1. Background

Imaging spectroscopy technology is a perfect combination of spectroscopy technology and image technology. It not only provides sample spatial information, but also provides tens to thousands of narrow-band spectral information for each pixel on each image. It can produce a complete and continuous spectral curve, so that the spectral data of any wavelength can generate an image, so as to realize the "integration of map and spectrum". In addition, the integrated features of imaging spectroscopy maps make it combine the advantages of visible-near infrared spectroscopy technology component detection and machine vision technology to reflect the advantages of spatial resolution information. The integrated features of imaging spectroscopy maps make it combine the advantages of visible-near infrared spectroscopy technology component detection and machine vision technology to reflect the advantages of spatial resolution information. Through the analysis of the spectrum and image, the content of the sample, the state of existence, the spatial distribution and the dynamic change can be detected.

The spectral range of SOC700 series hyperspectral imaging spectrometer covers 400-1000nm, 900-1700nm, 1000-2350nm and mid-infrared wave band. It has powerful functions. Very ideal hyperspectral imaging system.

Application of imaging spectrometer in the field of agricultural products / food inspection

Figure 1 SOC710 Hyperspectral Imager with a built-in scanning device, weighing only 3Kg

2. The image data of the "integrated map"

The data of hyperspectral imaging is a series of images of scenes or samples obtained by continuous imaging of multiple wavelength bands, which is commonly known as the image cube. This image cube has two spatial dimensions (X and Y), and the third dimension is the wavelength or radiation intensity of each pixel. See figure 2 below.

Application of imaging spectrometer in the field of agricultural products / food inspection

Figure 2 The cube of data generated by the imaging spectrometer

3. Specific application of imaging spectrometer in agricultural product / food inspection

3.1 Application of imaging spectrometer in the field of meat and poultry products

Meat safety is an important aspect of food safety testing in China at present, but due to the lack of rapid and non-destructive meat testing technology and equipment, the early stage relied mostly on sensory assessment, supplemented by physical and chemical methods. Sensory assessment methods are quick to detect but lack consistent standards, and are easily influenced by supervisors. Physical and chemical methods are time-consuming and difficult to perform rapid real-time analysis. Pork quality is usually expressed by color, texture, and exudate characteristics. Imaging spectroscopy technology can simultaneously obtain color information and composition information, and has broad application prospects in the diagnosis of meat quality. For example, beef quality testing.

The tenderness of the steak is the most important factor in determining consumer satisfaction. Naganathan et al. Applied hyperspectral imaging technology in the near infrared region to detect the tenderness of beef. The spectral range of the imaging system is 900-1700nm. A 150 × 300 pixel image in the central area of ​​the image was collected for analysis, and a standard recognition model was used to predict 3 different levels of beef tenderness. Among the 334 samples analyzed, 242 were correctly identified, and the recognition rate was 77%. The results show that the imaging spectroscopy instrument is expected to become a new instrument for beef tenderness detection.

Cluff and others also use imaging spectroscopy to detect the tenderness of steaks. They use a portable hyperspectral imaging spectrometer to detect fresh beef muscle. The acquired imaging spectrum includes more than 120 narrow bands with a spectral resolution of 4.54nm. Warner-Bratzler shear (WBS) value was used to characterize the standard tenderness of beef, and an improved Lorentz function was used to fit the imaging spectrum of beef. Stepwise regression is used to establish the relationship between WBS and Lorentz function parameters (such as the peak height and half-width of the curve) to realize the prediction of beef tenderness. The method of scattering characteristics is expected to become a rapid method for detecting the tenderness of beef.

At present, as a fast non-destructive, non-contact measurement technology, hyperspectral imaging technology has been widely used in the detection of meat and livestock products, including skin tumors, surface contaminants, tenderness, color, drip loss, pH Carcass marble pattern and predict meat edible quality, total bacteria etc.

3.2 Detection of hyperspectral imaging technology in the field of fruit quality

Fruit quality inspection is of great significance for determining the harvest period and product grading. At present, the more mature machine vision technology can obtain the appearance information of the fruit. Fruit internal quality detection technology based on visible and near infrared spectroscopy has been industrialized abroad and is still in the research stage in China. Hyperspectral imaging technology has the advantage of combining maps and spectra, and can obtain information on the appearance and internal quality of fruits at the same time, so the application research on fruit detection has been carried out earlier.

