CN108846203A - The method and device of fruit non-destructive testing - Google Patents
The method and device of fruit non-destructive testing Download PDFInfo
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Abstract
The present invention relates to a kind of method and devices of fruit non-destructive testing, including light source, for carrying out illumination to the fruit as evaluation object；With spectral detection portion, for the fruit transmitted light or reflected light carry out light splitting detection and will test result spectroscopic data output；Computing device exports calculated result for receiving the spectroscopic data and calculating；Include the following steps：Pol detection model and maturity detection model based on fruit high light spectrum image-forming technology are established in the computing device；The spectroscopic data of the transmitted light or reflected light that are detected fruit is obtained, and the spectroscopic data input pol detection model and maturity detection model are calculated, exports calculated result.The present invention can not only detect the pol of fruit, moreover it is possible to detect the maturity of fruit.
Description
Technical field
The present invention relates to fruit quality technical field of nondestructive testing more particularly to a kind of methods for fruit non-destructive testing
And device.
Background technique
To fruit carry out commercial treatment i.e. to fruit harvesting after reprocessed and reprocessed, be improve application and
The indispensable means of operator's economic benefit；Wherein how to carry out Fast nondestructive evaluation to fruit inside quality is to carry out a system
The important step of column fruit commercial treatment.
The document of Patent No. 201510644358.2 discloses one kind, and the present invention provides one kind based on EO-1 hyperion transmission
The navel orange pol of technology spectral peak area detects rapid modeling method, establishes prediction model to navel orange pol, effectively extracts EO-1 hyperion letter
Breath, improves modeling efficiency and detection accuracy.Since navel orange pol is to measure an important indicator of navel orange quality, transmission side
Method can both be such that internal spectral information relevant to navel orange pol is effectively obtained, without because light source power is excessively high
Cause navel orange internal injury；The differentiation of navel orange pol, the modeling of less spectral variables complexity are carried out using high-spectrum spectral peak area
Process, calculating speed is fast, and accuracy rate is higher, can satisfy the requirement to navel orange pol Fast nondestructive evaluation.Specifically, our
Method includes the following steps：1. navel orange sample half transmitting EO-1 hyperion map is obtained, using the bloom of bloom spectrometer detection navel orange sample
Spectrogram spectrum, setting acquisition mode, time for exposure, light source power, wave-length coverage, resolution ratio and acquisition speed；2. utilizing chemistry side
Method measures the pol value of navel orange sample, according to the pol value of the measurement navel orange sample of measuring method described in national standard GB/T8210；3. selecting
It takes navel orange sample average EO-1 hyperion map, chooses navel orange mean height spectrum atlas, according to navel orange high-spectrum spectral property,
Pretreated spectra is carried out to the curve of spectrum under MATLAB environment, removes non-targeted information, instrument noise, background interference and removal
After the irrelevant variables information such as water peak, simplify spectral information, retains important information；4. EO-1 hyperion map spectral peak area is calculated,
Under MATLAB environment, the curve of spectrum is fitted, crest value and valley value under adaptive chosen spectrum region pass through integrating meter
Calculate EO-1 hyperion map spectral peak area；5. establishing navel orange sample glucose prediction model, Quality Detection is carried out；It is adaptive using navel orange sample
Input variable of the ratio between the left and right spectrogram spectral peak area as model under chosen spectrum region is answered, pol is carried out to unknown navel orange sample
Quality Detection establishes linear regression quantitative detection model to navel orange pol, utilizes forecast set related coefficient and forecast set root mean square
Error evaluates the precision of detection model.The invention is based on hyperspectral technique and acquires navel orange sample EO-1 hyperion by half transmitting mode
Map can effectively obtain navel orange inside quality information, improve the detection level and detection efficiency of fruit internal quality；The modeling
Method modeling efficiency is high, accuracy rate is high, and model calculation speed is fast, the pol Internal quality index of fruit can be quickly detected, to water
The inside quality of fruit is evaluated.
The inventors discovered that using various sensors and image processing techniques in the screening technique (selecting fruit machine) of fruit
Size, weight, pol etc. are determined and are screened.In the screening technique that these are established, if fruit can be added
Maturity index, so that it may to more precisely control best safety process time or the best safety edible time of fruit, into
One step improves fruit process efficiency and processing quality.
