CN118032709A - Fruit and vegetable quality detection method and detection system - Google Patents
Fruit and vegetable quality detection method and detection system Download PDFInfo
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- 235000012055 fruits and vegetables Nutrition 0.000 title claims abstract description 132
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- 238000002835 absorbance Methods 0.000 claims abstract description 45
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Abstract
The invention discloses a fruit and vegetable quality detection method and a detection system, wherein the method comprises the following steps: collecting transmission spectrum data and transmission optical path of a first fruit and vegetable sample to be detected; performing absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and performing spectrum pretreatment on the absorbance data set by combining a transmission optical path to obtain first pretreatment data; determining a quality reference value of the first fruit and vegetable sample by a chemical analysis method; establishing a fruit and vegetable quality index correction model by utilizing the first preprocessing data and the quality reference value; and predicting the quality index value of the second fruit and vegetable sample to be detected by using the correction model. The method can realize rapid detection of the quality of fruits and vegetables, correct transmission spectrum data according to the transmission optical path of the detected fruits and vegetables, and improve detection precision.
Description
Technical Field
The invention relates to the technical field of quality detection of agricultural products, in particular to a fruit and vegetable quality detection method and system based on near infrared spectrum for detecting potatoes.
Background
The existing detection technology for the content of the internal components of the potatoes mainly relies on chemical analysis, and the chemical analysis method has the defects of great influence by human factors, great error, long analysis time, complicated steps, environmental pollution and the like. With the development of society and the rapid development of scientific technology and the need of timely supervision and control of fruit and vegetable quality safety by market regulatory departments, the field detection technology of potatoes is developed towards the rapid technology, so that the technology for detecting and analyzing potatoes by utilizing spectrum analysis software is also rapidly developed and widely applied.
The photoelectric detection technology for agricultural products has the advantages of high throughput and simultaneous detection of multiple indexes. The green analysis technology which is the first choice in the aspect of rapid nondestructive testing of fruit and vegetable quality is the near infrared spectrum analysis technology. The near infrared detection and evaluation method has the advantages of quick and high-precision fruit and vegetable quality, no damage and intelligence, and has a wide market prospect. However, the detection accuracy of the existing detection method is still to be improved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a fruit and vegetable quality detection method and a detection system, which are used for correcting transmission spectrum data of fruits and vegetables to be detected and improving detection precision.
In order to achieve the above purpose, in one aspect, the present invention provides a method for detecting quality of fruits and vegetables, comprising the following steps: collecting transmission spectrum data of a first fruit and vegetable sample to be detected; performing absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and performing spectrum pretreatment on the absorbance data set to obtain first pretreatment data; determining a quality reference value of the first fruit and vegetable sample by a chemical analysis method; establishing a fruit and vegetable quality index correction model by utilizing the first preprocessing data and the quality reference value; and predicting the quality index value of the second fruit and vegetable sample to be detected by using the correction model.
Further, the collecting the transmission spectrum data of the first fruit and vegetable sample to be detected further comprises collecting the transmission optical path of the first fruit and vegetable sample, and the spectrum pretreatment is performed by combining the transmission optical path.
Further, the spectral preprocessing comprises one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multiple scattering correction, orthogonal signal correction and baseline correction.
Further, the spectral preprocessing is baseline correction, including: the corrected absorbance a ′ is: Wherein a = log (1/T) is absorbance, T is transmittance, d is the transmission optical path of the first fruit and vegetable sample; the corrected transmittance T ′ is:
Furthermore, the establishment of the fruit and vegetable quality index correction model adopts a multi-element correction method.
On the other hand, the invention also provides a fruit and vegetable quality detection system, which comprises:
The acquisition module is used for acquiring transmission spectrum data of a first fruit and vegetable sample to be detected;
A data processing module, further comprising: the pretreatment module is used for carrying out absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and carrying out spectrum pretreatment on the absorbance data set to obtain first pretreatment data; the quality reference value measuring module is used for measuring the quality reference value of the first fruit and vegetable sample through a chemical analysis method; the correction model building module is used for building a fruit and vegetable quality index correction model by utilizing the first preprocessing data and the quality reference value;
And the prediction module is used for predicting the quality index value of the second fruit and vegetable sample to be detected by using the correction model.
