CN102252971A - Rapid detection method for mango hardness - Google Patents
Rapid detection method for mango hardness Download PDFInfo
- Publication number
- CN102252971A CN102252971A CN2011100848356A CN201110084835A CN102252971A CN 102252971 A CN102252971 A CN 102252971A CN 2011100848356 A CN2011100848356 A CN 2011100848356A CN 201110084835 A CN201110084835 A CN 201110084835A CN 102252971 A CN102252971 A CN 102252971A
- Authority
- CN
- China
- Prior art keywords
- mango
- hardness
- near infrared
- sample
- infrared spectrum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Abstract
In order to overcome the defects that the analysis method for detection of mango hardness is complex to operate and is laborious and time-consuming, and fruit bodies are damaged in the detection, the invention provides a rapid detection method for the mango hardness based on a near infrared spectroscopic analysis technology. The rapid detection method comprises the follow steps of: collecting mango samples for modeling; measuring the near infrared spectra of the samples; pre-processing the spectra; measuring the hardness of the mango samples by using a standard method; establishing a calibration model between the near infrared spectra and the mango hardness by using a chemometrics method; verifying and optimizing the model; collecting the near infrared spectra of the samples to be detected, and quantitatively detecting the hardness of the samples by using the model. The method provided by the invention is rapid in detection, has no damage to the fruit bodies, and provides a technical base for dynamic detection and tracking of mango quality in a logistic process.
Description
Technical field
The present invention relates to the mango hardness maintaining time method for quick, particularly, the mathematical model that relates between the hardness number of the near infrared spectrum that utilizes mango and mango is carried out Rapid Determination Method to mango hardness maintaining time.
Background technology
Mango is a kind of main tropical fruit (tree) of China, and its hardness is as an important indicator judging the mango degree of ripeness, no matter to the judgement of collecting period of mango, still preserves and all has crucial meaning for the back of adopting of mango.Now classic method mainly adopts sampling to the detection of the hardness of mango, wastes time and energy, and need remove the peel processing to fruit when detecting, and can't accomplish Non-Destructive Testing.Therefore, a kind of simple, fast, the detection method of harmless mango hardness maintaining time, can effectively realize detecting on the tree of mango degree of ripeness, differentiating, instruct to select the best collecting period, can realize dynamic, real-time monitoring in the logistics progresses such as back transportation, storage adopting of mango again the important indicator of this representative quality variation of mango hardness maintaining time.
Near-infrared spectrum technique is with fastest developing speed, one of the most noticeable spectral analysis technique.So-called near infrared light is meant wavelength in the 780-2526nm scope, a kind of electromagnetic wave between visible light and infrared light.Near infrared spectrum reflects that mainly the frequency multiplication and the sum of fundamental frequencies of hydrogeneous radicals X-H vibration absorb information.Be used at present the detection of oil, the medium organic principle of tobacco.The materials such as pectin of decision mango hardness maintaining time etc. all contain hydroxyl or carbonyl, and therefore, theoretically, near-infrared spectral analysis technology can be used for detecting the hardness of mango.Simultaneously, characteristics such as it is quick, simple to operate, harmless that near-infrared spectral analysis technology has are a kind of methods of comparatively desirable fast detecting mango hardness maintaining time.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, propose a kind of mango hardness maintaining time method for quick based on near-infrared spectral analysis technology.Technical scheme of the present invention is as follows:
A kind of mango hardness maintaining time method for quick may further comprise the steps:
1, the collection of modeling sample collection: gather representative mango sample and form the modeling sample collection;
2, the mensuration of near infrared spectrum: the near infrared spectrum of using the near infrared spectrometer collected specimens;
3, the pre-service of spectrum:, spectrum is carried out pre-service such as background removal, correction, denoising, characteristic point selection according to the characteristics of spectrum;
4, the mensuration of reference value: the hardness of measuring the mango sample according to standard method of analysis is as the reference value;
5, the foundation of model: adopt chemometrics method to set up calibration model between the reference value of near infrared spectrum and mango hardness maintaining time;
6, verification of model: gather main mango sample on the China market, gather and its near infrared spectrum of pre-service, its hardness is predicted, obtain predicted value according to the calibration model that step 5 is set up according to step 2 and 3; Measure the reference value of its hardness simultaneously according to step 4; The relatively reference value and the predicted value of this mango sample hardness, and according to the error requirements in the actual production, calibration model is optimized repeatedly;
7, the analysis of testing sample: gather the also near infrared spectrum of pre-service mango sample to be measured according to step 2 and 3, its hardness is made detection by quantitative with the calibration model of empirical tests.