High light spectrum image-forming technology is a kind of effective means of non-destructive testing fruit internal quality, soluble solid, sugar,
There is very big application potential in terms of the non-destructive testing of the inside qualities content such as acid, vitamin.Currently, used in being detected to fruit
Research method be more using full spectral band or variables choice is carried out based on full spectrum to go to select and fruit internal quality
The relevant characteristic wave bands of index, establish the prediction model of fruit internal quality.
Summary of the invention
The purpose of the present invention is to provide a kind of method and devices of fruit non-destructive testing, can not only detect the sugar of fruit
Degree, moreover it is possible to detect the maturity of fruit.
Technical scheme is as follows：A kind of method of fruit non-destructive testing, including light source, for as evaluation pair
The fruit of elephant carries out illumination；With spectral detection portion, for the fruit transmitted light or reflected light carry out light splitting detection and will
The spectroscopic data of testing result exports；Computing device exports calculated result for receiving the spectroscopic data and calculating；It is special
Sign is, includes the following steps：
Pol detection model and the maturity detection based on fruit high light spectrum image-forming technology are established in the computing device
Model；The spectroscopic data of the transmitted light or reflected light that are detected fruit is obtained, and the spectroscopic data is inputted into the pol and is examined
It surveys model and maturity detection model is calculated, export calculated result.
Further, the method for building up of the pol detection model includes：1. obtaining fruit sample half transmitting high-spectrum
Spectrum sets acquisition mode, time for exposure, light source power, wavelength model using the EO-1 hyperion map of bloom spectrometer detection fruit sample
Enclose, resolution ratio and acquisition speed；2. utilizing the pol value of chemical gauging fruit sample, surveyed according to described in national standard GB/T8210
Determine the pol value of method measurement fruit sample；3. choosing fruit sample mean height spectrum atlas, chooses fruit and be averaged high-spectrum
Spectrum carries out Pretreated spectra to the curve of spectrum under MATLAB environment, removes non-targeted letter according to fruit high-spectrum spectral property
After the irrelevant variables information such as breath, instrument noise, background interference and removal water peak, simplifies spectral information, retain important information；
4. calculating EO-1 hyperion map spectral peak area to be fitted the curve of spectrum under MATLAB environment, adaptive chosen spectrum region
Lower crest value and valley value pass through integral calculation EO-1 hyperion map spectral peak area；5. fruit sample glucose prediction model is established, into
Row Quality Detection；Using the ratio between left and right spectrogram spectral peak area under the adaptive chosen spectrum region of fruit sample as the input of model
Variable carries out pol Quality Detection to unknown fruit sample, linear regression quantitative detection model is established to sugar degree, using pre-
Collection related coefficient and forecast set root-mean-square error are surveyed to evaluate the precision of detection model.
Further, the method for building up of the maturity detection model includes：According to the selection of artificial evaluation criterion most
The fruit sample of differing maturity establishes a mature degree series according to the difference of the maturity, mature such as 1 grade of maturity
2 grades of degree, according to the mature degree series, carries out illumination to fruit sample by 3 grades of maturity；Transmitted light or anti-to the fruit
Light is penetrated to carry out light splitting detection and will test the spectroscopic data output of result；Analyze the spectroscopic data, summarize the fruit with
Maturity sequence variation and change the smallest absorbance absorbing wavelength I, and change maximum suction with maturity sequence variation
Photometric absorbance wavelength II extracts the absorbance data A that absorbing wavelength is I, from the spectroscopic data from the spectroscopic data
The absorbance data B that absorbing wavelength is II is extracted, the absorbance difference C=A-B of every 60min in the time series is calculated；Knot
Absorbance difference C and time series are closed, establishes maturity detection model using unitary bearing calibration or machine learning algorithm.
When detected fruit is peach, the absorbing wavelength I is 960nm, and the absorbing wavelength II is 810nm.
When detected fruit is apple, the absorbing wavelength I is 720nm, and the absorbing wavelength II is 670nm.
A kind of device of fruit non-destructive testing, including light source, for carrying out illumination to the fruit as evaluation object；And light
Compose test section, for the fruit transmitted light or reflected light carry out light splitting detection and will test result spectroscopic data it is defeated
Out；Computing device exports calculated result for receiving the spectroscopic data and calculating；It is characterized in that, the computing device packet
Include that pol detection model establishes module and maturity detection model establishes module, for obtaining the transmitted light of detected fruit or anti-
The spectroscopic data of light is penetrated, and the spectroscopic data input pol detection model and maturity detection model are calculated,
Export calculated result.