Qin et al. Used hyperspectral imaging technology to measure the optical properties of fresh fruits in the range of 500-1000 nm. They used a hyperspectral imaging spectrometer to obtain the reflectivity of fruits and vegetables such as apples, pears, peaches, kiwi, and plums. The inverse algorithm of the diffusion theory model is used to obtain the absorption and attenuation coefficients of the sample. The absorption coefficient in the spectrum is usually characterized by the main pigments (chlorophyll, anthocyanins, carotenoids), while the attenuation coefficient in the spectrum decreases with increasing wavelength. Qin et al. Used hyperspectral imaging technology to detect citrus ulcer disease, and used a hyperspectral imaging spectrometer with a spectral range of 400-1000. They measured abnormally "Ruby Red" grapefruit with different lesions. These lesions include ulcers, copper injuries, spots, scars, melanosis and so on. The principal component analysis method is used to compress the three-dimensional imaging spectrum and extract information that can be used to distinguish ulcers in normal and diseased fruits. The correct rate of ulcer detection reached 92.7%. They also selected 4 characteristic bands (553, 677, 718, 858nm) that can be used to build a multispectral system for ulcer detection. The research results indicate that the imaging spectrum can be used to identify other lesions of ulcer disease.

3.3 Detection of hyperspectral imaging technology in the field of vegetable quality

In recent years, China has vigorously promoted pollution-free vegetables and green vegetables, mainly to improve the quality of vegetables; however, China ’s current detection of safety issues such as excessive pesticides and animal waste pollution is still mainly destructive preparation, which has greatly reduced Productivity.

In China, Chai Ali et al. Used spectral imaging technology (400-1000nm) to combat cucumber powdery mildew, angular spot disease,

Downy mildew, brown spot disease and disease-free areas are identified. Stepwise discriminant analysis and typical discriminant analysis are used for dimension reduction, and the selected spectral characteristic parameters are used to establish a disease identification model. The results show that the discriminative accuracy of the model constructed by stepwise discrimination is 100% and 94% respectively for the training samples, and the accuracy of the model constructed by the typical discriminant is 100% for the training samples and test samples.

3.4 Application in nondestructive testing of food crops

After decades of rapid development in China's agriculture, the quantity and demand of domestic grain have been basically guaranteed. However, the 2010 chromium-containing rice incident and the 2011 Mengniu milk aflatoxin excess incident once again sounded the alarm, and food security problems still exist. However, there are still many difficulties in rapid non-destructive testing of food crops. In recent years, researchers have used hyperspectral imaging technology to detect food crops and have made good progress.

Yao et al. Used fluorescence hyperspectral reflection imaging technology to detect 504 corns contaminated with artificially inoculated aspergillus flavus, induced with 365nm violet light, and obtained 800 × 425 pixel spectral images in the wavelength range of 400 ~ 600nm; reuse SAS software performs multiple linear regression analysis and multivariate analysis of variance on the obtained data and establishes a discriminant model. The correlation coefficient is R2 = 0.72, and it is learned that <1, 1-20, 20-100, ≥100ng / g, etc. 4 Within a threshold range, Pr <0.01 reached a very significant difference level. The aflatoxin threshold was 20ng / g or 100ng / g, and the accuracy rate was 84% ​​to 91%. The results show that this method is feasible to detect single corn particles containing aflatoxin.

In China, Li Jiangbo et al. Used hyperspectral imaging technology (450-900nm) and ANN to test the moisture content of corn. The spectral characteristic wavelength reflecting the moisture content of corn kernels is obtained through the reflection spectrum image of corn kernels. ANN is used to establish a prediction model of the moisture content of corn kernels. The correlation coefficient of the model reaches 0.98. The absolute value of the prediction error is 2.1182, and the minimum is 0.0024; the average value of the absolute value of the relative error is 0.309, indicating that this technique is feasible for non-destructive testing of corn moisture content.

The use of hyperspectral imaging spectrometer can monitor fruits, poultry, vegetables or special crops online, and can quickly analyze and check food varieties, quality, maturity, freshness, crushing, pests, diseases, and pollution factors on the assembly line Is a powerful tool for the safe production of agricultural products and the monitoring of food processing.

Using hyperspectral imaging technology can not only qualitatively and quantitatively analyze the objects to be detected, but also locate and analyze them. Current research indicates that hyperspectral imaging spectroscopy technology has broad application prospects in agricultural products / food testing.

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