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CHNSpec Technology (Zhejiang)Co.,Ltd was found in 2008, and we are specialize in the R&D, production and sales of colorimeters.
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Lastest company cases about Three methods for color measurement
Three methods for color measurement
Color measurement is mainly divided into the measurement of the color of the light source and the measurement of the color of the object. The object color measurement is divided into fluorescent object measurement and non-fluorescent object measurement. In actual production and daily life, color measurement of non-fluorescent objects is widely used. It is mainly divided into two categories: visual color measurement and instrument color measurement. Among them, instrument color measurement includes photoelectric integration method and spectrophotometry method.   1. Visual method The visual method is the visual perception of light produced by the eyes, the brain, and our life experience. The light we see with the naked eye is generated by electromagnetic waves with a narrow wavelength range, and electromagnetic waves of different wavelengths show different colors The recognition of color is the visual nerve sensation caused by the naked eye after being stimulated by electromagnetic wave radiation energy. The unknown colors of the individual components are added together to describe the resulting unknown colors. Although it is most suitable for color evaluation. The way to rely on it is with the help of the human eye, and it is simple and flexible, but due to the experience of observers and psychological and physiological factors The impact of this method makes the method too many variables and cannot be described quantitatively, which affects the accuracy of the evaluation.   2.The photoelectric integration method For a long time, the density method has occupied a very high position in color measurement, but with the application of CIE1976L *, a *, b * gradually becoming widespread, and has covered the entire work flow from press to printing, people are more and more aware of color The importance of degree, and the rapid development of modern colorimetric have also laid the foundation for the objective evaluation of color by photoelectric integration instruments ( precision color difference meters). The photoelectric integration method is a common method used in instrument color measurement in the 1960s. It does not measure the color stimulus value of a certain wavelength, but measures the tristimulus values X, Y, and Z of the sample through integral measurement over the entire measurement wavelength interval, and then calculates the chromaticity coordinates and other parameters of the sample. When using such three photo detectors to receive light stimuli, the tristimulus values X, Y, and Z of the sample can be measured with one integration. The filter must meet Luther's conditions to accurately match the photo detector. The photoelectric integration instrument cannot accurately measure the tristimulus value and chromaticity coordinates of the excellent source, but can accurately measure the color difference between the two color sources, so it is also called a color difference meter. Foreign color difference meters have been mass-produced since the 1960s, and China has been developing such instruments since the early 1980s. Nowadays, the CS-210 precsision colorimeter produced by Hangzhou CHNSpec Technology Co.,Ltd has been used. CS-210 Precision Colorimeter   3. Spectrophotometry Spectrophotometry is also called spectrophotometer. It compares the light energy reflected (transmitted) by the sample with the standard reflected (transmitted) light energy under the same conditions to obtain the spectral reflectance of the sample at each wavelength, and then uses CIE The provided standard observer and standard light source are calculated according to the following formula to obtain the tristimulus values X, Y, and Z, and then X, Y, and Z are used to calculate the chromaticity coordinates x according to the formulas such as CIE Yxy and CIE Lab. y, CIELAB chromaticity parameters, etc. The spectrophotometer determines the color parameters by detecting the spectral components of the sample. It can not only give the absolute values of X, Y, Z and the color difference value △ E, but also give the spectral reflectance value of the object, and can draw the object. Therefore, it is widely used in color matching and color analysis. The use of such instruments can achieve high-accuracy color measurement, calibration of photoelectric integral color measurement instruments, and establishment of chromaticity standards. Such instruments were first developed in China. CS-600 Integrating Sphere Color Spectrophotometer is color spectrum. Therefore, the spectrophotometer is an authoritative instrument in color measurement.   Color Spectrophotometer CS-600   Company introduction Our CHNSpec Technology Co., Ltd are specialized on manufacturing haze meter, spectrophotometers, colorimeters and gloss meters. Our products have gotten 10 Invention Patents including 1 American Invention Patent, 8 Utility Model Patents, 4 Appearance Patents and 3 Software Copyrights till now.    
