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Li Z, Ni C, Wu R, Zhu T, Cheng L, Yuan Y, Zhou C. Online small-object anti-fringe sorting of tobacco stem impurities based on hyperspectral superpixels. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123084. [PMID: 37423100 DOI: 10.1016/j.saa.2023.123084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023]
Abstract
The use of tobacco stems as raw material for cigarettes reduces cost and improves the flammability of cigarettes. However, various impurities, such as plastic, reduce the purity of tobacco stems, degrade the quality of cigarettes, and endanger the health of smokers. Therefore, the correct classification of tobacco stems and impurities is crucial. This study proposes a method based on hyperspectral image superpixels and the use of light gradient boosting machine (LightGBM) classifier to categorize tobacco stems and impurities. First, the hyperspectral image is segmented using superpixels. Second, the gray-level co-occurrence matrix extracts the texture features of superpixels. Subsequently, an improved LightGBM is applied and trained with the spectral and textural features of superpixels as a classification model. Several experiments were implemented to evaluate the performance of the proposed method. The results show that the classification performance based on superpixels is better than that based on single-pixel points. The classification model based on superpixels (10 × 10 px) achieved the highest impurity recognition rate (93.8%). This algorithm has already been applied to industrial production in cigarette factories. It exhibits considerable potential in overcoming the influence of interference fringes to promote the intelligent industrial application of hyperspectral imaging.
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Affiliation(s)
- Zhenye Li
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
| | - Chao Ni
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu 210037, China.
| | - Rui Wu
- Jiangsu Xinyuan Tobacco Sheet Co. LTD, Huaian, Jiangsu 223002, China
| | - Tingting Zhu
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu 210037, China.
| | - Lei Cheng
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
| | - Yangchun Yuan
- Jiangsu Xinyuan Tobacco Sheet Co. LTD, Huaian, Jiangsu 223002, China
| | - Chao Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
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2
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Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C. Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1073346. [PMID: 36968402 PMCID: PMC10030857 DOI: 10.3389/fpls.2023.1073346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
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Affiliation(s)
- Mingzheng Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
| | - Tian’en Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Xiaohe Gu
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Dong Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Cong Wang
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Wenbiao Wu
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Chunjiang Zhao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
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Geng Y, Ni H, Shen H, Wang H, Wu J, Pan K, Wu Y, Chen Y, Luo Y, Xu T, Liu X. Feasibility of an NIR spectral calibration transfer algorithm based on optimized feature variables to predict tobacco samples in different states. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:719-728. [PMID: 36722963 DOI: 10.1039/d2ay01805e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The prediction accuracy of calibration models for near-infrared (NIR) spectroscopy typically relies on the morphology and homogeneity of the samples. To achieve non-homogeneous tobacco samples for non-destructive and rapid analysis, a method that can predict tobacco filament samples using reliable models based on the corresponding tobacco powder is proposed here. First, as it is necessary to establish a simple and robust calibrated model with excellent performance, based on full-wavelength PLSR (Full-PLSR), the key feature variables were screened by three methods, namely competitive adaptive reweighted sampling (CARS), variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV), and variable combination population analysis-genetic algorithm (VCPA-GA). The partial least squares regression (PLSR) models for predicting the total sugar content in tobacco were established based on three optimal wavelength sets and named CARS-PLSR, VCPA-IRIV-PLSR and VCPA-GA-PLSR, respectively. Subsequently, they were combined with different calibration transfer algorithms, including calibration transfer based on canonical correlation analysis (CTCCA), slope/bias correction (S/B) and non-supervised parameter-free framework for calibration enhancement (NS-PFCE), to evaluate the best prediction model for the tobacco filament samples. Compared with the previous two transfer algorithms, NS-PFCE performed the best under various wavelength conditions. The prediction results indicated that the most successful approach for predicting the tobacco filament samples was achieved by VCPA-IRIV-PLSR when coupled with the NS-PFCE method, which obtained the highest determination coefficient (Rp2 = 0.9340) and the lowest root mean square error of the prediction set (RMSEP = 0.8425). VCPA-IRIV simplifies the calibration model and improves the efficiency of model transfer (31 variables). Furthermore, it pledges the prediction accuracy of the tobacco filament samples when combined with NS-PFCE. In summary, calibration transfer based on optimized feature variables can eliminate prediction errors caused by sample morphological differences and proves to be a more beneficial method for online application in the tobacco industry.
