Detection of foreign materials in cocoa beans by hyperspectral imaging technology

Saeidan, Ali; Khojastehpour, Mehdi; Golzarian, Mahmood Reza; Mooenfard, Marziye; Khan, Haris Ahmad

doi:10.1016/j.foodcont.2021.108242

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…LDA is a powerful supervised learning technique that can significantly increase the discrimination ability between classes based on the distance between projections and effectively classify data [38]. LDA pays more attention to the inter-class distance and intra-class distance of the projected samples in the new dimension space, ensuring that the model has the best separability in the subspace [39].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Sun

et al. 2022

Foods

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Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Sun

et al. 2022

Foods

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“…Another category of food products prone to be evaluated by these systems were nuts, grains, beans, seeds such as chia seeds, 152 soybeans, 153 cocoa beans. 154 oils, 152,155 honeys, 156 and fish meat such as salmon fillets, 157 and dairy products punctuated by the evaluation of fresh cheese 158 and hard cheese 159 as well as beverages such as aged wines, 160 beers 161 and tequilas 162 were less likely to be evaluated by CVSs. Table 6 summarizes all the food matrices that were subjected to an evaluation as per e-eyes.…”

Section: Recent Applications Using E-eyes In Food Analysismentioning

confidence: 99%

“…Such results are promising in the removal of foreign materials on an industrial scale to help improve the quality of the final product. 154 Moreover, SVM was conducted to develop an efficient approach for the inspection of maize seeds and establish a classification model with a satisfying performance. 184 On the other hand, SVM and ANN were exploited to generate an on-line surface defect detection system using hyperspectral images of jujubes.…”

Section: Recent Applications Using E-eyes In Food Analysismentioning

confidence: 99%

“…159 In addition, these systems were also used for sorting purposes to categorize sesame seeds used in the manufacturing of sesame oils 166 and detect foreign materials in cocoa bean batches. 154 Moreover, monitoring the withering condition of leaves during black tea processing illustrates the implementation of CVSs in industrial process follow-up. 167 Further investigated objectives are detailed in Table 6.…”

Section: Recent Applications Using E-eyes In Food Analysismentioning

confidence: 99%

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Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

Abi-Rizk,

Jouan-Rimbaud Bouveresse,

Chamberland

et al. 2023

Anal. Methods

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“…Therefore, HSI has been widely applied for the quality evaluation of various food and agriculture products, such as discrimination of sesame oils, 1 determination of chia seed (Salvia hispanica) geographical origins, 2 differentiation of pine nuts, 3 and detection of foreign materials in cocoa beans. 4 The spatial-spectral hyperspectral images contain hundreds of spectral channels and thousands, even millions of spatial pixels. The different chemical characteristics and physical structures of the scanned materials reflect, absorb, and emit electromagnetic signals with distinctive patterns at different wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Hyperspectral Imaging Classification Performance with CNN and Batch Normalization

Zhang,

Abdulla

2023

Applied Spectroscopy Practica

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Background: Hyperspectral imaging systems face numerous challenges in acquiring accurate spatial-spectral hypercubes due to sample surface heterogeneity, environmental instability, and instrumental noise. Preprocessing strategies such as outlier detection, calibration, smoothing, and normalization are typically employed to address these issues, selecting appropriate techniques based on prediction performance evaluation. However, the risk of misusing inappropriate preprocessing methods remains a concern. Methods: In this study, we evaluate the impact of five normalization methods on the classification performance of six different classifiers using honey hyperspectral images. Our results show that different classifiers have varying compatible normalization techniques and that using Batch Normalization with Convolutional Neural Networks (CNN) can significantly improve classification performance and diminish the variations among other normalization techniques. The CNN with Batch Normalization can achieve a macro average F1 score of ≥0.99 with four different normalization methods and ≥0.97 without normalization. Furthermore, we analyze kernel weights' distribution in the CNN models' final convolutional layers using statistical measurements and kernel density estimation (KDE) graphs. Results: We find that the performance improvements resulting from adding BatchNorm layers are associated with kernel weight range, kurtosis, and density around 0. However, the differences among normalization methods do not show a strong correlation with kernel weight distribution. In conclusion, our findings demonstrate that the CNN with Batch Normalization layers can achieve better prediction results and avoid the risk of inappropriate normalization.

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Detection of foreign materials in cocoa beans by hyperspectral imaging technology

Cited by 14 publications

References 30 publications

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

Optimizing Hyperspectral Imaging Classification Performance with CNN and Batch Normalization

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