Lastest company cases about Objective Measurement of Transparency
Objective Measurement of Transparency
Measurement and analysis of haze and clarity guarantee a uniform and consistent product quality and help analyze influencing process parameters and material properties, e.g.cooling rate or compatibility of raw materials.   The figure on the picture shows the measurement principle of the haze meter:   A light beam strikes the specimen and enters an integrating sphere. The sphere's interior surface is coated uniformly with a matte white material to allow diffusion. A detector in the sphere measures total transmittance and transmission haze. A ring sensor mounted at the exit port of the sphere detects narrow angle scattered light ( clarity). Standard Methods The measurement of Total Transmittance and Transmission Haze is described in international standards. Two different test methods are specified: 1. IS013468 Compensation method 2. ASTM D1003 Non-compensation method The compensation method takes the light reflected on the sample surface into account. Differences between the two methods can be approximately 2 Total Transmittance on clear, glossy samples.   ASTM D 1003 Measurement conditions are different during calibration and actual measurement. During calibration, part of the light escapes through the open entrance port of the haze meter. While taking a measurement, the entrance port is covered with the sample, thus, the amount of light in the sphere is increased by the light reflected at the sample surface.     ISO13468 Measurement conditions are kept equal during calibration and measurement due to an additional opening in the sphere. During calibration the sample is placed at the compensation port. For the actual measurement, the sample is changed to the entrance port. Thus, the so-called sphere efficiency is independent of the reflection properties of the sample.     Two Standard Methods in one Unit The clarity and haze meter CS-720 complies with both ASTM and ISO measurement standards. It can meet the following measurement standards ASTM D1003 / D1044, ISO13468 / ISO14782, JIS K7105, JIS K7361, JIS K7163 and other international standards. If any inquiry, you are welcome to contact us.  
Lastest company cases about Factors affecting haze measurement
Factors affecting haze measurement
What is haze? Haze is also called turbidity. It indicates the degree of unclearness of transparent or translucent materials. It is the appearance of cloudiness or turbidity caused by light scattering inside or on the surface of the material. It is expressed as the percentage of the ratio of the scattered light flux to the light flux through the material.   Why measure haze? Haze measurement can be used to quantify the optical properties of plastics and packaging films. Obscure films in packaging applications can reduce consumer perception of quality, such as when packaging products look blurry. For plastics with haze, the visibility of the test material becomes more pronounced and reduces the contrast of the observed objects.   Factors affecting haze measurement Part1: light source Different light sources have different relative spectral energy distributions. Because various transparent plastics have their own spectral selectivity, the same material is measured with different light sources, and the obtained light transmittance and haze value are different. The darker the color, the greater the impact.In order to eliminate the influence of the light source, the International Institute of Illumination (CIE) has specified three standard light sources A, B, and C. This method uses a "C" light source.       Part2: Influence of surface condition The surface state of the sample mainly refers to whether the surface is flat and smooth, whether there are scratches and defects, and whether it is contaminated.       Part3: Effect of specimen thickness As the thickness of the sample increases, the light absorption increases, the light transmittance decreases, and light scattering increases, so the haze increases. Transmission and haze can only be compared at the same thickness.  
Lastest company cases about How to calculate haze of transparent acrylic plastic sheet?
How to calculate haze of transparent acrylic plastic sheet?
What is acrylic sheet? Acrylic is also called special-processed plexiglass. It is a replacement product of plexiglass. The light box made of acrylic has good light transmission, pure colors, rich colors, beautiful and flat, taking into account the two effects of day and night, long life, does not affect the use, and other features.   How to calculate transmittance? In the process of measuring the haze and light transmittance of the sample, it is necessary to measure the incident light flux (T1), the transmitted light flux (T2), the scattered light flux (T3) of the instrument, and the scattered light flux (T4) of the sample. Calculation method of Transmittance: Tt= T2/ t1x100%   How to calculate haze? Haze: H= [t4-t3 (T2/T1)]/ t2x100% The formula of haze value H can be simplified as: H(%)= [(T4/T2)-(T3/T1)]×100%   How to Measure Acrylic Plastic Sheet?(The products that measure haze are Color Spectrum TH-100, CS-700, CS-701 and CS-720) Take Color Spectrum Haze Meter TH-100 as an example 1.Start Connect the instrument to the power source, press the power key, the indicator light is always blue, and the instrument starts normally. 2.0% and 100% calibration. Put the 0% calibration cover on the test port so that the integrating sphere does not receive any light. Press the OK key on the side of the instrument to calibrate.100%: Keep the test port open, let the light from the light source pass through the test port, and press the OK key on the side of the instrument for calibration. 3.Measure After calibration, place the transparent acrylic plastic sheet in the test port and click the test button next to the instrument. The result will be available in 2 seconds. The operation process is very simple.  
