CN102564997A - Domestic near-infrared detection device for food quality - Google Patents
Domestic near-infrared detection device for food quality Download PDFInfo
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- CN102564997A CN102564997A CN2012100137081A CN201210013708A CN102564997A CN 102564997 A CN102564997 A CN 102564997A CN 2012100137081 A CN2012100137081 A CN 2012100137081A CN 201210013708 A CN201210013708 A CN 201210013708A CN 102564997 A CN102564997 A CN 102564997A
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Abstract
The invention discloses a domestic near-infrared detection device for food quality, which comprises a near-infrared light emitting source, a light filter, a Fresnel lens, a sample tank, a detector, a lock-in amplifier and a microprocessor. Near infrared rays emitted from the near-infrared light emitting source are converted into square-wave signals after passing through the light filter, the Fresnel lens, the sample tank, the detector and the lock-in amplifier in sequence; the square-wave signals are inputted into the microprocessor; and the microprocessor processes the received square-wave signals by utilizing fuzzy iterative self-organization data analysis and displays the signals. The domestic near-infrared detection device is simple in structure, convenient to operate and suitable to use domestically.
Description
Technical field
The present invention relates to a kind of based near infrared spectrum hand-held domestic food Quality Detection device.
Background technology
Near infrared food quality pick-up unit is according to different in the strongest absorbing wavelength of the optical absorption characteristic near infrared spectrum zone, each composition of various representational organic components in the food (meat, edible oil, dairy products, cereal with rice and flour etc.); Proportional relation between absorption intensity and grain organic content; Through to sample known chemical component content and the regretional analysis as a result of its near infrared ray; Set up calibration equation, can estimate unknown sample component content with a kind of similar type.Because a little less than absorbing, near infrared light have than in infrared and the stronger penetration capacity of visible light, need not dilute the directly principal ingredient of working sample, can see through thicker sample, realize long light path mensuration.The transmission beam method of long light path realizes through the diffuse reflection technology with the near infrared spectrum application in agricultural byproducts in early days mostly.The diffuse reflection method needs the effective collection scattered light for guaranteeing enough signal intensities, and light channel structure is complicated usually.Food safety detection on the market all is desk-top now, all is that dining rooms such as office, enterprises and institutions, school are bought basically, sets up the food safety detection chamber.
Along with people's is to the improving constantly of requiring of quality of life and the present situation of food products market at present, family to food quality require increasingly high, but the desk-top detector complex structure that adopts at present, and volume is big, is not suitable for family and uses.
Summary of the invention
To the objective of the invention is the defective that exists in the prior art in order solving, a kind of home-use near infrared food quality pick-up unit to be provided.
In order to achieve the above object, the invention provides a kind of home-use near infrared food quality pick-up unit, comprise near-infrared luminous light source 7, optical filter 8, Fresnel Lenses 9, sample cell 1, detecting device 2, lock-in amplifier 3 and microprocessor 4; The near infrared ray that said near-infrared luminous light source 7 sends is successively through converting square-wave signal to behind optical filter 8, Fresnel Lenses 9, sample cell 1, detecting device 2 and the lock-in amplifier 3, in the input microprocessor 4; Said microprocessor 4 employing fuzzy iterative self-organization data analyses are handled the square-wave signal that receives and are shown.
Further improvement of the present invention is: this pick-up unit also comprises display 10 and audible-visual annunciator 11; Microprocessor 4 links to each other with audible-visual annunciator 11 with display 10 respectively through interface module 5.Interface module 5 adopts fpga chip.
Wherein, near-infrared luminous light source 7 is 3 * 4 near-infrared luminous diode arrays, and wavelength is 800～1100nm.Detecting device 2 is optical sensor TSL245.Lock-in amplifier 3 is the heterodyne system lock-in amplifier.Microprocessor 4 is selected the dsp chip of TMS320C2XXX series for use.
The present invention compares prior art and has the following advantages: food quality pick-up unit of the present invention adopts the transmission beam method of shortwave near-infrared region, has improved the reliability of pick-up unit, and has reduced the light path cost.The present invention adopts photoelectric sensor TSL245 as detecting device; Convert infrared ray to square wave; Utilize the heterodyne system lock-in amplifier that the near infrared feeble signal is handled simultaneously, the signal after the processing realizes that through the DSP microprocessor wavelet transformation denoising pre-service and fuzzy iterative self-organization data analysis technique (ISODATA) carry out attribute classification.Food quality pick-up unit of the present invention adopts fpga chip to realize the interface module driving circuit, makes that apparatus structure is compact, volume is little, utilizes display and audible-visual annunciator to realize result visualization simultaneously, and is simple to operate, is fit to family and uses.
