CN114280002A - Abnormal fermented grain spectrum screening method based on characteristic peak determination - Google Patents
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Abstract
The invention relates to the technical field of brewing production, and provides a method for screening abnormal fermented grains spectrum based on characteristic peak judgment, which aims to ensure the accuracy of fermented grain sample spectrum data and reduce data processing capacity, 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 of the fermented grain sample; 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 receiving illumination intensity of the sensor; 4. calculating the standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illumination value of the spectral sensor and the characteristic peak light intensity value; 5. and setting a reasonable deviation threshold of the characteristic peak according to the error characteristics of the portable near infrared spectrum, and carrying out abnormity judgment and screening on the spectral data of the characteristic peak of the fermented grain sample by combining the reasonable deviation threshold of the characteristic peak. 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 a method for screening abnormal fermented grains by a spectrum based on characteristic peak judgment.
Background
Fermented grains are necessary products in a brewing link, the fermented grains are mainly fermented from grains, the components contain a large number of hydrogen-containing groups, including C-H, S-H, O-H, N-H and the like, and in the fermentation process of the fermented grains, the content of moisture, starch, acidity, sugar and other substance components in the fermented grains directly influences the quality of wine products, and the fermented grains are the main basis for judging whether the fermented grains are suitable and whether the fermentation process of the fermented grains is normal. However, fermented grains are a solid-liquid mixture, and have different particle sizes, uneven component distribution and serious volatilization, thereby causing great trouble to component analysis.
In recent years, large-scale near-infrared spectrometers are used in wineries to detect main components of fermented grains, although the method is high in quantitative accuracy and sensitivity, the equipment is large in size and high in requirements on environmental conditions, special detection rooms and professional analyzers are still needed, brewers cannot perform field detection, and the real-time performance is poor. Meanwhile, because the large near-infrared spectrometer is expensive, a large amount of devices cannot be arranged in a winery, and each pit and each batch of samples are difficult to detect, and the actual requirements of the winery are far away.
The portable near-infrared spectrometer has small volume and low price, and can be purchased in large quantities to realize the detection of each batch of fermented grains. However, the portable near infrared spectrometer is affected by a light source, a detector, a using method, environmental conditions and the like, spectral data acquired by the portable near infrared spectrometer is easy to distort, accuracy is poor, and spectral 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 sample, the spectrum data acquired by the portable near infrared spectrum equipment is easy to be abnormal, and the portable near infrared spectrum analysis technology is easily influenced by the abnormal spectrum data, so that the prediction analysis capability of the portable near infrared spectrum analysis technology is greatly reduced. Meanwhile, the spectral data acquired by the portable near infrared spectrum equipment are relatively redundant, too much data information with relatively small correlation with the fermented grain sample is included, and relatively large workload and difficulty are brought to modeling analysis work, so that the method for acquiring the spectral data of the fermented grain sample, which can reduce the data volume of the spectrum and maximally ensure the accuracy of the spectral data of the fermented grain sample, becomes a problem to be solved.
Disclosure of Invention
In order to ensure the accuracy of the fermented grain sample spectral data and reduce the data processing capacity, the invention provides an abnormal fermented grain spectral screening method based on characteristic peak judgment.
The technical scheme adopted by the invention for solving the problems is as follows:
a method for screening abnormal fermented grain spectrums based on characteristic peak judgment comprises the following steps:
step 1, collecting fermented grain sample spectral data, and performing second-order derivation on the fermented grain sample spectral data to obtain a characteristic peak of the fermented grain sample spectral data;
step 2, selecting M characteristic peak light intensity points to replace original spectrum data according to the near infrared spectrum wavelength point weight characteristics;
step 3, calculating the relation between the illumination intensity of the portable near infrared spectrum and the receiving illumination intensity of the sensor;
step 4, calculating a standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illumination value of the spectral sensor and the characteristic peak light intensity value;
and 5, setting a reasonable deviation threshold of the characteristic peak according to the error characteristics of the portable near infrared spectrum, and carrying out abnormity judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the reasonable deviation threshold of the characteristic peak.
Further, the calculation method of M in step 2 is as follows: m ═ K + (λ)2-λ1)/n+(λ4-λ3)/n+...+(λi-λi-1) In the formula, K represents the number of wave bands with weight coefficients meeting the requirement, (lambda)i-1，λi) And representing the waveband range of which the weight coefficient meets the requirement, wherein n is the resolution of the portable near-infrared spectrometer.