Lastest company cases about How to calculate haze
How to calculate haze
Haze : Wide Angle Scattering   The light before passing through the sample is called incident light, the entire light after passing through the sample is called transmitted light, and the scattered light with a scattering angle greater than 2.5 ° after the transmission sample is called scattered light, haze Is the scattered light than the transmitted light (as show in green color of picture 2) and Tt is the total transmitted light (as show in pink color of picture 1).   So haze equation is Haze = Td / Tt.     Haze Measuring Instrument   We will introduce how to measure haze by CHNSpec Haze Meter TH-100. It can meet both ISO and ASTM standards.   TH-100 haze meter   What is the measurement method of TH-100? This is the light path structure diagram of this haze meter. The light source emits parallel light, passes through the sample and enters the integrating sphere. Part of the transmitted light is parallel light and part is scattered light. A photoelectric sensor is installed on the inner wall of the integrating sphere perpendicular to the parallel beam to obtain the light flux signal. The light trap is used to absorb all the incident light when there is no sample in the test port. The light trap is equipped with a shutter, which is coated with the same high reflectivity coating as the integrating sphere wall. The shutter can be opened and closed as required. Light trap: When measuring the haze, the light trap will open (because the scattered light will be collected to calculate the haze); when measuring the total transmittance, the light trap will be closed; haze meter TH-100 can be automatically measured, all you have to do is place the sample at the test.     For more details of haze meter TH-100, you can refer to the following url   1). Haze Meter TH-100 Working Video   2). TH-100 Haze Meter Accuracy Test Video   CHNSpec Tech is specialized on provide color, gloss and haze measurement solutions. If any future inquiry, you are welcome to contact me for more details.
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Lastest company news about Hyperspectral cameras: Unlocking new horizons in the science behind color
Hyperspectral cameras: Unlocking new horizons in the science behind color
As an advanced optical imaging device, hyperspectral camera has shown great application potential in many fields in recent years. It can not only obtain spatial information of objects, but also obtain rich spectral information at the same time, which provides unique and valuable data for scientific research and practical applications. What is a hyperspectral camera? A hyperspectral camera is an imaging device capable of capturing information about light reflected or emitted by a target object in multiple continuous and narrow spectral bands. Unlike traditional cameras or the limited range of colors that the human eye can perceive, hyperspectral cameras cover a broad spectral region from ultraviolet to infrared, capable of generating data cubes containing rich spectral information. These data not only record the spatial position information of the target object (two-dimensional image), but also contain the spectral response characteristics of each pixel at different wavelengths (third-dimensional spectral information), so as to achieve a more comprehensive and in-depth analysis of the target object. How hyperspectral cameras work The work of the hyperspectral camera is based on spectroscopy technology, that is, the use of a splitter to decompose the incident light into different wavelengths of monochromatic light, and through a series of sophisticated optical systems and detectors, respectively, to measure the reflection or emission intensity of the target object at each wavelength. This data is then integrated into 3D data cubes for subsequent processing and analysis. The high-resolution spectral characteristics of a hyperspectral camera enable it to capture subtle spectral differences that traditional cameras cannot detect, revealing information such as the chemical composition, physical state and environmental conditions of the surface of an object.   Application areas of hyperspectral cameras 1. Agriculture and forestry: Hyperspectral cameras are particularly widely used in agriculture. By analyzing the spectral data of leaf, fruit and other parts of the crop, it can accurately evaluate the growth status of the crop, nutrient level, disease and pest situation and yield prediction. In forestry, hyperspectral cameras can be used to monitor changes in forest cover, identify tree species, and assess forest health. 2. Environmental monitoring and protection: hyperspectral cameras can identify and quantify various pollutants in the environment, such as oil pollution in water, heavy metal pollution and harmful gases in the air. It can also be used to monitor land degradation, ecological restoration and the impact of climate change on the natural environment. 3. Mineral resource exploration: hyperspectral cameras can detect specific mineral components in surface rocks, soil and vegetation, providing important clues for the exploration of mineral resources. By analyzing spectral features in hyperspectral images, it is possible to quickly locate mineral deposits and assess their size and quality. 4. Military and defense: In the military field, hyperspectral cameras can be used for target identification, camouflage detection, and battlefield environment monitoring. Its high-resolution spectral data can help military personnel more accurately identify enemy targets, assess battlefield situations, and formulate corresponding tactical strategies. 5. Cultural heritage protection: Hyperspectral cameras also play an important role in the protection of cultural heritage. Through the analysis of the spectral data of the surface of cultural relics, the material, production process and historical changes of cultural relics can be revealed, providing scientific basis for the restoration, protection and display of cultural relics. With its unique imaging ability and wide application potential, hyperspectral camera is becoming a bright star in modern scientific research and technology application. FigSpec®FS1X series hyperspectral cameras contain visible light (400-700nm), near-infrared (400-1000nm) and short-wave near-infrared (900-1700nm) three spectral regions, widely used in printing, textile and other industrial products surface color texture detection, component identification, substance identification, machine vision, color detection. Agricultural product quality and other areas. With the continuous progress of technology and the gradual reduction of costs, hyperspectral cameras will play an important role in more fields, contributing more wisdom and strength to the sustainable development of human society.