Above-mentioned steps 1 will be gathered and can fully represent the place of production of mango and the mango sample of variety characteristic within the specific limits according to model range of application in the future.
Above-mentioned steps 2 is normally used near infrared spectrometer, with diffuse reflectance measurement mode collected specimens at 3600-12500cm
-1Near infrared spectrum in the wavelength coverage.
3 pairs of spectrum of above-mentioned steps carry out that method that pre-service adopts comprises that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, level and smooth, small echo denoising, differentiate conversion, genetic algorithm Wavelength optimization etc., and it is more definite repeatedly at concrete sample and spectrum specifically to adopt any or several preprocess methods to need.
Above-mentioned steps 4 described standard method of analyses generally are meant the method for GB regulation.
The chemometrics method that above-mentioned steps 5 adopts includes but not limited to one or more the combination in multiple linear regression, partial least squares regression, artificial neural network, the support vector machine.
The method for quick of mango hardness maintaining time provided by the invention can fast, accurately, nondestructively detect the hardness number of mango, and tool is sayed it, and method of the present invention has following advantage:
1, fast and convenient.The acquisition time of near infrared spectrum is very short, and in a single day model is set up, and the time of Model Calculation can be ignored substantially.
2, Non-Destructive Testing.Adopt near-infrared spectral analysis technology, only need, do not need that the mango sample is removed the peel processing and wait infringement to handle by its diffuse reflection spectrum of collecting fiber.
3, the present invention provides technical foundation for the detection of dynamic and the tracking of mango quality in logistics progress.
Description of drawings
Fig. 1 is the near infrared spectrum of platform farming mango sample in the embodiment of the invention.
Fig. 2 is the predicted value of the hardness of platform farming mango sample in the embodiment of the invention and the scatter diagram between the measured value.
Embodiment
Enforcement of the present invention mainly comprises using and safeguarding of modelling and model, and software and hardware facilities comprise parts such as near infrared spectrometer, y-type optical fiber, Chemical Measurement software, computing machine.With embodiment the specific embodiment of the present invention is described below.
Embodiment: the CCD-NIR Non-Destructive Testing of mango hardness maintaining time
Specimen preparation: the mango sample is selected the mango of different sizes, color for gathering 108 ' platform farming ' mango during collection, make the content range of its hardness big as far as possible, and the representativeness of sample is better.These mango are placed on laboratory environment (～20 ℃) and deposited 5 hours buying, and make its temperature consistent with laboratory temperature.
Near infrared spectra collection: adopt AvaSpec-2048CCD near infrared spectrometer (Avantes company) to measure the diffuse reflection spectrum of mango sample.Light source is halogen tungsten lamp (AvaLight-HAL), and detecting device is 2048 picture dot linear array CCD detecting devices, joins 75mm focusing optical platform, spectral range 580～1100nm, and CCD integral time is 2ms, scanning times is 100.With diameter 30mm, thickness is the BaSO of 5mm
4Standard reflecting plate is as reference.In 20 ℃ of constant temperature laboratories, measure, adopt y-type optical fiber to measure the diffuse reflection spectrum of mango, y-type optical fiber can transmit and receive near infrared light simultaneously, the optical fiber head contacts with the mango surface, light is by fiber-optic illuminated to mango, after the inner chemical constitution effect of mango, return by optical fiber again and receive.During test, a point is respectively got in center at positive and negative 2 longitudinal axis of the fruit body of the kidney shape of each mango, puts signs on, and avoids tangible surface imperfection (damage, scar) during mark, again the label place is carried out spectral scan, obtain the near infrared spectrum of 216 sampled points.
The mensuration of reference value: behind spectra collection, in the position of correspondence, according to the hardness (N/cm of NY/T 492-2002 with GY-1 type hardness tester manometry fruit
2).
