Identification of storage years of black tea using near-infrared hyperspectral imaging with deep learning methods

Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.

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Prediction of specialty coffee flavors based on near‐infrared spectra using machine‑ and deep‐learning methods

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.11116 ◽

2021 ◽

Author(s):

Yu‐Tang Chang ◽

Meng‐Chien Hsueh ◽

Shu‐Pin Hung ◽

Juin‐Ming Lu ◽

Jia‐Hung Peng ◽

...

Keyword(s):

Deep Learning ◽

Infrared Spectra ◽

Near Infrared ◽

Learning Methods ◽

Near Infrared Spectra ◽

Specialty Coffee

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Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2020.105438 ◽

2020 ◽

Vol 173 ◽

pp. 105438 ◽

Cited By ~ 1

Author(s):

Manjree Agarwal ◽

Thamer Al-Shuwaili ◽

Anupiya Nugaliyadde ◽

Penghao Wang ◽

Kok Wai Wong ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging Technique ◽

Whole Body ◽

Trogoderma Granarium ◽

Trogoderma Variabile

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Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

Molecules ◽

10.3390/molecules24183268 ◽

2019 ◽

Vol 24 (18) ◽

pp. 3268 ◽

Cited By ~ 13

Author(s):

Zhu ◽

Zhou ◽

Gao ◽

Bao ◽

He ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Cotton Seed ◽

Near Infrared ◽

Principal Component ◽

Critical Factor ◽

Support Vector ◽

Classification Models ◽

Cotton Seeds ◽

Better Than

Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.

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Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Sensors ◽

10.3390/s21092899 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2899

Author(s):

Youngwook Seo ◽

Giyoung Kim ◽

Jongguk Lim ◽

Ahyeong Lee ◽

Balgeum Kim ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging System ◽

Imaging Technique ◽

Support Vector ◽

Organic Residues ◽

Efficient Detection ◽

Non Destructive

Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

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Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

RSC Advances ◽

10.1039/d0ra06925f ◽

2020 ◽

Vol 10 (68) ◽

pp. 41936-41945

Author(s):

Tianying Yan ◽

Long Duan ◽

Xiaopan Chen ◽

Pan Gao ◽

Wei Xu

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Geographical Origin ◽

Learning Methods ◽

Radix Glycyrrhizae ◽

Origin Identification

Hyperspectral imaging provides an effective way to identify the geographical origin of Radix Glycyrrhizae to assess its quality.

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Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing

Minerals ◽

10.3390/min10090809 ◽

2020 ◽

Vol 10 (9) ◽

pp. 809 ◽

Cited By ~ 1

Author(s):

Natsuo Okada ◽

Yohei Maekawa ◽

Narihiro Owada ◽

Kazutoshi Haga ◽

Atsushi Shibayama ◽

...

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Visual Information ◽

Near Infrared ◽

Wavelength Region ◽

Mineral Processing ◽

Hyperspectral Data ◽

Identification Accuracy ◽

Identification System ◽

Mining Operations

In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.

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