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Affiliation(s)
- Yingrui Geng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Hongfei Ni
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Huanchao Shen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Hui Wang
- Technology Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou 310008, China
| | - Jizhong Wu
- Technology Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou 310008, China
| | - Keyu Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Yongjiang Wu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Yong Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Yingjie Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Tengfei Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
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Pu H, Yu J, Sun DW, Wei Q, Shen X, Wang Z. Distinguishing Fresh and Frozen-thawed Beef Using Hyperspectral Imaging Technology Combined with Convolutional Neural Networks. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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5
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Zhou B, Liang YM, Bin J, Ding MJ, Yang M, Kang C. Rapid Determination of Phosphogypsum in Soil Based by Infrared (IR) and Near-Infrared (NIR) Spectroscopy with Multivariate Calibration. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2152829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Bo Zhou
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, China
| | - Yan-Mei Liang
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, China
| | - Jun Bin
- College of Tobacco Science, Guizhou University, Guiyang, China
| | - Meng-Jiao Ding
- College of Tobacco Science, Guizhou University, Guiyang, China
| | - Min Yang
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, China
| | - Chao Kang
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, China
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Liang Y, Zhao L, Guo J, Wang H, Liu S, Wang L, Chen L, Chen M, Zhang N, Liu H, Nie C. Just-in-Time Learning-Integrated Partial Least-Squares Strategy for Accurately Predicting 71 Chemical Constituents in Chinese Tobacco by Near-Infrared Spectroscopy. ACS OMEGA 2022; 7:38650-38659. [PMID: 36340111 PMCID: PMC9631892 DOI: 10.1021/acsomega.2c04139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Near-infrared spectroscopy has been widely used to characterize the chemical composition of tobacco because it is fast, economical, and nondestructive. However, few predictive models perform ideally when applied to large spectral libraries of tobacco and its various chemical indicators. In this study, the just-in-time learning-integrated partial least-squares (JIT-PLS) modeling strategy was applied for the first time to quantitatively analyze 71 chemical components in Chinese tobacco. Approximately 18000 tobacco samples from China were analyzed to find appropriately similar measurements and propose suitable and flexible similar subsets from the calibration for each test sample. In total, 879 representative aged tobacco leaf samples and 816 cigarette samples were used as external instances to evaluate the practical predicting ability of the proposed method. The most suitable similar subsets for each test sample could be selected by limiting the Euclidean distance and number of similar subsets to 0-3.0 × 10-9 and 10-300, respectively. The majority of the JIT-PLS models performed significantly better than traditional PLS models. Specifically, using JIT-PLS instead of traditional PLS models increased the R 2 values from 0.347-0.984 to 0.763-0.996, and from 0.179-0.981 to 0.506-0.989 for the prediction of 67 and 71 components in aged tobacco leaf and cigarette samples, respectively. Good prediction ability was demonstrated for routine chemical components, polyphenolic compounds, organic acids, and other compounds, with the mean ratios of prediction to deviation (RPDmean) being 7.74, 4.39, 4.05, and 5.48, respectively). The proposed methodology could simultaneously determine 67 major components in large and complicated tobacco spectral libraries with high precision and accuracy, which will assist tobacco and cigarette quality control in collecting as well as processing stages.
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Affiliation(s)
- Youyan Liang
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Le Zhao
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Junwei Guo
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Hongbo Wang
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Shaofeng Liu
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Luoping Wang
- Technology
Center of China Tobacco Yunnan Industrial Co. Ltd., Kunming650231, China
| | - Li Chen
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Mantang Chen
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Nuohan Zhang
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Huimin Liu
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
| | - Cong Nie
- Zhengzhou
Tobacco Research Institute of CNTC, Zhengzhou, Henan450001, China
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Rodrigues M, Berti de Oliveira R, Leboso Alemparte Abrantes Dos Santos G, Mayara de Oliveira K, Silveira Reis A, Herrig Furlanetto R, Antônio Yanes Bernardo Júnior L, Silva Coelho F, Rafael Nanni M. Rapid quantification of alkaloids, sugar and yield of tobacco (Nicotiana tabacum L.) varieties by using Vis-NIR-SWIR spectroradiometry. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121082. [PMID: 35248861 DOI: 10.1016/j.saa.2022.121082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/03/2022] [Accepted: 02/24/2022] [Indexed: 05/27/2023]
Abstract
Tobacco genetic improvement programs, as well as the tobacco industry, require techniques that allow the estimation of its attributes in a fast and cheap way. The use of remote sensing through visible, near infrared and short-wave spectroscopy (Vis-NIR-SWIR) has been studied aiming to meet such demand. Thus, the aim of this work was to evaluate the use of Vis-NIR-SWIR spectroradiometer as a rapid tool to estimate alkaloids, sugars and yield of tobacco varieties. For that purpose, a study was carried out in a greenhouse with plants grown in pots (18 dm-3) containing nutrient solutions. The experimental design was completely randomized, with 30 treatments (tobacco varieties) and 10 repetitions. Tobacco leaf reflectance was collected at 13, 34 and 68 days after transplantation (DAT) with a plant-probe device connected to the spectroradiometer by an optical fiber. Subsequently, leaf analysis of alkaloids, sugars and yield were performed, and such attributes were estimated by using the Partial Least Squares Regression (PLSR), combined with the following pre-processing (PP) techniques: multiplicative scatter correction (MSC), Savitzky-Golay (SG) and standard normal variate (SNV). The results showed presence of typical inflections of chemical and structural components of the plants, which allowed obtaining PLSR models with R2p and RPDp superior to 0.71 and 2.27, respectively, for all PP techniques and attributes evaluated. The most important wavelengths were well distributed within the three operating ranges of the spectroradiometer (Vis-NIR-SWIR). Thus, the methodology proposed by this research was able to simultaneously determine all the three attributes (alkaloids, sugars and yield) with excellent predictive capacity. This is a promising result for genetic improvement and processing of tobacco (as well as other crops), since it is necessary to evaluate a large number of samples within a short period and at a low cost.