Further, the method for building up of the pol detection model includes：1. obtaining fruit sample half transmitting high-spectrum
Spectrum sets acquisition mode, time for exposure, light source power, wavelength model using the EO-1 hyperion map of bloom spectrometer detection fruit sample
Enclose, resolution ratio and acquisition speed；2. utilizing the pol value of chemical gauging fruit sample, surveyed according to described in national standard GB/T8210
Determine the pol value of method measurement fruit sample；3. choosing fruit sample mean height spectrum atlas, chooses fruit and be averaged high-spectrum
Spectrum carries out Pretreated spectra to the curve of spectrum under MATLAB environment, removes non-targeted letter according to fruit high-spectrum spectral property
After the irrelevant variables information such as breath, instrument noise, background interference and removal water peak, simplifies spectral information, retain important information；
4. calculating EO-1 hyperion map spectral peak area to be fitted the curve of spectrum under MATLAB environment, adaptive chosen spectrum region
Lower crest value and valley value pass through integral calculation EO-1 hyperion map spectral peak area；5. fruit sample glucose prediction model is established, into
Row Quality Detection；Using the ratio between left and right spectrogram spectral peak area under the adaptive chosen spectrum region of fruit sample as the input of model
Variable carries out pol Quality Detection to unknown fruit sample, linear regression quantitative detection model is established to sugar degree, using pre-
Collection related coefficient and forecast set root-mean-square error are surveyed to evaluate the precision of detection model.
8, the device of fruit non-destructive testing according to claim 6 or 7, which is characterized in that the maturity detection
The method for building up of model includes：According to the fruit sample of artificial evaluation criterion most differing maturities of selection, according to it is described at
The difference of ripe degree establishes a mature degree series, such as 1 grade of maturity, 2 grades of maturity, 3 grades of maturity, according to the maturity sequence
Column carry out illumination to fruit sample；Transmitted light or reflected light to the fruit carry out light splitting detection and will test the light of result
Modal data output；The spectroscopic data is analyzed, the fruit is summarized with maturity sequence variation and changes the smallest absorbance
Absorbing wavelength I, and change maximum absorbance absorbing wavelength II with maturity sequence variation, it is mentioned from the spectroscopic data
Taking absorbing wavelength is the absorbance data A of I, and the absorbance data B that absorbing wavelength is II is extracted from the spectroscopic data, is calculated
The absorbance difference C=A-B of every 60min in the time series；In conjunction with absorbance difference C and time series, using unitary school
Correction method or machine learning algorithm establish maturity detection model.
When detected fruit is peach, the absorbing wavelength I is 960nm, and the absorbing wavelength II is 810nm.
When detected fruit is apple, the absorbing wavelength I is 720nm, and the absorbing wavelength II is 670nm.
The beneficial effects of the present invention are：In the ripe period that chases after of fruit, water soluble pectin extracted amount is as time increases
And increase, therefore in the hydrolytic process of pectin, it can use near-infrared spectrum technique, by the suction for reading different absorbing wavelengths
Luminosity difference forms absorbance difference index, the actual content of water soluble pectin directly in reflection fruit, so as to quantify
The maturity of fruit.
Moreover, the present invention can not only detect the pol of fruit, moreover it is possible to detect the maturity of fruit.
Detailed description of the invention
Fig. 1 is flow diagram of the embodiment of the present invention.
Specific embodiment
It elaborates below in conjunction with attached drawing and embodiment to technical solution of the present invention.
As shown in Figure 1, a kind of method of fruit non-destructive testing, including light source, for the fruit as evaluation object into
Row illumination；With spectral detection portion, for the fruit transmitted light or reflected light carry out light splitting and detection and will test result
Spectroscopic data output；Computing device exports calculated result for receiving the spectroscopic data and calculating；Include the following steps：
Pol detection model and the maturity detection based on fruit high light spectrum image-forming technology are established in the computing device
Model；The spectroscopic data of the transmitted light or reflected light that are detected fruit is obtained, and the spectroscopic data is inputted into the pol and is examined
Survey model and maturity detection model；With calculating detected fruit according to the pol detection model and maturity detection model
Pol and maturity data export calculated result.
