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Novel Instrumental Developments and Applications of Near-Infrared Spectroscopy

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Keywords
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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 26144

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Special Issue Editors

Prof. Dr. Heinz Wilhelm Siesler

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Guest Editor

Department of Physical Chemistry, University of Duisburg-Essen, Schuetzenbahn 70, D 45117 Essen, Germany

Prof. Dr. Hui Yan

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Guest Editor

School of Biotechnology, Jiangsu University of Science and Technology, Sibaidu, Zhenjiang 212018, China

Special Issue Information

Dear Colleagues,

Over the last few decades, near-infrared spectroscopy has emerged—in combination with light-fiber optics, new in- and online probe accessories, and chemometric evaluation procedures—as an extremely powerful tool for industrial quality control and process monitoring. Recently, its widespread use has been further enhanced by miniaturized, handheld spectrometers for on-site and in-the-field measurements. With these developments, the wavelength gap between the visible and the mid-infrared (MIR) region that has over a long period been idle is finally filled with life and exploited in accordance with its real potential. The objective of this Special Issue is to demonstrate that this analytical technique not only is a routine tool but also has tremendous research potential that can provide unique information not accessible via other methods.

We cordially invite experts working with this technique to submit manuscripts. Particular attention will be given to innovations in the field that have enhanced the performance and capability of NIR spectroscopy.

Prof. Dr. Heinz Siesler
Prof. Dr. Hui Yan
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. Molecules 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 2700 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

Near-infrared spectroscopy
Instrumentation
Portable NIR spectrometers
Chemometrics
Material science
Quality/process control
Imaging/mapping
Agro-food analysis
Pharmaceutical
(to be included in material science) Medicine
Herb/plant
Biotechnology

Published Papers (3 papers)

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Research

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14 pages, 3726 KiB

Open AccessArticle

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

by Shaolong Zhu, Jinyu Zhang, Maoni Chao, Xinjuan Xu, Puwen Song, Jinlong Zhang and Zhongwen Huang

Molecules 2020, 25(1), 152; https://doi.org/10.3390/molecules25010152 - 30 Dec 2019

Cited by 50 | Viewed by 3874

Abstract

Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds. Full article

(This article belongs to the Special Issue Novel Instrumental Developments and Applications of Near-Infrared Spectroscopy)

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13 pages, 2000 KiB

Open AccessArticle

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

by Yue Ma, Guo-Zheng Zhang and Sedjoah Aye-Ayire Rita-Cindy

Molecules 2019, 24(24), 4439; https://doi.org/10.3390/molecules24244439 - 04 Dec 2019

Cited by 11 | Viewed by 3006

Abstract

Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set (

R_{C}^{2}

), R-square of the cross-validation set (

R_{C V}^{2}

) and R-square of the prediction set (

R_{P}^{2}

) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The

R_{C}^{2}

R_{C V}^{2}

and

R_{P}^{2}

of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has

R_{C}^{2}

R_{C V}^{2}

, and

R_{P}^{2}

of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves. Full article

(This article belongs to the Special Issue Novel Instrumental Developments and Applications of Near-Infrared Spectroscopy)

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Review

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37 pages, 9207 KiB

Open AccessReview

Near-Infrared Spectroscopy in Bio-Applications

by Krzysztof B. Beć, Justyna Grabska and Christian W. Huck

Molecules 2020, 25(12), 2948; https://doi.org/10.3390/molecules25122948 - 26 Jun 2020

Cited by 184 | Viewed by 18729

Abstract

Near-infrared (NIR) spectroscopy occupies a specific spot across the field of bioscience and related disciplines. Its characteristics and application potential differs from infrared (IR) or Raman spectroscopy. This vibrational spectroscopy technique elucidates molecular information from the examined sample by measuring absorption bands resulting from overtones and combination excitations. Recent decades brought significant progress in the instrumentation (e.g., miniaturized spectrometers) and spectral analysis methods (e.g., spectral image processing and analysis, quantum chemical calculation of NIR spectra), which made notable impact on its applicability. This review aims to present NIR spectroscopy as a matured technique, yet with great potential for further advances in several directions throughout broadly understood bio-applications. Its practical value is critically assessed and compared with competing techniques. Attention is given to link the bio-application potential of NIR spectroscopy with its fundamental characteristics and principal features of NIR spectra. Full article

(This article belongs to the Special Issue Novel Instrumental Developments and Applications of Near-Infrared Spectroscopy)

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