid detection method based on near infrared spectroscopy analysis |
| CN109856081A (en) * | 2019-03-13 | 2019-06-07 | 西北农林科技大学 | Portable kiwi fruit sugar fast non-destructive detection method and device |
| CN110702628A (en) * | 2019-10-26 | 2020-01-17 | 山东科技大学 | Spectral index model of chlorophyll content of vegetation leaf based on continuous wavelet analysis |
| CN111220568A (en) * | 2020-03-12 | 2020-06-02 | 中国科学院合肥物质科学研究院 | Apple sugar determination device and method based on near infrared spectrum analysis technology |
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- 2021-03-03 CN CN202110233713.2A patent/CN113030009A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5324945A (en) * | 1991-10-04 | 1994-06-28 | Director Of National Food Research Institute, Ministry Of Agriculture, Forestry And Fisheries | Method of nondestructively measuring sugar content of fruit by using near infrared transmittance spectrum |
| CN105044021A (en) * | 2015-07-08 | 2015-11-11 | 湖南环境生物职业技术学院 | Mid-autumn crispy jujube sugar degree nondestructive test method |
| CN106018320A (en) * | 2015-10-26 | 2016-10-12 | 沈阳农业大学 | Carotenoid detection method based on near infrared spectroscopy analysis |
| CN109856081A (en) * | 2019-03-13 | 2019-06-07 | 西北农林科技大学 | Portable kiwi fruit sugar fast non-destructive detection method and device |
| CN110702628A (en) * | 2019-10-26 | 2020-01-17 | 山东科技大学 | Spectral index model of chlorophyll content of vegetation leaf based on continuous wavelet analysis |
| CN111220568A (en) * | 2020-03-12 | 2020-06-02 | 中国科学院合肥物质科学研究院 | Apple sugar determination device and method based on near infrared spectrum analysis technology |
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