CN106932361A - The method for building up of Peach fruits maturity forecast model - Google Patents
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- 244000174681 Michelia champaca Species 0.000 description 1
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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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Abstract
The invention discloses the method for building up of Peach fruits maturity forecast model, its step includes：Colour-change period is harvested respectively and the Peach fruits of harvesting ripe degree are reached, and is respectively labeled as maturity I and maturity II；To the Peach fruits measurement pericarp absorbance difference I for pickingAD, determine the hardness of Peach fruits；By maturity I, the hardness of maturity II fruits and IADValue is fitted, and sets up Peach fruits maturity predictive equation y=ax2+bx+c.Fruit rind absorbance difference I of the present invention based near infrared technology non-destructive determinationADPeach fruits maturity forecast model is established, through regression forecasting, estimate is not different with actual observation value difference notable, illustrate that forecast model has accuracy higher, measurement, computational methods are simple, are capable of achieving Peach fruits timely collecting.
Description
Technical field
The present invention relates to a kind of method for building up of Peach fruits maturity forecast model.
Background technology
In peach market circulation, fruit harvesting maturity is the principal element for restricting its commodity value all the time.Peach fruits into
The accurate judgement of ripe degree is most important to timely collecting, classification, packaging, transport, guarantee commodity etc..Peach jumps as one kind breathing
Modification fruit, acetate releasing quantity steeply rises its fruit during maturing, the transcription of simultaneous gene, and these changes are often
All adjusted by hormone.In the process, the hardness of fruit, inclusion constituent content and ratio and pericarp background color etc. be all
Corresponding change can occur, the fruit correlation merit index of differing maturity has notable difference.Based on destructive or non-demolition
Property the method existing more research of detection Peach fruits maturity.Zhang Binbin etc. disclose based on Peach fruits soluble solid content with
The correlation of maturity, by research ' lake scape honeydew ' fruit soluble solids content and the regression relation of the index of quality, adopts
The predictive equation of belt leather hardness, peeling hardness and soluble solid content is tentatively established with the quadratic polynomial Return Law.
Near infrared spectroscopy prediction Peach fruits maturity aspect, Nascimento etc. are to short low temperature peach soluble solid content and fruit
The LEAST SQUARES MODELS FITTING of hardness is studied, and the prediction mould of soluble solid content and the hardness of fruit is established respectively
Type, but also indicate that the model still needs to optimization simultaneously.It is lossless that Matteoli etc. proposes a kind of Peach fruits maturity based on spectrum
Stage division, it is by by Many-faceted researching technology being applied to obtained reflectance spectrum and being estimated by fuzzy classification system
Flesh firmness and then maturity classification is carried out, to realize that Peach fruits maturity automatic classification is laid a good foundation.
Pericarp absorbance difference index (IAD) be based on fruit maturation early stage pericarp chlorophyll degradation and its with maturity
Substantial connection and a kind of index for setting up, it utilizes near-infrared spectrum technique, poor by the absorbance for reading 670nm and 720nm
Value forms IADIndex, directly reflects the actual content of chlorophyll a.IADNon-destructive determination it is not disruptive to fruit, reading is fast
Victory is convenient, and advantage is better than the Destructive determination of hardness and soluble solid content, and fruit quality is carried out to the fruit supply end of the chain
Estimation has preferable application effect.
The content of the invention
It is an object of the invention to provide a kind of method for building up of Peach fruits maturity forecast model.
The purpose of the present invention is achieved through the following technical solutions：
A kind of method for building up of Peach fruits maturity forecast model, its step includes：
(1) colour-change period is harvested respectively and the Peach fruits of harvesting ripe degree are reached, and is respectively labeled as maturity I and maturity
II；Peach fruits substantially 7 are ripe during colour-change period, pericarp cyan, and color starts to change to red；The fruit for reaching harvest maturity is big
8.5 maturations, the cyan of pericarp background color are caused gradually to take off, red colored degree is high；
(2) the Peach fruits measurement pericarp absorbance difference I to pickingAD, determine the hardness of Peach fruits；
(3) by maturity I, the hardness of maturity II fruits and IADValue is fitted, and sets up Peach fruits maturity prediction side
Journey y=ax2+ bx+c, wherein y are hardness, and x is IADValue, the quadratic polynomial coefficient that a, b, c are fitted for regression analysis.