Furthermore, the collection module is further used for collecting a transmission optical path of the first fruit and vegetable sample, and the spectrum pretreatment is performed in combination with the transmission optical path.
Further, the spectral preprocessing comprises one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multiple scattering correction, orthogonal signal correction and baseline correction.
Further, the spectral preprocessing is baseline correction, including: the corrected absorbance a ′ is: Wherein a = log (1/T) is absorbance, T is transmittance, d is the transmission optical path of the first fruit and vegetable sample; the corrected transmittance T ′ is:
Furthermore, the establishment of the fruit and vegetable quality index correction model adopts a multi-element correction method.
Further, the acquisition module comprises an acquisition device, and comprises a shell, a near infrared spectrometer and a clamping part which are arranged on the shell, and a light source arranged on the clamping part.
Further, the acquisition device includes: the push rod is arranged on the shell and connected with the clamping part to control the clamping part to move.
Further, the acquisition device includes: and the distance measuring module is arranged at the position of the shell at the push rod and used for measuring the moving distance of the push rod.
Further, the data processing module is a processor, is installed on the shell, and is electrically connected to the near infrared spectrometer and the ranging module.
Further, the detection system further includes: the power supply and the electric quantity monitoring module are arranged on the shell, and the electric quantity monitoring module is used for monitoring the electric quantity of the power supply in real time.
Further, the collecting device further includes: and the rubber gasket is arranged at the lower end of the clamping part and is positioned at the upper end of the light source.
Further, the detection system further includes: the switch is arranged on the shell and is electrically connected with the light source to start or stop the light source; the switch is a dual MOS switch.
Further, the detection system further includes: the touch screen is arranged on the shell and is electrically connected with the processor.
Further, the detection system further includes: and the voltage stabilizing module is arranged on the shell and is electrically connected with the processor.
The advantages of the invention are as follows:
according to the method, the transmission spectrum data of the fruit and vegetable sample to be detected is corrected, and the detection precision is improved.
When the method is used for correction, the transmission optical path is adopted to carry out the original transmission spectrum data, so that the detection precision of the correction model is improved.
According to the detection system disclosed by the invention, the data processing module is used for controlling the near infrared spectrometer to carry out secondary development, the distance measuring module and the push rod are matched to complete automatic detection of the transmission optical path, and the transmission optical path is combined to carry out baseline correction on the transmission spectrum data, so that the detection of the quality of fruits and vegetables can be rapidly realized, and the detection precision is improved.
The detection system disclosed by the invention is characterized in that the rubber gasket is in contact with the fruit and vegetable sample to be detected, so that the space between the fruit and vegetable sample to be detected and the light source is closed, and the damage of fruits and vegetables and the interference of stray light can be avoided.
In addition, the detection system provided by the invention is provided with the touch screen, so that the control and information of the whole detection process can be output and displayed in real time; the voltage stabilizing module realizes stable power supply of each component and prevents damage caused by unstable working voltage; the double CMOS switch realizes the closing of the light source; the distance measuring module transmits the distance detected by the push rod part to the microprocessor, and the transmission optical path is obtained through operation; the electric quantity monitoring module monitors the electric quantity of the battery in real time, so that the performance reduction and the damage risk of the detection system are prevented; the power supply is a detachable power supply, so that the standby battery can be replaced, and the convenience is improved.
Drawings
FIG. 1 is a flow chart of a fruit and vegetable quality detection method of the invention;
FIGS. 2 and 3 are schematic diagrams of the detection system of the present invention;
FIG. 4 is a schematic diagram of the structure of the collecting device of the present invention;
Wherein, the reference numerals:
1-a fruit and vegetable quality detection method;
S10-S14-step;
2,2' -fruit and vegetable quality detection system;
20-an acquisition module;
21-a collection device;
22-a data processing module;
220-a preprocessing module;
221-a quality reference value determination module;
222-a correction model building module;
a 23-prediction module;
200-a housing;
201-a near infrared spectrometer;
202-a clamping part;
203-a light source;
204-pushing rod;
206-a processor;
207-rubber gasket;
208-double CMOS switches;
209—touch screen;
210-a voltage stabilizing module;
211-a power supply;
212-a power monitoring module.