Further, the method for building up of the pol detection model includes：1. obtaining fruit sample half transmitting high-spectrum
Spectrum sets acquisition mode, time for exposure, light source power, wavelength model using the EO-1 hyperion map of bloom spectrometer detection fruit sample
Enclose, resolution ratio and acquisition speed；2. utilizing the pol value of chemical gauging fruit sample, surveyed according to described in national standard GB/T8210
Determine the pol value of method measurement fruit sample；3. choosing fruit sample mean height spectrum atlas, chooses fruit and be averaged high-spectrum
Spectrum carries out Pretreated spectra to the curve of spectrum under MATLAB environment, removes non-targeted letter according to fruit high-spectrum spectral property
After the irrelevant variables information such as breath, instrument noise, background interference and removal water peak, simplifies spectral information, retain important information；
4. calculating EO-1 hyperion map spectral peak area to be fitted the curve of spectrum under MATLAB environment, adaptive chosen spectrum region
Lower crest value and valley value pass through integral calculation EO-1 hyperion map spectral peak area；5. fruit sample glucose prediction model is established, into
Row Quality Detection；Using the ratio between left and right spectrogram spectral peak area under the adaptive chosen spectrum region of fruit sample as the input of model
Variable carries out pol Quality Detection to unknown fruit sample, linear regression quantitative detection model is established to sugar degree, using pre-
Collection related coefficient and forecast set root-mean-square error are surveyed to evaluate the precision of detection model.
Further, the method for building up of the maturity detection model includes：According to the selection of artificial evaluation criterion most
The fruit sample of differing maturity establishes a mature degree series according to the difference of the maturity, mature such as 1 grade of maturity
2 grades of degree, according to the mature degree series, carries out illumination to fruit sample by 3 grades of maturity；Transmitted light or anti-to the fruit
Light is penetrated to carry out light splitting detection and will test the spectroscopic data output of result；Analyze the spectroscopic data, summarize the fruit with
Maturity sequence variation and change the smallest absorbance absorbing wavelength I, and change maximum suction with maturity sequence variation
Photometric absorbance wavelength II extracts the absorbance data A that absorbing wavelength is I, from the spectroscopic data from the spectroscopic data
The absorbance data B that absorbing wavelength is II is extracted, the absorbance difference C=A-B of every 60min in the time series is calculated；Knot
Absorbance difference C and time series are closed, establishes maturity detection model using unitary bearing calibration or machine learning algorithm.
When detected fruit is peach, the absorbing wavelength I is 960nm, and the absorbing wavelength II is 810nm.
When detected fruit is apple, the absorbing wavelength I is 720nm, and the absorbing wavelength II is 670nm.
Above description merely relates to certain specific embodiments of the invention, and any those skilled in the art is based on this
The replacement or improvement that the spirit of invention is done should be protection scope of the present invention and covered, protection scope of the present invention Ying Yiquan
Subject to sharp claim.
Claims (10)
1. a kind of method of fruit non-destructive testing, including light source, for carrying out illumination to the fruit as evaluation object；And spectrum
Test section, for the fruit transmitted light or reflected light carry out light splitting detection and will test result spectroscopic data output；
Computing device exports calculated result for receiving the spectroscopic data and calculating；It is characterised in that it includes following steps：
Pol detection model and maturity detection model based on fruit high light spectrum image-forming technology are established in the computing device；
The spectroscopic data of the transmitted light or reflected light that are detected fruit is obtained, and the spectroscopic data is inputted into the pol detection model
It is calculated with maturity detection model, exports calculated result.
2. the method for fruit non-destructive testing according to claim 1, which is characterized in that the foundation of the pol detection model
Method includes：1. obtaining fruit sample half transmitting EO-1 hyperion map, the EO-1 hyperion map of fruit sample is detected using bloom spectrometer,
Set acquisition mode, time for exposure, light source power, wave-length coverage, resolution ratio and acquisition speed；2. utilizing chemical gauging water
The pol value of fruit sample, according to the pol value of the measurement fruit sample of measuring method described in national standard GB/T8210；3. choosing fruit sample
Product mean height spectrum atlas chooses fruit mean height spectrum atlas, according to fruit high-spectrum spectral property, under MATLAB environment
Pretreated spectra is carried out to the curve of spectrum, removes the unrelated changes such as non-targeted information, instrument noise, background interference and removal water peak
After measuring information, simplify spectral information, retains important information；4. EO-1 hyperion map spectral peak area is calculated, under MATLAB environment,
The curve of spectrum is fitted, crest value and valley value under adaptive chosen spectrum region pass through integral calculation EO-1 hyperion map
Spectral peak area；5. establishing fruit sample glucose prediction model, Quality Detection is carried out；Utilize the adaptive chosen spectrum area of fruit sample
Input variable of the ratio between the left and right spectrogram spectral peak area as model under domain carries out pol Quality Detection to unknown fruit sample, right
Sugar degree establishes linear regression quantitative detection model, and inspection is evaluated using forecast set related coefficient and forecast set root-mean-square error
Survey the precision of model.