Preferably, the hardness is belt leather hardness.
Preferably, the hardness is peeling hardness.
Preferably, the Peach fruits are the Peach fruits of same kind.
Preferably, the kind of the Peach fruits is late-maturing common peach rosy clouds sunshine 8 or the late-maturing nectarine rays of sunlight.
Preferably, by belt leather hardness and IADData are arranged in SPSS softwares, are carried out using quadratic polynomial regression block
Fitting, the Peach fruits maturity predictive equation being fitted.
Preferably, hardness and I will be removed the peelADData are arranged in SPSS softwares, are carried out using quadratic polynomial regression block
Fitting, the Peach fruits maturity predictive equation being fitted.
Preferably, the Peach fruits of maturity I are 30 in the step (1), and the Peach fruits of maturity II are 30.
Preferably, the IADValue is determined using DA-Meter.
Fruit rind absorbance difference I of the present invention based near infrared technology non-destructive determinationADEstablish Peach fruits maturity
Forecast model, through regression forecasting, estimate is different with actual observation value difference not significantly, illustrate forecast model with higher accurate
Property, measurement, computational methods simply, are capable of achieving Peach fruits timely collecting.
Brief description of the drawings
Fig. 1 is ' rosy clouds sunshine 8 ' Peach fruits hardness and IADThe regression curve of value.
Fig. 2 is ' rays of sunlight ' Peach fruits hardness and IADThe regression curve of value.
Specific embodiment
Embodiment 1
The method for building up of this Peach fruits maturity forecast model, its step includes：
(1) fruit with the late-maturing common peach ' rosy clouds sunshine 8 ' of life in 7 years and late-maturing nectarine ' rays of sunlight ' 2 kinds is test material.
Fructescence, in the fair weather morning 8:The colour-change period (about 7 that 00 harvesting is grown more consistent per kind tree body middle part periphery
Maturation, maturity I) and each 30 of the fruit of harvest maturity (about 8.5 ripe, maturity II) is reached, laboratory is taken back immediately.
By each fruit numbering, and 2 sides middle part of each fruit is marked.Participate in the experiment the plant tree vigo(u)r golden mean of the Confucian school, north and south row is to cultivation
Plant, tree-like is natural open centre shape, Root growth, routinely cultivation step management.
(2) I of pericarp is determined using DA-Meter (TR Turoni srl, Forl ì, Italy)ADValue, uses
The belt leather hardness and peeling hardness of TA.XT.Plus type instrumental test fruits, probe diameter 8mm, MTD 5mm, injection speed
Rate 1mms-1。
(3) respectively with ' rosy clouds sunshine 8 ', the belt leather hardness of ' rays of sunlight ' whole fruit and IAD, peeling hardness and IADCarry out secondary
Polynomial regression, the figure for fitting is shown in Fig. 1, Fig. 2, the Peach fruits maturity predictive equation y=ax of foundation2+ bx+c such as the institutes of table 1
Show, wherein y represents belt leather hardness or peeling hardness, and y is hardness, and x is IADValue.As can be seen that the R of each regression model2It is higher,
P values are 0.0001, and regression relation reaches the pole level of signifiance.Further the quadratic term to each regression equation carries out variance analysis,
Significance test result shows (table 2), the two of the belt leather of ' rosy clouds sunshine 8 ', peeling hardness model and ' rays of sunlight ' belt leather hardness model
Secondary item the results of analysis of variance partial regression coefficient is up to the pole level of signifiance (P<0.01), and to the peeling hardness model of ' rays of sunlight ' fruit and
Speech, the partial regression coefficient of quadratic term is the level of signifiance (P<0.05), Durbin-Watson statistics are not apparent from deviation 2.Through returning
Prediction, estimate is not different with actual observation value difference notable, and regression equation fitting effect is good.
