CHNSpec Technology （Zhejiang）Co.,Ltd

Application of hyperspectral imaging technology to the detection of protein content in milk

In the evaluation of dairy nutrition, the protein content is the most important indicator that milk is an essential source of protein absorption in People's Daily life. In recent years, the health of consumers and the development of the dairy industry are closely related to the quality of milk. Therefore, the detection of milk protein content is a very important link. Traditional detection methods consume a long time, waste a lot of human resources, and lead to environmental deterioration. Therefore, it is of great significance to find a faster and more accurate method for detecting milk protein content. Therefore, this paper uses machine learning combined with hyperspectral imaging technology to quantitatively evaluate milk protein content, providing a feasible scheme for milk protein content detection on the market. Specific research work and conclusions are as follows:   一、Experimental materials We bought seven different brands of pure milk, including Mengniu, New Hope, Yili and Guangming, and stored them in the refrigerator. Protein content is shown in Table 1. 二、Experimental equipment 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). 三、Experimental setting method The hyperspectral images of milk samples were collected by using the hyperspectral spectrometer. Samples were collected three times for each type of milk, and then a clear image was selected from ENVI5.3. The collected spectral image had a resolution of 777x1004 pixels. The exposure time of the hyperspectral imager was 10ms, the pixel mixing times were 6, the resolution was 4.8nm, the average interval was 0.8nm, the vertical distance was 30cm, and the acquisition condition was room temperature (23~25°C). The imaging spectrometer and scanning head are installed together during the shooting, and the average spectral data of the milk is derived from the hyperspectral image using the ENVI software." 四、Extraction and preprocessing of hyperspectral data Extracting hyperspectral reflectance data from hyperspectral images is the basis of traditional machine learning modeling. Generally, the spectral reflectance data of samples is obtained by extracting the average spectral reflectance of all pixels in the region of interest (ROD). In this paper, ENVI software was used to open the corrected hyperspectral image of milk sample, and the pixel near the center of each hyperspectral image was selected as the ROI with the rectangle tool. A total of 30 ROI and 7 hyperspectral images were selected, and 210 ROI were selected. The average spectral reflectance of all pixels in ROI was calculated as the spectral data of the sample, a total of 210 spectral data. The spectral data is saved in ASCI format. The following figure shows the process of extracting ROI. In this paper, hyperspectral imaging technology combined with machine learning was used to predict milk protein content in order to improve the accuracy of milk protein content prediction. Hyperspectral imaging system was built, hyperspectral images of 7 kinds of milk brands on the market were collected, spectral data were extracted by ENVI software, milk hyperspectral data set was established, and 210 hyperspectral data were extracted finally. Hyperspectral imaging technology has shown great potential in the field of milk protein content detection, although there are some challenges at this stage, but with the integration of interdisciplinary technology innovation, it will gradually revolutionize the traditional milk detection mode. Through continuous optimization of the technical system and solving practical application problems, hyperspectral imaging will become an indispensable and powerful tool for dairy quality control, help improve the economic and social benefits of the milk industry, and meet the growing demand of consumers for high-quality dairy products.