3. the method for fruit non-destructive testing according to claim 1 or 2, which is characterized in that the maturity detection model
Method for building up include：According to the fruit sample of most differing maturities of artificial evaluation criterion selection, according to the maturity
Difference establish a mature degree series, such as 1 grade of maturity, 2 grades of maturity, 3 grades of maturity, according to the mature degree series,
Illumination is carried out to fruit sample；Transmitted light or reflected light to the fruit carry out light splitting detection and will test the spectrum number of result
According to output；The spectroscopic data is analyzed, the fruit is summarized and changes the smallest absorbance absorption with maturity sequence variation
Wavelength I, and change maximum absorbance absorbing wavelength II with maturity sequence variation, it extracts and inhales from the spectroscopic data
The absorbance data A that wavelength is I is received, absorbing wavelength is extracted from the spectroscopic data for the absorbance data B of II, described in calculating
The absorbance difference C=A-B of every 60min in time series；In conjunction with absorbance difference C and time series, using unitary correction side
Method or machine learning algorithm establish maturity detection model.
4. the method for fruit non-destructive testing according to claim 3, which is characterized in that the absorbing wavelength I is 960nm,
The absorbing wavelength II is 810nm.
5. the method for fruit non-destructive testing according to claim 3, which is characterized in that the absorbing wavelength I is 720nm,
The absorbing wavelength II is 670nm.
6. a kind of device of fruit non-destructive testing, including light source, for carrying out illumination to the fruit as evaluation object；And spectrum
Test section, for the fruit transmitted light or reflected light carry out light splitting detection and will test result spectroscopic data output；
Computing device exports calculated result for receiving the spectroscopic data and calculating；It is characterized in that, the computing device includes
Pol detection model establishes module and maturity detection model establishes module, for obtaining the transmitted light or reflection of detected fruit
The spectroscopic data of light, and the spectroscopic data input pol detection model and maturity detection model are calculated, it is defeated
Calculated result out.
7. the device of fruit non-destructive testing according to claim 6, which is characterized in that the foundation of the pol detection model
Method includes：1. obtaining fruit sample half transmitting EO-1 hyperion map, the EO-1 hyperion map of fruit sample is detected using bloom spectrometer,
Set acquisition mode, time for exposure, light source power, wave-length coverage, resolution ratio and acquisition speed；2. utilizing chemical gauging water
The pol value of fruit sample, according to the pol value of the measurement fruit sample of measuring method described in national standard GB/T8210；3. choosing fruit sample
Product mean height spectrum atlas chooses fruit mean height spectrum atlas, according to fruit high-spectrum spectral property, under MATLAB environment
Pretreated spectra is carried out to the curve of spectrum, removes the unrelated changes such as non-targeted information, instrument noise, background interference and removal water peak
After measuring information, simplify spectral information, retains important information；4. EO-1 hyperion map spectral peak area is calculated, under MATLAB environment,
The curve of spectrum is fitted, crest value and valley value under adaptive chosen spectrum region pass through integral calculation EO-1 hyperion map
Spectral peak area；5. establishing fruit sample glucose prediction model, Quality Detection is carried out；Utilize the adaptive chosen spectrum area of fruit sample
Input variable of the ratio between the left and right spectrogram spectral peak area as model under domain carries out pol Quality Detection to unknown fruit sample, right
Sugar degree establishes linear regression quantitative detection model, and inspection is evaluated using forecast set related coefficient and forecast set root-mean-square error
Survey the precision of model.
