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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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 invention relates to a handheld household food quality detection device based on near infrared spectrum.
Background
The near infrared food quality detector is based on the optical absorption characteristics of various representative organic components in food (meat, edible oil, milk, grain, rice and flour, etc.) in near infrared spectrum region, the difference of the strongest absorption wavelength of each component, and the proportional relation between the absorption intensity and the organic content of grain, and establishes a calibration equation by regression analysis of the known chemical component content of the sample and its near infrared spectrum measurement result, so as to estimate the component content of the same similar unknown sample. Because of weak absorption, near infrared light has stronger penetrating power than intermediate infrared light and visible light, the main components of the sample can be directly measured without dilution, and the thick sample can be penetrated, thereby realizing long optical path measurement. The long-optical-path transmission method is applied to agricultural and sideline products in the early stage by using near infrared spectrum, and is mostly realized by a diffuse reflection technology. Diffuse reflection methods require efficient collection of scattered light to ensure sufficient signal intensity, and the optical path structure is generally complex. Food safety detection in the market is desktop at present, and is basically purchased in canteens such as organs, enterprises and public institutions, schools and the like to establish a food safety detection chamber.
With the continuous improvement of the requirements of people on the living quality and the current situation of the current food market, the requirements of families on the food quality are higher and higher, but the currently adopted desk-top detector has a complex structure and a large volume, and is not suitable for being used by families.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a household near-infrared food quality detection device.
In order to achieve the aim, the invention provides a household near-infrared food quality detection device, which comprises a near-infrared light-emitting source 7, an optical filter 8, a Fresnel lens 9, a sample cell 1, a detector 2, a lock-in amplifier 3 and a microprocessor 4; near infrared rays emitted by the near infrared light-emitting source 7 are converted into square wave signals after passing through the optical filter 8, the Fresnel lens 9, the sample cell 1, the detector 2 and the lock-in amplifier 3 in sequence, and then are input into the microprocessor 4; the microprocessor 4 processes and displays the received square wave signal by using fuzzy iterative self-organizing data analysis.
The invention is further improved in that: the detection device also comprises a display 10 and an audible and visual alarm 11; the microprocessor 4 is connected with a display 10 and an audible and visual alarm 11 through an interface module 5. The interface module 5 adopts an FPGA chip.
Wherein, the near-infrared light source 7 is a 3X 4 near-infrared light emitting diode array with the wavelength of 800-1100 nm. Detector 2 is a photosensor TSL 245. The lock-in amplifier 3 is a heterodyne lock-in amplifier. The microprocessor 4 selects a TMS320C2XXX series DSP chip.
Compared with the prior art, the invention has the following advantages: the food quality detection device adopts a transmission method in a short wave near infrared region, improves the reliability of the detection device and reduces the cost of a light path. The invention adopts a photoelectric sensor TSL245 as a detector, converts infrared rays into square waves, simultaneously processes near-infrared weak signals by utilizing a heterodyne lock-in amplifier, and realizes wavelet transform denoising pretreatment and fuzzy iterative self-organizing data analysis (ISODATA) quality classification by the processed signals through a DSP microprocessor. The food quality detection device provided by the invention adopts the FPGA chip to realize the interface module driving circuit, so that the device has a compact structure and a small volume, and simultaneously utilizes the display and the audible and visual alarm to realize result visualization, is simple to operate and is suitable for families.
Drawings
Fig. 1 is a schematic structural diagram of the household near-infrared food quality detection device of the present invention.
Fig. 2 is a circuit diagram of a current adjusting circuit of the near-infrared light source in fig. 1.
Fig. 3 is a flow chart of the operation of the lock-in amplifier of fig. 1.
FIG. 4 is a flow chart of the present invention for food quality assessment using fuzzy iterative self-organizing data analysis.
In the figure, 1-a sample cell, 2-a detector, 3-a lock-in amplifier, 4-a microprocessor, 5-an interface module, 6-a power module, 7-a near-infrared light source, 8-a light filter, 9-a Fresnel lens, 10-a display and 11-an audible and visual alarm.
Detailed Description
The present invention will be described in detail with reference to the following embodiments.
