Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

This paper proposes the use of Near Infrared Hyperspectral Imaging (NIR-HSI) as a new strategy for fast and non-destructive classification of cotton seeds with respect to variety.

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<i>Visible and near infrared hyperspectral imaging for non-destructive grading and classification of chicken breast fillets</i>

10.13031/aim.201800826 ◽

2018 ◽

Author(s):

Beibei Jia ◽

Wei Wang ◽

Seung-Chul Yoon ◽

Hong Zhuang ◽

Yi Yang ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Chicken Breast ◽

Non Destructive

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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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Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.9360 ◽

2018 ◽

Vol 99 (4) ◽

pp. 1709-1718 ◽

Cited By ~ 6

Author(s):

Xuyang Pan ◽

Laijun Sun ◽

Yingsong Li ◽

Wenkai Che ◽

Yamin Ji ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Non Destructive

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Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products

Journal of Food Engineering ◽

10.1016/j.jfoodeng.2018.06.015 ◽

2018 ◽

Vol 238 ◽

pp. 70-77 ◽

Cited By ~ 28

Author(s):

Puneet Mishra ◽

Alison Nordon ◽

Julius Tschannerl ◽

Guoping Lian ◽

Sally Redfern ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Non Destructive

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Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽

10.3390/pr9020196 ◽

2021 ◽

Vol 9 (2) ◽

pp. 196

Author(s):

Araz Soltani Nazarloo ◽

Vali Rasooli Sharabiani ◽

Yousef Abbaspour Gilandeh ◽

Ebrahim Taghinezhad ◽

Mariusz Szymanek ◽

...

Keyword(s):

Near Infrared ◽

Cross Validation ◽

Low Cost ◽

Moving Average ◽

Nir Spectroscopy ◽

Control Treatment ◽

Residue Detection ◽

Non Destructive ◽

Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

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