Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Khan, Muhammad Hussain; Saleem, Zainab; Sohaib, Ahmed; Ayaz, Hamail; Mazzara, Manuel; Raza, Rana Aamir

doi:10.1007/s00521-021-06094-4

Cited by 35 publications

(19 citation statements)

References 53 publications

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“…However, as the concentration of food adulteration increases, the penetration ability of spectral imaging is limited, and the accuracy of algorithm identification decreases. Therefore, ANN with nonlinear mapping was also used in the study for classification, such as adulteration classification of chocolate powder [ 90 ], nutmeg [ 65 ], red chili powder [ 69 ], colla corii asini [ 63 ], marine fishmeal [ 71 ], and cereal [ 72 ]. The results demonstrate the superiority of the nonlinear mapping model.…”

Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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“…H YPERSPECTRAL images (HSIs) contain very rich spatial-spectral information, thus providing a substantial opportunity to explore and understand earth surface characteristics. Among existing HSI processing technologies, classification is by far the most well-established field and has been widely applied to various scenarios in Earth science, such as monitoring coastal wetlands [1], precision agriculture [2], water quality analysis [3], and other fields [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fully Connected Tensorized Compression Network for Hyperspectral Image Classification

Lin

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Deep learning models, such as convolutional neural networks (CNNs), have made significant progress in hyperspectral image (HSI) classification. However, these models require a large number of parameters, which occupy a lot of storage space and suffer from overfitting, thus resulting in performance loss. To solve the above problems, in this article, we propose a new compression network [namely, a Hybrid Fully Connected Tensorized Compression Network (HybridFCTCN)] by considering the high dimensionality of HSI data. First, using the low-rank fully connected tensor network decomposition (FCTND), three novel units, i.e., FCTN-FC, FCTNConv2D, and FCTNConv3D, are designed to compress the weight tensor of standard fully connected (FC) layer and kernel tensor of convolutional layer, reducing their parameters. In the novel units, the intrinsic correlation of the decomposed factors is adequately exploited by the FC structures, which enhances their feature extraction and classification abilities. Then, benefiting from the hybrid network backbone composed of the FCTNConv3D and FCTNConv2D units, HybridFCTCN can extract more discriminative features with fewer parameters, while it has great generalization capability and robustness, enabling better HSI classification. Finally, the rank of above-designed units is defined, and its determination is discussed to facilitate the application of the proposed model. Extensive experiments on three widely used HSI datasets reveal that the proposed model achieves state-of-the-art classification performance for different training sample sizes with a very small number of parameters.

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“…HSI is an evolving pioneering statistical and heuristic spectrometric technology 21 , 22 . It is a nondestructive technique that is widely used to examine a broad spectrum of light rather than merely examining the primary colors, i.e., red, green, and blue (RGB) in the pixels of an image 23 , 24 . HSI has been used in various field and applications, such as cancer detection 25 , air pollution monitoring 26 , remote sensing 27 , agriculture 28 , astronomy 29 , quality control 30 , environment monitoring 31 , and vegetable classification 32 .…”

Section: Introductionmentioning

confidence: 99%

Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm

Mukundan

Tsao

Lin

et al. 2022

Sci Rep

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One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key.

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Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Cited by 35 publications

References 53 publications

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Hybrid Fully Connected Tensorized Compression Network for Hyperspectral Image Classification

Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm

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