CN114280002B - Abnormal fermented grain spectrum screening method based on characteristic peak judgment - Google Patents
Abnormal fermented grain spectrum screening method based on characteristic peak judgment Download PDFInfo
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Abstract
The invention relates to the technical field of brewing production, and provides an abnormal fermented grain spectrum screening method based on characteristic peak judgment, which aims to ensure the accuracy of spectral data of a fermented grain sample and reduce data processing amount, and comprises the following steps: 1. collecting spectral data of a fermented grain sample, and performing second-order derivation on the spectral data to obtain a characteristic peak; 2. selecting M characteristic peak light intensity points to replace original spectrum data according to the near infrared spectrum wavelength point weight characteristics; 3. calculating the relation between the illumination intensity of the portable near infrared spectrum and the illumination intensity received by the sensor; 4. calculating a standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illuminance value of the spectrum sensor and the characteristic peak light intensity value; 5. and setting a characteristic peak reasonable deviation threshold according to the portable near infrared spectrum error characteristic, and carrying out anomaly judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the characteristic peak reasonable deviation threshold. By adopting the mode, the accuracy is ensured and the data processing amount is reduced.
Description
Technical Field
The invention relates to the technical field of brewing production, in particular to an abnormal fermented grain spectrum screening method based on characteristic peak judgment.
Background
The fermented grains are necessary products in the brewing link, the fermented grains are mainly prepared by fermenting grains, the components of the fermented grains contain a large amount of hydrogen-containing groups, the hydrogen-containing groups comprise C-H, S-H, O-H, N-H and the like, and the contents of the components of the substances such as moisture, starch, acidity, sugar and the like in the fermented grains directly influence the quality of the wine in the fermentation process of the fermented grains, so that the fermented grains are the main basis for measuring whether the fermented grains are suitable or not and whether the fermentation process of the fermented grains is normal or not. However, the fermented grains are solid-liquid mixtures, have different particle sizes, uneven component distribution and serious volatilization, and cause great trouble to component analysis.
In recent years, wineries begin to use large near infrared spectrometers to detect main components of fermented grains, and the method has the advantages of huge volume of equipment and high requirements on environmental conditions although the quantitative accuracy and the sensitivity are high, special detection chambers and professional analyzers are still needed, and the wineries cannot detect the main components on site and have poor real-time performance. Meanwhile, because the large near infrared spectrometer is expensive, the winery cannot be configured in a large quantity, and each pit and each batch of samples are difficult to detect, and the method is still quite different from the actual needs of the winery.
The portable near infrared spectrometer is small in size and low in price, and can be purchased in a large quantity to realize the detection of fermented grains in each batch. However, the portable near infrared spectrometer is affected by a light source, a detector, a using method, environmental conditions and the like, the acquired spectrum data of the portable near infrared spectrometer is easy to distort, the accuracy is poor, and the spectrum prediction analysis capability of the portable near infrared spectrometer is further affected. In the practical application process, due to the complexity of the fermented grain samples, the spectrum data acquired and acquired by the portable near infrared spectrum equipment are easy to be abnormal, and the portable near infrared spectrum analysis technology is easy to be influenced by the abnormal spectrum data, so that the predictive analysis capability of the fermented grain samples is greatly reduced. Meanwhile, the portable near infrared spectrum equipment collects and acquires the spectrum data which is redundant, contains too much data information with smaller correlation with the fermented grain sample, and brings larger workload and difficulty to modeling analysis work, so that the method for acquiring the spectrum data can reduce the data quantity of the spectrum itself and can ensure the accuracy of the spectrum data of the fermented grain sample to the greatest extent becomes a problem which must be solved.
Disclosure of Invention
In order to ensure the accuracy of the spectrum data of the fermented grain sample and reduce the data processing capacity, the invention provides an abnormal fermented grain spectrum screening method based on characteristic peak judgment.
The invention solves the problems by adopting the following technical scheme:
an abnormal fermented grain spectrum screening method based on characteristic peak judgment comprises the following steps:
step 1, collecting spectrum data of a fermented grain sample, and performing second order derivation on the spectrum data of the fermented grain sample to obtain characteristic peaks of the fermented grain sample;
step 2, selecting M characteristic peak light intensity points to replace original spectrum data according to the weight characteristics of the near infrared spectrum wavelength points;
step 3, calculating the relation between the illumination intensity of the portable near infrared spectrum and the illumination intensity received by the sensor;
step 4, calculating a standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illuminance value of the spectrum sensor and the characteristic peak light intensity value;
and 5, setting a characteristic peak reasonable deviation threshold according to the portable near infrared spectrum error characteristic, and carrying out anomaly judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the characteristic peak reasonable deviation threshold.