Detailed Description
The following detailed description of the present invention is provided with reference to the accompanying drawings and specific embodiments, so as to further understand the purpose, the scheme and the effects of the present invention, but not to limit the scope of the appended claims.
References in the specification to "an embodiment," "another embodiment," "this embodiment," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Furthermore, such phrases are not intended to refer to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
Certain terms are used throughout the description and following claims to refer to particular components or features, as one of ordinary skill in the art will appreciate that a technical user or manufacturer may refer to the same component or feature in different terms or terms. The specification and claims do not identify differences in names as a way of distinguishing components or parts, but rather differences in functions of the components or parts as a criterion of distinguishing. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. In addition, the term "coupled" as used herein includes any direct or indirect electrical connection. Indirect electrical connection means include connection via other devices.
It should be noted that, in the description of the present invention, terms such as "transverse," "longitudinal," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and "about," or "about," "substantially," "left and right," etc. indicate orientations or positional relationships or parameters, etc. based on the orientation or positional relationships shown in the drawings, are merely for convenience of description and simplicity of description, and do not indicate or imply that the apparatus or elements being referred to must have a specific orientation, a specific size, or be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
The embodiment of the invention provides a fruit and vegetable quality detection method and a detection system, which are used for correcting spectral data of fruits and vegetables to be detected and improving detection accuracy.
In addition, the influence of the transmission optical path on the spectrum data is considered, and the correction and the processing are carried out by a proper method so as to obtain an accurate and reliable spectrum analysis result, and the fruit and vegetable quality detection method and the detection system based on transmission mode acquisition and transmission optical path measurement are designed, so that the problem that the transmission optical path is not influenced in detection effect is solved.
The technical scheme of the invention aims to solve the problems, and the general idea is that a microprocessor is used for controlling a near infrared spectrometer to carry out secondary development, a designed clamping type detection system is used for carrying out automatic detection on spectrum data, the original spectrum data is corrected by combining a transmission optical path, then a reference value (quality reference value) of the content of internal components of a fruit and vegetable sample and near infrared spectrum data are collected to establish a fruit and vegetable internal component content detection correction model, and the spectrum data of an unknown fruit and vegetable sample is detected through the correction model to obtain an internal component content predicted value of the unknown sample.
Embodiment one:
fig. 1 is a flowchart of a fruit and vegetable quality detection method 1 according to an embodiment of the present invention, which includes the following steps:
s10: collecting transmission spectrum data of a first fruit and vegetable sample to be detected;
S11: absorbance calculation is carried out on the transmission spectrum data to obtain an absorbance data set, and spectrum pretreatment is carried out on the absorbance data set to obtain first pretreatment data;
S12: determining a quality reference value of the first fruit and vegetable sample by a chemical analysis method;
S13: establishing a fruit and vegetable quality index correction model by using the first preprocessing data and the quality reference value;
S14: and predicting the quality index value of the second fruit and vegetable sample to be detected by using the correction model.
In step S10, the method further includes collecting a transmission optical path of the first fruit and vegetable sample. Specifically, representative potato samples to be detected are collected in batches to serve as first fruit and vegetable samples, the first fruit and vegetable samples are required to be intact, and the detection indexes have a certain coverage; near infrared transmission spectrum data [ T 1,T2,T3,…Tn ] and a transmission optical path d of the first fruit and vegetable sample are collected.
In step S11, the spectral preprocessing is performed in combination with the transmission optical path.
The spectral preprocessing comprises one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multi-element scattering correction, orthogonal signal correction and baseline correction.