Lastest company news about Application of hyperspectral camera in non-destructive testing of fruit quality
Application of hyperspectral camera in non-destructive testing of fruit quality
With the continuous progress of agricultural technology, the demand for fruit quality detection is also increasing. Traditional fruit quality detection methods often need to destroy the sample, which is not only time-consuming and laborious, but also may lead to a lot of waste. As an advanced imaging technology, hyperspectral camera has shown great application potential in the field of non-destructive testing of fruit quality with its unique advantages. The technical principle of hyperspectral camera The basic principle of hyperspectral camera is to use spectral imaging technology to convert the spectral information of the target object into image information. By measuring the reflection or emission spectrum of the target object at different wavelengths, the spectral characteristics of the target object are obtained, and then the target object is recognized and classified. Hyperspectral camera combines spectral imaging technology with imaging technology to generate hyperspectral images, which contain not only the spatial information of the target object, but also its spectral information, so as to realize multi-dimensional analysis of the target object.   Features of hyperspectral cameras 1. Hyperspectral resolution: The hyperspectral camera can obtain the spectral data of the target object at hundreds or even thousands of wavelengths to achieve fine identification and analysis of the target object. 2. High spatial resolution: the technology can not only obtain spectral information, but also accurately obtain the spatial information of the target object to achieve high-precision positioning. 3. High sensitivity: The hyperspectral camera can also obtain clear hyperspectral images under lower lighting conditions, improving the recognition ability of the target object. 4. Multi-dimensional information fusion: spectral information is fused with spatial information to generate multi-dimensional hyperspectral images, which provides rich information for subsequent image processing and analysis. Application of hyperspectral camera in nondestructive testing of fruit quality 1. Maturity detection The ripeness of fruit is a key factor in determining its quality and taste. Traditional methods are often judged by appearance, color, or touch, but this method is subjective and prone to error. Hyperspectral cameras can capture the spectral characteristics of fruits at different wavelengths, and these characteristics can be used to accurately judge the maturity of fruits. 2. Identification of pests and diseases Pests and diseases are important factors affecting fruit quality. Hyperspectral cameras can capture the spectral changes caused by diseases and pests on the surface or inside of the fruit to achieve accurate identification of diseases and pests. This is of great significance for early detection of pests and diseases and timely measures to improve fruit yield and quality. 3. Quality assessment In addition to ripen and pests, fruit quality also involves many aspects, such as sweetness, acidity, moisture content and so on. Hyperspectral camera can obtain multi-dimensional spectral information of fruit, and combine with the corresponding algorithm model, these quality indexes can be accurately evaluated. For example, hyperspectral technology can be used to identify defects such as surface damage of sweet apples and red dates, which provides scientific basis for fruit grading and sales. The application of hyperspectral camera in the field of non-destructive testing of fruit quality has broad prospects. With the continuous progress of the technology and the reduction of the cost, the technology is expected to be applied in more kinds of fruit detection. At the same time, combining artificial intelligence and big data analysis technology can further improve the detection accuracy and efficiency, and realize the intelligence and automation of fruit quality detection.   However, hyperspectral cameras also face some challenges in fruit quality detection. For example, the spectral characteristics of different fruits are quite different, so it is necessary to establish a detection model for different fruits. At the same time, environmental factors such as light and temperature may also affect the detection results, and corresponding measures should be taken to correct them.   In short, as an advanced imaging technology, hyperspectral camera has shown great application potential and broad prospects in the field of non-destructive testing of fruit quality. FigSpec® series imaging hyperspectral cameras can realize the rapid acquisition of spectral images, not only for the analysis and detection of vegetables and fruits, but also widely used in spectral analysis, material sorting, agricultural remote sensing, industrial detection and other fields. With the continuous development and improvement of technology, it is believed that hyperspectral cameras will play a more important role in agricultural production in the future, contributing to improving fruit quality and promoting sustainable development of agriculture.