Table 1 is based on hardness and IADThe regression model of value
The variance analysis of the regression coefficient of table 2
The Color Quest XE colour difference meters produced using Hunter Lab companies of the U.S. determine pericarp brightness value (L*), it is red
Green difference (a*) and champac value of chromatism (b*), and calculate color saturation (C), hue angle (h) (Voss, 1992；Koukounaras
Et al., 2009) and a*/b*.The pulp of 2 sides is cut down respectively, pulp homogenate, pulp soluble solid content (SSC)
Determined with the portable digital display refractometers (PAL-1) of ATAGO, the efficient liquid phases of Agilent 1100 produced with Agilent companies of the U.S.
Chromatograph determines sucrose, glucose, fructose, sorbierite, malic acid, chinic acid and citric acid content (Zhang et in pulp
Al., 2015).Total sugar content is the summation of each soluble sugar constituent content, and total acid content is the total of each organic acid composition content
With, according to total reducing sugar, total acid content calculate sugar-acid ratio.
Calculate respectively the average value of 2 kind maturity I and each index of maturity II fruits, standard deviation, luffing, extreme difference and
The coefficient of variation, the results are shown in Table 3- tables 6.It can be seen that, the I of ' rosy clouds sunshine 8 ' and ' rays of sunlight ' fruit in maturity IAD, aberration, hardness,
SSC, sucrose, fructose, sorbierite, citric acid and total sugar content are respectively provided with similar height compared with the fruit of maturity II and become
Gesture.The I of maturity II fruitsAD、L*、b*, h, belt leather hardness, peeling hardness, sorbierite, citric acid content is compared with maturity I's
Fruit reduction, but a*、C、a*/b*, SSC, sucrose, total sugar content it is then opposite.In glucose, malic acid, chinic acid, total acid content
In terms of sugar-acid ratio, 2 kinds of differing maturity have difference：' the rosy clouds sunshine 8 ' glucose content and sugar-acid ratio of maturity I
Low compared with the fruit of maturity II and malic acid, chinic acid and total acid are then higher, these indexs of ' rays of sunlight ' fruit then show as phase
Anti- trend.Show that Peach fruits maturity is higher, IADValue is smaller, and the hardness of fruit declines, and red shade is deepened, and inclusion level increases
Plus, but soluble sugar and organic acid composition because variety type is different difference.From table 3,4,2 ' rosy clouds of maturity
Sunshine 8 ' fruit IAD, sorbierite, chinic acid, citric acid content the coefficient of variation it is all larger.Additionally, a of maturity I fruits*、
h、a*/b*, total acid, saccharic acid when the belt leather hardness of maturity II fruits, peeling hardness also have the larger coefficient of variation.By table
5th, 6 understand, 2 I of ' rays of sunlight ' fruit of maturityAD、h、a*/b*, belt leather hardness, peeling hardness, sorbierite, chinic acid, lemon
Lemon acid content is respectively provided with the coefficient of variation higher, and maturity I fruits a*, maturity II fruit cane sugar contents the coefficient of variation
It is higher.Show IADIt is that most sensitive index is determined to Peach fruits maturity；The larger index of quality of the coefficient of variation and IADPass
System is more close, acts on bigger in terms of fruit maturity is influenceed.