Description of drawings
Fig. 1 is the structural representation of the home-use near infrared food quality of the present invention pick-up unit.
Fig. 2 is the matrix current adjustment circuit figure of near-infrared luminous light source among Fig. 1.
Fig. 3 is the workflow diagram of lock-in amplifier among Fig. 1.
Fig. 4 passes judgment on process flow diagram for the present invention utilizes fuzzy iterative self-organization data analysis technique to carry out food quality.
Among the figure, 1-sample cell, 2-detecting device, 3-lock-in amplifier, 4-microprocessor, 5-interface module, 6-power module, the near-infrared luminous light source of 7-, 8-optical filter, 9-Fresnel Lenses, 10-display, 11-audible-visual annunciator.
Embodiment
Below in conjunction with embodiment the home-use near infrared food quality of the present invention pick-up unit is elaborated.
1. structure of this pick-up unit and concrete testing process
Referring to Fig. 1, the home-use near infrared food quality of the present invention pick-up unit comprises near-infrared luminous light source 7, optical filter 8, Fresnel Lenses 9, sample cell 1, detecting device 2, lock-in amplifier 3, microprocessor 4, interface module 5, display 10, audible-visual annunciator 11 and power module 6.The near infrared ray that near-infrared luminous light source 7 sends (is put food to be detected through optical filter 8, Fresnel Lenses 9, sample cell 1 successively in the sample cell; Like meat, edible oil, dairy products, cereal and rice and flour etc.), convert square-wave signal to behind detecting device 2 and the lock-in amplifier 3, send in the microprocessor 4.Microprocessor 4, display 10 and audible-visual annunciator 11 link to each other with interface module respectively.Power module 6 links to each other with interface module 5 with near-infrared luminous light source 7, detecting device 2, microprocessor 4 respectively.After microprocessor 4 receives spectroscopic data, carry out the wavelet transformation pre-service, eliminate or reduce the randomness difference that instrument light stability and sample characteristic cause, thereby eliminate or reduce spectral noise.
The pretreated spectroscopic data of wavelet transformation is computing fuzzy iterative self-organization data analysis technique (ISODATA) algorithm on the dsp chip of microprocessor 4; Overcome common cluster only note similarity measure between sample with type merger; And ignore the deficiency of the choose reasonable of sample properties index itself, consider the inner link of index simultaneously.The meaning that fuzzy ISODATA judges is: which kind of new samples XR just is grouped into it to which kind of (fuzzy subset that X is last) maximum; New samples XR and which cluster centre are the most approaching, just which kind of it are grouped into.Food quality is passed judgment on flow process and is seen accompanying drawing 4; According to the food that in the microprocessor of pick-up unit of the present invention, has stored (meat, edible oil, dairy products, cereal and rice and flour etc.) Quality Detection master sample; These samples are with all kinds of samples of day island proper Tianjin IRPrestige-21 type fourier-transform infrared spectrophotometer to food (meat, edible oil, dairy products, cereal and rice and flour etc.) quality in the laboratory; And these testing results are blured ISODATA analyze; Carry out the sample attribute classification to analyzing data then; Pick-up unit of the present invention is gathered food (meat, edible oil, dairy products, cereal and rice and flour etc.) sample at the scene; Carry out food (meat, edible oil, dairy products, cereal and rice and flour etc.) Quality Detection (monitoring) simultaneously, standard of comparison carries out food (meat, edible oil, dairy products, cereal and rice and flour etc.) quality judging.On display 10, show evaluation result, send sound and light signal through audible-visual annunciator 11 simultaneously.
2. the explanation of each ingredient of this pick-up unit
1) near-infrared luminous light source 7 is made up of 3 * 4 near-infrared luminous diode (LED) arrays; Wavelength is 800 ~ 1100nm; In order to guarantee the stable of light source; Each all has independent adjustable constant-current circuit, uses 4-16 code translator CD4515 to realize the near infrared diode array is carried out break-make control, has increased the current driving ability of LED at the ULN2003 of collector (phase inverter).In conjunction with Fig. 2; LM336 is as voltage reference R; Be its current-limiting resistance, one of regulator potentiometer R2 terminates at the output terminal of ULN2003, and the other end is connected with the emitter of triode 2N222; Increase near infrared current driving ability through triode 2N222, also can obtain stable electric current on the near-infrared LED simultaneously.