Determination of amylose content in fresh lotus by hyperspectral imaging

With the improvement of living standards, people have higher and higher requirements for the taste and nutrition of lotus seeds. Lotus seed as a medicine is also a kind of tonic, its amylose content directly affects the quality and taste of lotus seed. The amylose content of lotus seeds varies greatly among different varieties, so the determination of amylose content of lotus seeds is of great significance for subsequent processing. The traditional amylose detection is generally using iodine colorimetry, iodine affinity titration and cross-cutting infection method, these methods are time-consuming and laborious, and easy to be affected by experimental conditions! Hyperspectral imaging technology is a non-destructive testing technology that can obtain rich spectrum and image information. Compared with chemical detection methods, it has the advantages of saving time, labor and environmental protection. In this paper, hyperspectral imaging technology was used to detect amylose of fresh lotus. 一、Materials and methods   1.1 Test materials The samples were from Fujian province, and the varieties of Xuanlian, Guangchanglian, Jianxuan 36, Mantianxing, Space lotus and Xianglian were selected. After picking at maturity, the fresh lotus seed was stored in liquid nitrogen and transported to the laboratory, where it was refrigerated at 4 ° C for 12 hours. 1.2 Hyperspectral image acquisition and correction The main components of hyperspectral imaging system include hyperspectral imager, light source, stage, black box and hyperspectral data acquisition software. The whole system can use the color spectrum hyperspectral camera fs-13, which can collect the spectral range of 400nm~1000nm, and the spectral resolution is 2.5nm. The hyperspectral imaging system is shown in Figure 1. The moving speed of the payload platform is set to 3.5mm/s and the exposure time is 30ms. The lens is 40cm away from the moving platform and straight down. Adjust the focal length of the camera of the spectrometer for black and white correction of the system. 1.3 Data Processing Analysis software was used to extract the average spectrum of the region of interest (ROI) from the spectral image of lotus seeds. In order to eliminate the influence of noise and external stray light, the modeling effect of pre-processing methods such as first derivative, second derivative, SG smoothing, multiple scattering correction (MSC) standard normal variable conversion was compared, and the best pretreatment method was selected. 二、Results and analysis   2.1 Average spectrum of the region of interest In this paper, the spectral curve of each pixel in the region of interest of a single sample is used for subsequent processing. The average spectral diagram after removing the head and tail noise (400nm~971nm) is shown in Figure 2. It can be seen from the figure that the variation trend of spectral values of different samples is consistent. The band has an obvious upward shift between 460nm and 570nm, which may be caused by the shift in the water band. The band has a relatively obvious absorption between 500nm and 920nm. It may be related to quaternary frequency doubling, O-H secondary frequency doubling and O-H primary frequency doubling of C-H group in amylose molecule. 2.2 Amylose content of lotus seeds The results of correction set and prediction set of amylose content divided by SPXY method are shown in Table 1. It can be seen from the table that the amylose content of fresh lotus seeds varies greatly. The maximum value of amylose content of corrected lotus seeds is 227.90 mg/g, the minimum value is 100.82 mg/g, and the standard deviation is 44.73mg/g. The amylose content of the predicted sample is within the range of the correction set sample, so the sample division is reasonable. 三、Conclusion In this paper, hyperspectral imaging technology was used to rapidly detect amylose content. The results show that the modeling effect is the best after using first derivative and multiple scattering correction MSC). Then SPA was used to extract 9 feature bands. The corrected set correlation coefficient (R) of the PLSR prediction model was 0.835, the corrected set root mean square error (RMSEC) was 1.802, the predicted set correlation coefficient (R) was 0.856, and the predicted set root mean square error (RMSEP) was 1.752. The relative analysis error (RPD) was 1.944. The correlation coefficient of prediction set of PLSR prediction model established by RC method (R. The prediction set root mean square error (RMSEP) was 1.897. The relative analysis error (RPD) was 1.761. This study provided a thought for further developing an on-line detection instrument for amylose content, and laid a good foundation.

