CN109164062A - A kind of method of near infrared ray "Hami" melon titratable acid content value - Google Patents
A kind of method of near infrared ray "Hami" melon titratable acid content value Download PDFInfo
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- CN109164062A CN109164062A CN201811305647.XA CN201811305647A CN109164062A CN 109164062 A CN109164062 A CN 109164062A CN 201811305647 A CN201811305647 A CN 201811305647A CN 109164062 A CN109164062 A CN 109164062A
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- 241000219112 Cucumis Species 0.000 title claims abstract description 49
- 235000015510 Cucumis melo subsp melo Nutrition 0.000 title claims abstract description 49
- FJJCIZWZNKZHII-UHFFFAOYSA-N [4,6-bis(cyanoamino)-1,3,5-triazin-2-yl]cyanamide Chemical compound N#CNC1=NC(NC#N)=NC(NC#N)=N1 FJJCIZWZNKZHII-UHFFFAOYSA-N 0.000 title claims abstract description 46
- 239000002253 acid Substances 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000001514 detection method Methods 0.000 claims abstract description 22
- 241000366676 Justicia pectoralis Species 0.000 claims abstract description 20
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 17
- 238000005259 measurement Methods 0.000 claims abstract description 8
- 230000008569 process Effects 0.000 claims abstract description 6
- 238000007689 inspection Methods 0.000 claims abstract description 5
- 238000010561 standard procedure Methods 0.000 claims abstract description 5
- 238000001228 spectrum Methods 0.000 claims description 20
- 238000012937 correction Methods 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 12
- 238000009499 grossing Methods 0.000 claims description 11
- 239000012141 concentrate Substances 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 238000005303 weighing Methods 0.000 claims 1
- 239000000126 substance Substances 0.000 abstract description 6
- 238000012360 testing method Methods 0.000 abstract description 5
- 238000004448 titration Methods 0.000 abstract description 3
- 239000000523 sample Substances 0.000 description 30
- 235000013399 edible fruits Nutrition 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000009102 absorption Effects 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004611 spectroscopical analysis Methods 0.000 description 2
- 244000144730 Amygdalus persica Species 0.000 description 1
- 241000207199 Citrus Species 0.000 description 1
- 235000016623 Fragaria vesca Nutrition 0.000 description 1
- 240000009088 Fragaria x ananassa Species 0.000 description 1
- 235000011363 Fragaria x ananassa Nutrition 0.000 description 1
- 241000220225 Malus Species 0.000 description 1
- 235000011430 Malus pumila Nutrition 0.000 description 1
- 235000015103 Malus silvestris Nutrition 0.000 description 1
- 235000006040 Prunus persica var persica Nutrition 0.000 description 1
- 241000220324 Pyrus Species 0.000 description 1
- 125000003636 chemical group Chemical group 0.000 description 1
- 235000020971 citrus fruits Nutrition 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 235000021017 pears Nutrition 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000007811 spectroscopic assay Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
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- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
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- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention belongs to technical field of agricultural product detection, and in particular to a kind of method of near infrared ray "Hami" melon titratable acid content value, including collect "Hami" melon sample；With the titratable acid content value of national standard method measurement fresh-cut "Hami" melon；Acquire the atlas of near infrared spectra of fresh-cut "Hami" melon sample；The atlas of near infrared spectra is pre-processed, disturbing factor, chosen wavelength range and pretreatment mode are eliminated；Establish the calibration model between the titratable acid content value of fresh-cut "Hami" melon and near infrared spectrum and inspection.The present invention measuring process cumbersome without chemical titration, saves testing cost, reduces detection time, improve detection efficiency, promote detection accuracy.
Description
Technical field
The invention belongs to technical field of agricultural product detection, and in particular to a kind of near infrared ray "Hami" melon titratable acid
The method of content value.
Background technique
Near infrared reflectance spectroscopy is the material information abundant for including using near-infrared spectra area, absorption band
Absorption intensity is related with the content of molecular composition or chemical group, can be used for measuring the ingredient of chemical substance and analyzes physical
Matter.To the substance of certain no Near-infrared Spectral Absorptions, can also be changed by the near infrared spectrum for the bulk mass that it coexists,
Ground connection reflects its information.
When near infrared light fruit, due to the inside and outside feature difference of fruit, near infrared light can be generated different degrees of
Absorption or the characteristics such as reflection, be reflected to the constituent of fruit and structure feature in relevant atlas of near infrared spectra,
The Fast nondestructive evaluation to fruit quality can be realized from the quality information for spectrally extracting fruit in turn.By grinding for many years
Study carefully and show to be able to achieve under the premise of not destroying sample using near-infrared spectrum technique, to pears, apple, strawberry, peach, citrus etc.