The foundation of model:, set up the forecast model of hardness for 216 mango samples.Earlier remove 6 unusual samples during modeling, adopt the K-S method to divide calibration set, checking collection then according to external studentization residual error-lever value figure.The sample number and the distribution range of dividing each index of back see Table 1.The calibration set sample is used for setting up model, and checking collection sample is used for model is estimated.With wavelet transformation spectrum is carried out pre-service, the influence that prediction is caused with the noise of removing spectra collection is set up calibration model between near infrared spectrum and the reference value with partial least-squares regression method.
The statistical value of table 1. calibration set and checking collection mango sample hardness number
Modelling verification:, all adopt checking collection sample to verify for the calibration model of setting up.
Hardness with the model prediction sample obtains predicted value, measure the reference value (being measured value) of hardness with standard method, predicted value and measured value are compared, result such as table 2 and shown in Figure 2, show that the model of being set up can predict this parameter index of mango hardness maintaining time, its facies relationship number average higher (0.83) exactly.Above modeling result explanation near infrared spectrum can be measured the hardness of mango rapidly and accurately.
The near infrared of table 2. mango hardness maintaining time predicts the outcome
More than invention has been described by embodiment, should be understood that so openly can not explain the restriction of the present invention of opposing.To those skilled in the art, in connotation of the present invention and scope, various conversion and modification all are possible.Therefore, appended claims should be interpreted as having contained all conversion and modification.
Claims (7)
1. mango hardness maintaining time method for quick may further comprise the steps:
(1) collection of the representative sample sets of modeling;
(2) mensuration of near infrared spectrum;
(3) to the pre-service of near infrared spectrum;
(4) measure mango sample reference value according to standard method of analysis;
(5) adopt chemometrics method to set up calibration model between the reference value of near infrared spectrum and mango hardness maintaining time.
(6) gather main mango sample on the China market, gather and its near infrared spectrum of pre-service, its hardness is predicted, obtain predicted value according to the calibration model that step (5) is set up according to step (2) and (3); Measure the reference value of its hardness simultaneously according to step (4); The relatively reference value and the predicted value of this mango sample hardness, and according to the error requirements in the actual production, calibration model is optimized repeatedly;
(7) analysis of testing sample: gather the also near infrared spectrum of pre-service mango sample to be measured according to step (2) and (3), its hardness is made detection by quantitative with the calibration model of empirical tests.
2. the method for claim 1, it is characterized in that: described step (1) is according to the range of application of calibration model, and the place of production of mango and the mango sample of variety characteristic can be fully represented in interior collection.
3. the method for claim 1, it is characterized in that: described step (2) collected specimens is at 3600-12500cm
-1Diffuse reflection near infrared spectrum in the wavelength coverage.
4. the method for claim 1, it is characterized in that: described step (2) adopts y-type optical fiber to measure the diffuse reflection spectrum of mango, the y-type optical fiber head contacts with the mango surface, light is by fiber-optic illuminated to mango, after the inner chemical constitution effect of mango, return by optical fiber again and receive, avoid tangible surface imperfection (damage, scar) during measure spectrum, carry out spectral scan again.
5. the method for claim 1 is characterized in that: described step (3) to spectrum carry out that method that pre-service adopts is selected from that centralization, canonical variable conversion, additional scatter correction, orthogonal signal are proofreaied and correct, in level and smooth, small echo denoising, differentiate conversion and the genetic algorithm Wavelength optimization one or more.
6. the method for claim 1 is characterized in that: standard method of analysis described in the step (4) is the method for GB regulation.