In step 3, the reflectivity of the fermented grain sample is set to be α, the cavity attenuation rate of the portable near-infrared spectrometer is set to be β, the value of the illumination intensity emitted by the portable near-infrared light source is X, and the sensor receiving illumination intensity is Z, which is (1- β) × α × (1- β) × X.
Further, the light intensity value of the standard characteristic peak of the fermented grain sample in the step 4
In the formula, PMThe light intensity value corresponding to the Mth characteristic peak, Z1Is the value of the illumination light 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 threshold T of a light intensity value set of characteristic peaks of the fermented grain sample is:
the lower threshold S is:
compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of selecting partial light intensity points with higher weight coefficients through characteristic peak information to replace original spectrum data, further reducing spectrum data amount to a great extent on the premise of keeping spectrum data characteristic information, meanwhile, conducting abnormity judgment and screening on the spectrum data of the fermented grains sample by combining a characteristic peak reasonable deviation threshold value, rejecting abnormal spectrum data, ensuring spectrum data accuracy, and solving the problem that the portable infrared spectrum analysis technology is easily influenced by the abnormal spectrum data to reduce the prediction analysis capability.
Drawings
FIG. 1 is a flow chart of the method for screening abnormal fermented grains by spectrum;
FIG. 2 is a graph of spectral data of a fermented grain sample after second-order derivation.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a method for screening abnormal fermented grains spectrum based on characteristic peak determination comprises:
step 1, collecting fermented grain sample spectral data, and performing second-order derivation on the fermented grain sample spectral data to obtain a characteristic peak of the fermented grain sample spectral data; the fermented grain sample spectral data can be acquired by adopting a wavelength-sharing portable near-infrared spectrometer, and the sample spectral data can be acquired to the maximum uniformity degree by adopting the wavelength-sharing portable near-infrared spectrometer to acquire the spectral data of the fermented grain sample. In this embodiment, a portable near-infrared spectrometer with a wavelength range of 1758nm to 2150nm and a resolution of 8nm is used to collect spectral data, it can be calculated that light intensity points included in each fermented grain spectral data are M ═ 1+ (2150-. In the collection process, each sample correspondingly collects 5 pieces of spectral data, the 5 pieces of spectral data are subjected to mean value operation, and the data after the mean value is the actual spectral data of the sample, so that the advantages that the collection error can be effectively reduced, and the reliability of the data is improved are achieved. As shown in fig. 2, the second derivation is performed on the collected spectral data of the fermented grain sample, and obvious spectral peaks appear at the positions of 1838nm, 1926nm and 2030nm, and these peak points are the characteristic peaks of the fermented grain sample.
The second-order derivation is carried out on the spectrum data of the fermented grain sample, the second-order derivation spectrum has the full width at half maximum of about 1/3 of the full width at half maximum of the original spectrogram, small shoulder peaks on two sides of a strong peak can be simply distinguished, the peak value of the fermented grain sample spectrogram, namely the position of the characteristic peak wavelength point, can be clearly distinguished by the second-order derivation, and the accurate peak position and shoulder peak position determination is extremely effective.
Step 2, selecting M characteristic peak light intensity points to replace original spectrum data according to the near infrared spectrum wavelength point weight characteristics; according to the second-order derivative spectrum graph of the spectrum data of the sample to be detected, the wavelength points with higher weight coefficients around the characteristic peak are selected to replace original spectrum data, and by adopting the mode, the spectrum data amount 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 derivative spectrum pattern of the spectrum data of the fermented grain sample, the characteristic peaks of the spectrum have obvious peaks at the positions of 1838nm, 1926nm and 2030nm, wherein the light intensity points around the characteristic peaks have higher weight coefficients, and in this embodiment, two points of the secondary peak are selected, that is, the wavelength ranges with higher weight coefficients are 1822nm to 1854nm, 1910nm to 1942nm and 2014nm to 2046 nm. As can be seen from the above, the recombined spectral data replacing the original 50 light intensity value point spectral data includes 15 light intensity value points, the 1 st to 15 th wavelength points correspond to wavelength ranges of (1822nm, 1830nm, 1838nm, 1846nm, 1854nm, 1910nm, 1918nm, 1926nm, 1934nm, 1942nm, 2014nm, 2022nm, 2030nm, 2038nm and 2046nm), and the spectral data of each fermented grain sample after reconstruction is actually represented as a matrix set of light intensity values at 15 wavelength points. Compared with the original spectrum of the fermented grain sample, the method has the advantages that the characteristic information of the sample is retained to the maximum extent, the number of light intensity value points of each spectrum data is greatly reduced, and the spectral analysis efficiency is effectively improved.
