CHNSpec Technology （Zhejiang）Co.,Ltd Company News

In modern industrial production, the color consistency of products is directly related to the quality of products and market competitiveness. Especially for products such as pipe materials, the consistency of color not only affects the appearance, but also may involve the identification and application of the product. As an accurate measuring tool, color difference meter can effectively help enterprises to ensure the consistency of product color and improve product quality. This article will introduce in detail the steps of measuring the color difference of pipe materials by using color difference meter, and discuss its importance and necessity. 一、Preparation before the test Before making a color difference measurement, it is first necessary to ensure that the color difference meter has been calibrated and is in normal working condition. At the same time, choose a uniform and stable test environment to avoid interference of external light on the measurement results.   二、the color difference measurement of different positions of the same pipe Select a representative pipe as the test sample. It is critical to place the measuring probe of the colorimeter perpendicular to the pipe surface, as a tilted measurement Angle can lead to a deviation in the measurement results. Starting from one end of the tube and along the length of the tube, a number of measuring points are evenly selected for measurement. For example, a measurement point can be selected every 10 cm.   At each measuring point, keep the color difference meter stable, press the measuring button, and record the measurement data. 三、the color difference measurement of another pipe The other pipe is measured in the same way.   It is also necessary to ensure that the measuring probe is perpendicular to the pipe surface, and that the selection and distribution of the measuring points are consistent with the first pipe for effective comparison.   四、instrument feedback color difference and Lab value analysis After the color difference measurement is completed, it will feedback the outstanding difference and Lab value. Color difference is usually expressed as ΔE, which reflects the overall degree of difference between two colors. The Lab value represents the brightness of the color (L), the range from green to red (a), and the range from blue to yellow (b), respectively. By analyzing the ΔE and Lab values, the magnitude and direction of color differences can be quantified. For example, if the ΔE value is small, it means that the color difference between the two pipes is small and the color consistency is good; If the ΔE value is large, the color difference is significant. By comparing the Lab values, we can understand the specific changes of color in brightness, red green and blue yellow directions.   The measurement results of the first pipe are roughly L1 = 30, a1 = -2, b1 = -9, and the color difference ΔE value at different positions is around 0.5. This is within the acceptable range. The measurement results for the second pipe are roughly L2 = 30, a2 = -5, b2 = -6. It is calculated that the color difference ΔE in different positions also fluctuates around 0.5, indicating that the color difference of the same pipe is small, but the color difference ΔE between different pipes reaches more than 5. Far beyond what is acceptable. As can be seen from the specific Lab value, the second pipe is equivalent in brightness (L2 is about equal to L1), more green in the red and green direction (a2 b1).   Using a color difference meter can accurately measure and quantify color differences, helping companies ensure color consistency for each batch of products. Through regular testing and monitoring, enterprises can find and correct color deviations in time to avoid quality problems caused by color inconsistencies. Companies can ensure that product colors meet customer requirements and improve customer satisfaction and loyalty.   Timely detection and adjustment of color deviations can reduce rework and scrap rates caused by color inconsistencies, thereby saving production costs and resources. At the same time, through accurate measurement, the production process can be optimized and production efficiency can be improved.