7. the method for claim 1 is characterized in that: the chemometrics method that described step (5) adopts is selected from one or more in multiple linear regression, partial least squares regression, artificial neural network, the support vector machine.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100848356A CN102252971A (en) | 2011-04-06 | 2011-04-06 | Rapid detection method for mango hardness |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100848356A CN102252971A (en) | 2011-04-06 | 2011-04-06 | Rapid detection method for mango hardness |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102252971A true CN102252971A (en) | 2011-11-23 |
Family
ID=44980361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100848356A Pending CN102252971A (en) | 2011-04-06 | 2011-04-06 | Rapid detection method for mango hardness |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102252971A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590129A (en) * | 2012-01-11 | 2012-07-18 | 中国农业科学院农产品加工研究所 | Method for detecting content of amino acid in peanuts by near infrared method |
| CN102636455A (en) * | 2012-05-21 | 2012-08-15 | 山东理工大学 | Method for measuring hardness of agaricus bisporus by using near infrared spectrum |
| CN103091257A (en) * | 2013-01-16 | 2013-05-08 | 浙江工商大学 | Apple storage period detection method based on spectrum analysis |
| CN103487397A (en) * | 2013-09-23 | 2014-01-01 | 浙江农林大学 | Quick detecting method for hardness of phyllostachys pracecox shoots and device |
| CN104089885A (en) * | 2014-03-31 | 2014-10-08 | 浙江工商大学 | Beef quality rapid detection system and method |
| CN105606473A (en) * | 2016-01-20 | 2016-05-25 | 华中农业大学 | Red globe grape hardness nondestructive detection method based on machine vision |
| CN106596414A (en) * | 2016-11-14 | 2017-04-26 | 浙江大学 | Imaging method for internal hardness space distribution of peach fruit |
| CN106644958A (en) * | 2016-11-14 | 2017-05-10 | 浙江大学 | Method for imaging spatial distribution of pectin content in peach fruit |
| CN108535250A (en) * | 2018-04-27 | 2018-09-14 | 浙江大学 | ' Fuji ' ripe apples degree lossless detection method based on Streif indexes |
| CN108593594A (en) * | 2018-04-27 | 2018-09-28 | 浙江大学 | A kind of apple rigidity nondestructive testing method |
| CN108613951A (en) * | 2018-03-21 | 2018-10-02 | 浙江大学 | Portable fruit hardness non-destructive testing device and detection method |
| CN112179871A (en) * | 2020-10-22 | 2021-01-05 | 南京农业大学 | Method for nondestructive detection of caprolactam content in sauce food |
| CN112611722A (en) * | 2020-11-25 | 2021-04-06 | 攀枝花市农业技术推广服务中心 | Mango fruit maturity check out test set |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101158651A (en) * | 2007-11-16 | 2008-04-09 | 南京农业大学 | Device and method for detecting apples inside and outside quality based on laser image analyse |
-
2011
- 2011-04-06 CN CN2011100848356A patent/CN102252971A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101158651A (en) * | 2007-11-16 | 2008-04-09 | 南京农业大学 | Device and method for detecting apples inside and outside quality based on laser image analyse |
Non-Patent Citations (2)
| Title |
|---|
| 屠振华 等: "基于遗传算法和间隔偏最小二乘的苹果硬度特征波长分析研究", 《光谱学与光谱分析》, vol. 29, no. 10, 31 October 2009 (2009-10-31), pages 2760 - 2764 * |
| 屠振华 等: "芒果内部品质的CCD-NIR无损检测试验研究", 《光谱学与光谱分析》, vol. 28, no. 10, 31 October 2008 (2008-10-31), pages 111 - 112 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590129B (en) * | 2012-01-11 | 2014-03-26 | 中国农业科学院农产品加工研究所 | Method for detecting content of amino acid in peanuts by near infrared method |
| CN102590129A (en) * | 2012-01-11 | 2012-07-18 | 中国农业科学院农产品加工研究所 | Method for detecting content of amino acid in peanuts by near infrared method |
| CN102636455A (en) * | 2012-05-21 | 2012-08-15 | 山东理工大学 | Method for measuring hardness of agaricus bisporus by using near infrared spectrum |
| CN103091257A (en) * | 2013-01-16 | 2013-05-08 | 浙江工商大学 | Apple storage period detection method based on spectrum analysis |
| CN103487397A (en) * | 2013-09-23 | 2014-01-01 | 浙江农林大学 | Quick detecting method for hardness of phyllostachys pracecox shoots and device |
| CN103487397B (en) * | 2013-09-23 | 2015-10-28 | 浙江农林大学 | A kind of thunder bamboo shoots hardness method for quick and device |
| CN104089885A (en) * | 2014-03-31 | 2014-10-08 | 浙江工商大学 | Beef quality rapid detection system and method |
| CN105606473B (en) * | 2016-01-20 | 2018-06-15 | 华中农业大学 | A kind of red grape rigidity nondestructive testing method based on machine vision |