Lastest company news about How to use color difference meter to detect paint color difference
How to use color difference meter to detect paint color difference
In industrial production and daily life, the accuracy of color is becoming more and more important. Whether it is automobile manufacturing, cosmetics production, or home decoration, the accuracy of color will affect the quality and market acceptance of the product. In order to ensure the accuracy of color, many industries began to use color difference meters to detect color differences. This article will explain how to use the color difference meter to detect whether the paint color has color difference.   一、he working principle of the color difference meter A color difference meter is an instrument that evaluates color differences by measuring the color brightness, saturation, and hue of an object's surface. It can convert the color of an object into numerical values, and then calculate these values with standard color values to obtain color differences. A color difference meter usually consists of a light source, a receiver and a processor.   二、the steps of using color difference meter 1. Sample preparation Select a representative paint sample and apply it evenly on the cardboard to ensure that the surface of the sample is smooth to avoid the deviation of light when reflected on the surface. Dry in a cool place to avoid sticking and contaminating the instrument and affecting the measurement results. 2. Measurement phase Place the color difference meter on the sample surface and adjust the Angle so that the light source shines vertically on the sample. Then, press the measurement key and the color difference meter will automatically measure the color of the sample and produce the data. Typically, the color difference meter will output three values: L, a, and b. L represents the color brightness, a represents the red-green value, and b represents the yellow-blue value. 3. Data analysis The color difference is calculated by comparing the data from the color difference meter with the standard color data. In general, the smaller the color difference value, the closer the color is to the standard color. Commonly used color difference formulas include ΔEab, ΔE00, etc. 4. Result report According to the calculated color difference value, the conformity of the sample is evaluated. If the color difference value is within the acceptable range, it indicates that the paint color meets the requirements; If the color difference value exceeds the acceptable range, the sample formula can be adjusted according to the data of the color difference meter, and then the sample can be obtained to meet the requirements. (The range of assessment can be set by the system itself)   三、precautions 1. Keep the instrument clean: the color difference meter needs to be cleaned and maintained before and after use to extend its service life. 2. Correct operation: Read the instruction manual carefully before use, and measure according to the operating steps. 3. Calibration: It is necessary to check whether the instrument has been calibrated before use to ensure the accuracy of the measurement results.
Lastest company news about Identification of rice sheath blight by imaging hyperspectral camera
Identification of rice sheath blight by imaging hyperspectral camera
In this study, a 400-1000nm hyperspectral camera was applied, and FS23, a product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. FigSpec® series imaging hyperspectral cameras use a transmission grating beam splitting module with high diffraction efficiency and a high sensitivity surface array camera, combined with built-in scanning imaging and auxiliary camera technology, to solve the traditional hyperspectral cameras require external push-scan imaging mechanism and complex focusing and other difficult problems. It can be directly integrated with the standard C interface imaging lens or microscope to achieve fast acquisition of spectral images. Precision agriculture is an important way to achieve low consumption, high efficiency, high quality and safety in agriculture. As the largest grain crop in our country, the stable yield and high yield of rice have always been the focus of our agricultural production, and timely and effective disease control is an important guarantee to achieve stable yield and high yield. Rice leaf blight is one of the three major diseases of rice. If the cause and degree of damage of damaged crops can be detected in the early stage of rice disease, combined with variable application in fine agriculture, the disease rate of rice disease infection can be effectively reduced, the harm scope can be narrowed, and the rice yield can be effectively increased. Variable application mainly refers to the timely diagnosis of the cause and degree of damage of the affected crops according to the information of crop pests and diseases, and the application of chemical agents according to the appropriate disease treatment, local conditions and demand, so as to reduce the use of chemical agents and achieve the purpose of timely prevention and control. In this study, hyperspectral imaging technology was used to recognize rice sheath blight. The PLS-DA discriminant analysis model was established after different pretreatment of the original spectra, and good results were obtained. Under SG, SNV and MSC pretreatment methods, the accuracy of prediction sample discrimination was 82.8%, 92.1% and 89.1%, respectively. The PLS-DA model established by SNV pretreatment spectrum had the highest accuracy, while the PLS-DA model established by SG pretreatment spectrum had the lowest accuracy, but the accuracy was more than 80%. So these three methods are feasible. The accuracy of the prediction set of LDA and BPNN discriminant models based on MNF feature information extraction is 95.3% and 98.4%, respectively, which is better than the PLS-DA model based on all bands. After comprehensive comparison of the three models, the BPNN model based on MNF feature information extraction achieves the optimal discriminant effect, and the accuracy of modeling set and prediction set is 99.1% and 98.4%, respectively. The experimental results show that the hyperspectral imaging technology can be used to identify rice grain wilt, and the MNF algorithm can be used to extract characteristic information to represent the original spectrum, and greatly reduce the calculation amount. The algorithm has a wide application prospect in the process of rapid recognition and modeling of rice disease.