The quick detection of many indexs such as pol, acidity, hardness, the Vc of various fruits.The purpose of quantitative analysis modeling is that foundation is close red
The correlative connection of external spectrum technology and sample component.The regression model that Partial Least Squares (PLS method) is established compared with other methods is more
Readily discernible system information and noise, also can in independent variable there are carrying out regression modeling under conditions of serious multiple correlation,
Its modelling effect has degree of precision relative to other method of discrimination.The information that modeling sample spectrum is included will pass through PLS method
It is associated with the information of group score value.This method assume from optical spectroscopy to system variation be the variation of group score value as a result, and
The correlativity of group score value and its signal intensity needs not be linear.The repetition that can eliminate sample is pre-processed to original spectrum
The interference of the factors such as property, noise, impurity and thickness of sample difference, to improve the accuracy of model.
Although near infrared light spectrometry applied it is more, not yet discovery near infrared light spectroscopic assay "Hami" melon
The application of titratable acid content value lacks the corresponding model of "Hami" melon titratable acid content value measurement.Existing "Hami" melon can drip
Determine acid content value detection technique and still relies on chemical titration to measure, since its is cumbersome, complicated, detection time is long, effect
Rate is low, is not easy to the quick detection to "Hami" melon titratable acid content value.
Summary of the invention
A kind of method of near infrared ray "Hami" melon titratable acid content value provided by the invention, solves chemical drop
Determine the problem of method measurement "Hami" melon titratable acid bring is cumbersome, complicated, detection time is long, inefficiency.
The present invention provides a kind of methods of near infrared ray "Hami" melon titratable acid content value, including following step
It is rapid:
The selection of sample: S1 takes several pieces of fresh-cut Hami melon pulps, as modeling sample collection；
S2, National Standard Method detect the titratable acid content value of all fresh-cut Hami melon pulps, obtain all samples of modeling sample collection
The titratable acid content value of product；
S3, near infrared spectrum detection: measurement modeling sample concentrates the atlas of near infrared spectra of all samples, obtains modeling sample
Concentrate the primary light spectrogram of all samples；
S4 establishes best correction model using modeling sample collection primary light spectrogram
The primary light spectrogram that modeling sample concentrates all samples is called in OPUS software, is then corresponded input and is measured
Modeling sample collection all samples titratable acid content value, using Partial Least Squares by modeling sample in spectrum wave-number range
It concentrates all samples primary light spectrogram to carry out the pretreatment of different modes by 7.5 software of OPUS, obtains the pre- place of different samples
Manage data；System of being tested by the way of crosscheck Automatic Optimal filters out optimal wave-number range and pretreatment side
Formula, and the best correction model of final output.
Preferably, in S1, fresh-cut Hami melon pulp is the square block of 1.8~2.1cm of side length.
Preferably, in the near infrared spectrum detection process of S3, environment temperature is 25 ± 1 DEG C of room temperature, relative humidity 20%
~30%, 12000~4000cm of spectrum wave-number range- 1, resolution ratio 8cm- 1, scan 64 times.
Preferably, external information interference is eliminated at interval of 1h run-down background guarantees that the stability of spectrum is missed to reduce
Difference.
Preferably, the pretreatment mode of primary light spectrogram includes following methods: eliminating constant offset, subtracts one
Straight line, vector normalization, min-max normalization, polynary scatter correction, internal standard, first derivative+5,9,13,17,21,
25 smoothing processings, second dervative+5,9,13,17,21,25 smoothing processings, first derivative+subtract straight line+5,9,13,
17,21,25 smoothing processings, first derivative+SNV+ is smooth, the smoothing processing of first derivative+MSC+5,9,13,17,21,25.
Preferably, system of being tested by the way of crosscheck Automatic Optimal filters out optimal wave-number range and pre-
Processing mode, by measuring root-mean-square error RMSECV and directional gain R2To measure the quality of model, R2Numerical value is closer
100% is predicted content value closer to true value；RMSECV numerical value is the smaller the better.
Optimal wave-number range and pretreatment mode are 4 249.8~9400.9cm respectively-1Range and subtract straight line
Pretreatment mode.