1. Structure and specific detection process of detection device
Referring to fig. 1, the near-infrared food quality detection device for home use of the invention comprises a near-infrared light source 7, a filter 8, a fresnel lens 9, a sample cell 1, a detector 2, a lock-in amplifier 3, a microprocessor 4, an interface module 5, a display 10, an audible and visual alarm 11 and a power module 6. Near infrared rays emitted by the near infrared light emitting source 7 are converted into square wave signals after passing through the optical filter 8, the Fresnel lens 9, the sample cell 1 (food to be detected such as meat, edible oil, dairy products, grains, rice flour and the like are placed in the sample cell), the detector 2 and the lock-in amplifier 3 in sequence, and then the square wave signals are sent into the microprocessor 4. The microprocessor 4, the display 10 and the audible and visual alarm 11 are respectively connected with the interface module. The power module 6 is respectively connected with the near-infrared light-emitting source 7, the detector 2, the microprocessor 4 and the interface module 5. After the microprocessor 4 receives the spectrum data, wavelet transformation preprocessing is carried out to eliminate or reduce the instrument light intensity stability and the randomness difference caused by sample characteristics, thereby eliminating or reducing the spectrum noise.
The spectrum data after the wavelet transformation pretreatment is operated on a DSP chip of the microprocessor 4 by an algorithm of fuzzy iterative self-organizing data analysis technology (ISODATA), the defect that the ordinary clustering only pays attention to the merging of similarity measurement and class among samples and ignores the reasonable selection of the characteristic indexes of the samples is overcome, and the internal relation of the indexes is considered. The significance of the fuzzy ISODATA decision is that which class (fuzzy subset on X) the new sample XR is the largest, it is classified into which class; the new sample XR is assigned to which cluster center it is closest. The food quality evaluation process is shown in FIG. 4, and according to the standard samples for food quality detection (meat, edible oil, milk, cereal, rice and flour, etc.) stored in the microprocessor of the detection device of the present invention, these samples were of various types of food (meat, edible oil, milk, grain, rice flour, etc.) quality in a laboratory using a Shimadzu IRPrestige-21 Fourier transform infrared spectrophotometer, and the detection results were subjected to fuzzy ISODATA analysis, then the quality of the sample is classified according to the analysis data, the detection device of the invention collects the samples of food (meat, edible oil, dairy products, grains, rice and flour, etc.) on site, and simultaneously, detecting (monitoring) the quality of the food (meat, edible oil, dairy products, grains, rice flour and the like), and judging the quality of the food (meat, edible oil, dairy products, grains, rice flour and the like) by comparing with the standard sample. The evaluation result is displayed on the display 10, and an audible and visual signal is emitted by the audible and visual alarm 11.
2. Description of the respective Components of the present detection apparatus
1) The near-infrared light-emitting source 7 is composed of a 3 x 4 near-infrared light-emitting diode (LED) array, the wavelength is 800-1100 nm, in order to guarantee stability of the light source, each branch is provided with an independent adjustable constant current circuit, on-off control of the near-infrared diode array is achieved through a 4-16 decoder CD4515, and current driving capability of the LED is increased through an ULN2003 (phase inverter) of a collector. Referring to fig. 2, the LM336 is used as a voltage reference R, and for its current limiting resistance, one end of the adjustment potentiometer R2 is connected to the output end of the ULN2003, and the other end is connected to the emitter of the transistor 2N222, so that the current driving capability of the near infrared is increased by the transistor 2N222, and a stable current can be obtained on the near infrared LED.
2) The photoelectric sensor TSL245 that the detector 2 chooses, it is a new infrared light frequency converter produced by Texas Instruments (TI) company of America, it combines a silicon photodiode and a current-frequency conversion on a single-chip CMOS integrated circuit, when the infrared ray that the near infrared luminescent diode sends out shines on the silicon photodiode of the detector 2 after passing the optical filter 8, Fresnel lens 9 and sample cell 1 sequentially, the silicon photodiode produces and turns on the current in proportion to the illumination intensity, and then change this current into a square wave (the duty cycle is 50%) whose frequency is proportional to it by the current-frequency converter, namely TSL245 outputs a square wave, and the frequency and illumination intensity that is exerted are the accurate direct proportional relation, have good linearity; it is also a complete visible light cut-off filter.
3) The square wave output by the detector 2 is amplified by the lock-in amplifier 3 to a weak signal and then output to an I/O port of a microprocessor DSP. The lock-in amplifier 3 uses a heterodyne lock-in amplifier which first converts the measured signal x (t) to a fixed intermediate frequency
Then, band-pass filtering and phase-sensitive detection are performed, so that drift and variation of the frequency of the signal passing through the BPF (band-pass filter) can be avoided. With reference to FIG. 3, a heterodyne LIA (lock-in amplifier) is designed to operate at a frequency ofIs input to a frequency synthesizer, and the frequency synthesizer generates a high-stability reference signal r (t)
Andtwo frequencies are output. Wherein,
as reference signal for PSD (phase sensitive detector);
is fed to a mixer with a frequency of
The signals of (a) are mixed. The mixer is also effectively a multiplier that produces a two-way input difference frequency term (frequency of
) And sum frequency term (frequency of
) Again at a center frequency of
The phase sensitive detection of the BPF and the low pass filtering of the LPF (low pass filter) realize the measurement of the signal amplitude.