Lastest company news about Study on detection of black neutral pen by hyperspectral imaging
Study on detection of black neutral pen by hyperspectral imaging
The human eye is sensitive to light in the visible range and distinguishes materials based on color. However, humans are unable to distinguish between two identical colors. Black neutral ink stable, writing does not fade. Many important documents are written with a black neutral pen, such as contracts, receipts, certificates, checks and other documents, the numbers on these documents, time, text and so on. Easy to be added or tampered with, the identification of tampered handwriting and the reproduction of covered handwriting are important evidence in criminal proceedings, therefore, in most civil and criminal cases, a lot of document identification requires the identification of black neutral pen handwriting. There are two main methods for identifying handwriting: lossy detection and nondestructive detection. Hyperspectral imaging, an effective non-destructive tool, has been widely used in the identification of agricultural products in recent years. In this paper, 18 kinds of black neutral pens sold in the market are taken as the object to explore a more effective method of handwriting recognition, which provides a research basis for handwriting criminal investigation and identification.   In this paper, a 400-1000nm hyperspectral camera is used. FS13, a product of Hangzhou Color Spectrum Technology Co., LTD., can be used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection).     1. Materials and equipment   Preparation of experimental materials and experimental samples   The experimental samples were 18 brands of black neutral pens that were popular in the market, and 18 brands of neutral pens tampered with and covered each other. After writing the number "1" with 18 brands of neutral pens, the number "40" was altered by other brands of neutral pens 24 hours later, and 306 samples of tampering experiments were made. (a) and (b) in Figure 1 are the pictures before and after pen 1 was tampered with by pen 2. As can be seen from Figure 1, after No. 1 pen is tampered with by No. 2 pen of the same color, the traces of tampering are completely invisible to the naked eye. 18 brands of neutral pens were used to write their respective numeric serial numbers, which were covered by other brands of neutral pens 24 hours later, and 306 masking experiment samples were made. (c) and (d) in Figure 1 show the pictures of No. 14 pen before and after being covered by No. 15 pen. As can be seen from Figure 1, the obscured writing is completely unrecognizable to the naked eye.     2. Results and discussion   Handwriting tampering and masking of reproduced identification results   Take pen No. 1 and pen No. 17 for example, as shown in Figure 2, (a) is a digital photo, (b) is the result of principal component analysis processing without background removal, (c) is the result of principal component analysis processing without background removal, and (d) is the result of false color synthesis processing. As can be seen from Figure 2, the processing results are clearer after the interference of background information is removed. A large amount of data analysis shows that false color synthesis has the best recognition effect on handwriting tampering. People who have not seen the original data can successfully identify the tampered handwriting, that is, the group of samples can be identified. Taking No. 2 neutral pen as an example to cover the sample with No. 13 neutral pen, FIG. 3 (a) is the digital photo of the sample, (b) is the result of principal component analysis processing without background removal, (c) is the result of principal component analysis processing without background removal, and (d) is the result of false color synthesis processing. A large amount of data analysis shows that the principal component analysis processing with the removal of background has the best effect on the recognition of handwriting masking recurrence.     3. Conclusion (1) In the range of 720-1000nm band, the spectral reflectance of different brands of neutral pens is very different, and it is the best band for recognizing handwriting.   (2) The recognition effect of domestic pens and Nissan pens can reach 100%, which provides a theoretical basis for the counterfeiting of goods.   (3) The research shows that after removing the background information, the identification effect is updated clearly after analysis and processing again.   (4) In this paper, the handwriting is recognized by noise reduction, IsoData, eye mask establishment, background removal and PCA analysis. After processing by different methods, different sample data will be recognized. Among 306 groups of tampered sample data of black neutral pens, 232 groups of data could be identified, with a recognition rate of 75.8%. Among 306 groups of black neutral pen masking samples, 175 groups of data could be reproduced, and the recognition rate reached 57.3%.   (5) The research results show that hyperspectral imaging technology can be used to identify the tampering and coverup between different brands of black neutral pens, which provides a research basis for the criminal investigation and identification of handwriting.