In example one, the spectral pretreatment was performed by baseline correction of absorbance dataset a in conjunction with the transmission path length. Specifically: a=log (1/T) =kdc, where K is the molar absorption coefficient, related to the nature of the absorbing species, according to the lambert-beer law mathematical expression; c is the substance concentration of the first fruit and vegetable sample; d is the thickness of the absorption layer; the substance concentration can numerically represent the detection index, the thickness of the absorption layer is expressed as the transmission optical path of the first fruit and vegetable sample in the transmission detection of the first embodiment, and the absorbance A and the transmission optical path d are in a linear relation.
Then, the corrected absorbance a ′ is: where a=log (1/T) is absorbance, T is transmittance, a ′ is absorbance after correction, and d is transmission optical path.
The corrected transmittance T ′ is:
In step S13, the calibration model establishes a calibration equation by using a multi-element calibration method, specifically, establishes a calibration equation of fruit and vegetable quality index by using the corrected spectrum data and the transmission optical path through the multi-element calibration method, and examples are as follows:
Y＝a0d+a1T′1+a2T′2+…+anT′n+W；
Wherein Y is a quality index value of the fruits and vegetables, a i is a coefficient corresponding to a wavelength T 'i, T' i is spectral transmittance corresponding to the wavelength, W is a fitting constant, and d is a transmission optical path.
The quality index is one or more quality indexes of fruits and vegetables to be detected, such as one or more of sugar content, moisture content, cellulose content and the like of potatoes, and the invention is not limited by the quality index.
In step S14, the second fruit and vegetable sample is an unknown potato sample to be detected.
The following is an example of a detection system corresponding to the above method example, and this embodiment may be implemented in cooperation with the above embodiment. The related technical details mentioned in the above embodiments are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the related technical details mentioned in the present embodiment can also be applied to the above-described embodiments.
Embodiment two:
fig. 2 is a schematic structural diagram of a fruit and vegetable quality detecting system 2 according to a second embodiment of the present invention. The detection system 2 includes: an acquisition module 20, a data processing module 22 and a prediction module 23.
The collection module 20 is configured to collect transmission spectrum data of a first fruit and vegetable sample to be detected; the data processing module 22 further comprises: the preprocessing module 220 is configured to perform absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and then perform spectrum preprocessing on the absorbance data set to obtain first preprocessed data; a quality reference value determining module 221, configured to determine a quality reference value of the first fruit and vegetable sample by using a chemical analysis method; the correction model building module 222 is configured to build a fruit and vegetable quality index correction model by using the first preprocessing data and the quality reference value; and the prediction module 23 is used for applying the correction model to predict the quality index value of the second fruit and vegetable sample to be detected.
Specifically, the collecting module 20 further includes a transmission optical path for collecting the first fruit and vegetable samples, specifically, the collecting module 20 collects representative potato samples to be detected in batches as the first fruit and vegetable samples, which requires that the first fruit and vegetable samples are intact, and the detection index has a certain coverage; near infrared transmission spectrum data [ T 1,T2,T3,…Tn ] and a transmission optical path d of the first fruit and vegetable sample are collected.
The spectral preprocessing comprises one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multi-element scattering correction, orthogonal signal correction and baseline correction.
In example two, the spectral pretreatment was a baseline correction of absorbance dataset a in combination with the transmission path length. Specifically, a=log (1/T) =kdc, where K is the molar absorption coefficient, related to the nature of the absorbing species, according to the lambert-beer law mathematical expression; c is the substance concentration of the first fruit and vegetable sample; d is the thickness of the absorption layer; the substance concentration can numerically represent the detection index, the thickness of the absorption layer is expressed as the transmission optical path of the first fruit and vegetable sample in the transmission detection of the embodiment, and the absorbance A and the transmission optical path d are in a linear relation.
Then, the corrected absorbance a ′ is: Where a=log (1/T) is absorbance, T is transmittance, and d is transmission optical path.
