Detection of Internal Defect in Pickling Cucumbers Using Hyperspectral Transmittance Imaging

High speed measurement of corn seed viability using hyperspectral imaging

<abstract> <bold>Abstract.</bold> Soluble solid is an important quality attribute of vegetables and fruits. Development of nondestructive technology for soluble solid measurement will help the industry provide better vegetables and fruits for consumers. A prototype of hyperspectral transmittance imaging system with two 50W halogen lamps and one height-limiting board was developed. Spectral transmittance images of tomatoes were acquired from the loophole of the height limiting board before background segmentation using special bands. The spectral data were analyzed using the partial least squares (PLS) analysis. PLS model was established to predict the soluble solid content (SSC) of tomatoes using average spectral information of regions of interest. The correlation coefficients (r) with the spectral range (720-990 nm) for predicting SSC was 0.83 with RMSEC of 0.175 and RMSEP of 0.357 respectively. The results of this work show that the hyperspectral diffuse transmittance imaging technique is promising for assessing soluble solid content of tomatoes.

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

Fat content is one of the most important parameters of beef grading. In this study, a hyperspectral imaging (HSI) system, combined with multivariate data analysis, was adopted for the classification of beef grades. Three types of beef samples, namely Akaushi (AK), USDA prime, and USDA choice, were used for HSI image acquisition in the spectral range of 400–1000 nm. Spectral information was extracted from the image by applying the partial least squares discriminant analysis (PLS-DA) for the three classifications. A total of eight different types of data pre-processing procedures were tested during PLS-DA to evaluate their individual performance, with the accepted pre-processing method selected based on the highest accuracy. Chemical and binary images were generated to visualize the fat mapping of the samples. Quantitative analysis of the samples was performed for the reference measurement of the dry matter and fat content. The highest overall accuracy, 86.5%, was found using the Savitzky–Golay second derivatives pre-processing method for PLS-DA analysis. The optimal wavelength values were found from the beta coefficient curve. The chemical and binary images showed significant differences in fat mapping among the three groups of samples, with AK having the greatest intramuscular fat content and USDA choice having the least. Similar results were observed during the proximate analysis. The findings of this study demonstrate that the HSI technique is a potential tool for the fast and non-destructive determination of beef grades based on fat mapping.

Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis.

Abstract. Internal bruise damage that occurs in blueberry fruit during harvest operations and postharvest handling lowers the overall quality and causes significant economic losses. The main goal of this study was to nondestructively detect internal bruises in blueberries after mechanical damage using hyperspectral transmittance imaging. A total of 600 hand-harvested blueberries were divided into 20 groups of four storage times (30 min, 3 h, 12 h, and 24 h), two storage temperatures (22°C and 4°C), and three treatments (stem bruise, equator bruise, and control). A near-infrared hyperspectral imaging system was used to acquire transmittance images from 970 to 1400 nm with 5 nm bandwidth. Images were acquired from three orientations (calyx-up, stem-up, and equator-up) for fruit in the control and stem bruise groups and from four orientations (calyx-up, stem-up, equator-up, and equator-down) in the equator bruise groups. Immediately after imaging, the fruit samples were sliced, and the sliced surfaces were photographed. The color images of sliced fruit were used as references. By comparing with the reference color images, the profiles of spatial and spectral intensities were evaluated to observe the effect of orientation and help extract regions of interest (ROIs) of bruised and healthy tissues. A support vector machine (SVM) classifier was trained and tested to classify pixels of bruised and healthy tissues. Classification maps were produced, and the bruise ratio was calculated to identify bruised blueberries (bruise ratio &amp;gt;25%). The average accuracy of blueberry identification was 94.5% with the stem-up orientation. The results indicate that detecting bruised blueberries as soon as 30 min after mechanical damage is feasible using hyperspectral transmittance imaging. Keywords: Blueberry, Bruise detection, Classification, Hyperspectral imagery, Transmittance mode.

Blood spots are one of undesired inclusions in eggs, whose detection success is highly dependent on shell color. This research reports a method for detecting blood spots in light brown-shelled eggs on the basis of hyperspectral transmittance images. The normalized spectra of intact eggs and their shells were acquired. Five feature wavelengths of intact eggs selected by the successive projections algorithm and 3 absorption peak locations of eggshells were regarded as characteristic bands. The k-nearest neighbor (kNN) and support vector machine (SVM) algorithms were adopted to develop detection models. The latter achieved better performance. The overall classification accuracy increased to 100% by merging normalized spectra of intact eggs at 5 feature wavelengths with 3 absorption peaks of eggshells as input variables of SVM-based model. Moreover, a practical SVM-based model with 96.43% overall classification accuracy was established by replacing inputs with normalized spectra of intact eggs at characteristic bands. Keywords: hyperspectral transmittance imaging, non-destructive detection, blood-spot, egg DOI: 10.25165/j.ijabe.20191206.5376 Citation: Feng Z, Ding C Q, Li W H, Cui D. Detection of blood spots in eggs by hyperspectral transmittance imaging. Int J Agric & Biol Eng, 2019; 12(6): 209–214.