Lastest company news about An example of detecting milk colorimeter CS-821N
An example of detecting milk colorimeter CS-821N
In the milk industry, the color of milk is an important quality indicator, which reflects the composition, freshness and processing of milk, and is of great significance for evaluating the quality and safety of milk. For example, excessive heat treatment or oxidation may lead to yellow milk color, which is usually undesirable, so strict quality control of milk color is required to ensure that it complies with relevant standards and regulations, while traditional color evaluation methods may be affected by human factors, ambient light or observer subjectivity, leading to large deviations in evaluation. However, the desktop spectrophotometer can accurately quantify the color difference by measuring the spectral distribution of the reflection or transmission of the sample and converting it into objective color parameters, such as Lab values. This paper introduces a method of measuring the color difference of milk by using a desktop spectrophotometer.   The working principle of desktop spectrophotometer A desktop spectrophotometer is an instrument that evaluates the color of an object by measuring the reflected or transmitted light of the color. It splits the light reflected by the object into different wavelengths of monochromatic light and measures the intensity of light at each wavelength. By measuring the color of the object and the target color, the desktop spectrophotometer can calculate the color difference between the two, and then judge the quality of milk.   Measurement procedure 一、Prepare materials (1) Color spectrum desktop spectrophotometer CS-821N (2) Standard milk sample (3) Milk sample to be tested (4) Colorimetric dishes Among them, the desktop spectrophotometer CS-821N is the main instrument used to measure the color of milk, and the circular colorimetric dish is the instrument used to hold milk samples.   二、Sample preparation (1) Pour the milk into the cupola (make sure that the milk is poured into more than 3/4 of the cupola volume)   三、Sample measurement (1) Turn on the desktop spectrophotometer CS-821N (2) Set parameters: Select reflection measurement mode, D65 light source, 10° observer Angle, etc (3) Perform black and white calibration in reflection measurement mode (4) Erect CS-821N so that the test port is measured upward (5) Place the colorimetric dish poured into the standard milk on the test port to ensure that it completely covers the test port (6) Press the measurement key and wait for the instrument to complete the measurement and display the result   (7) Record the measurement results (8) Clean the comparator and instrument to prepare for the next measurement   四、Result analysis This experiment can evaluate the color difference of the sample to be tested by comparing the color difference between the sample to be tested and the standard sample. This approach can help milk producers ensure product quality and improve the consumer experience. At the same time, in the new product development stage, color adjustment and optimization is a key step. By using benchtop spectrophotometers, researchers can precisely measure and adjust the color of new products to meet market and consumer expectations.  
Lastest company news about Application of color spectrum hyperspectral camera to the detection of whiteness classification of ores in open pit mines
Application of color spectrum hyperspectral camera to the detection of whiteness classification of ores in open pit mines
一、 Introduction It is an important work to test ore whiteness classification in open pit mine, which has a decisive influence on the effective utilization and fine processing of mineral resources. Traditional detection methods mainly rely on manual operation, which is not only inefficient, but also susceptible to subjective factors. Therefore, it is very important to adopt advanced detection technology to improve the accuracy and efficiency of ore whiteness classification detection. This paper introduces the application of color spectrum hyperspectral camera in the detection of whiteness classification of ores in open pit mines.   二、Background The customer needs to test the whiteness of mine ore in a large area, but the detection efficiency by manual or hand-held whiteness meter is low, and a more efficient detection method is urgently needed. A 400-1000nm hyperspectral camera was used for this classification detection, and FS13, a product of Color Spectrum Technology (Zhejiang) Co., LTD., was used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection).     三、 Laboratory testing The reflectance of calcium carbonate with different whiteness at 400-1000nm was obtained after the four ores were placed on the transmission platform and tested with FS-13.     It can be seen from Figure 4 that the primary whiteness and secondary whiteness are similar. According to the overall waveform, the primary and secondary whiteness can be classified into one category, and the tertiary and quaternary distinctions are obvious. The four-stage whiteness slope is high, the three-stage whiteness slope is low, and the overall difference with the first-stage and second-stage is large, and it is easy to distinguish.   四、On-site detection Shooting time: 15:00, November 07, 2023   Figure 5   Figure 5 shows the hyperspectral camera FS-23 set up on site and the bench for detection.   Figure 6   Technicians selected a piece of calcium carbonate with second-grade whiteness in FIG. 6 and photographed it about 50m away. After modeling, the band curve was calibrated to invert the ore in the figure.   Figure 7   FIG. 7 shows the field shooting map of secondary calcium carbonate calibration at 20m and the inversion effect map.   