Further, the calculating manner of M in the step 2 is as follows: m=k+ (λ) 2 -λ 1 )/n+(λ 4 -λ 3 )/n+...+(λ i -λ i-1 ) Wherein K represents the number of bands for which the weight coefficient satisfies the requirement, (lambda) i-1 ，λ i ) And (3) representing a wave band range in which the weight coefficient meets the requirement, wherein n is the resolution of the portable near infrared spectrometer.
In step 3, the reflectance of the fermented grain sample is set to be α, the attenuation rate of the optical cavity of the portable near infrared spectrometer is set to be β, the illumination value emitted by the portable near infrared light source is set to be X, and the sensor receiving illumination is set to be z= (1- β) ×α× (1- β) ×x.
Further, the standard characteristic peak light intensity value of the fermented grain sample in the step 4
Wherein P is M For the intensity value corresponding to the M th characteristic peak, Z 1 For the illumination intensity value received by the spectral sensor.
Further, in the step 5, a reasonable deviation range of the portable near infrared spectrum data is set to be c%, and then an upper limit threshold T of the set of characteristic peak light intensity values of the fermented grain sample is set to be:
the lower threshold S is: />
Compared with the prior art, the invention has the following beneficial effects: the characteristic peak information is used for selecting partial light points with higher weight coefficients to replace original spectrum data, so that the spectrum data volume is greatly reduced on the premise of retaining the characteristic information of the spectrum data, meanwhile, the characteristic peak reasonable deviation threshold value is combined for carrying out abnormal judgment and screening on the spectrum data of the fermented grain sample, abnormal spectrum data is removed, the accuracy of the spectrum data is guaranteed, and the problem that the predictive analysis capability of the portable infrared spectrum analysis technology is reduced due to the fact that the portable infrared spectrum analysis technology is easily influenced by the abnormal spectrum data is solved.
Drawings
FIG. 1 is a flow chart of a method for screening abnormal fermented grains according to the present application;
fig. 2 is a graph of spectral data of the fermented grain sample after second order derivation.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the method for screening abnormal fermented grains based on characteristic peak determination comprises the following steps:
step 1, collecting spectrum data of a fermented grain sample, and performing second order derivation on the spectrum data of the fermented grain sample to obtain characteristic peaks of the fermented grain sample; the wavelength average portable near infrared spectrometer can be used for collecting spectrum data of the fermented grain samples, and the wavelength average portable near infrared spectrometer can be used for collecting spectrum data of the fermented grain samples to obtain sample spectrum data with the greatest uniformity. In the embodiment, a portable near infrared spectrometer with a wavelength range of 1758nm-2150nm and a resolution of 8nm is used for collecting spectrum data, so that the number of light intensity points contained in each piece of fermented grain spectrum data is M=1+ (2150-1758)/8=50, the wavelength range corresponding to the 1 st-50 th wavelength points is (1758 nm,1766nm, the number of the wavelengths is equal to the number of the wavelengths, 2150 nm), and the spectrum data obtained by collecting each sample of the fermented grains to be detected is actually expressed as a matrix set of light intensity values on 50 wavelength points. In the acquisition process, each sample correspondingly acquires 5 pieces of spectrum data, the average value operation is carried out on the 5 pieces of spectrum data, and the data after the average value is the actual spectrum data of the sample, so that the acquisition error can be effectively reduced, and the reliability of the data is improved. As shown in figure 2, the second order derivative is carried out on the collected spectrum data of the fermented grain sample, obvious spectrum peaks appear at the positions of 1838nm,1926nm and 2030nm of wavelength points, and the peak points are the characteristic peaks of the fermented grain sample.
The second order derivative is carried out on the spectrum data of the fermented grain sample, the half-width of the second order derivative spectrum is only about 1/3 of the half-peak width of the original spectrogram, small shoulder peaks at two sides of a strong peak can be simply distinguished, the method is extremely effective for correctly measuring the peak position and the shoulder peak position, and the peak value of the spectrogram of the fermented grain sample, namely the characteristic peak wavelength point position, can be clearly distinguished through the second order derivative.