As can be seen from the above working process, even during the measurement processChanged, the frequency of the mixer outputStill remain stable. So that the stages after the mixer can be targeted at fixed frequencies
Optimized design, including specially designed bandpass filters with fixed center frequency, improves both system noise rejection and harmonic responseThe trouble of adjusting the BPF is avoided. For different measured signal frequencies
The heterodyne lock-in amplifier is adaptable as long as it is consistent with the frequency of the reference input (which is generally easy to do, and the reference input signal r (t) in practical applications is often a modulated sine wave from the generated signal x (t) to be measured or a square wave for chopping).
The microprocessor 4 selects TMS320C2XXX series DSP chip produced by Texas Instruments (TI) company of America, the chip has strong processing capability, the shortest instruction period is 25ns, the operational capability reaches 40MIPS, a large flash memory is arranged in the chip, the power consumption is low, and the resource allocation is flexible. After the microprocessor 4 receives the spectrum data, the wavelet transformation preprocessing is carried out to eliminate or reduce the randomness difference caused by the stability of the light intensity of the instrument and the characteristics of the sample, thereby eliminating or reducing the spectrum noise,
the spectrum data after the wavelet transformation pretreatment is operated on a DSP chip of the microprocessor 4 by an algorithm of fuzzy iterative self-organizing data analysis technology (ISODATA), the defect that the ordinary clustering only pays attention to the merging of similarity measurement and class among samples and ignores the reasonable selection of the characteristic indexes of the samples is overcome, and the internal relation of the indexes is considered. The significance of the fuzzy ISODATA decision is that which class (fuzzy subset on X) the new sample XR is the largest, it is classified into which class; the new sample XR is assigned to which cluster center it is closest. The food quality judging process is shown in the attached figure 4, a laboratory near-infrared spectrometer is used for carrying out quality classification detection on standard samples of food (meat, edible oil, dairy products, grains, rice and flour and the like) to obtain standard spectral data samples, fuzzy ISODATA analysis is carried out on the standard spectral data samples to obtain sample quality classification, and compared with the near-infrared food quality detecting device for families, the device for judging the quality of the food is used for detecting samples to be judged and finally judging the samples. The evaluation result is displayed on the display 10, and an audible and visual signal is emitted by the audible and visual alarm 11.
The fuzzy ISODATA analysis method comprises the following steps:
1. fuzzy classification
J.c. Bezdek raises the problem of fuzzy classification (also called soft segmentation) using the concept of fuzzy sets, considering samples in the set X of objects to be classified
Belonging to a certain class to some extent, that is, all samples are respectively from different classes. Therefore, each class is considered as a fuzzy subset of the sample set X, and the classification matrix corresponding to each class of classification result is a fuzzy matrix
In the formula:
。
is provided with
For all sets of blurring matrices R satisfying the above condition, i.e.
BalanceThe integrated sample set X is classified into a class C fuzzy classification space.
In the clustering analysis, if the optimal fuzzy classification matrix R under a certain condition can be found according to the characteristic index matrices of the n samples, the fuzzy human corresponding to R is the optimal fuzzy classification of the sample set X under the condition.
2. Fuzzy ISODATA clustering analysis method
Set the classified objects as
Each of which is a sample
All have m characteristic indexes, i.e.
. The characteristic index matrix is
（6）
To classify the sample set X into class C
Let C cluster center vectors be
To obtain an optimal fuzzy classification, the following clustering criteria can be followed from the fuzzy classification space
The best fuzzy classification is preferred.
3. Fuzzy classified clustering criterion and criterion
In the case of fuzzy classification, in order to obtain the optimal classification, the clustering criterion is generalized as follows. Even an objective function
Reaching a minimum value. Wherein
，
Has the same meaning as formula (7), q>0. In order to flexibly change the relative membership degree, q may have a certain value (generally q = 2), and information distortion may be caused when the value is too large.
