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Ma X, Guo X, Lin B, Wang H, Dong Q, Huang S, Li L, Zang H. Detection and analysis of hyaluronic acid raw materials from different sources by NIR and aquaphotomics. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:537-550. [PMID: 38180114 DOI: 10.1039/d3ay01963b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Hyaluronic acid (HA), a polysaccharide, is widely used for its essential physiological functions. Although the structures of low molecular weight HA produced by both acid and enzyme degradation methods are extremely similar, there are still differences due to the different degradation principles. There is currently no clear way to distinguish between HA prepared by acidolysis and enzymatic hydrolysis. Based on near-infrared (NIR) spectroscopy and aquaphotomics technology, a method for distinguishing HA raw materials and their mixtures from different sources was proposed, and HA with different mixed ratios was accurately quantified. First, NIR spectra of the HA samples were collected. The spectra were then preprocessed to improve the spectral resolution. Spectral information was extracted based on wavelet transform and principal component analysis, resulting in a final selection of 12 characteristic wavelengths containing classification information. The discriminative and quantitative models were then constructed using the 12 wavelengths. The discriminative model achieved a 100% identification rate for HA from different sources. The correlation coefficient of calibration (Rc), validation (Rp), external test (Rt), root mean square error of cross validation (RMSECV), calibration (RMSEC), validation (RMSEP), and external test (RMSET) of the mixed proportion quantitative model were 0.9876, 0.9876, 0.9898, 0.0546, 0.0433, 0.0440, and 0.0347, respectively. In this study, the problem of structural similarity and non-identifiability of HA produced by acidolysis and enzymatic hydrolysis was addressed, and quality monitoring of HA feedstock in HA circulating links was achieved. This is the first time to achieve accurate quantification of solid mixtures using the aquaphotomics method.
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Affiliation(s)
- Xiaobo Ma
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Xueping Guo
- Bloomage Biotechnol Corp Ltd, Jinan 250012, PR China
| | - Boran Lin
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Haowei Wang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Qin Dong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Siling Huang
- Bloomage Biotechnol Corp Ltd, Jinan 250012, PR China
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, 250012, Shandong, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, 250012, Shandong, China
- National Glycoengineering Research Center, Shandong University, Jinan, 250012, Shandong, China
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Hara R, Kobayashi W, Yamanaka H, Murayama K, Shimoda S, Ozaki Y. Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media. APPLIED SPECTROSCOPY 2023; 77:521-533. [PMID: 36765462 DOI: 10.1177/00037028231160197] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.
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Affiliation(s)
- Risa Hara
- Department of Research and Development, Yokogawa Electric Corporation, Musashino, Japan
| | - Wataru Kobayashi
- Department of Life Business, Yokogawa Electric Corporation, Musashino, Japan
| | - Hiroaki Yamanaka
- Department of Life Business, Yokogawa Electric Corporation, Musashino, Japan
| | - Kodai Murayama
- Department of Research and Development, Yokogawa Electric Corporation, Musashino, Japan
| | - Soichiro Shimoda
- Department of Life Business, Yokogawa Electric Corporation, Musashino, Japan
| | - Yukihiro Ozaki
- School of Biological and Environmental Sciences, Kwansei Gakuin University, Sanda, Japan
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Li F, Zhang X, Lu A, Xu L, Ren D, You T. Estimation of metal elements content in soil using x-ray fluorescence based on multilayer perceptron. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:95. [PMID: 35029753 DOI: 10.1007/s10661-022-09750-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/31/2021] [Indexed: 06/14/2023]
Abstract
X-ray fluorescence (XRF) is widely used to rapidly detect heavy metals in soil. Spectra processing has been an important research topic to improve accuracy. In this study, 80 soil samples were analyzed by XRF under indoor conditions, where different preprocessing and quantitative analysis methods were compared in terms of prediction accuracy. Denoising algorithms were used to preprocess the soil spectra before establishing prediction models for As, Pb, Cu, Cr, and Cd in soil. The influence of denoising methods on the prediction effects of different models was compared and discussed. The results on five heavy metal spectra show that the proper spectral preprocessing method can effectively improve the prediction performance of the model. The multilayer perceptron model provides promising analysis and modeling for the five metal elements. The determination coefficients (R2) of the models were 0.857, 0.976, 0.977, 0.995, and 0.886, respectively. The proposed method provides the potential to support accurate quantitation by XRF analysis.
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Affiliation(s)
- Fang Li
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
- Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Xiaofeng Zhang
- College of Computer and Information Technology, Three Gorges University, Yichang, 443002, Hubei, China
| | - Anxiang Lu
- Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Li Xu
- Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Dong Ren
- College of Computer and Information Technology, Three Gorges University, Yichang, 443002, Hubei, China
| | - Tianyan You
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
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The usefulness of NIRS calibrations based on feed and feces spectra to predict nutrient content, digestibility and net energy of pig feeds. Anim Feed Sci Technol 2021. [DOI: 10.1016/j.anifeedsci.2021.115091] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves. Sci Rep 2021; 11:4169. [PMID: 33603126 PMCID: PMC7892543 DOI: 10.1038/s41598-021-83847-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 01/31/2023] Open
Abstract
Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.
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Macavilca EA, Condezo-Hoyos L. Assessment of total antioxidant capacity of altiplano colored quinoa (Chenopodium quinoa willd) by visible and near-infrared diffuse reflectance spectroscopy and chemometrics. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.110182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Yamashita H, Sonobe R, Hirono Y, Morita A, Ikka T. Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms. Sci Rep 2020; 10:17360. [PMID: 33060629 PMCID: PMC7566634 DOI: 10.1038/s41598-020-73745-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/21/2020] [Indexed: 11/09/2022] Open
Abstract
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325-1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.
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Affiliation(s)
- Hiroto Yamashita
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan
- United Graduate School of Agricultural Science, Gifu University, Gifu, Japan
| | - Rei Sonobe
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan.
| | - Yuhei Hirono
- Division of Tea Research, Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), Shimada, Japan
| | - Akio Morita
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan
| | - Takashi Ikka
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan.
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Yuan D, Jiang J, Qiao X, Qi X, Wang W. An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Pasquini C. Near infrared spectroscopy: A mature analytical technique with new perspectives – A review. Anal Chim Acta 2018; 1026:8-36. [DOI: 10.1016/j.aca.2018.04.004] [Citation(s) in RCA: 363] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 04/05/2018] [Accepted: 04/06/2018] [Indexed: 12/19/2022]
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Zhou J, Wang R, Wu X, Xu B. Fiber-Content Measurement of Wool-Cashmere Blends Using Near-Infrared Spectroscopy. APPLIED SPECTROSCOPY 2017; 71:2367-2376. [PMID: 28537417 DOI: 10.1177/0003702817713480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool-cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool-cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere-wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool-cashmere blend and have potential to be used for tackling the cashmere adulteration problem.
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Affiliation(s)
- Jinfeng Zhou
- 1 Department of Merchandising and Digital Retailing, University of North Texas, Denton, TX, USA
- 2 Key Laboratory of Textile Science & Technology Ministry of Education College of Textiles, Donghua University, China
| | - Rongwu Wang
- 2 Key Laboratory of Textile Science & Technology Ministry of Education College of Textiles, Donghua University, China
| | - Xiongying Wu
- 2 Key Laboratory of Textile Science & Technology Ministry of Education College of Textiles, Donghua University, China
| | - Bugao Xu
- 1 Department of Merchandising and Digital Retailing, University of North Texas, Denton, TX, USA
- 2 Key Laboratory of Textile Science & Technology Ministry of Education College of Textiles, Donghua University, China
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