Preferably, further include model verification step: several pieces of fresh-cut Hami melon pulps are in addition taken, as Prediction；
National Standard Method detects the titratable acid content value of all fresh-cut Hami melon pulps, obtains Prediction all samples
Titratable acid content value；
The atlas of near infrared spectra for measuring all samples in Prediction obtains the original of all samples in Prediction
Spectrogram；
The primary light spectrogram of all samples in Prediction is called in OPUS software, is then corresponded input and is measured
Prediction all samples titratable acid content value, the verification mode examined using inspection set existed using Partial Least Squares
Under the conditions of optimal wave-number range and pretreatment mode that S4 is filtered out, by the root-mean-square error that Prediction is calculated
RMSEP value, if when RMSEP≤RMSECV, illustrating that model built prediction effect is splendid, precision is high.
Compared with prior art, the method for near infrared ray "Hami" melon titratable acid content value of the invention have with
It is lower the utility model has the advantages that
The present invention develops the detection method of the "Hami" melon titratable acid content value based near infrared spectrum, nothing for the first time
The measuring process that chemical titration is cumbersome is needed, testing cost is saved, reduces detection time, improves detection efficiency, promotes detection essence
Degree.
Detailed description of the invention
Fig. 1 is the best correction model that the present invention establishes；
Fig. 2 is the prediction model that the present invention establishes.
Specific embodiment
The present invention is described in detail combined with specific embodiments below, but should not be construed as limitation of the invention.It is following
The test method of actual conditions is not specified in embodiment, operates usually according to normal condition, due to not being related to inventive point, thus it is not right
Its step is described in detail.
Embodiment 1
The present invention provides a kind of methods of near infrared ray "Hami" melon titratable acid content value, including following step
It is rapid:
The selection of sample: S1 takes several pieces of fresh-cut Hami melon pulps, as modeling sample collection；In addition several pieces of fresh-cuts are taken
Hami melon pulp, as Prediction；Fresh-cut Hami melon pulp selection standard is as follows:
20 "Hami" melons are chosen, 80 pieces of fresh-cut Hami melon pulps are cut on different "Hami" melons at random, as modeling sample collection；
In addition 10 "Hami" melons are chosen, 40 pieces of fresh-cut Hami melon pulps are cut at random, as Prediction.Fresh-cut Hami melon pulp is
The square block of 1.8~2.1cm of side length, now cuts current.
S2 measures all fresh-cut Hami melon pulp samples according to GB/T 12456-2008 " measurement of total acid in food "
Titratable acid content value respectively obtains the titratable acid content value and Prediction all samples of modeling sample collection all samples
Titratable acid content value；The titratable acid content value of modeling sample collection all samples is in 0.04~0.15g/100g；
The Texture instrument parameter of total acidity test are as follows: the model P/50 of Texture instrument probe；It is 0.5mm/ that the rate before surveying, which is arranged,
s；The rate of test is 0.5mm/s；Rate after survey is 0.5mm/s；Decrement is 30%；Trigger force is 5g.
S3, near infrared spectrum detection: the infrared spectrogram of all samples in measurement modeling sample collection and Prediction, point
The primary light spectrogram that modeling sample concentrates all samples in the primary light spectrogram and Prediction of all samples is not obtained；Specifically
It operates as follows:
TENSOR II type Fourier Transform Near Infrared instrument is preheated into 30min, opens 7.5 software of OPUS via inspection
Signal saves peak position, scanning background single channel spectrum, after the interference of background to be canceled, by being put into for fresh-cut Hami melon pulp
TENSOR II type Fourier Transform Near Infrared instrument detection mouth, measurement modeling sample are concentrated the spectrum of all samples, are built
Apperance product concentrate the primary light spectrogram of all samples, measure the spectrum of all samples in Prediction, obtain Prediction
The primary light spectrogram of middle all samples；
In detection process, external information interference is eliminated at interval of 1h run-down background and guarantees the stability of spectrum to subtract
Few error.
In detection process, environment temperature is 25 ± 1 DEG C of room temperature, and relative humidity is 20%~30%, spectrum wave-number range 12
000~4 000cm- 1, resolution ratio 8cm- 1, scan 64 times.