The clustering criteria are: and (4) taking out a proper fuzzy classification matrix R and a clustering center vector V so that the objective function represented by the formula (8) reaches a minimum value. In general, the solution of the extremum of the above objective function is rather difficult, but Bezdek has demonstrated that: when in use
The iterative operation can be performed in the following manner, and the operation process is convergent, which is the fuzzy ISODATA method. The method comprises the following steps:
(1) selecting the C,
taking an initial fuzzy classification matrix
The steps are iterated step by step,
。
(2) for the
Calculating a cluster center vector
（9）
In the formula,
。
(3) correcting fuzzy classification matrix
（10）
ComparisonAnd
if it is decided to
，
Then
Andi.e., the iteration is stopped, otherwise,
and (5) returning to the step (2) for repeating.
The fuzzy classification matrix is obtained by applying the algorithm
And a cluster center
Is relative to the classification number C, the initial fuzzy classification matrix、
And an optimal solution for the parameter q.
Due to the algorithm requirements
And the cause of equations (9) and (10) themselves, the initial fuzzy classification matrix
Except that 2 of the 3 conditions of the fuzzy classification matrix have to be met.
（1） 
；
（2） 
In addition, an initial fuzzy matrix must be formed
The following restrictions apply;
(3) initial matrix
It cannot be a constant matrix with each element being equal;
(4) initial matrix
It cannot be a matrix of the same value of a certain row of elements;
(5) initial matrix
For the class with only one sample, the class is removed before clustering and then put in after clustering.
The initial fuzzy classification matrix selected by simultaneously satisfying the above 5 conditions
Distortion phenomena cannot be caused in the fuzzy ISODATA ground calculation iteration process, otherwise, clustering analysis fails. This must be taken into account when selecting the initial fuzzy classification matrix.
The new sample attributes are identified according to the following principle:
decision principle 1Setting the finally obtained clustering center vector as
If, if
Then the sample is taken
Classified as i.
Decision principle 2Setting the finally obtained fuzzy classification matrix as
（13）
Then the sample is taken
Classified as i.
4. Inspection of clustering effects
It has been pointed out previously that the fuzzy clustering obtained by applying the fuzzy ISODATA method is relative to the classification number C, the initial fuzzy classification matrix
Error, error of
And an optimal solution for the parameter q. If C is changed,、
And q, a number of locally optimal solutions can be obtained. If the best solution is selected from the optimal solutions, an index for identifying the fuzzy ISODATA clustering effect is required. The following indexes can be used for identifying the clustering effect:
coefficient of classification
When in use
When the temperature of the water is higher than the set temperature,
therefore, the process of the present invention is,the closer to 1; the less fuzzy the final classification is, the better the clustering effect is;
mean fuzzy entropy
（15）
The average blur entropy is better as it approaches zero.
Claims (7)
1. The utility model provides a near-infrared food quality detection device for family which characterized in that: the device comprises a near-infrared light-emitting source (7), an optical filter (8), a Fresnel lens (9), a sample cell (1), a detector (2), a lock-in amplifier (3) and a microprocessor (4); near infrared rays emitted by the near infrared light emitting source (7) are sequentially converted into square wave signals through the optical filter (8), the Fresnel lens (9), the sample cell (1), the detector (2) and the lock-in amplifier (3) and then input into the microprocessor (4); and the microprocessor (4) processes and displays the received square wave signals by adopting fuzzy iterative self-organizing data analysis.
2. The near-infrared food quality detection device for home use according to claim 1, characterized in that: the detection device also comprises a display (10) and an audible and visual alarm (11); and the microprocessor (4) is respectively connected with the display (10) and the audible and visual alarm (11) through the interface module (5).
3. The near-infrared food quality detection device for home use according to claim 2, characterized in that: the interface module (5) adopts an FPGA chip.
4. The near-infrared food quality detection apparatus for home use according to claim 1 or 2, characterized in that: the near-infrared light source (7) is a 3X 4 near-infrared light emitting diode array, and the wavelength is 800-1100 nm.
5. The near-infrared food quality detection apparatus for home use according to claim 1 or 2, characterized in that: the detector (2) is a light sensor TSL 245.
6. The near-infrared food quality detection apparatus for home use according to claim 1 or 2, characterized in that: the lock-in amplifier (3) is a heterodyne lock-in amplifier.
7. The near-infrared food quality detection apparatus for home use according to claim 1 or 2, characterized in that: the microprocessor (4) selects a TMS320C2XXX series DSP chip.
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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 |
| CN106092940A (en) * | 2016-08-27 | 2016-11-09 | 方有菊 | A kind of food safety fast detecting device |
| CN106092917A (en) * | 2016-06-01 | 2016-11-09 | 刘天军 | A kind of trace chemical Non-Destructive Testing intelligent terminal |
| 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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