Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique

Agricultural products quality which included intrinsic attribute and extrinsic characteristic, closely related to the health of consumer and the exported cost. Now, imaging (machine vision) and spectrum are two main nondestructive inspection technologies to be applied. Hyperspectral imaging, a new emerging technology developed for detecting quality of the food and agricultural products in recent years, combined techniques of conventional imaging and spectroscopy to obtain both spatial and spectral information from an objective simultaneously. This paper compared the advantage and disadvantage of imaging, spectrum and hyperspectral imaging technique, and provided a description to basic principle, feature of hyperspectral imaging system and calibration of hyperspectral reflectance images. In addition, the recent advances for the application of hyperspectral imaging to agricultural products quality inspection were reviewed in other countries and China.

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Detection of the Protein Content of Ningxia Tan Sheep Using Hyperspectral Reflectance Imaging

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.4235 ◽

2014 ◽

Vol 513-517 ◽

pp. 4235-4238

Author(s):

Song Lei Wang ◽

Gui Shan Liu ◽

Xue Fu Li ◽

Rui Ming Luo

Keyword(s):

Protein Content ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Mean Squared Error ◽

Imaging Technique ◽

Linear Regression Analysis ◽

Scatter Correction ◽

Squared Error ◽

Tan Sheep ◽

Well Correlation

Near-infrared (NIR) hyperspectral imaging technique (900-1700nm) was evaluated to predict the protein content of Tan sheep. This research adopted NIR hyperspectral imaging to get imaging information of 72 mutton samples, multiplicative scatter correction was used to spectral data preprocessing. The optimal wavelengths were obtained through linear-regression analysis, BP neural network combined with actual measured values were established the prediction model and verified this model. The results showed that the prediction effect of model was very well. Correlation coefficient (Rp) and root mean squared error of prediction (RMSEP) of the protein were 0.87 and 1.19. The results indicated that it is feasible to predict the protein content of Tan sheep for NIR hyperspectral imaging technique.

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Comparison of different CCD detectors and chemometrics for predicting total anthocyanin content and antioxidant activity of mulberry fruit using visible and near infrared hyperspectral imaging technique

Food Chemistry ◽

10.1016/j.foodchem.2016.12.037 ◽

2017 ◽

Vol 224 ◽

pp. 1-10 ◽

Cited By ~ 36

Author(s):

Lingxia Huang ◽

Yibin Zhou ◽

Liuwei Meng ◽

Di Wu ◽

Yong He

Keyword(s):

Antioxidant Activity ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging Technique ◽

Anthocyanin Content ◽

Total Anthocyanin ◽

Mulberry Fruit ◽

Total Anthocyanin Content ◽

Ccd Detectors

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Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique

Quality Assurance and Safety of Crops & Foods ◽

10.3920/qas2015.0606 ◽

2016 ◽

Vol 8 (1) ◽

pp. 129-135 ◽

Cited By ~ 2

Author(s):

K. Kheiralipour ◽

H. Ahmadi ◽

A. Rajabipour ◽

S. Rafiee ◽

M. Javan-Nikkhah ◽

...

Keyword(s):

Fungal Infection ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging Technique ◽

Long Wave ◽

Pistachio Kernel

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Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2020.105438 ◽

2020 ◽

Vol 173 ◽

pp. 105438 ◽

Cited By ~ 1

Author(s):

Manjree Agarwal ◽

Thamer Al-Shuwaili ◽

Anupiya Nugaliyadde ◽

Penghao Wang ◽

Kok Wai Wong ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging Technique ◽

Whole Body ◽

Trogoderma Granarium ◽

Trogoderma Variabile

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Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Sensors ◽

10.3390/s21092899 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2899

Author(s):

Youngwook Seo ◽

Giyoung Kim ◽

Jongguk Lim ◽

Ahyeong Lee ◽

Balgeum Kim ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging System ◽

Imaging Technique ◽

Support Vector ◽

Organic Residues ◽

Efficient Detection ◽

Non Destructive

Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

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