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Applied Sciences
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Applied Machine Learning in NIR Technology
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Applied Machine Learning in NIR Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 10844

Special Issue Editors

Dr. Enrique Fernandez-Blanco

E-Mail Website
Guest Editor
CITIC, Faculty of Computer Science, Campus de Elviña, University of A Coruña, 15071 A Coruña, Spain
Interests: signal processing; deep learning; machine learning; evolutionary computarion
Dr. Daniel Rivero

E-Mail Website
Guest Editor
CITIC, Faculty of Computer Science, Campus de Elviña, University of A Coruña, 15071 A Coruña, Spain
Interests: artificial neural networks; genetic algorithms; genetic programming; adaptative systems

Special Issue Information

Dear Colleagues,

As you already know, since the 1980s, Near-Infrared Reflectance (NIR) scanners have been a common companion in laboratories. This technology allows the analysis of the electromagnetic spectrum in bands close to the visible spectrum. As a result, there is a large amount of information that can be analyzed in order to make predictions about a factor such as the quality of the sample.  This advantage of being able to quickly analyze the material without having to destroy the samples have made it so that, in recent times, this kind of equipment has been moved from the laboratory to portable devices in some cases as small as a credit card. Therefore, numerous applications have been developed under the umbrella of this technology, such as analysis of sewer water, determination of the food safety, diagnosis of construction material, support on medical and vet diagnosis; the number of applications are near endless.

However, this blossom of applications usually goes along with the requirement of a model which identifies and uses the information in the captured spectra. The relationship between both factors is usually non-linear and complex, so it is necessary to use machine learning techniques to obtain it. Although, it has usually been performed through an analytical process, currently, the application of machine learning has been a step forward in terms of the precision and adjustment of the results. As NIR and machine learning are multidisciplinary technologies, many fields of science and industry could benefit from their use.

Especially, if we focus on feature selection of the bands on the spectra that contain the information required to solve the problem, the application of machine learning needs a process of adjustment to optimize the performance. The aim of this Special Issue is to present the latest advances that have been made in this field, combining both techniques, as well as to show the remaining challenges and the future in this area of research. That is why this Special Issue solicits submissions in, but not limited to, the following areas:

  • Selection of features or bands of the NIR spectra, with a special interest in those applications using nano-scale NIR scanners due to its limit operational range;
  • Studies of different preprocessing techniques and comparison with automatic features extraction process, such as PCA, LDA, evolutionary computation;
  • Developments combining cloud computing, fog computing or edge computing with the processing of NIR spectra in different industrial processes;
  • Application and comparative of the latest approaches of machine learning techniques, such as deep learning or ensemble models;
  • Works with a particular interest to cover the explainable artificial intelligence in the frame of decision-making using NIR data and machine learning.

Dr. Enrique Fernandez-Blanco
Dr. Daniel Rivero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal processing
  • near-infrared
  • machine learning
  • artificial neural networks
  • support vector machines
  • k-nearest neighbour
  • Naïve Bayes
  • random forest
  • ensemble models
  • evolutionary computation
  • explainable artificial intelligence
  • ensemble methods
  • deep learning
  • feature extraction
  • Principal Component Analysis (PCA)
  • food security
  • material analysis
  • drug identification
  • medical diagnosis
  • vet diagnosis
  • Internet of things (IoT)

Published Papers (4 papers)

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Research

18 pages, 1413 KiB  
Article
Identification of a Suitable Machine Learning Model for Detection of Asymptomatic Ganoderma boninense Infection in Oil Palm Seedlings Using Hyperspectral Data
by Aiman Nabilah Noor Azmi, Siti Khairunniza-Bejo, Mahirah Jahari, Farrah Melissa Muharram and Ian Yule
Appl. Sci. 2021, 11(24), 11798; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411798 - 12 Dec 2021
Cited by 7 | Viewed by 2233
Abstract
In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is [...] Read more.
In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124 s when run on a personal computer with an Intel® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry. Full article
(This article belongs to the Special Issue Applied Machine Learning in NIR Technology)
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16 pages, 1943 KiB  
Article
Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine
by Siti Khairunniza-Bejo, Muhamad Syahir Shahibullah, Aiman Nabilah Noor Azmi and Mahirah Jahari
Appl. Sci. 2021, 11(22), 10878; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210878 - 17 Nov 2021
Cited by 14 | Viewed by 2246
Abstract
Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses [...] Read more.
Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses on the early detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral data and a support vector machine (SVM). The study aims to use a small number of wavelengths by using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results of detection obtained from the vegetation indices developed using spectral reflectance taken from the same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two classes would be more suitable for G. boninense detection compared to the others, both for single-band reflectance and vegetation index datasets. A linear SVM which was developed using a single-band reflectance at 934 nm was identified as the best model of detection since it was not only economical, but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and area under the receiver operating characteristic curve (AUC) (0.95). Full article
(This article belongs to the Special Issue Applied Machine Learning in NIR Technology)
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13 pages, 2173 KiB  
Article
Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
by Ivan Ramirez-Morales, Lenin Aguilar, Enrique Fernandez-Blanco, Daniel Rivero, Jhonny Perez and Alejandro Pazos
Appl. Sci. 2021, 11(22), 10751; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210751 - 15 Nov 2021
Cited by 2 | Viewed by 2323
Abstract
Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and [...] Read more.
Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%. Full article
(This article belongs to the Special Issue Applied Machine Learning in NIR Technology)
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25 pages, 3221 KiB  
Article
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods
by Véronique Gomes, Ricardo Rendall, Marco Seabra Reis, Ana Mendes-Ferreira and Pedro Melo-Pinto
Appl. Sci. 2021, 11(21), 10319; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110319 - 03 Nov 2021
Cited by 5 | Viewed by 1929
Abstract
This paper presents an extended comparison study between 16 different linear and non-linear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, [...] Read more.
This paper presents an extended comparison study between 16 different linear and non-linear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the Savitzky−Golay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters. Full article
(This article belongs to the Special Issue Applied Machine Learning in NIR Technology)
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