Step 2, selecting M characteristic peak light intensity points to replace original spectrum data according to the weight characteristics of the near infrared spectrum wavelength points; according to the second-order derivative spectrum graph of the spectrum data of the sample to be detected, wavelength points with higher weight coefficients around characteristic peaks are selected to replace the original spectrum data, and by adopting the mode, the spectrum data volume can be greatly reduced on the premise of keeping the characteristic information of the spectrum data, and the spectrum analysis efficiency is improved.
As shown in FIG. 2, in the second order derivative spectrum graph of the spectrum data of the fermented grain sample, the characteristic peak appears obvious spectrum peaks at the positions of 1838nm,1926nm and 2030nm, wherein the light intensity points around the characteristic peak have higher weight coefficients, and in the embodiment, two points of the secondary peak are selected, namely, the two points are in 1822nm-1854nm,1910nm-1942nm and 2014nm-2046nm in the wave band range with higher weight coefficients. From the above, the number of light intensity value points included in the recombined spectrum data replacing the original 50 light intensity value point spectrum data is m=3+ (1854-1822)/8+ (1942-1910)/8+ (2046-2014)/8=15, and the wavelength range corresponding to the 1 st to 15 th wavelength points is (1822 nm,1838nm,1846nm,1854nm,1910nm,1918nm,1926nm,1934nm,1942nm,2014nm,2022nm,2030nm,2038nm,2046 nm), and each of the light intensity sample spectrum data after the reconstruction is actually expressed as a matrix set of light intensity values at 15 wavelength points. Compared with the original spectrum of the fermented grain sample, the number of the light intensity value points of each spectrum data is greatly reduced, and the spectrum analysis efficiency is effectively improved besides the sample characteristic information is reserved to the greatest extent.
Step 3, calculating the relation between the illumination intensity of the portable near infrared spectrum and the illumination intensity received by the sensor; the portable near infrared light source emits near infrared light to reach the surface of the object to be detected after being attenuated by the near infrared light cavity, the near infrared light is converged into sampling light spots, the sampling light spots are subjected to light reflection by the object to be detected, the sampling light spots reach the spectrum sensor after being attenuated by the light cavity, and the spectrum sensor receives the reflected light intensity information to generate corresponding spectrum data values. And the relation between the illumination intensity of the portable near infrared spectrum and the illumination intensity received by the sensor can be calculated by combining the attenuation rate of the optical cavity and the reflectivity of the object to be detected.
In this embodiment, the reflectance of the fermented grain sample is set to be α, the attenuation rate of the optical cavity of the portable near infrared spectrometer is set to be β, the illumination value emitted by the portable near infrared light source is set to be X, and the operation principle of the portable near infrared spectrometer is as follows:
the portable near infrared light source emits near infrared light which is attenuated by the near infrared light cavity and reaches the surface of the object to be detected, the near infrared light is converged into a sampling light spot, and the illuminance value Y of the sampling light spot is as follows: y= (1- β) ×x; the sampling light spot is subjected to light reflection by an object to be detected, and reaches a spectrum sensor through light cavity attenuation, wherein the illuminance value received by the spectrum sensor is as follows: z= (1- β) ×α×y; in summary, the relationship between the illumination intensity X of the portable near infrared spectrum and the illumination intensity Z received by the sensor is: z= (1- β) ×α× (1- β) ×x.
The light cavity attenuation rate of the same portable near infrared spectrometer is a fixed value, and the illumination intensity is also a fixed value, so that the illumination intensity value received by the sensor is only related to the reflectivity of the object to be detected and is in linear positive correlation.
Step 4, calculating a standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illuminance value of the spectrum sensor and the characteristic peak light intensity value; after receiving the near infrared spectrum information of the fermented grain sample, the spectrum sensor transmits an initial spectrum signal to the operational amplifier, the operational amplifier amplifies the initial spectrum signal and transmits the amplified initial spectrum signal to the ADC, and the ADC converts the initial spectrum signal to the ARM chip for storing spectrum data. According to the spectral data transmission and processing steps, the conversion relation between the illuminance value of the spectral sensor and the light intensity value of the characteristic peak can be known, and the standard characteristic peak light intensity value of the fermented grain sample can be further calculated.
