Wavelength Selection for Surface Defects Detection on Tomatoes by Means of a Hyperspectral Imaging System

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of R p = 0.966 and R M S E P = 5.105 % , which is better than those of successive projections algorithm ( R p = 0.932 and R M S E P = 7.329 % ), and the uninformative viable elimination algorithm ( R p = 0.928 and R M S E P = 7.416 % ). The results obtained by BFWA were more stable, with a smaller standard deviation of R p and R M S E P than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.

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Wavelength selection for development of a near infrared imaging system for early detection of bruise damage in mushrooms (Agaricus bisporus)

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.1014 ◽

2012 ◽

Vol 20 (5) ◽

pp. 537 ◽

Cited By ~ 14

Author(s):

Carlos Esquerre ◽

Aoife Gowen ◽

Gerard Downey ◽

Colm O'Donnell

Keyword(s):

Early Detection ◽

Agaricus Bisporus ◽

Near Infrared ◽

Infrared Imaging ◽

Imaging System ◽

Wavelength Selection ◽

Near Infrared Imaging ◽

Infrared Imaging System ◽

Selection For

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Hyperspectral Imaging for Tomato Bruising Damage Assessment of Simulated Harvesting Process Impact Using Wavelength Interval Selection and Multivariate Analysis

Applied Engineering in Agriculture ◽

10.13031/aea.13734 ◽

2020 ◽

Vol 36 (4) ◽

pp. 533-547

Author(s):

Jinshi Cui ◽

Myongkyoon Yang ◽

Daesik Son ◽

Seong-In Cho ◽

Ghiseok Kim

Keyword(s):

Multivariate Analysis ◽

Hyperspectral Imaging ◽

Imaging System ◽

Classification Model ◽

Fast Method ◽

Wavelength Selection ◽

Internal Damage ◽

Discrimination Analysis ◽

Interval Selection ◽

Non Destructive

Highlights The hidden internal damage of falling impact on tomatoes will reduce the quality of products. Hyperspectral imaging and VIS/NIR spectrum analysis, including wavelength selection and classification model construction, have the possibility as a non-destructive and fast method to predict the effect of drop impact grades on tomato bruising damage. Abstract . Mechanical damage usually causes hidden internal damage to tomatoes (Solanum lycopersicum L.), which can reduce the product quality and can cause economic losses to farmers. The visible and near-infrared (VIS/NIR) spectra of tomato fruits were analyzed by using the wavelength selection algorithm (the combination of ant colony optimization and variable importance in projection), and the influence of impact grades of simulated transport on tomato fruit bruising was evaluated. A VIS/NIR hyperspectral imaging system was developed to capture hyperspectral images of tomatoes from 392–1034 nm spectral region and the part used in actual data analysis was 442-984 nm. Multivariate analysis classifier models (partial least squares discrimination analysis and ANN) were set up based on the original spectral dataset. On the basis of selected wavelength intervals, multivariate analysis classifier models were re-established. The overall classification accuracies of all models in the validation set are good, ranging from 64.29% to 100%. Especially in the two types of classification (bruising and normal), the range of correct accuracy is 89.29% to 100%, which shows very high predicted performance. The prediction performance of the model based on the selected wavelengths decreases slightly, but the prediction time is shortened by more than 70%. The results demonstrated that hyperspectral imaging and VIS/NIR spectrum analysis, including wavelength selection and classification model construction, have the possibility as a non-destructive and fast method to predict the effect of drop impact grades on tomato bruising damage. Keywords: ANN, Ant colony optimization (ACO), Partial least squares discrimination analysis (PLS-DA), Variable importance in projection (VIP), VIS/NIR hyperspectral imaging system, wavelength interval selection.

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