Application of hyperspectral camera to detect pumpkin seed vitality

As an important cash crop, pumpkin seed vitality is directly related to the emergence rate, seedling growth potential and final yield after sowing. The traditional methods of seed vitality detection, such as germination test, are time-consuming and laborious, and can not meet the needs of rapid and large-scale seed quality detection in modern agriculture. Hyperspectral imaging technology combines the advantages of spectroscopy and imaging, and can obtain the spectral information and spatial information of samples at the same time, which shows great potential in the field of seed viability nondestructive testing. 一、Preparation of experimental materials Divide the pumpkin seeds into 4 groups of 100 seeds and place them in a nylon mesh bag, as shown in Figure 3-2. Put a group of pumpkin seeds in the dryer every other day. The specific procedure is as follows: take out 3 groups of samples, put the first group of samples in the dryer, put the second group of samples in the dryer 24 hours later, put the third group of samples in the dryer 24 hours later, and take out all the samples with aging time of 1 to 3 days respectively after 3 days (the first group is the samples with aging time of 3 days). Group 2 is for samples aged for 2 days, and Group 3 is for samples aged for 1 day). The remaining 1 of the 4 groups was not subjected to aging treatment and was placed at room temperature for 3 days during the aging group experiment. 二、Hyperspectral data acquisition Seeds with different aging days were collected by a color spectrum hyperspectral camera, and hyperspectral images of 400-1000nm were taken for all samples. After the spectral data were extracted, a total of 400 spectral curves were obtained, as shown in the figure. Observe the growth every day, and pour the right amount of water to ensure the water needed for germination. The germination was recorded once on the third and fifth days respectively. The following is the pre-germination test diagram of pumpkin seeds. According to the vitality level of each seed, the average spectral data of each seed was classified, and the overall spectral curve of each grade was shown in the figure below. 三、Spectral data processing The original hyperspectral image is susceptible to noise and uneven illumination. Median filter is adopted to remove salt and pepper noise, and the illumination difference is eliminated based on the reflectivity correction of the standard whiteboard. The region of interest (ROI) is extracted from the corrected image, focusing on the seed embryo and endosperm to ensure the accuracy of subsequent feature extraction. Dimensionality reduction methods such as principal component analysis (PCA) are used to compress data initially, retain key information and reduce computation. 四、Conclusion and Prospect In this study, a pumpkin seed vitality detection model based on hyperspectral imaging technology was successfully constructed to realize rapid, non-destructive and high-precision vitality identification, and provide an efficient technical solution for the quality control of pumpkin seed industry. Follow-up research can be extended to more crop seeds, and multi-modal data (such as fluorescence spectrum, thermal imaging, etc.) can be integrated to further improve the detection accuracy in complex environments. Combined with Internet of Things technology, an online monitoring system for seed vitality can be built to help real-time control and accurate screening of seed quality in smart agriculture.

Application of hyperspectral camera to tea pests and diseases

Tea inchworm is one of the common pests in tea gardens, which seriously affects the yield and quality of tea. The traditional method of monitoring the damage degree of tea inchworm mainly relies on manual investigation, which has some problems such as low efficiency, strong subjectivity and difficult to realize real-time monitoring in large area. Hyperspectral remote sensing technology has the characteristics of high spectral resolution and rich spectral information, which provides a new way for rapid and accurate monitoring of the harm degree of tea inchworm. 一、Environmental conditions The spectral reflectance of tea canopy was measured from 10:00 to 14:00 on a sunny day with no wind, no cloud and good solar visibility. In this study, a 400-1000nm hyperspectral camera was applied, and FS13, a product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. During the observation, the field of view Angle was 25, and the height between the detection head of the hyperspectral camera and the top of the tea canopy was about 0.5m. The diameter of the observation range was about 0.22m. In order to reduce the experimental error, the measurements were repeated three times in each sample area, and the average value was taken as the spectral reflection value.   二、 Data processing and analysis 1. Comparison of leaf surface appearance between normal tea and tea inchworms. In this experiment, a series of tea leaves harmed by tea inchworms to different degrees were collected as research subjects, and their spectral data, leaf area index and the number of tea inchworms per mu of tea ruler were respectively collected. The comparison between tea leaves without insect pests and those harmed by tea inchworms was shown in Figure 1: The leaves were intact, the leaves were crowded together, and the leaves of the insect-damaged tea were bitten into irregular shapes, their external color became dark yellow, and the structure of the leaves also changed accordingly. 2. Comparison of leaf area index between normal tea and tea inchworm. As can be seen from FIG. 2, the leaf area index was greatly affected by the degree of harm caused by tea geometrid. The more tea ruler there were, the more tea leaves were eaten, and the smaller the leaf area index would be. 3. The influence of tea inchworms on the reflectance spectral characteristics of tea canopy. The influence of insect infestation on tea leaves will lead to some changes in the physical and chemical properties of tea leaves, including the color, structure, water content, chlorophyll content and nutritional status of the leaves. The change of these physical and chemical properties will cause some changes in the value of its spectral characteristic parameters, such as spectral reflectivity, transmittance, absorptivity, red peak and its wavelength position and blue peak and its wavelength position. Therefore, to grasp the normal tea spectral characteristics and related information is the premise and basis of studying the damage of tea by other diseases and pests. 三、Research significance and prospect Research significance: This study provides a new technical means for the rapid and accurate monitoring of the harm degree of tea inchworms, helps to timely grasp the occurrence of tea inchworms in tea gardens, provides scientific basis for the accurate prevention and control of diseases and pests in tea gardens, reduces the use of pesticides, and improves the yield and quality of tea. Research prospects: Future studies can further optimize hyperspectral remote sensing models and improve the accuracy and stability of the models. At the same time, it can be combined with UAV remote sensing, satellite remote sensing and other technologies to achieve a larger range of tea inchworm harm degree monitoring. In addition, the relationship between the harm of tea inchworms and the physiological and ecological changes of tea trees can be deeply studied, and the mechanism of hyperspectral remote sensing monitoring can be revealed from a deeper level.