The corrected transmittance T ′ is:
In the second embodiment, the calibration model in the data processing module 22 adopts a multi-component calibration method to establish a calibration equation, specifically, uses the corrected spectrum data together with the transmission optical path, and establishes a calibration equation of the fruit and vegetable quality index through the multi-component calibration method, which is exemplified as follows:
Y＝a0d+a1T′1+a2T′2+…+anT′n+W；
Wherein Y is a quality index value of the fruits and vegetables, a i is a coefficient corresponding to a wavelength T 'i, T' i is spectral transmittance corresponding to the wavelength, W is a fitting constant, and d is a transmission optical path.
In the second embodiment, the second fruit and vegetable sample is an unknown potato sample to be detected.
Embodiment III:
Fig. 3 is a schematic structural diagram of a fruit and vegetable quality detection system 2 'according to a third embodiment of the present invention, where the detection system 2' includes: the system comprises an acquisition module 20, a data processing module 22, a prediction module 23, a voltage stabilizing module 210, a touch screen 209, a power supply 211 and a power monitoring module 212.
The collection module 20 is used for collecting transmission spectrum data and transmission optical paths of a first fruit and vegetable sample to be detected; the prediction module 23 is configured to predict a quality index value of a second fruit and vegetable sample to be detected using the correction model; the data processing module 22 is a processor 206, and the processor 206 further includes: a preprocessing module 220, a quality reference value determination module 221 and a correction model establishment module 222.
Specifically, the preprocessing module 220 is configured to perform absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and then perform spectrum preprocessing on the absorbance data set to obtain first preprocessed data; a quality reference value determining module 221, configured to determine a quality reference value of the first fruit and vegetable sample by using a chemical analysis method; the correction model building module 222 is configured to build a fruit and vegetable quality index correction model using the first preprocessing data and the quality reference value.
The collecting module 20 further includes a collecting device 21, as shown in fig. 3 or fig. 4, the collecting device 21 collects representative potato samples to be detected in batches as first fruit and vegetable samples, and the first fruit and vegetable samples are required to be intact, and the detection indexes have a certain coverage; near infrared transmission spectrum data [ T 1,T2,T3,…Tn ] and a transmission optical path d of the first fruit and vegetable sample are collected.
The spectral preprocessing comprises one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multi-element scattering correction, orthogonal signal correction and baseline correction.
In this example three, the spectral pretreatment was performed by baseline correction of absorbance dataset a in combination with the transmission optical path length. Specifically, a=log (1/T) =kdc, where K is the molar absorption coefficient, related to the nature of the absorbing species, according to the lambert-beer law mathematical expression; c is the substance concentration of the first fruit and vegetable sample; d is the thickness of the absorption layer; the substance concentration can numerically represent the detection index, the thickness of the absorption layer is expressed as the transmission optical path of the first fruit and vegetable sample in the transmission detection of the embodiment, and the absorbance A and the transmission optical path d are in a linear relation.
Then, the corrected absorbance a ′ is: Where a=log (1/T) is absorbance, T is transmittance, and d is transmission optical path.
The corrected transmittance T ′ is:
In the third embodiment, the calibration model in the data processing module 22 uses a multi-component calibration method to establish a calibration equation, specifically, uses the corrected spectrum data together with the transmission optical path, and establishes a calibration equation of the fruit and vegetable quality index through the multi-component calibration method, which is exemplified as follows:
Y＝a0d+a1T′1+a2T′2+…+anT′n+W；
Wherein Y is a quality index value of the fruits and vegetables, a i is a coefficient corresponding to a wavelength T 'i, T' i is spectral transmittance corresponding to the wavelength, W is a fitting constant, and d is a transmission optical path.
In the third embodiment, the second fruit and vegetable sample is an unknown potato sample to be detected.
In the third embodiment, as shown in fig. 4, the collecting device 21 further includes a housing 200, a near infrared spectrometer 201 disposed at the upper end and a clamping portion 202 disposed at the lower end of the housing 200, and a light source 203 disposed at the lower end (bottom) of the clamping portion 202.
In the third embodiment, the collecting device 21 further includes a push rod 204 disposed at the upper end of the housing 200, where the push rod 204 is connected to the clamping portion 202 and can control the movement of the clamping portion 202; a ranging module (not shown) is installed at the push rod 204, and a moving distance of the push rod 204 can be detected; the near infrared spectrometer 201 is disposed opposite to the light source 203, and is used for collecting transmission spectrum data of fruits and vegetables (e.g. potatoes) to be detected.