S4 establishes best correction model using modeling sample collection primary light spectrogram
The primary light spectrogram that modeling sample concentrates all samples is called in OPUS software, is then corresponded input and is measured
Modeling sample collection all samples titratable acid content value, will be built in spectrum wave-number range using Partial Least Squares (PLS)
Mould sample sets all samples primary light spectrogram carries out the pretreatment of different modes by 7.5 software of OPUS, obtains different samples
Preprocessed data；System of being tested by the way of crosscheck Automatic Optimal filters out suitable wave-number range and pretreatment
Mode, and initially export best correction model.Specific step is as follows:
7.5 software of OPUS is opened, quantitative approach is established in selection, calls in the original spectrum that modeling sample concentrates all samples
Then figure corresponds the modeling sample collection all samples titratable acid content value that input measures, using PLS method in spectrum wave
Number 12 000~4 000cm of range- 1It is interior, modeling sample collection primary light spectrogram is subjected to different modes by 7.5 software of OPUS
Pretreatment, obtain the preprocessed data of different samples；The pretreatment mode of original spectrum includes following methods: being eliminated normal
Number offset subtracts straight line, vector normalization (Standard Normal Variate, SNV), min-max normalizing
Change, polynary scatter correction (Multiplicative Scatter Correction, MSC), internal standard, first derivative+smooth
(5,9,13,17,21,25 smoothing processings), second dervative+smooth (5,9,13,17,21,25 smoothing processings), first derivative
+ subtract that straight line+smooth (5,9,13,17,21,25 smoothing processings), first derivative+SNV+ be smooth, first derivative+MSC+
Smoothly (5,9,13,17,21,25 smoothing processings)；
System of being tested by the way of crosscheck Automatic Optimal filters out optimal wave-number range and pretreatment side
Formula, by measuring root-mean-square error (RMSECV) and directional gain (R2) measure the quality of model.Wherein R2Numerical value is closer
100% is predicted content value closer to true value；RMSECV numerical value is the smaller the better；
Finally obtain in 4 249.8~9400.9cm-1It is used in optimal wave-number range and subtracts straight line pretreatment side
The correction model effect that formula is established is preferable, as best correction model, for measuring "Hami" melon titratable acid content value, and
Best correction model file is saved backup, the best correction model that 7.5 software of OPUS is opened is as shown in Figure 1, RMSECV is
0.00293, R2It is 98.38；
S5, model verifying
7.5 software of OPUS is opened, selection establishes quantitative approach, calls in the original spectrum of all samples in Prediction
Then figure corresponds the Prediction all samples titratable acid content value that input measures, the side examined using inspection set
Formula is using PLS method in 4 249.8~9400.9cm of wave number-1It is smart to model using straight line pretreatment mode is subtracted in range
Degree is predicted, by the way that root-mean-square error (RMSEP) value is calculated, if when RMSEP≤RMSECV, illustrating built best model
It is good to measure effect, prediction, test effect are splendid, and precision is high, can be used for measuring "Hami" melon titratable acid content value.
The prediction model that the embodiment of the present invention is established using Prediction primary light spectrogram is as shown in Fig. 2, prediction mould
The RMSEP of type is 0.0031, R2It is 98.48.Illustrate that the best correction model precision shown in FIG. 1 that we establish is high.
It should be noted that when the present invention provides numberical range, it should be appreciated that except non-present invention is otherwise noted, every number
Being worth any one numerical value between two endpoints and two endpoints of range can be selected.Unless otherwise defined, make in the present invention
All technical and scientific terms are identical as the normally understood meaning of those skilled in the art of the present technique.Although this hair has been described
Bright preferred embodiment, once a person skilled in the art knows basic creative concepts, then can be to these embodiments
Make other change and modification.So the following claims are intended to be interpreted as including preferred embodiment and falls into the present invention
All change and modification of range.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (8)
1. a kind of method of near infrared ray "Hami" melon titratable acid content value, which comprises the following steps:
The selection of sample: S1 takes several pieces of fresh-cut Hami melon pulps, as modeling sample collection；
S2, National Standard Method detect the titratable acid content value of all fresh-cut Hami melon pulps, obtain modeling sample collection all samples
Titratable acid content value；
S3, near infrared spectrum detection: measurement modeling sample concentrates the atlas of near infrared spectra of all samples, obtains modeling sample concentration
The primary light spectrogram of all samples；
S4 establishes best correction model using modeling sample collection primary light spectrogram
The primary light spectrogram that modeling sample concentrates all samples is called in OPUS software, what then one-to-one correspondence input measured builds
Mould sample sets all samples titratable acid content value, is concentrated modeling sample in spectrum wave-number range using Partial Least Squares
All samples primary light spectrogram carries out the pretreatment of different modes by 7.5 software of OPUS, obtains the pretreatment number of different samples
According to；System of being tested by the way of crosscheck Automatic Optimal filters out optimal wave-number range and pretreatment mode, and
The best correction model of final output.
2. the method for near infrared ray "Hami" melon titratable acid content value according to claim 1, which is characterized in that
In S1, fresh-cut Hami melon pulp is the square block of 1.8~2.1cm of side length.
3. the method for near infrared ray "Hami" melon titratable acid content value according to claim 1, which is characterized in that
In the near infrared spectrum detection process of S3, environment temperature is 25 ± 1 DEG C of room temperature, and relative humidity is 20%~30%, spectrum wave number
12 000~4 000cm of range- 1, resolution ratio 8cm- 1, scan 64 times.