Table 3 ' rosy clouds sunshine 8 ' maturity I Peach fruits index of quality analysiss of variance
Table 4 ' rosy clouds sunshine 8 ' maturity II Peach fruits index of quality analysiss of variance
The I Peach fruits index of quality analysiss of variance of table 5 ' rays of sunlight ' maturity
The II Peach fruits index of quality analysiss of variance of table 6 ' rays of sunlight ' maturity
Respectively by maturity I, maturity II and aberration, hardness, soluble solid content, the soluble sugar of whole fruits
Component, organic acid composition index and IADValue carries out correlation analysis, the results are shown in Table 7.For ' rosy clouds sunshine 8 ', except chinic acid contains
Amount is outer, and other each indexs of whole fruits are and IADWith notable or extremely significant dependency relation；And ' rays of sunlight ' whole fruit
Aberration, hardness, glucose and sorbitol content and IADNotable or extremely significantly correlated, other indexs have no obvious correlation.Often
3 index of quality of fruit colony of individual kind respectively with IADThe result for carrying out correlation analysis shows, most indexs such as L*、h、
a*/b*, sucrose, chinic acid, total acid, some indexs of the only a certain maturity such as sugar-acid ratio and IADIt is notable or extremely significantly correlated, product
Inter-species uniformity is poor.And C, belt leather hardness, peeling hardness and sorbitol content and IADCorrelation analysis result show, it is different
The These parameters of colony are and IADIt is notable or extremely it is significantly correlated (belt leather of ' rosy clouds sunshine 8 ' maturity I fruits and peeling hardness, into
Except the sorbitol content of ripe degree II fruits and the C of ' rays of sunlight ' maturity I fruits), illustrate 4 indexs to fitting Peach fruits into
Ripe degree forecast model operability is stronger.Compare this 4 indexs and IADThe absolute value of coefficient correlation can find, each colony's fruit
Belt leather, peeling hardness are high compared with C and sorbitol content, show that Peach fruits hardness judges closely related with maturity.Respectively to ' rosy clouds
Sunshine 8 ', ' rays of sunlight ' whole fruits belt leather hardness and peeling hardness carry out correlation analysis, obtain coefficient correlation and be respectively
0.966**、0.955**, therefore, respectively by belt leather, peeling hardness based on data and IADCarry out regression analysis and then set up peach really
Real maturity forecast model is respectively provided with high feasibility.
Table 7 is participated in the experiment fruit quality index and IADThe correlation analysis of value
Claims (9)
1. a kind of method for building up of Peach fruits maturity forecast model, its step includes：
(1) colour-change period is harvested respectively and the Peach fruits of harvesting ripe degree are reached, and is respectively labeled as maturity I and maturity II；
(2) the Peach fruits measurement pericarp absorbance difference I to pickingAD, determine the hardness of Peach fruits；
(3) by maturity I, the hardness of maturity II fruits and IADValue is fitted, and sets up Peach fruits maturity predictive equation y=
ax2+ bx+c, wherein y are hardness, and x is IADValue, the quadratic polynomial coefficient that a, b, c are fitted for regression analysis.
2. the method for building up of Peach fruits maturity forecast model according to claim 1, it is characterised in that：The hardness is
Belt leather hardness.
3. the method for building up of Peach fruits maturity forecast model according to claim 1, it is characterised in that：The hardness is
Peeling hardness.
4. the method for building up of Peach fruits maturity forecast model according to claim 1, it is characterised in that：The Peach fruits
It is the Peach fruits of same kind.
5. the method for building up of Peach fruits maturity forecast model according to claim 4, it is characterised in that：The Peach fruits
Kind be late-maturing common peach rosy clouds sunshine 8 or the late-maturing nectarine rays of sunlight.
6. the method for building up of Peach fruits maturity forecast model according to claim 2, it is characterised in that：By belt leather hardness
With IADData are arranged in SPSS softwares, are fitted using quadratic polynomial regression block, and the Peach fruits being fitted are ripe
Degree predictive equation.
7. the method for building up of Peach fruits maturity forecast model according to claim 3, it is characterised in that：Hardness will be removed the peel
With IADData are arranged in SPSS softwares, are fitted using quadratic polynomial regression block, and the Peach fruits being fitted are ripe
Degree predictive equation.
8. the method for building up of Peach fruits maturity forecast model according to claim 2, it is characterised in that：The step
(1) Peach fruits of maturity I are 30 in, and the Peach fruits of maturity II are 30.
9. the method for building up of Peach fruits maturity forecast model according to claim 2, it is characterised in that：The IADValue makes
Determined with DA-Meter.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107368699A (en) * | 2017-09-19 | 2017-11-21 | 强岳昭 | A kind of hospital admission rate Forecasting Methodology based on existing consultation rate information |
| CN108414376A (en) * | 2018-02-13 | 2018-08-17 | 上海市农业科学院 | The not damaged Peach fruits Determination of Hardness method for establishing model of portable |
| CN109100311A (en) * | 2018-07-11 | 2018-12-28 | 中国农业大学 | Strawberry ripening degree method for quickly identifying and device |
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