Specifically, near infrared spectrometer 201 may be a miniature near infrared spectrometer with a resolution of 2.2nm, a scan rate of 160Hz, an entrance slit of 25 μm, and input requirements of 5VDC and 500mA. The near infrared spectrometer 201 converts the collected sample information (transmission spectrum data of the first fruit and vegetable sample) into an electrical signal and transmits the electrical signal to the processor 206.
The ranging module can be VL53L0X, has 940nm laser VCSEL, is embedded with an advanced microcontroller, measures distance 2m, can rapidly range, and obtains transmission optical path through the processor 206. The processor 206 may be a microprocessor, such as raspberry group 4B, for receiving instructions for the various modules.
In the third embodiment, the acquisition device 21 may be designed such that the push rod 204 and the clamping portion 202 are in a linear relationship, and the processor 206 measures the moving distance of the push rod 204 through a ranging module (not shown), and then obtains the final transmission optical path d through a linear equation. The transmission optical path d is calculated as follows: the known maximum distance of the clamping portion 202 is D max, and the minimum distance D min; the distance measurement of the push rod 204 part is L max at the maximum distance and L min at the minimum distance; the transmission optical path d=ax-B can be obtained, X is the value measured by the ranging module, and a and B can be obtained according to the linear relationship between the clamping part 202 and the push rod 204;
For example, the clamping portion 202 has a maximum distance of 133.5mm and a minimum distance of 66mm; the push rod 204 has a distance measuring maximum distance of 179mm and a distance measuring minimum distance of 112mm; the transmission path d=1.007X-46.784, X is the value measured by the ranging module, where a is 1.007 and b is 46.784.
In the third embodiment, the collecting device 21 further includes: a rubber gasket 207 is provided on the holding portion 202 and located at the upper end of the light source 203. The rubber grommet 207 is in direct contact with the fruit and vegetable sample to be tested (including the first fruit and vegetable sample and the second fruit and vegetable sample). The rubber gasket 207 is in contact with the fruit and vegetable sample to be detected, and seals the space between the fruit and vegetable sample to be detected and the light source 203, so that the fruit and vegetable damage can be avoided, and meanwhile, the interference of stray light can be avoided.
In the third embodiment, the detection system 2' further includes: a switch disposed on the housing 200 and electrically connected to the light source 203 to turn on or off the light source 203; illustratively, the switch may be a dual CMOS switch 208 with a PMW control panel that controls the on and off and brightness of the light source 203.
In the third embodiment, the data processing module 22 is a processor 206, and is mounted on the upper end of the housing 200, such as a microprocessor, for example, a raspberry group 4B, for receiving instructions from the respective modules, and the raspberry group 4B is electrically connected to the near infrared spectrometer 201 and a ranging module (not shown).
In the third embodiment, the detection system 2' further includes: the touch screen 209 is disposed on the housing 200 and electrically connected to the processor 206. The touch screen 209 is an exemplary JRP4301 display screen, and is connected with the raspberry group 4B through HDMI, and is used for outputting and displaying control and information of the whole detection process in real time.
In the third embodiment, the voltage stabilizing module 210 is disposed on the housing 200 and electrically connected to the processor 206. For example, the voltage stabilizing module 210 is model DM02-ADJ, with a wide voltage input of 6.5-28 VDC, a fixed output voltage of 5V, and a continuous current of 0.5A to ensure that the operating voltage of the processor 206 and the micro near infrared spectrometer 201 is reached.
In the third embodiment, the detection system 2' further includes: a power supply 211 is disposed at the bottom of the housing 200 for supplying power to the various modules of the detection system 2'. Specifically, the power supply 211 is connected to the voltage stabilizing module 210, the double-CMOS switch 208, and the ranging module (not shown) through power lines (not shown), the micro near infrared spectrometer 201 and the processor 206 are powered by the voltage stabilizing module 210 through power lines (not shown), and the light source 203 is powered by the double-CMOS switch 208 connected to the power lines (not shown); the power supply 211 is a removable power supply, and can replace a backup battery.