8. the device of fruit non-destructive testing according to claim 6 or 7, which is characterized in that the maturity detection model
Method for building up include：According to the fruit sample of most differing maturities of artificial evaluation criterion selection, according to the maturity
Difference establish a mature degree series, such as 1 grade of maturity, 2 grades of maturity, 3 grades of maturity, according to the mature degree series,
Illumination is carried out to fruit sample；Transmitted light or reflected light to the fruit carry out light splitting detection and will test the spectrum number of result
According to output；The spectroscopic data is analyzed, the fruit is summarized and changes the smallest absorbance absorption with maturity sequence variation
Wavelength I, and change maximum absorbance absorbing wavelength II with maturity sequence variation, it extracts and inhales from the spectroscopic data
The absorbance data A that wavelength is I is received, absorbing wavelength is extracted from the spectroscopic data for the absorbance data B of II, described in calculating
The absorbance difference C=A-B of every 60min in time series；In conjunction with absorbance difference C and time series, using unitary correction side
Method or machine learning algorithm establish maturity detection model.
9. the device of fruit non-destructive testing according to claim 8, which is characterized in that the absorbing wavelength I is 960nm,
The absorbing wavelength II is 810nm.
10. the device of fruit non-destructive testing according to claim 8, which is characterized in that the absorbing wavelength I is 720nm,
The absorbing wavelength II is 670nm.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109596561A (en) * | 2018-12-29 | 2019-04-09 | 芯视界（北京）科技有限公司 | A kind of long-range real-time online fruit quality monitoring system and monitoring method |
| CN110780044A (en) * | 2019-10-11 | 2020-02-11 | 华中农业大学 | Nondestructive testing method for sugar-acid ratio of fresh citrus fruits |
| CN110969090A (en) * | 2019-11-04 | 2020-04-07 | 口碑(上海)信息技术有限公司 | Fruit quality identification method and device based on deep neural network |
| CN111968075A (en) * | 2020-07-21 | 2020-11-20 | 天津大学 | Hyperspectrum-based plant maturity detection system and method |
| CN111982835A (en) * | 2020-08-17 | 2020-11-24 | 吉林求是光谱数据科技有限公司 | Fruit sugar degree nondestructive testing device and method based on silicon-based multispectral chip |
| CN112240842A (en) * | 2020-09-18 | 2021-01-19 | 苏州市美益添生物科技有限公司 | A domestic food detects sampling device for fruit maturity |
| CN112964719A (en) * | 2021-04-26 | 2021-06-15 | 山东深蓝智谱数字科技有限公司 | Hyperspectrum-based food fructose detection method and device |
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| CN116007753A (en) * | 2022-12-09 | 2023-04-25 | 重庆理工大学 | Real-time nondestructive testing method for fruit quality based on FPGA and hyperspectrum |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109596561A (en) * | 2018-12-29 | 2019-04-09 | 芯视界（北京）科技有限公司 | A kind of long-range real-time online fruit quality monitoring system and monitoring method |
| CN110780044A (en) * | 2019-10-11 | 2020-02-11 | 华中农业大学 | Nondestructive testing method for sugar-acid ratio of fresh citrus fruits |
| CN110780044B (en) * | 2019-10-11 | 2022-04-19 | 华中农业大学 | Nondestructive testing method for sugar-acid ratio of fresh citrus fruits |
| CN110969090A (en) * | 2019-11-04 | 2020-04-07 | 口碑(上海)信息技术有限公司 | Fruit quality identification method and device based on deep neural network |
| CN113218889A (en) * | 2020-01-21 | 2021-08-06 | 青岛海尔电冰箱有限公司 | Fruit detection method, refrigerator and storage medium |
| CN111968075A (en) * | 2020-07-21 | 2020-11-20 | 天津大学 | Hyperspectrum-based plant maturity detection system and method |
| CN111968075B (en) * | 2020-07-21 | 2022-11-08 | 天津大学 | Hyperspectrum-based plant maturity detection system and method |
| CN111982835A (en) * | 2020-08-17 | 2020-11-24 | 吉林求是光谱数据科技有限公司 | Fruit sugar degree nondestructive testing device and method based on silicon-based multispectral chip |
| CN112240842A (en) * | 2020-09-18 | 2021-01-19 | 苏州市美益添生物科技有限公司 | A domestic food detects sampling device for fruit maturity |
| CN112964719A (en) * | 2021-04-26 | 2021-06-15 | 山东深蓝智谱数字科技有限公司 | Hyperspectrum-based food fructose detection method and device |
| CN116007753A (en) * | 2022-12-09 | 2023-04-25 | 重庆理工大学 | Real-time nondestructive testing method for fruit quality based on FPGA and hyperspectrum |
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