Application of hyperspectral camera to measure moisture content of wood

Wood moisture content is an important attribute of wood quality, which has an important impact on wood processing, use and storage. Although the traditional methods of measuring wood moisture content such as weighing method and resistance method have certain accuracy, they have some disadvantages such as cumbersome operation, long measurement time and damage to wood. Hyperspectral imaging provides a fast, non-destructive and efficient method for measuring wood moisture content. 一、hyperspectral camera test principle Hyperspectral cameras can obtain spectral information of the wood surface, which includes the reflectivity or transmission of the wood at different wavelengths. Since the moisture content of wood will affect its spectral characteristics, the moisture content can be deduced by analyzing the spectral information of wood. Specifically, the spectral data of wood can be collected by hyperspectral imaging technology, and the prediction model between wood moisture content and spectral information can be established by pre-processing, feature extraction and modeling, so as to realize the rapid test of wood moisture content. 二、Application examples Instrument: Color spectrum built-in push sweep FS-17 near infrared high spectrometer Auxiliary equipment: Constant spectral light source - for indoor modeling Light source: linear halogen light source Experimental materials: A number of wood samples with different moisture content are used as experimental materials, and these wood blocks are cyclically dried to obtain different moisture content states. Data acquisition: Spectral image acquisition of wood samples was carried out using hyperspectral imaging system. In the acquisition process, it is necessary to ensure that the lighting conditions are stable to avoid the impact of light changes on the spectral information. At the same time, in order to obtain more accurate results, spectral image acquisition can be carried out at multiple locations of the wood sample, and the average value is taken as the final spectral data. Data processing: Pre-processing the collected spectral data, such as removing noise, correcting spectrum, etc. Then feature selection algorithm is used to extract the characteristic wavelength related to wood moisture content to simplify the model and improve the prediction accuracy. Model building: Based on the extracted characteristic wavelength, the prediction model between wood moisture content and spectral information was established. Common modeling methods include Gaussian process regression (GPR), partial least squares regression (PLSR) and so on. These models can quickly predict the moisture content of wood based on its spectral information. Model validation: The established model is validated using an independent validation set to assess its predictive performance and accuracy. Common evaluation indexes include correlation coefficient (R²) and root mean square error (RMSE). 三、Application advantages Fast test: The hyperspectral camera can obtain the spectral information of the wood surface in a short time, so as to realize the rapid test of the wood moisture content. Non-destructive testing: Compared with traditional testing methods, hyperspectral imaging technology does not cause damage to the wood, so it is more suitable for testing valuable wood or wood that needs to be maintained in integrity. High accuracy: By establishing an accurate prediction model, hyperspectral cameras can achieve high-precision testing of wood moisture content, meeting the stringent quality control requirements of the wood processing industry. 四、Application prospect With the continuous development and improvement of hyperspectral imaging technology, its application prospects in wood moisture content testing will be more broad. In the future, we can look forward to the emergence of hyperspectral cameras with higher precision, faster speed and easier operation to meet the needs of the wood processing industry for quality control and intelligent production. At the same time, combined with advanced technologies such as machine learning and deep learning, the accuracy and intelligence level of wood moisture content testing can be further improved. In summary, hyperspectral cameras have significant advantages in testing wood moisture content, providing an efficient, accurate and non-destructive inspection method for the wood processing industry.

How do hyperspectral cameras make color measurements?