2) the photoelectric sensor TSL245 that selects for use of detecting device 2; It is the novel infrared light-to-frequency converter that company of Texas Instruments (TI) produces; It has made up a silicon photoelectric diode and an electric current-frequency inverted on a monolithic cmos integrated circuit; When infrared ray that near-infrared luminous diode sends successively when shining the silicon photoelectric diode of detecting device 2 behind optical filter 8, Fresnel Lenses 9 and the sample cell 1; Silicon photoelectric diode produces and the directly proportional conducting electric current of illuminance, by electric current-frequency converter this current conversion is become a frequency and its proportional square wave (dutycycle is 50%) again, and what promptly TSL245 exported is a square wave; And frequency becomes accurate proportional relationship with the illuminance that is applied, and has good linearty; It also is a visible light cut-off filter completely simultaneously.
3) after the square wave of being exported by detecting device 2 amplifies feeble signals via lock-in amplifier 3, export the I/O mouth of microprocessor DSP to.Lock-in amplifier 3 adopts the heterodyne system lock-in amplifier; The heterodyne system lock-in amplifier is that measured signal x (t) at first is converted to a fixing intermediate frequency
; Carry out bandpass filtering and phase-sensitive detection then, so just can avoid drift and variation through the signal frequency of BPF (BPF.).In conjunction with Fig. 3; Heterodyne system LIA (lock-in amplifier) is input to frequency synthesizer to the reference signal r (t) of frequency for
, is produced
and
two kinds of frequency outputs of high stability by frequency synthesizer.Wherein,
is as the reference signal of PSD (phase sensitive detector);
gives frequency mixer, carries out mixing with the signal of frequency for
.Frequency mixer in fact also is a multiplier; The difference frequency term (frequency is
) that it produces the two-way input reaches and frequency (frequency is
); Through the phase-sensitive detection of the BPF that centre frequency is
and the LPF of LPF (low-pass filter), realize measurement again to signal amplitude.
Visible from the above-mentioned course of work; Even variation has taken place
in measuring process, the frequency
of frequency mixer output still keeps stablizing constant.The frequencies
of fixing that can be directed against at different levels after the frequency mixer made optimal design like this; Comprise the BPF. that adopts custom-designed dead center frequency; This both raising system suppresses the ability of noise harmonic response, has avoided the trouble of adjustment BPF again.For different measured signal frequencies
; As long as it and (this general easy the accomplishing that is consistent with reference to the frequency of importing; Reference-input signal r in the practical application (t) often is exactly modulated sinusoid or the used square wave of copped wave that comes self-generating measured signal x (t)), then the heterodyne system lock-in amplifier can both adapt to.
The TMS320C2XXX series DSP chip that microprocessor 4 selects for use company of Texas Instruments (TI) to produce, this chip processing power is strong, and the instruction cycle is the shortest to be 25ns; Arithmetic capability reaches 40MIPS; Have bigger flash memory in the sheet, low in energy consumption, resource distribution is flexible.After microprocessor 4 receives spectroscopic data, carry out the wavelet transformation pre-service, eliminate or reduce the randomness difference that instrument light stability and sample characteristic cause, thereby eliminate or reduce spectral noise,
The pretreated spectroscopic data of wavelet transformation is computing fuzzy iterative self-organization data analysis technique (ISODATA) algorithm on the dsp chip of microprocessor 4; Overcome common cluster only note similarity measure between sample with type merger; And ignore the deficiency of the choose reasonable of sample properties index itself, consider the inner link of index simultaneously.The meaning that fuzzy ISODATA judges is: which kind of new samples XR just is grouped into it to which kind of (fuzzy subset that X is last) maximum; New samples XR and which cluster centre are the most approaching, just which kind of it are grouped into.Food quality is passed judgment on flow process and is seen accompanying drawing 4; With near infrared spectrometer the master sample of food (meat, edible oil, dairy products, cereal and rice and flour etc.) being carried out the branch attribute classification through the laboratory detects and obtains the standard spectrum data sample; The standard spectrum data sample is blured ISODATA to be analyzed; Obtain the sample attribute classification, the home-use near infrared food quality of contrast the present invention pick-up unit is treated the judgement sample detection, carries out sample at last and judges.On display 10, show evaluation result, send sound and light signal through audible-visual annunciator 11 simultaneously.