In the third embodiment, the detection system 2' further includes: the power monitoring module 212 is disposed on the housing 200 and connected to the power supply 211 to monitor the power of the power supply 211 in real time.
When the device works, the light source 203 is turned on, fruits and vegetables (such as potatoes) are fixed through the push rod 204, light is transmitted inside the fruits and vegetables, transmission spectrum data is obtained by the near infrared spectrometer 201, and the light signals are converted into electric signals and transmitted to the processor 206 for display and storage by the near infrared spectrometer 201; during detection, the processor 206 and the double-CMOS switch 208 control the light source 203 to be turned on and off, the ranging module obtains a transmission optical path and transmits the transmission optical path to the processor 206 for spectral data correction, and the corrected spectral data is input into a detection model (namely a calibration equation) to realize rapid detection of fruit and vegetable quality.
Specifically, the workflow of the detection system 2' is:
Firstly, before spectrum measurement, the device is started and preheated for 5 minutes, so that the near infrared spectrometer 201 and the light source 203 reach a stable working state, and the fluctuation of random noise is reduced.
Then, performing black-and-white correction on the near infrared spectrometer 201, aligning a polytetrafluoroethylene white reference plate to a detection port in front of a detection system 2', turning on a light source 203, and collecting white reference spectrum data; the light source 203 is turned off, the reference plate is used to shield the front detection port of the detection system 2', black reference spectrum data are collected, if the black (white) correction is missed before measurement, the touch screen 209 automatically jumps out of the prompt window to prompt the user that the black (white) correction is not completed.
Then the potato is fixed in the middle of the clamping part 202 through the push rod 204, the potato is directly contacted with the rubber gasket 207, corrected transmission spectrum data are collected, the collected data are stored in the SD card by the processor 206 (raspberry group 4B), and the data storage names are named according to time.
And finally, the acquired transmission spectrum data is imported into a correction model (namely a calibration equation) to obtain a prediction result. And (5) ending the detection and closing the system operation.
In the detection process, the electric quantity monitoring module 212 is connected with the USB-TTL chip, and the USB-TTL chip converts TTL signals and raspberry-set USB signals output by the electric quantity monitoring chip to read the electric quantity of the battery of the power supply 211 in real time so as to prevent performance limitation and data loss caused by insufficient electric quantity.
In summary, the method of the invention adopts the transmission optical path to correct the original transmission spectrum data, thereby improving the detection precision of the model (calibration equation).
According to the detection system disclosed by the invention, the processor is used for controlling the near infrared spectrometer to carry out secondary development, the distance measurement module and the push rod are matched to complete automatic detection of the transmission optical path, and the transmission optical path is combined to carry out baseline correction on transmission spectrum data, so that the detection of the quality of fruits and vegetables can be rapidly realized, and the detection precision is improved.
The detection system disclosed by the invention is characterized in that the rubber gasket is in contact with the fruit and vegetable sample to be detected, so that the space between the fruit and vegetable sample to be detected and the light source is closed, and the damage of fruits and vegetables and the interference of stray light can be avoided.
In addition, the detection system provided by the invention is provided with the touch screen, so that the control and information of the whole detection process can be output and displayed in real time; the voltage stabilizing module realizes stable power supply of each component and prevents damage caused by unstable working voltage; the double CMOS switch realizes the closing of the light source; the distance measuring module transmits the distance detected by the push rod part to the microprocessor, and the transmission optical path is obtained through operation; the electric quantity monitoring module monitors the electric quantity of the battery in real time, so that the performance reduction and the damage risk of the detection system are prevented; the power supply is a detachable power supply, so that the standby battery can be replaced, and the convenience is improved.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are also within the scope of the present invention.