In today's era of rapid development of science and technology, color measurement has a vital position in many fields, from product quality control, artistic creation to scientific research. As an advanced optical device, hyperspectral camera brings a new, more accurate and comprehensive solution for color measurement. 一、the basic principle of hyperspectral camera The working principle of hyperspectral cameras is based on the fine capture of spectral information. Unlike traditional cameras, which can only record the color information of the three channels of red, green and blue, hyperspectral cameras can divide the spectrum into many narrow bands in a wide spectral range such as visible light to near infrared, usually up to hundreds or even more. For example, it can divide the spectral range of 400-1000nm into bands with very small intervals, such as 1nm or smaller intervals. When light shines on the surface of the measured object, the reflection, absorption and transmission characteristics of the object to different wavelengths of light are different. Through its special optical system and detector, the hyperspectral camera collects the intensity of the light signal of each band in turn, so as to construct the spectral reflectance curve of the object. This curve records in detail the reflectivity of objects at various wavelengths and is the basic data source for color measurement.   二、the specific process of color measurement (1) Calibration Calibration is a critical step before using a hyperspectral camera for color measurement. The purpose of calibration is to establish an accurate correspondence between the spectral data captured by the camera and the true color values. Standard whiteboards with known spectral properties are often used as calibration references. Standard whiteboards have stable and precisely known reflectance at various wavelengths. The hyperspectral camera takes pictures of the standard whiteboard, records its optical signal intensity in each band, and calculates the response function of the camera according to the known spectral reflectance data of the standard whiteboard, so as to correct the possible spectral deviation, dark current noise and other error factors of the camera, and ensure the accuracy and reliability of the subsequent measurement data.   (2) Image collection After the calibration is completed, the image of the target object can be acquired. When a hyperspectral camera takes pictures of an object, it obtains the intensity information of the light reflected by the object band by band according to the preset spectral band range and resolution. For example, for each pixel in an image, its reflected light data across multiple spectral bands is recorded. If the camera divides the spectral range into 200 bands, then each pixel will have 200 corresponding spectral reflectance values. Together, these data form a three-dimensional data cube, where the two-dimensional plane represents the spatial position information of the image (x, y coordinates), and the third dimension represents the spectral band information (λ). In this way, the hyperspectral camera not only records the color and appearance information of the object, but also contains its spectral characteristics information, which provides more abundant data than traditional cameras.   (3) Data processing and color calculation The massive spectral data collected need to go through complex data processing to get the final color measurement results. First of all, the data should be preprocessed, including removing noise, correcting spectral distortion and other operations. Then, the color is calculated according to a specific color model and algorithm. In the field of color science, the commonly used color models are CIE XYZ, CIELAB, etc. Taking the CIELAB color model as an example, it represents color as three coordinate values based on the human eye's perception characteristics of color: L represents the brightness, a represents the red-green degree component, and b * represents the yellow-blue degree component. By combining the spectral reflectance data collected by the hyperspectral camera with the spectral power distribution of the standard illumination body (such as the D65 standard light source), and integrating according to the color matching function, the coordinate value of the object in the CIELAB color space can be calculated, so as to accurately describe the color attribute of the object. Such as color depth, tone and saturation. In addition, color difference can also be calculated by comparing the color coordinate values of different objects or different parts of the same object, which is used to evaluate the consistency or degree of change of color. 三、the advantages of hyperspectral camera color measurement (1) High precision and high resolution Hyperspectral cameras provide extremely high spectral resolution, which allows them to capture extremely fine color differences in color measurements. For example, in some industries that require very high color accuracy, such as high-end printing, cosmetics production, etc., it can accurately distinguish color changes that are difficult for the human eye to detect, ensuring the consistency of product color and high quality standards. Its high-precision measurement results help to improve the quality control level of products and reduce the rate of defective products caused by color deviation.   (2) Rich spectral information In addition to the tristimulus value information of the color, the spectral reflectance curve obtained by the hyperspectral camera contains detailed information about the object over the entire measured spectral range. This has unique advantages for the color analysis of some special materials or objects. For example, in the field of cultural relics restoration and protection, by analyzing the spectral characteristics of pigments on the surface of cultural relics, we can understand their composition and age information, which provides an important basis for restoration work. In the field of agriculture, the growth status, nutrient content and disease and insect pests of plants can be monitored according to the changes in the spectral reflectance of plant leaves, because the absorption and reflection characteristics of different wavelengths of light will change in different growth stages and health states of plants.   (3) Non-contact measurement Hyperspectral cameras do not need to make direct contact with the object being measured, which is important in many cases. For some fragile, precious or difficult to reach objects, such as art, cultural relics, biological samples, etc., non-contact measurement can avoid damage or pollution to the object. At the same time, it can also achieve fast, large area color measurement, improve the measurement efficiency. For example, in the color detection of large-scale mural paintings, the color information of the entire mural can be quickly obtained, providing comprehensive data support for protection and restoration work.   四、Experimental test of hyperspectral camera in color measurement 1. Experimental purpose Test the Lab value of the sample below 2. List of experimental testing instruments Device name Model number Configuration details Remark CHNSpec hyperspectral camera FS-13 Spectral range: 400-1000nm; Spectral resolution: 2.5nm Spectral band: 1200       3. Experimental content The reflectance curve was obtained by external push scan detection of 400-1000nm hyperspectral camera The experimental measurement process is shown in the figure below: 4. Conclusion The hyperspectral camera FS-13 was used to shoot the customer's samples, and the Lab value of each sample was obtained from the hyperspectral image analysis, which could be used to replace the color difference meter, and the test stability was good, the sampling position of the test sample was flexible, and multi-point measurement could be made to realize automatic detection.