| CN105606473A (en) * | 2016-01-20 | 2016-05-25 | 华中农业大学 | Red globe grape hardness nondestructive detection method based on machine vision |
| CN106596414A (en) * | 2016-11-14 | 2017-04-26 | 浙江大学 | Imaging method for internal hardness space distribution of peach fruit |
| CN106644958A (en) * | 2016-11-14 | 2017-05-10 | 浙江大学 | Method for imaging spatial distribution of pectin content in peach fruit |
| CN106596414B (en) * | 2016-11-14 | 2019-03-29 | 浙江大学 | A kind of method of Peach fruits inner hardness spatial distribution imaging |
| CN106644958B (en) * | 2016-11-14 | 2019-04-05 | 浙江大学 | A kind of method of Peach fruits inside pectin content spatial distribution imaging |
| CN108613951A (en) * | 2018-03-21 | 2018-10-02 | 浙江大学 | Portable fruit hardness non-destructive testing device and detection method |
| CN108535250A (en) * | 2018-04-27 | 2018-09-14 | 浙江大学 | ' Fuji ' ripe apples degree lossless detection method based on Streif indexes |
| CN108593594A (en) * | 2018-04-27 | 2018-09-28 | 浙江大学 | A kind of apple rigidity nondestructive testing method |
| CN112179871A (en) * | 2020-10-22 | 2021-01-05 | 南京农业大学 | Method for nondestructive detection of caprolactam content in sauce food |
| CN112611722A (en) * | 2020-11-25 | 2021-04-06 | 攀枝花市农业技术推广服务中心 | Mango fruit maturity check out test set |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102252971A (en) | Rapid detection method for mango hardness | |
| EP2710353B1 (en) | SPECTROSCOPIC APPARATUS AND METHOD of DETERMINING COMPONENTS PRESENT IN A SAMPLE | |
| CN107703097B (en) | Method for constructing model for rapidly predicting crude oil property by using near-infrared spectrometer | |
| CN104897607A (en) | Food modeling and rapid detecting integration method and system adopting portable NIRS (near infrared spectroscopy) | |
| CN101738383A (en) | Mid-infrared spectrum-based method for quickly detecting contents of heavy metal elements in soil | |
| CN102636450A (en) | Method for detecting wolfberry polyose content in Chinese wolfberry in a nondestructive way based on near infrared spectrum technology | |
| CN104062258B (en) | Method for rapid determination of soluble solids in compound ass-hide glue pulp by near infrared spectroscopy | |
| CN1831516A (en) | Method for nondistructive discriminating variety and true and false of cigarette using visible light and near-infrared spectrum technology | |
| CN110082429B (en) | Tunnel lining nondestructive testing auxiliary judgment method combining machine learning | |
| CN110702663B (en) | Nondestructive rapid detection method for storage time of frozen meat | |
| CN109932333B (en) | Fruit firmness measuring system and method with acoustic vibration and near infrared spectrum fused | |
| CN105486655A (en) | Rapid detection method for organic matters in soil based on infrared spectroscopic intelligent identification model | |
| CN106018332A (en) | Near-infrared-spectrum citrus yellow shoot disease field detection method | |
| CN103487397B (en) | A kind of thunder bamboo shoots hardness method for quick and device | |
| CN115993344A (en) | Quality monitoring and analyzing system and method for near infrared spectrum analyzer | |
| CN109211829A (en) | A method of moisture content in the near infrared spectroscopy measurement rice based on SiPLS | |
| CN108956545A (en) | A kind of fruit internal quality Establishment of Nondestructive Testing Model method and system | |
| CN105300954A (en) | Method for Raman spectrum characterization of heterogeneous foam layer minerals in antimony ore flotation | |
| CN102937575B (en) | Watermelon sugar degree rapid modeling method based on secondary spectrum recombination | |
| CN107860743A (en) | Utilize the method and its application of the model of reflective near infrared fibre-optical probe structure fast prediction oil property | |
| CN111537469A (en) | Apple quality rapid nondestructive testing method based on near-infrared technology | |
| CN105092436A (en) | Sediment particle size spectrum analysis method and device | |
| CN102128805A (en) | Method and device for near infrared spectrum wavelength selection and quick quantitative analysis of fruit | |
| CN105223140A (en) | The method for quickly identifying of homology material | |
| CN103743705A (en) | Rapid detection method for sorghum halepense and similar species |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20111123 |