The color of furniture paint is a crucial part of the furniture manufacturing and decoration process, which can not only give the furniture a unique appearance and color effect, but also meet the needs of consumers for personalized and diversified. With the development of science and technology, the application of color matching software in the furniture paint industry has brought great convenience and innovation to the color matching work. The basic principle and method of furniture paint color The basic principle of furniture paint color is based on the three primary colors of color (red, yellow, blue) and their mutual mixing relationship. By adjusting the proportion of different colors, you can deploy a variety of desired colors. In practice, there are usually the following methods:   Manual color mixing: is the most traditional method of color mixing, the colorist with their own experience and visual judgment, the paste or pigment is gradually added to the base paint, constantly stirring and mixing until the ideal color is achieved. This method requires high experience and skills of the colorist, and is easily affected by human factors, resulting in certain limitations in the accuracy and repeatability of the color mixing.   Instrument color matching: It is the use of spectrophotometer and other equipment to measure and analyze the target color, obtain the color chroma parameters (such as hue, brightness, saturation, etc.), and then calculate the required color paste formula according to these parameters. Color matching software color matching method and process 1. Establish a color matching database Basic data collection: This is the basis of the entire color matching process, which requires accurate measurement and recording of the reflectivity or L, a, b chromaticity values of each base color paint at different concentration ratios. These data will be used as the basis of color matching calculation and directly affect the accuracy of color matching. Database updates: As materials, environments, or equipment change, the information in the database may no longer be accurate. Therefore, it is essential to update the database regularly to ensure the continuous accuracy of the color scheme.   2, enter the standard sample color related data Standard color source: The standard color can be a physical object, chroma value or spectral curve, depending on how the customer or manufacturer provides it. Standard value setting: Input the relevant data of the standard sample color into the color matching system to establish the standard value of the standard sample and provide a reference for the subsequent color matching.   3. Operation and scheme selection of color matching software Color matching calculation: The color matching software calculates according to the input standard sample color data, the information in the color matching database and the restrictions set by the user (such as color difference △E, color matching cost, color paint characteristics, etc.). Scheme selection: The system may give a variety of optional color schemes, users need to choose according to the actual situation (such as cost, feasibility, etc.).   4, paint formula proofing and color development Formula selection: According to the enterprise needs to select the best paint formula for proofing. Uniform color distribution: Ensure that the paint splines are evenly distributed to reflect the true color effect.   5. Color measurement and analysis Color measuring instrument: Use a professional color measuring instrument to measure the chroma value of the proofing color to ensure the accuracy of the data. Color difference calculation: The measured color value is compared with the standard value of the standard sample color, and the color difference value △E is calculated between the two.   6. Formula revision and iteration Iterative correction: According to the color difference value △E, the initial paint formula is iterated until a satisfactory furniture paint formula is obtained. Repeat testing: If the formula does not meet the requirements after the initial correction, repeat steps 4 and 5 for testing and correction.   7, formula archive and color stability Formula archiving: The final satisfactory furniture paint formula will be archived for future production and reproduction. File content: The file should include the name of the basic paint, the proportion of the paint formula, the thickness of the proofing, the proofing time and other key information to ensure the stability of the furniture paint phase between different batches.   When choosing color matching software, enterprises need to consider the function, performance, ease of use, price and after-sales service of the software. At the same time, it is also necessary to ensure that the software matches the production equipment and process of the enterprise and can meet the actual needs of the enterprise. The advantage of color spectrum computer color matching software is that it can provide a variety of formulations, which can be selected according to cost and actual situation. At the same time, sample data and formula data are electronic, reducing labor costs and ensuring stable production. Simple operation, no threshold, fast hand, precise formula. High color matching efficiency can use waste, used materials for color matching, saving costs.

In the field of nail polish production, color matching software is gradually becoming an important tool for manufacturers to enhance product competitiveness, optimize production processes and meet the diversified needs of the market. For nail polish manufacturers, color matching software first plays a key role in the product development stage. Manufacturers can use color matching software to quickly simulate and test different color combinations, greatly shortening the development cycle of new products. By entering the parameters and characteristics of various pigments, the software can quickly calculate the pigment formula needed to achieve a specific color effect, helping the research and development team to accurately develop new colors and new series of products in line with market trends and consumer preferences. The traditional color matching process of nail polish glue often relies on the experience and intuition of the designer, which is not only time-consuming and laborious, but also difficult to ensure the accuracy and innovation of each color matching. Color matching software provides designers with powerful color matching tools through advanced algorithms and rich color libraries. Designers can obtain a variety of color schemes with simple operation, which greatly improves the design efficiency. At the same time, the intelligent recommendation function in the software can also automatically generate novel and unique color schemes according to the designer's preferences and market demand, and stimulate the designer's creative inspiration. ​In the production process, the color matching software enables precise control of pigment use and cost optimization. The software calculates the exact amount of each pigment according to the color formula required by the order, avoiding pigment waste due to manual estimation errors. At the same time, by analyzing the cost and performance of different pigments, the software can provide manufacturers with the lowest cost and best quality color scheme, effectively reduce the production cost, improve the product cost performance and market competitiveness. ​In today's increasingly prominent consumer sovereignty, personalized customization has become the mainstream trend of the market. Nail polish glue manufacturers through the color matching software, can provide customers with more rich color selection and more accurate color matching services. Customers can select or customize colors in the software according to their preferences and needs, and preview the effect in real time. This personalized customized service not only meets the diversified needs of consumers, but also enhances the sense of participation and satisfaction of customers, and enhances the brand image and loyalty. ​The application of color matching software in nail polish glue manufacturers covers all aspects from research and development, production, quality control to market expansion, creating significant economic benefits for manufacturers and building a strong competitive advantage. ​The advantages of color spectrum computer color matching software are reflected in the ability to provide multiple formulations, which can be selected according to cost and actual conditions. In addition, the sample data and formula data are electronic, which not only reduces the labor cost, but also guarantees the stability of production. The software is easy to operate, there is no threshold, easy to use, and the formula is accurate. The color matching efficiency is outstanding, and the waste and old materials can also be used for color matching to achieve cost savings. With the continuous progress of technology and the constant change of market demand, it is believed that the application of color matching software in the nail polish industry will become more in-depth and extensive, and promote the entire industry to continue to enter a new stage of development.

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.

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.

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.

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.

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.

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.

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.  

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.

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.