Wherein, fuzzy ISODATA analytical approach is described below:
1, fuzzy classification
J.C. Bezdek utilizes the notion of fuzzy set to propose fuzzy classification (also crying soft division) problem; Think by the sample
among the object of classification set X; Be under the jurisdiction of a certain type with certain degree; That is to say that all samples are all respectively from different be under the jurisdiction of a certain types.Therefore, each type just thought the fuzzy subset of sample set X, so the pairing classification matrix of each this type classification results is exactly a fuzzy matrix
In the formula:
.
If
is for all satisfy the set of the fuzzy matrix R of above condition, promptly
Claim
and become sample set X to be divided into the fuzzy classification space of C class.
In cluster analysis, then human if can seek out best fuzzy classification matrix R under certain condition with the R corresponding fuzzy according to the characteristic index matrix of n sample, be exactly the fuzzy classification of sample set X the best under this condition.
2, fuzzy ISODATA clustering method
If done by the object of classification set
Wherein each sample
all has m characteristic index, i.e.
.The characteristic index matrix does
（6）
To sample set X be divided into C class
, establish C cluster centre vector and do
In order to obtain the fuzzy classification an of the best; Can be according to following clustering criteria, a preferred best fuzzy classification from fuzzy classification space
.
3, the clustering criteria of fuzzy classification and clustering criterion
Under the situation of fuzzy classification,, do following the popularization to clustering criterion in order to obtain optimum classification.Even objective function
Reach minimal value.Wherein
; The meaning of
is identical with formula (7), q>0.In order to change relative subjection degree neatly, the desirable certain value of q (generally getting q=2), value is crossed conference and is caused information distortion.
Clustering criteria is: take out suitable fuzzy classification matrix R and cluster centre vector V, make the represented objective function of formula (8) reach minimal value.Generally speaking; The extreme value of above-mentioned objective function is found the solution quite difficulty; But Bezdek is verified: during as
; Can carry out interative computation through following mode, and calculating process is convergent, fuzzy ISODATA method that Here it is.The steps include:
(1) selected C;
; Get an initial fuzzy classification matrix
; Iteration progressively,
.
(2), calculate the cluster centre vector for
（9）
In the formula,
.
(3) revise fuzzy classification matrix
（10）
Relatively
and
; If to getting fixed
Then
and
be the institute ask; Stop iteration; Otherwise;
gets back to step (2) and repeats.
Use that above algorithm obtains fuzzy classification matrix
and cluster centre
is with respect to number of categories C, initially the optimum solution of fuzzy classification matrix
,
and parameter q.
Because this algorithm requires
; And the reason of formula (9) and formula (10) itself, initially fuzzy classification matrix
chooses except 2 in 3 conditions that must satisfy the fuzzy classification matrix.
（1）
；
(2)
; Outside; Also must be to the choosing of initial fuzzy matrix
, and in addition like limit;
(3) initial matrix
can not be a constant matrices that each element all equates;
(4) initial matrix
can not be a matrix that a certain row element is equivalent;
(5) in the initial matrix
to have only a sample the class; To remove before the cluster, put into again after to be clustered.
Satisfy the selected initial fuzzy classification matrix
of above 5 conditions simultaneously; Just can not calculate in the iterative process and cause distortion phenomenon, otherwise will make the cluster analysis failure on fuzzy ISODATA ground.This point must cause enough attention when choosing initial fuzzy classification matrix.
What type new samples belongs to is discerned by following principle:
Decision principle 1If the cluster centre vector of trying to achieve at last does
If,
Then sample
is belonged to the i class.
Decision principle 2If the fuzzy classification matrix of trying to achieve at last does
（13）
Then sample
is belonged to the i class.
4, the check of cluster effect
The front points out, uses fuzzy clustering that fuzzy ISODATA method obtains and is with respect to number of categories C, the initial optimum solution of fuzzy classification matrix
, error
and parameter q.If change C,
,
and q, then can obtain many locally optimal solutions.If from these optimum solutions, select the best, then need the index of differentiating fuzzy ISODATA cluster effect.Discriminating cluster effect can be used following index:
Classification factor
During as
;
. therefore,
is more near 1; The ambiguity of final classification is littler, and the cluster effect better;
The average blur entropy
（15）
When the average blur entropy approach more zero good more.