Step 3, calculating the relation between the illumination intensity of the portable near infrared spectrum and the receiving illumination intensity of the sensor; the portable near-infrared light source emits near-infrared light which is attenuated by the near-infrared light cavity and then reaches the surface of an object to be detected to be converged into a sampling light spot, the sampling light spot is subjected to light reflection by the object to be detected and reaches the spectrum sensor through the attenuation of the light cavity, and the spectrum sensor receives reflected light intensity information to generate a corresponding spectrum data value. The relation between the illumination intensity of the portable near infrared spectrum and the receiving illumination intensity of 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 reflectivity of the fermented grain sample is set to be α, the optical cavity attenuation rate of the portable near-infrared spectrometer is set to be β, and the illumination value emitted by the portable near-infrared light source is set to be X, which can be known according to the working principle of the portable near-infrared spectrometer:
the portable near-infrared light source emits near-infrared light, the near-infrared light is attenuated by the near-infrared light cavity and reaches the surface of an object to be detected, the near-infrared light is converged into a sampling light spot, and the illumination 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 the spectrum sensor through the attenuation of the optical cavity, and the illumination value received by the spectrum sensor is Z: z ═ 1- β × α × Y; in summary, the relationship between the illumination intensity X of the portable near infrared spectrum and the receiving illumination intensity Z of the sensor is as follows: z ═ 1- β × α × (1- β) × X.
The attenuation rate of the optical cavity of the same portable near-infrared spectrometer is a fixed value, the illumination intensity of the same portable near-infrared spectrometer is also a fixed value, and the illumination intensity value received by the sensor is only related to the reflectivity of the object to be measured and is in a 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 illumination value of the spectral sensor and the characteristic peak light intensity value; and after receiving the near infrared spectrum information of the fermented grain sample, the spectrum sensor transmits the 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 performs analog-to-digital conversion and transmits the amplified initial spectrum signal to the ARM chip to store spectrum data. According to the spectral data transmission and processing steps, the conversion relation between the illumination value of the spectral sensor and the light intensity value of the characteristic peak can be known, and the light intensity value of the standard characteristic peak of the fermented grain sample can be further calculated.
In this embodiment, the spectral sensor receives an illumination intensity value of Z1When the light intensity value is within the characteristic peak wavelength range (1822nm, 1830nm, 1838nm, 1846nm, 1854nm, 1910nm, 1918nm, 1926nm, 1934nm, 1942nm, 2014nm, 2022nm, 2030nm, 2038nm and 2046nm), the light intensity value is (P2 nm, 1830nm, 1838nm, 1846nm, 1854nm, 1910nm, 2014nm, 2022nm, 2030nm, 2038nm and 2046nm)1，P2，......，P15) And when the portable near-infrared spectrometer collects the fermented grain sample with the reflectivity of alpha calculated in step 103, the illumination 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 reasonable deviation threshold of the characteristic peak according to the error characteristics of the portable near infrared spectrum, and carrying out abnormity judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the reasonable deviation threshold of the characteristic peak; due to the convenience of the portable near infrared spectrum equipment, the spectral 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 the spectral data, and the spectral data are also determined to be reasonable data. According to the characteristic, the deviation of the spectral characteristic peak data within a certain threshold value range is regarded as a reasonable deviation value. When the reflectivity of the fermented grain sample is fixed, the spectral characteristic peak threshold value is also a determined value, the number of the characteristic peak light intensity points of the fermented grain sample spectral data is judged, and if the light intensity values are all in the threshold range, the spectral data is judged to be normal. If some light intensity point values exceed the threshold range, the spectrum data is judged to be abnormal, and the abnormal spectrum data is rejected.
In this embodiment, the reasonable deviation range of the portable near infrared spectrum data is set as c%, and the upper threshold T of the light intensity value set of the characteristic peak of the fermented grain sample is determined by combining the light intensity value set P of the standard characteristic peak 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: the actual spectral data of the characteristic peak of the fermented grain sample collected by the portable near infrared spectrum equipment is set as (H)1，H2，......，H15). And judging 15 points in the spectral data one by one, if the following conditions are met: st<Ht<TtAnd if t is 1, 2, 15, judging that the spectrum data is normal, otherwise, judging that the spectrum data is abnormal spectrum, and removing the abnormal spectrum.