The application of hyperspectral camera in the measurement of building surface defects

In the field of building science, ensuring the quality and safety of buildings is always the focus and core concern of research. With the continuous development of the construction industry and the increasing requirements of people on the living environment, the accurate detection and evaluation of the surface defects of the house has become very important. Traditional inspection methods for building surface defects, such as artificial naked eye observation and simple measuring tools, often have many limitations, such as strong subjectivity, low detection efficiency, and difficulty in finding potential minor defects. The emergence of hyperspectral camera technology has brought a new opportunity for the measurement of building surface defects. Hyperspectral cameras are able to acquire information about objects in multiple narrow and continuous spectral bands, which can not only provide spatial images of the surface of the house, but also reveal the differences in spectral characteristics of different materials. This unique technical advantage makes it have great application potential in the detection, identification and analysis of housing surface defects. The purpose of this study is to deeply explore the application principle, method and practical effect of hyperspectral camera in the measurement of building surface defects, so as to provide new ideas and technical support for the quality inspection and evaluation in the construction industry.   Take FS-23 imaging high spectrometer with built-in push sweep in color spectrum as an example Application principle Hyperspectral cameras work by capturing the light reflected or scattered by a target object and breaking it down into spectral data of different wavelengths. These spectral data reflect the material composition, structure and other characteristics of the surface of the object. In the measurement of building surface defects, the hyperspectral camera can capture the spectral changes caused by aging, damage, pollution, etc., so as to achieve accurate identification of defects. Application advantage 1. High-precision identification: hyperspectral cameras can capture subtle spectral differences, so they can identify various defects on the surface of the house with high precision, such as cracks, shedding, corrosion, etc. 2. Non-contact measurement: The hyperspectral camera adopts a non-contact measurement method, which will not cause secondary damage to the surface of the house, and also avoid the direct contact of the surveyor with the potentially dangerous environment. 3. Fast and efficient: The hyperspectral camera can complete the scanning and data analysis of the surface of a large area of the house in a short time, which greatly improves the measurement efficiency. 4. Comprehensive analysis: Combined with spectral information and spatial information, the hyperspectral camera can conduct comprehensive analysis of the defects on the surface of the house, including the type, location and severity of the defects, providing strong support for the subsequent repair work. Application example In the field of housing detection, hyperspectral cameras can be combined with other modern detection methods, such as acoustic detection, infrared detection, etc., to form a comprehensive detection system. The spectral data obtained through the hyperspectral camera can be integrated with the data of other inspection means to evaluate the structural performance and safety condition of the house more comprehensively. For example, when detecting the aging of the exterior paint of the house, the hyperspectral camera can capture the spectral changes caused by the aging of the paint surface, combined with the infrared detection method to measure the temperature distribution of the paint surface, which can comprehensively evaluate the degree of aging of the paint and potential safety hazards.   As shown below In summary, hyperspectral cameras have significant application advantages and broad application prospects in the measurement of building surface defects. With the continuous progress of technology and the reduction of cost, hyperspectral camera is expected to be more widely used and promoted in the field of house inspection.

