Identification of mechanical damage in the 'Fuji' apple cv. using artificial hyperspectral vision

One problem in the post-harvest phase of apples is the mechanical impact damage; its identification prevents quality issues during storage. The objective was to identify the wavelengths at which the damage is detected early in apples of the 'Fuji' cultivar, simulating the damage with a controlled stroke and taking hyperspectral images from 400 to 1700 nm. Three experiments were carried out at different temperatures (4 and 20 ° C) and with varying sampling times. It was found that in the NIR zone ranging between 1050 and 1100 nm, it was possible to classify healthy and bruised zones by means of a discriminant analysis by partial least squares (PLS-DA). Additionally, the evolution of the damage over time was not significant for the classification of the pixels (healthy and bruised classes), since bumps were detected in all three experiments from the first time.

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A frame-based ANN for classification of hyperspectral images: assessment of mechanical damage in mushrooms

Neural Computing and Applications ◽

10.1007/s00521-016-2376-7 ◽

2016 ◽

Vol 28 (S1) ◽

pp. 969-981 ◽

Cited By ~ 11

Author(s):

Rodrigo Rojas-Moraleda ◽

Nektarios A. Valous ◽

Aoife Gowen ◽

Carlos Esquerre ◽

Steffen Härtel ◽

...

Keyword(s):

Mechanical Damage ◽

Hyperspectral Images

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Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods

Applied Sciences ◽

10.3390/app10196724 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6724

Author(s):

Youngwook Seo ◽

Ahyeong Lee ◽

Balgeum Kim ◽

Jongguk Lim

Keyword(s):

Discriminant Analysis ◽

Hyperspectral Imaging ◽

Partial Least Square ◽

Hyperspectral Images ◽

Classification Model ◽

Support Vector ◽

Chemometric Methods ◽

Infrared Wavelength ◽

Linear Discriminant

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

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