Claims (6)
1. A method for screening abnormal fermented grain spectrum based on characteristic peak determination is characterized by comprising the following steps:
step 1, collecting fermented grain sample spectral data, and performing second-order derivation on the fermented grain sample spectral data to obtain a characteristic peak of the fermented grain sample spectral data;
step 2, selecting M characteristic peak light intensity points to replace original spectrum data according to the near infrared spectrum wavelength point weight characteristics;
step 3, calculating the relation between the illumination intensity of the portable near infrared spectrum and the receiving illumination intensity of the sensor;
step 4, calculating a standard characteristic peak light intensity value of the fermented grain sample according to the conversion relation between the illumination value of the spectral sensor and the characteristic peak light intensity value;
and 5, setting a reasonable deviation threshold of the characteristic peak according to the error characteristics of the portable near infrared spectrum, and carrying out abnormity judgment and screening on the characteristic peak spectrum data of the fermented grain sample by combining the reasonable deviation threshold of the characteristic peak.
2. The method for screening abnormal fermented grains spectrum based on characteristic peak determination according to claim 1, wherein M in the step 2 is calculated in a mode of: m ═ K + (λ)2-λ1)/n+(λ4-λ3)/n+...+(λi-λi-1) In the formula, K represents the number of wave bands with weight coefficients meeting the requirement, (lambda)i-1，λi) And representing the waveband range of which the weight coefficient meets the requirement, wherein n is the resolution of the portable near-infrared spectrometer.
3. The method for screening abnormal fermented grain spectrum based on characteristic peak determination according to claim 2, wherein in the step 3, the reflectance of the fermented grain sample is set to α, the optical cavity attenuation rate of the portable near-infrared spectrometer is set to β, the value of illumination emitted by the portable near-infrared light source is X, and the sensor receiving illumination is Z, which is (1- β) X α X (1- β) X.
4. The method for screening abnormal fermented grain spectrum based on characteristic peak determination according to claim 3, wherein the light intensity value of the standard characteristic peak of the fermented grain sample in the step 4 is
In the formula, PMThe light intensity value corresponding to the Mth characteristic peak, Z1Is the value of the illumination light received by the spectral sensor.
5. The method for screening abnormal fermented grain spectrum based on characteristic peak determination according to claim 4, wherein in the step 5, a reasonable deviation range of the portable near infrared spectrum data is set as c%, and an upper threshold T of a light intensity value set of characteristic peaks of the fermented grain sample is as follows:
the lower threshold S is:
6. the method for screening the spectrum of the abnormal fermented grains based on the characteristic peak judgment according to any one of claims 1 to 5, wherein a wavelength-averaging portable near-infrared spectrometer is adopted to collect the spectral data of the fermented grain sample.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008116432A (en) * | 2006-07-06 | 2008-05-22 | Ricoh Co Ltd | Raman spectrometric measuring instrument, and raman spectrometry using same |
| CN101303305A (en) * | 2008-07-04 | 2008-11-12 | 中国检验检疫科学研究院 | Portable Raman optical spectrum olive oil discrimination data processing system |
| CN104062256A (en) * | 2013-04-15 | 2014-09-24 | 山东东阿阿胶股份有限公司 | Soft measurement method based on near infrared spectroscopy |
| EP2882994A1 (en) * | 2012-08-07 | 2015-06-17 | University of South Alabama | Spectral illumination device and method |
| CN105784678A (en) * | 2016-01-31 | 2016-07-20 | 华南理工大学 | Method for identifying laser plasma spectrum of grain flow through standard deviation of characteristic peak strength |
| WO2017197123A1 (en) * | 2016-05-11 | 2017-11-16 | Cornell University | Systems, methods and programs for denoising signals using wavelets |
| US20180147556A1 (en) * | 2014-12-11 | 2018-05-31 | Consejo Nacional De Investigaciones Científicas Y Técnicas (Conicet) | Bi-structured matrix for solid reactants purification and handling and methods for obtaining said matrix |