Application of hyperspectrum in the field of ore silicates

In the research and application of ore silicates, we are always faced with many challenges. How to accurately identify the different kinds of ore silicate minerals? How to understand the structure and composition changes of ore silicates? How to explore and develop mineral resources efficiently? These questions have long puzzled geologists and mineral resource developers. With the continuous development of hyperspectral technology, these problems seem to usher in new solutions. Hyperspectral technology can capture the unique spectral characteristics of ore silicates, and through the analysis of these characteristics, we can realize the accurate identification of ore silicates, structural analysis and rapid exploration of mineral resources. Therefore, it is of great practical significance to explore the application of hyperspectrum in ore silicates to solve these long-standing problems. 一、 Application Scenarios 1. Identification and classification of ore silicates: Mineral type identification: Different ore silicate minerals have unique spectral characteristics, hyperspectral technology can accurately identify the types of silicate minerals contained in the ore through the analysis of these characteristics. For example, by detecting information such as the location, intensity and shape of absorption or reflection peaks in a specific wavelength range, it is possible to distinguish between different types of phyllosilicate minerals such as kaolinite, montmorillonite and illite. Ore grade assessment: For ores containing multiple mineral components, hyperspectroscopy can evaluate the overall grade of the ore based on the spectral characteristics of different minerals and their relative content. This helps to quickly determine the value and utilization direction of ore during ore mining and processing. 2, ore silicate structure and crystallinity analysis: Structural study: Hyperspectroscopy can detect the structural information of ore silicate minerals. For example, by analyzing the spectral characteristics generated by the vibration of metal ions and hydroxyl groups (-OH) in minerals, it is possible to understand the crystal structure of minerals, the nature of chemical bonds and the coordination of cations. It is of great significance to further understand the physical and chemical properties and formation mechanism of ore silicates. Crystallinity judgment: crystallinity is an important factor affecting the properties of silicate minerals. Hyperspectral technology can judge the crystallinity of minerals according to the changes in their spectral characteristics. For example, with the increase of crystallinity, the intensity, width and shape of the spectral absorption peak or reflection peak of some minerals in a specific wavelength range will change regularly. By monitoring and analyzing these changes, the crystallinity of ore silicates can be accurately assessed. 3, mining area geological mapping and mineral resources exploration: Geological mapping: Hyperspectrum can carry out detailed exploration and analysis of the geological conditions of mining areas, and draw high-precision geological mapping. By identifying the spectral characteristics of different rocks and minerals, it can accurately divide geological units, determine stratigraphic boundaries, identify geological structures, etc., and provide basic data for geological research and mineral resource exploration in mining areas. Mineral resource exploration: In mineral resource exploration, hyperspectral technology can quickly scan a large area of mining areas to detect potential mineral resources. By analyzing the spectral characteristics of silicate minerals, we can find the hidden mineralization information, determine the distribution range and enrichment degree of minerals, and provide strong support for the exploration and development of mineral resources.   二、Practical application Instrument used: Color spectrum FS-23 hyperspectral camera Test effect Conclusion The reflectance of the spectral curve is obvious. In the case of halogen light, the part containing silicate will be obviously bright, and the spectral curve will have obvious characteristic peaks (the setting of exposure time and white calibration are key). 三、Development prospects In the future, the spectral resolution, spatial resolution and signal-to-noise ratio of hyperspectral instruments will continue to improve. The higher spectral resolution allows for more precise capture of the fine spectral characteristics of ore silicate minerals, helping to more accurately identify mineral species and analyze their structures. For example, for some silicate minerals with similar crystal structures and small differences in spectral characteristics, high-resolution spectral instruments can better distinguish them. At the same time, the improvement of spatial resolution will enable the hyperspectral technology to analyze smaller ore particles or mineral structures and provide more detailed mineral distribution information, which is of great significance for the study of the microstructure of ores and the relationship between minerals. With the development of technology, hyperspectral instruments will gradually develop in the direction of miniaturization and portability. This will make the application of hyperspectral technology in field geological exploration, mine site monitoring and other fields more convenient. Geologists can directly detect and analyze the ore in the field, obtain the mineral composition, structure and other information of the ore in time, and provide more timely and accurate data support for the exploration and development of mineral resources.