In this embodiment, the illumination intensity value received by the spectrum sensor is Z 1 The light intensity values corresponding to the peak wavelength ranges (1822 nm,1838nm,1846nm,1854nm,1910nm, 1926nm,1934nm,1942nm,2014nm,2022nm,2030nm,2038nm,2046 nm) are (P) 1 ，P 2 ，......，P 15 ) When the fermented grain sample with the reflectivity alpha is collected by the portable near infrared spectrometer, the illuminance value received by the sensor is Z, and the standard characteristic peak light intensity value set P of the fermented grain sample can be further calculated as follows:
step 5, setting a characteristic peak reasonable deviation threshold according to the portable near infrared spectrum error characteristic, and carrying out anomaly judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the characteristic peak reasonable deviation threshold; because of the convenience of the portable near infrared spectrum equipment, the spectrum performance of the portable near infrared spectrum equipment is greatly influenced, and when the same sample is collected, a small amount of deviation exists in spectrum data of the portable near infrared spectrum equipment, and the spectrum data is also considered as reasonable data. Based on this characteristic, deviations of the spectral feature peak data within a certain threshold range are considered as reasonable deviation values. When the reflectivity of the fermented grain sample is fixed, the spectral characteristic peak threshold value is also a determined value, and the characteristic peak light intensity point number value of the spectral data of the fermented grain sample is judged, if the light intensity values of the characteristic peak light intensity point number value are all within the threshold value range, the spectral data is judged to be normal. If some light intensity point values exceed the threshold range, judging the spectrum data as abnormal, and eliminating the abnormal spectrum data.
In this embodiment, a reasonable deviation range of the portable near infrared spectrum data is set as c%, and the upper limit threshold T of the fermented grain sample characteristic peak light intensity value set is known by combining the standard characteristic peak light intensity value set P of the fermented grain sample:
the lower threshold S is: />
Judging whether the spectrum data of the fermented grain sample is abnormal or not, wherein the specific method comprises the following steps: setting the actual spectral data of characteristic peaks of a fermented grain sample collected by portable near infrared spectrum equipment as (H) 1 ，H 2 ，......，H 15 ). Judging 15 points in the spectrum data one by one, if the condition is satisfied: s is S t <H t <T t T=1, 2, & gt, 15, if the spectrum data is judged to be normal, otherwise, the spectrum data is abnormal, and the abnormal spectrum is subjected to a rejection operation.
Claims (3)
1. The abnormal fermented grain spectrum screening method based on characteristic peak judgment is characterized by comprising the following steps of:
step 1, collecting spectrum data of a fermented grain sample, and performing second order derivation on the spectrum data of the fermented grain sample to obtain characteristic peaks of the fermented grain sample;
step 2, selecting M characteristic peak light intensity points to replace original spectrum data according to the weight characteristics of the near infrared spectrum wavelength points;
step 3, calculating the relation between the illumination intensity of the portable near infrared spectrum and the illumination intensity received by the sensor;
step 4, calculating a standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illuminance value of the spectrum sensor and the characteristic peak light intensity value;
step 5, setting a characteristic peak reasonable deviation threshold according to the portable near infrared spectrum error characteristic, and carrying out anomaly judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the characteristic peak reasonable deviation threshold;
specifically, the calculating manner of M in the step 2 is as follows: m=k+ (λ) 2 -λ 1 )/n+(λ 4 -λ 3 )/n+...+(λ i -λ i-1 ) Wherein K represents the number of bands for which the weight coefficient satisfies the requirement, (lambda) i-1 ，λ i ) The wave band range that the weight coefficient meets the requirement is represented, and n is the resolution of the portable near infrared spectrometer;
in the step 3, the reflectivity of the fermented grain sample is set to be alpha, the attenuation rate of an optical cavity of the portable near infrared spectrometer is set to be beta, the illumination value emitted by the portable near infrared light source is set to be X, and the sensor receiving illumination is set to be Z, so that Z= (1-beta) X alpha X (1-beta) X;
the standard characteristic peak light intensity value of the fermented grain sample in the step 4
Wherein P is M For the intensity value corresponding to the M th characteristic peak, Z 1 For the illumination intensity value received by the spectral sensor.
2. The abnormal fermented grain spectrum screening method based on characteristic peak determination according to claim 1, wherein in the step 5, a reasonable deviation range of portable near infrared spectrum data is set to be c%, and an upper limit threshold T of a characteristic peak light intensity value set of the fermented grain sample is:
the lower threshold S is: />
3. The abnormal fermented grain spectrum screening method based on characteristic peak determination according to claim 1 or 2, wherein the spectral data of the fermented grain sample is collected by a wavelength-division portable near infrared spectrometer.
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