4. the method for near infrared ray "Hami" melon titratable acid content value according to claim 3, which is characterized in that
External information interference is eliminated at interval of 1h run-down background guarantees the stability of spectrum to reduce error.
5. the method for near infrared ray "Hami" melon titratable acid content value according to claim 4, which is characterized in that
The pretreatment mode of primary light spectrogram includes following methods: eliminating constant offset, subtracts straight line, vector normalizing
Change, min-max normalization, polynary scatter correction, internal standard, first derivative+5,9,13,17,21,25 smoothing processings,
Second dervative+5,9,13,17,21,25 smoothing processings, first derivative+subtract straight line+5,9,13,17,21,25 points it is flat
Sliding processing, first derivative+SNV+ is smooth, the smoothing processing of first derivative+MSC+5,9,13,17,21,25.
6. the method for near infrared ray "Hami" melon titratable acid content value according to claim 5, which is characterized in that
System of being tested by the way of crosscheck Automatic Optimal filters out optimal wave-number range and pretreatment mode, passes through weighing apparatus
Measure root-mean-square error RMSECV and directional gain R2To measure the quality of model, R2Numerical value predicts that content value is cured closer to 100%
Close to true value；RMSECV numerical value is the smaller the better.
7. the method for near infrared ray "Hami" melon titratable acid content value according to claim 6, which is characterized in that
Optimal wave-number range and pretreatment mode are 4 249.8~9400.9cm respectively-1Range and subtract straight line pretreatment side
Formula.
8. the method for near infrared ray "Hami" melon titratable acid content value according to claim 1, which is characterized in that
Further include model verification step: several pieces of fresh-cut Hami melon pulps is in addition taken, as Prediction；
National Standard Method detects the titratable acid content value of all fresh-cut Hami melon pulps, obtains dripping for Prediction all samples
Determine acid content value；
The atlas of near infrared spectra for measuring all samples in Prediction, obtains the original spectrum of all samples in Prediction
Figure；
The primary light spectrogram of all samples in Prediction is called in OPUS software, then one-to-one correspondence input measures pre-
Sample collection all samples titratable acid content value, the verification mode examined using inspection set are sieved using Partial Least Squares in S4
Under the conditions of the optimal wave-number range and pretreatment mode selected, by the root-mean-square error that Prediction is calculated
RMSEP value, if when RMSEP≤RMSECV, illustrating that model built prediction effect is splendid, precision is high.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114486803A (en) * | 2022-02-16 | 2022-05-13 | 西南大学 | Modeling method of fruit and vegetable performance parameter quantitative analysis model and detection method of fruit and vegetable performance parameters |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104062263A (en) * | 2014-07-11 | 2014-09-24 | 中国农业大学 | Near-infrared universal model detection method for quality indexes of fruits with similar optical and physical properties |
| CN107132197A (en) * | 2017-05-25 | 2017-09-05 | 江苏大学 | A kind of detection method and device of vinegar total acid content |
| CN108304970A (en) * | 2018-02-05 | 2018-07-20 | 西北农林科技大学 | The method for quick predicting and system of Apple, air conditioned storage monitoring system |
-
2018
- 2018-11-05 CN CN201811305647.XA patent/CN109164062A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104062263A (en) * | 2014-07-11 | 2014-09-24 | 中国农业大学 | Near-infrared universal model detection method for quality indexes of fruits with similar optical and physical properties |
| CN107132197A (en) * | 2017-05-25 | 2017-09-05 | 江苏大学 | A kind of detection method and device of vinegar total acid content |
| CN108304970A (en) * | 2018-02-05 | 2018-07-20 | 西北农林科技大学 | The method for quick predicting and system of Apple, air conditioned storage monitoring system |
Non-Patent Citations (4)
| Title |
|---|
| 宋雪健 等: "近红外光谱技术对鲜切哈密瓜品质检测的研究", 《农产品加工》 * |
| 王茜 等: "枇杷内部品质近红外光谱无损检测", 《FOOD&MACHINERY》 * |
| 章海亮 等: "近红外漫反射无损检测赣南脐橙中可溶性固形物和总酸", 《食品科学》 * |
| 袁雷 等: "柑橘品质的近红外光谱无损检测", 《光谱实验室》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114486803A (en) * | 2022-02-16 | 2022-05-13 | 西南大学 | Modeling method of fruit and vegetable performance parameter quantitative analysis model and detection method of fruit and vegetable performance parameters |
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Application publication date: 20190108 |