Claims (15)
1. The fruit and vegetable quality detection method is characterized by comprising the following steps of:
collecting transmission spectrum data of a first fruit and vegetable sample to be detected;
performing absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and performing spectrum pretreatment on the absorbance data set to obtain first pretreatment data;
determining a quality reference value of the first fruit and vegetable sample by a chemical analysis method;
establishing a fruit and vegetable quality index correction model by utilizing the first preprocessing data and the quality reference value;
and predicting the quality index value of the second fruit and vegetable sample to be detected by using the correction model.
2. The method for detecting fruit and vegetable quality according to claim 1, wherein the collecting transmission spectrum data of the first fruit and vegetable sample to be detected further comprises collecting a transmission optical path of the first fruit and vegetable sample;
The spectral pretreatment is performed in conjunction with the transmission optical path.
3. The fruit and vegetable quality detection method according to claim 1 or 2, wherein the spectral preprocessing comprises one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multiple scattering correction, orthogonal signal correction, baseline correction.
4. The fruit and vegetable quality detection method according to claim 2, wherein the spectral preprocessing uses baseline correction, comprising:
The corrected absorbance a ′ is: wherein a = log (1/T) is absorbance, T is transmittance, d is the transmission optical path of the first fruit and vegetable sample;
The corrected transmittance T ′ is:
5. The method for detecting the quality of fruits and vegetables according to claim 1,2 or 4, wherein the establishment of the fruit and vegetable quality index correction model adopts a multi-element correction method.
6. A fruit and vegetable quality inspection system, comprising:
The acquisition module is used for acquiring transmission spectrum data of a first fruit and vegetable sample to be detected;
A data processing module, further comprising: the pretreatment module is used for carrying out absorbance calculation on the transmission spectrum data to obtain an absorbance data set, and carrying out spectrum pretreatment on the absorbance data set to obtain first pretreatment data; the quality reference value measuring module is used for measuring the quality reference value of the first fruit and vegetable sample through a chemical analysis method; the correction model building module is used for building a fruit and vegetable quality index correction model by utilizing the first preprocessing data and the quality reference value;
and the prediction module is used for predicting the quality index value of the second fruit and vegetable sample to be detected by using the correction model.
7. The fruit and vegetable quality inspection system of claim 6 wherein the collection module further comprises a transmission optical path for collecting the first fruit and vegetable sample; the spectral pretreatment is performed in conjunction with the transmission optical path.
8. The fruit and vegetable quality detection system according to claim 6 or 7, wherein the spectral preprocessing is one or a combination of several of S-G smoothing/first derivative preprocessing, standard variable transformation, multiple scatter correction, quadrature signal correction, baseline correction.
9. The fruit and vegetable quality inspection system of claim 7 wherein the spectral pre-processing employs baseline correction, comprising:
The corrected absorbance a ′ is: wherein a = log (1/T) is absorbance, T is transmittance, d is the transmission optical path of the first fruit and vegetable sample;
The corrected transmittance T ′ is:
10. The fruit and vegetable quality inspection system according to claim 6, 7 or 9, wherein the establishment of the fruit and vegetable quality index correction model uses a multi-component correction method.
11. The fruit and vegetable quality detection system of claim 6, 7 or 9, wherein the acquisition module comprises an acquisition device comprising: the infrared spectrometer comprises a shell, a near infrared spectrometer arranged on the shell, a clamping part and a light source arranged on the clamping part.
12. The fruit and vegetable quality inspection system according to claim 11, wherein the collection device further comprises: the push rod is arranged on the shell and connected with the clamping part to control the clamping part to move.
13. The fruit and vegetable quality inspection system according to claim 12, wherein the collection device further comprises: and the distance measuring module is arranged at the position of the shell at the push rod and used for measuring the moving distance of the push rod.
14. The fruit and vegetable quality inspection system of claim 13 wherein the data processing module is a processor mounted on the housing and electrically connected to the near infrared spectrometer and the ranging module.
15. The fruit and vegetable quality inspection system of claim 11, further comprising: the power supply and the electric quantity monitoring module are arranged on the shell, and the electric quantity monitoring module is used for monitoring the electric quantity of the power supply in real time.
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