| CN109993155A (en) * | 2019-04-23 | 2019-07-09 | 北京理工大学 | For the characteristic peak extracting method of low signal-to-noise ratio uv raman spectroscopy |
| CN110672546A (en) * | 2019-10-11 | 2020-01-10 | 四川长虹电器股份有限公司 | Vinasse model modeling method based on portable near-infrared spectrometer |
| CN111398209A (en) * | 2020-05-04 | 2020-07-10 | 中钢集团郑州金属制品研究院有限公司 | Infrared spectrum method for measuring polyvinyl chloride content in hard polyvinyl chloride pipe |
| CN111579528A (en) * | 2020-06-30 | 2020-08-25 | 四川长虹电器股份有限公司 | Calibration method of micro near-infrared spectrometer |
| CN111965140A (en) * | 2020-08-24 | 2020-11-20 | 四川长虹电器股份有限公司 | Wavelength point recombination method based on characteristic peak |
| CN112525869A (en) * | 2020-07-13 | 2021-03-19 | 淮阴工学院 | Sectional type detection method for pesticide residues |
| US20210172800A1 (en) * | 2019-12-10 | 2021-06-10 | Perkinelmer Health Sciences Canada, Inc. | Systems and Methods for Analyzing Unknown Sample Compositions Using a Prediction Model Based On Optical Emission Spectra |
| US20210310959A1 (en) * | 2018-10-24 | 2021-10-07 | Technion Research & Development Foundation Limited | Means and methods for detection and characterization of spectrally structured, continuously changing, diffuse radiation sources |
-
2021
- 2021-12-16 CN CN202111541368.5A patent/CN114280002B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008116432A (en) * | 2006-07-06 | 2008-05-22 | Ricoh Co Ltd | Raman spectrometric measuring instrument, and raman spectrometry using same |
| CN101303305A (en) * | 2008-07-04 | 2008-11-12 | 中国检验检疫科学研究院 | Portable Raman optical spectrum olive oil discrimination data processing system |
| EP2882994A1 (en) * | 2012-08-07 | 2015-06-17 | University of South Alabama | Spectral illumination device and method |
| CN104062256A (en) * | 2013-04-15 | 2014-09-24 | 山东东阿阿胶股份有限公司 | Soft measurement method based on near infrared spectroscopy |
| US20180147556A1 (en) * | 2014-12-11 | 2018-05-31 | Consejo Nacional De Investigaciones Científicas Y Técnicas (Conicet) | Bi-structured matrix for solid reactants purification and handling and methods for obtaining said matrix |
| CN105784678A (en) * | 2016-01-31 | 2016-07-20 | 华南理工大学 | Method for identifying laser plasma spectrum of grain flow through standard deviation of characteristic peak strength |
| WO2017197123A1 (en) * | 2016-05-11 | 2017-11-16 | Cornell University | Systems, methods and programs for denoising signals using wavelets |
| US20210310959A1 (en) * | 2018-10-24 | 2021-10-07 | Technion Research & Development Foundation Limited | Means and methods for detection and characterization of spectrally structured, continuously changing, diffuse radiation sources |
| CN109993155A (en) * | 2019-04-23 | 2019-07-09 | 北京理工大学 | For the characteristic peak extracting method of low signal-to-noise ratio uv raman spectroscopy |
| CN110672546A (en) * | 2019-10-11 | 2020-01-10 | 四川长虹电器股份有限公司 | Vinasse model modeling method based on portable near-infrared spectrometer |
| US20210172800A1 (en) * | 2019-12-10 | 2021-06-10 | Perkinelmer Health Sciences Canada, Inc. | Systems and Methods for Analyzing Unknown Sample Compositions Using a Prediction Model Based On Optical Emission Spectra |
| CN111398209A (en) * | 2020-05-04 | 2020-07-10 | 中钢集团郑州金属制品研究院有限公司 | Infrared spectrum method for measuring polyvinyl chloride content in hard polyvinyl chloride pipe |
| CN111579528A (en) * | 2020-06-30 | 2020-08-25 | 四川长虹电器股份有限公司 | Calibration method of micro near-infrared spectrometer |
| CN112525869A (en) * | 2020-07-13 | 2021-03-19 | 淮阴工学院 | Sectional type detection method for pesticide residues |
| CN111965140A (en) * | 2020-08-24 | 2020-11-20 | 四川长虹电器股份有限公司 | Wavelength point recombination method based on characteristic peak |
Non-Patent Citations (8)
| Title |
|---|
| LI XP ET AL: "Characterization of intermolecular interaction between two substances when one substance does not possess any characteristic peak" * |
| LIU HH ET AL: "Cobalt disulfide nanosphere dispersed on multi-walled carbon nanotubes: an efficient and stable electrocatalyst for hydrogen evolution reaction" * |
| PAN XY ET AL: "Highly Antibacterial and Toughened Polystyrene Composites with Silver Nanoparticles Modified Tetrapod-Like Zinc Oxide Whiskers" * |
| SU LM ET AL: "Tunable luminescence properties and energy transfer of Tm3+, Dy3+, and Eu3+ co-activated InNbO4 phosphors for warm-white-lighting" * |
| 胡艳等: "光诱导约束刻蚀体系中羟基自由基生成的影响因素" * |
| 贾利红等: "基于集成建模方法的便携式近红外光谱仪酒醅成分检测研究" * |
| 邢素霞等: "高光谱成像及近红外技术在鸡肉品质无损检测中的应用" * |
| 金鑫等: "巢湖水体漫衰减系数空间差异及其遥感反演" * |
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