Application of hyperspectral technology in detection of agricultural products and food: A Review

Zhu, Min; Huang, Dan; Hu, Xinjun; Tong, Wenhua; Han, Bao-Lin; Tian, Jianping; Luo, Huibo

doi:10.1002/fsn3.1852

Cited by 51 publications

(24 citation statements)

References 56 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…he spectral imaging system that integrates spectroscopy and imaging technology plays an indispensable role in many analytical tools [1], [2]. Spectral imaging technology meets the requirements for specific scenarios, which makes its application very wide, including aerial remote sensing [3], biometric identification [4], medical diagnosis [5], environmental monitoring [6], [7], astronomical observation [8], and food detection [9], [10]. Due to the capability of target recognition with high precision, several different types of spectral imaging systems have been developed, which are divided by the number of spectral channels.…”

Section: Introductionmentioning

confidence: 99%

Planar Hyperspectral Imager With Small Smile and Keystone Based on Two Metasurfaces

et al. 2022

View full text Add to dashboard Cite

Metasurfaces, which are ultrathin planar artificial materials flexibly controlling the phase, amplitude, and polarization of the light, have facilitated the high integration and miniaturization of hyperspectral imaging systems. However, the pushbroom hyperspectral imaging systems based on metasurfaces usually face the large smile and keystone, which reduces the spectral uniformity and the fidelity of the spectral information. Moreover, the employment of many metasurfaces would cause manufacturing difficulties. In this paper, a planar hyperspectral imager only using two metasurfaces is proposed to achieve a spectral resolution of 3.5 nm in 850-1000 nm band with the near diffraction-limited focusing capability, and its maximum of smile and keystone reach to 124.4 μm and 33.6 μm for the image plane size of 4.75 mm × 1.35 mm and pixel size of 26 μm × 26 μm, respectively. This imager can provide a promising avenue for portable environmental monitoring systems and wearable electronic consumer products.

show abstract

Section: Introductionmentioning

confidence: 99%

Planar Hyperspectral Imager With Small Smile and Keystone Based on Two Metasurfaces

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Due to the electromagnetic absorption and scattering characteristics of the material, traditional spectroscopy technologies cannot be used for imaging detection in different areas, whereas near-infrared band technologies can be easily used for non-destructive testing. Hyperspectral imaging technology has been widely used in food [ 6 ], medicine [ 7 ], and crops [ 8 ] in recent years, owing to its fast and accurate characteristics. Applied research in crops mainly focuses on testing indicators of plant leaves, including water, nitrogen, chlorophyll, and other indicators [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter

Zhang

Liu

et al. 2021

Plant Methods

View full text Add to dashboard Cite

Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. Results We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. Conclusions Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway.

show abstract

“…Hyperspectral imaging technology can fuse image data with spectral data, detect geometric spatial distribution information on fermentation, and achieve complete characterization of target items. Hyperspectral imaging is becoming a core approach in the assessment of agricultural products [ 5 ]. However, the amount of hyperspectral information acquired is huge, and processing is complicated.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong

Yang

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

show abstract

Application of hyperspectral technology in detection of agricultural products and food: A Review

Cited by 51 publications

References 56 publications

Planar Hyperspectral Imager With Small Smile and Keystone Based on Two Metasurfaces

Planar Hyperspectral Imager With Small Smile and Keystone Based on Two Metasurfaces

Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Contact Info

Product

Resources

About