Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production

Quantitative Assessment of Resting State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

10.21203/rs.3.rs-69376/v1 ◽

2020 ◽

Author(s):

Dalin Yang ◽

Keum-Shik Hong

Keyword(s):

Machine Learning ◽

Resting State ◽

Near Infrared ◽

Feature Representation ◽

Learning Approach ◽

Theory Analysis ◽

Learning Methods ◽

Functional Near Infrared Spectroscopy ◽

Machine Learning Methods ◽

Graph Theory Analysis

Abstract Background: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer’s disease, which is the sixth leading cause of death in the United State. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. Currently, the combination of machine learning and neuroimaging plays a role in identifying and understanding neuropathological diseases. However, some challenges still remain, and these limitations need to be optimized for clinical MCI diagnosis. Methods: In this study, for stable identification with functional near-infrared spectroscopy (fNIRS) using the minimum resting-state time, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated based on 30 s intervals using a traditional machine learning approach and graph theory analysis. The machine learning methods were trained using temporal features of the resting-state fNIRS signal and included linear discriminant analysis (LDA), support vector machine, and K-nearest neighbor (KNN) algorithms. To enhance the diagnostic accuracy, feature representation- and classification-based transfer learning (TL) methods were used to detect MCI from the healthy controls through the input of connectivity maps with 30 and 90 s durations. Results: As in the results of the traditional machine learning and graph theory analysis, there was no significant difference among the different time windows. The accuracy of the conventional machine learning methods ranged from 55.76% (KNN, 120 s) to 67.00% (LDA, 90 s). The feature representation-based TL showed improved accuracy in both the 30 and 90 s cases (i.e., mean accuracy of 30 s: 79.37%, mean accuracy of 30 s: 74.05%). Notably, the classification-based TL method achieved the highest accuracy of 97.01% using the VGG19 pre-trained CNN model trained with the 30 s duration connectivity map. Conclusion: The results indicate that a 30 s measurement of the resting state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) may be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

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Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

Foods ◽

10.3390/foods8120620 ◽

2019 ◽

Vol 8 (12) ◽

pp. 620 ◽

Cited By ~ 3

Author(s):

Pan Gao ◽

Wei Xu ◽

Tianying Yan ◽

Chu Zhang ◽

Xin Lv ◽

...

Keyword(s):

Machine Learning ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

Elaeagnus Angustifolia ◽

Learning Methods ◽

Machine Learning Methods ◽

Discriminant Models ◽

Second Derivative Spectra ◽

Origin Identification

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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Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Drug Testing and Analysis ◽

10.1002/dta.2895 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1404-1418

Author(s):

Ruben F. Kranenburg ◽

Joshka Verduin ◽

Yannick Weesepoel ◽

Martin Alewijn ◽

Marcel Heerschop ◽

...

Keyword(s):

Machine Learning ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Scene Detection ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

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Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods

Remote Sensing ◽

10.3390/rs13204149 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4149

Author(s):

Soo-In Sohn ◽

Young-Ju Oh ◽

Subramani Pandian ◽

Yong-Ho Lee ◽

John-Lewis Zinia Zaukuu ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Support Vector ◽

Adaxial Side ◽

Learning Methods ◽

Machine Learning Methods ◽

Geographical Regions

The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications.

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