Claims (7)
1. a home-use near infrared food quality pick-up unit is characterized in that: comprise near-infrared luminous light source (7), optical filter (8), Fresnel Lenses (9), sample cell (1), detecting device (2), lock-in amplifier (3) and microprocessor (4); The near infrared ray that said near-infrared luminous light source (7) sends is successively through converting square-wave signal to behind optical filter (8), Fresnel Lenses (9), sample cell (1), detecting device (2) and the lock-in amplifier (3), in the input microprocessor (4); Said microprocessor (4) employing fuzzy iterative self-organization data analysis is handled the square-wave signal that receives and is shown.
2. home-use near infrared food quality pick-up unit according to claim 1 is characterized in that: said pick-up unit also comprises display (10) and audible-visual annunciator (11); Said microprocessor (4) links to each other with audible-visual annunciator (11) with display (10) respectively through interface module (5).
3. home-use near infrared food quality pick-up unit according to claim 2 is characterized in that: said interface module (5) adopts fpga chip.
4. home-use near infrared food quality pick-up unit according to claim 1 and 2 is characterized in that: said near-infrared luminous light source (7) is 3 * 4 near-infrared luminous diode arrays, and wavelength is 800～1100nm.
5. home-use near infrared food quality pick-up unit according to claim 1 and 2 is characterized in that: said detecting device (2) is optical sensor TSL245.
6. home-use near infrared food quality pick-up unit according to claim 1 and 2 is characterized in that: said lock-in amplifier (3) is the heterodyne system lock-in amplifier.
7. home-use near infrared food quality pick-up unit according to claim 1 and 2 is characterized in that: said microprocessor (4) is selected the dsp chip of TMS320C2 XXX series for use.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103353438A (en) * | 2013-07-30 | 2013-10-16 | 长春长光思博光谱技术有限公司 | Near-infrared grain composition analysis instrument |
| CN103487400A (en) * | 2013-10-15 | 2014-01-01 | 无锡艾科瑞思产品设计与研究有限公司 | Near infrared household food detection device and method |
| CN104864964A (en) * | 2015-06-02 | 2015-08-26 | 中国科学院上海技术物理研究所 | Infrared detector signal modulation and narrow band filter system and realization method |
| CN106092918A (en) * | 2016-06-01 | 2016-11-09 | 刘天军 | A kind of miniature UV, visible light/Infrared Non-destructive Testing spectrogrph |
| CN106092917A (en) * | 2016-06-01 | 2016-11-09 | 刘天军 | A kind of trace chemical Non-Destructive Testing intelligent terminal |
| CN106092940A (en) * | 2016-08-27 | 2016-11-09 | 方有菊 | A kind of food safety fast detecting device |
| CN107807106A (en) * | 2016-09-07 | 2018-03-16 | 中兴通讯股份有限公司 | A kind of food determines method and device, mobile terminal |
| CN109425715A (en) * | 2017-09-04 | 2019-03-05 | 浙江粮泰智能科技有限公司 | A kind of full-automatic unmanned grain assay system on duty |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103353438A (en) * | 2013-07-30 | 2013-10-16 | 长春长光思博光谱技术有限公司 | Near-infrared grain composition analysis instrument |
| CN103487400A (en) * | 2013-10-15 | 2014-01-01 | 无锡艾科瑞思产品设计与研究有限公司 | Near infrared household food detection device and method |
| CN104864964A (en) * | 2015-06-02 | 2015-08-26 | 中国科学院上海技术物理研究所 | Infrared detector signal modulation and narrow band filter system and realization method |
| CN106092918A (en) * | 2016-06-01 | 2016-11-09 | 刘天军 | A kind of miniature UV, visible light/Infrared Non-destructive Testing spectrogrph |
| CN106092917A (en) * | 2016-06-01 | 2016-11-09 | 刘天军 | A kind of trace chemical Non-Destructive Testing intelligent terminal |
| CN106092940A (en) * | 2016-08-27 | 2016-11-09 | 方有菊 | A kind of food safety fast detecting device |
| CN107807106A (en) * | 2016-09-07 | 2018-03-16 | 中兴通讯股份有限公司 | A kind of food determines method and device, mobile terminal |
| CN109425715A (en) * | 2017-09-04 | 2019-03-05 | 浙江粮泰智能科技有限公司 | A kind of full-automatic unmanned grain assay system on duty |
| CN109425715B (en) * | 2017-09-04 | 2023-10-13 | 浙江粮泰智能科技有限公司 | Full-automatic unmanned grain testing system |
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Application publication date: 20120711 |