Rooibos (Aspalathus linearis) and Honeybush (Cyclopia species) are teas indigenous to South Africa that are popularly consumed locally as well as internationally for its purported health benefits. Studies have confirmed anti-oxidant, antimutagenic and anticancer activity mainly due to the presence of polyphenols. Hyperspectral imaging (HSI) integrates conventional spectroscopy and imaging to obtain spectral and spatial information from a sample. The potential of short wave infrared (SWIR) hyperspectral imaging in combination with chemometric data analysis as a rapid quality control method for commercially important herbal teas was investigated. Images were acquired with the sisuChema short wave infrared (SWIR) hyperspectral pushbroom imaging system at a spectral range of 920 – 2514nm. Principal Component Analysis (PCA) was performed to remove background pixels and to visualize the data. Classes (i.e. Rooibos or Honeybush) were interactively assigned using the score images and plots. Partial Least Squares Discriminant Analysis (PLS-DA) classification models developed were subsequently used to accurately predict the species identity of commercially available herbal tea samples introduced as an external dataset, producing a visually interpretable result. The results showed that the labeling information is correct but that Honeybush tea is in most cases present in very low amounts. It is evident that hyperspectral imaging is an objective and non-destructive quality control method that can be successfully used to determine the botanical species included in herbal teas in addition to giving an indication of the abundance of each species in polyherbal tea mixtures.

The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.

Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis

Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P < 0.01). There was a negative association between pixel resolution and classification accuracy, while spectral binning equivalent to up to 98% data reduction had negligible effect on classification accuracies. This study supports the potential use of reflection-based technologies in the quality control of food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

Mushy Halibut Syndrome (MHS) is a condition that appears in Greenland halibut and manifests itself as abnormally opaque, flaccid and jelly-like flesh. Fish affected by this syndrome show poor meat quality, which results in negative consequences for the fish industry. The research community has not carefully investigated this condition, nor novel technologies for MHS detection have been proposed. In this research work, we propose using hyperspectral imaging to detect MHS. After collecting a dataset of hyperspectral images of halibut affected by MHS, two different goals were targeted. Firstly, the estimation of the chemical composition of the samples (specifically fat and water content) from their spectral data by using constrained spectral unmixing. Secondly, supervised classification using partial least squares discriminant analysis (PLS-DA) was evaluated to identify specimens affected by MHS. The outcomes of our study suggest that the prediction of fat from the spectral data is possible, but the prediction of the water content was not found to be accurate. However, the detection of MHS using PLS-DA was precise for hyperspectral images from both fillets and whole fish, with lower bounds of 75% and 83% for precision and recall, respectively. Our findings suggest hyperspectral imaging as a suitable technology for the early screening of MHS.

Finite element simulation of laser-generated ultrasonic waves for quantitative detection of internal defects in welds

Accurately identifying the location and type of internal defects in gas‐insulated switchgear (GIS) is a challenge. To address this challenge, this study proposes a novel method for the nondestructive detection of GIS internal defects. This method is based on x‐ray digital radiography (X‐DR) technology and an improved real‐time models for object detection (RTMdet) algorithm, namely GIS‐specific localised internal defect‐RTMdet. Firstly, the X‐DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast. Then, a convolution shuffle upsample module for upsampling is proposed, which enlarges the defect feature map by multi‐convolution and pixel shuffling, reduces the information loss, and enhances the interaction between the feature information. Finally, both the multi‐channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association. Experiments demonstrate that the proposed method achieves a mean average precision @0.5:0.95 of 94.9%, showcasing excellent overall performance and generalisation ability, and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.

Quantification of n-3 polyunsaturated fatty acids (PUFA) is an important aspect in quality control of designer eggs. Prediction of these fatty acids would be of value for egg grading stations. The aim of the present study was to evaluate the ability of hyperspectral imaging (HSI) to classify the eggs according their content in n-3 PUFA as a rapid and non-destructive technique. Furthermore, HSI was used to predict the content of n-3 PUFA in designer eggs. Normal eggs, eggs containing 75 mg and eggs containing 125 mg docosahexaenoic acid were purchased from a grocery store. After hyperspectral transmittance images were collected in the spectral region of 900–1,700 nm, the eggs were broken and analyzed chemically in order to measure fatty acid content. Regions of interest (ROIs) of about 3,000 pixels from each egg were selected. K-means analysis was performed to classify eggs into the three aforementioned types. The classification accuracy was 100%. A partial least-squares regression model was built and used to link the ROIs with results obtained from the chemical analysis. The correlation coefficients between the measured and predicted values of alpha-linolenic acid, eicosapentaenoic acid, and docosahexaenoic acid were 0.94, 0.73 and 0.87, respectively. The root mean square error values for the validation sets were 38.65, 3.31 and 7.03, respectively. These results showed that HSI could be used both for discriminating the eggs between normal and designer eggs and for prediction of n-3 PUFA content in eggs.