Figure 8   FIG. 8 shows the field shooting map of primary calcium carbonate calibration at 20m and the inversion effect map.   Figure 9   FIG. 9 shows the field shooting map of primary calcium carbonate calibration at 50m and the inversion effect map.   Figure 10   As shown in Figure 10, after the parameter value (similarity threshold value) is adjusted from 0.993 to 0.99 at 50m, the proportion of primary calcium carbonate in similar bands after reverse selection is greatly increased.   Figure 11   Figure 12   In FIG. 11 and FIG. 12, an adjustment threshold with whiteness of secondary calcium carbonate is selected 50m away for inversion effect.   五、Conclusion 1. Laboratory testing The 400-1000nm hyperspectral camera FS-13+ platform can be used to detect the whiteness classification of calcium carbonate, which is completely feasible in terms of identification feasibility. At the same time, it is found that the reflectance difference between primary whiteness and secondary whiteness is very small, and only two small differences are found, as shown in the following figure:     2. On-site inspection The portable hyperspectral camera FS-23 can be used to shoot the field situation and invert the specific position, mainly inverting the primary and secondary calcium carbonate. When the model threshold is adjusted, the accuracy is gradually improved, so the primary and secondary whiteness of this area can be inverted to the general area. The disadvantage is that only a single model is used, and the accuracy still has great room for improvement.   3. Uav hyperspectral detection If it is necessary to detect the whiteness level of calcium carbonate in a large area and efficiently in the future, the UAV-based hyperspectral measurement system can be used for detection. The UAV-based hyperspectral measurement system has the characteristics of high efficiency and low power consumption, and can provide high stability spectral image acquisition.     The application of color spectrum hyperspectral camera in the whiteness classification of ores in open pit has achieved some success. Through the acquisition and analysis of the hyperspectral data of color spectrum, the accurate detection of ore whiteness is realized, the accuracy and efficiency of detection are improved, and the error of manual operation is reduced. It is believed that in the future, with the further development of technology, color spectrum hyperspectral cameras will also play a greater role in the field of whiteness classification detection of open-pit ores, and provide more powerful technical support for the effective use of mineral resources and fine processing.
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Uav hyperspectral remote sensing for efficient crop phenotype analysis
一、 Background   In the face of challenges such as food shortages, population growth and climate change, increasing crop yields is an urgent need. Crop phenotype analysis provides valuable information for improving yield by deeply understanding the relationship between crop growth and environment.   二、Problems with traditional methods: The traditional vehicle-mounted platform has some problems in sample testing and crop character parameter determination, such as time and effort, limited space coverage, etc., which limits the development of crop science research.   三、the application of UAV hyperspectral remote sensing in the field of agriculture Color Spectrum Technology's unmanned Hyperspectral Measurement System (FS-60) provides an efficient and accurate solution for crop phenotyping.   Here are the key features and applications of the technology: 1. Uav Hyperspectral Measurement System (FS-60) : FS-60 of color spectrum technology is a high-throughput near-earth remote sensing phenotype platform, which has high flexibility, low cost and wide spatial coverage, and becomes an effective way to obtain field phenotype information.   2. System composition and characteristics: Dji M350RTK is adopted as the flight bearing platform. Ultra-high-speed spectral scanning imaging devices with high signal-to-noise ratio provide highly stable spectral image acquisition.   Self-developed high-efficiency and low-power image processing algorithm, which prolongs the flight time of the whole machine and reduces the power consumption of the system. Operating wavelength range of 400 to 1000nm with high spectral and spatial resolution, high sensitivity and high signal-to-noise ratio.     3. Application scenario The system can measure the spectral image information of plants, water bodies, soil and other ground objects in real time, which is widely used in precision agriculture, crop growth and yield assessment, forest pest monitoring and fire prevention monitoring, coastline and Marine environment monitoring, lake and watershed environmental monitoring and other fields.   4. Crop phenotype analysis The normalized vegetation index (NDVI) and plant Aging Reflex index (PSRI) can be evaluated by collecting spectral data of wheat at different periods. These indicators can be used to judge crop nitrogen requirements, guide fertilizer application and determine harvest time.   四、Value and application Prospect: Uav hyperspectral measurement system has high value and broad application prospect in agricultural production. Its high spectral resolution helps to detect pests and diseases early and monitor their evolution on crops, providing strong support for the protection and prediction of crop growth. Through the use of color spectrum technology UAV hyperspectral measurement system, agricultural researchers can be more comprehensive, more in-depth understanding of crop growth conditions, providing powerful tools and data support for scientific decision-making in agricultural production.