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Affiliation(s)
- Marlon Rodrigues
- Department of Agronomy, State University of Maringá, Maringá, Brazil.
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A Long Short-Term Memory Neural Network Based Simultaneous Quantitative Analysis of Multiple Tobacco Chemical Components by Near-Infrared Hyperspectroscopy Images. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10050164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Near-infrared (NIR) spectroscopy has been widely used in agricultural operations to obtain various crop parameters, such as water content, sugar content, and different indicators of ripeness, as well as other potential information concerning crops that cannot be directly obtained by human observation. The chemical compositions of tobacco play an important role in the quality of cigarettes. The NIR spectroscopy-based chemical composition analysis has recently become one of the most effective methods in tobacco quality analysis. Existing NIR spectroscopy-related solutions either have relatively low analysis accuracy, or are only able to analyze one or two chemical components. Thus, a precise prediction model is needed to improve the analysis accuracy of NIR data. This paper proposes a tobacco chemical component analysis method based on a neural network (TCCANN) to quantitatively analyze the chemical components of tobacco leaves by using NIR spectroscopy, including nicotine, total sugar, reducing sugar, total nitrogen, potassium, chlorine, and pH value. The proposed TCCANN consists of both residual network (ResNet) and long short-term memory (LSTM) neural network. ResNet is applied to the feature extraction of high-dimension NIR spectroscopy, which can effectively avoid the gradient-disappearance issue caused by the increase of network depth. LSTM is used to quantitatively analyze the multiple chemical compositions of tobacco leaves in a simultaneous manner. LSTM selectively allows information to pass through by a gated unit, thereby comprehensively analyzing the correlation between multiple chemical components and corresponding spectroscopy. The experimental results confirm that the proposed TCCANN not only predicts the corresponding values of seven chemical components simultaneously, but also achieves better prediction performance than other existing machine learning methods.
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Arefi A, Sturm B, von Gersdorff G, Nasirahmadi A, Hensel O. Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.112297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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10
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Daikos O, Scherzer T. Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging. Talanta 2021; 221:121567. [PMID: 33076115 DOI: 10.1016/j.talanta.2020.121567] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/29/2020] [Accepted: 08/10/2020] [Indexed: 12/11/2022]
Abstract
Hyperspectral imaging was used for large-scale monitoring of the residual moisture in wide textile webs at the end of the drying process that follows their washing or finishing by impregnation in aqueous solutions or dispersions. Such data are essential for optimizing the energy efficiency and the precise control of the drying process. Quantitative analysis of the recorded spectral data was carried out with multivariate regression methods such as the partial least squares (PLS) algorithm. Reference data for calibration of the prediction models were determined by gravimetry. The drying of textile materials from both natural or synthetic fibers possessing different water absorption capacities (cotton, polyamide, polyester), which were partially finished with an optical brightener, was investigated. Moisture contents in the range from 0 to about 12 wt% were considered in the calibration models. For all systems, the root mean square error of prediction (RMSEP) for the residual moisture was found to be about 0.5 wt%, that is, about 1 g/m2. In addition to the quantitative determination of the water content, hyperspectral imaging provides detailed information about its spatial distribution across the textile web, which may help to improve the control of the drying process. In particular, it was demonstrated that the developed methods were capable of detecting and visualizing inhomogeneous moisture distributions. Averaging of the individual values of the moisture content predicted from all spectra across the surface of the textile samples resulted in a very close correlation with the corresponding gravimetric reference values. Due to the averaging process, the difference between both values is generally lower than RMSEP even in case of samples with inhomogeneous distribution of the moisture. The high precision and the broad capabilities of the developed analytic methods for in-line monitoring of the moisture content hold the potential for an efficient process control in technical textile converting processes.
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Affiliation(s)
- Olesya Daikos
- Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany
| | - Tom Scherzer
- Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany.
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Mansoldo FRP, Cardoso VDS, Neves Junior A, Cedrola SML, Maricato V, Rosa MDSS, Vermelho AB. Quantification of schizophyllan directly from the fermented broth by ATR-FTIR and PLS regression. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:5468-5475. [PMID: 33141124 DOI: 10.1039/d0ay01585g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Non-destructive methods that allow the quantification of bioproducts in a simple and quick manner during fermentation are extremely desirable from a practical point of view. Therefore, a 9 day fermentation experiment with Schizophyllum commune was carried out to investigate the possibility of using ATR-FTIR to quantify the schizophyllan biopolymer (SPG) directly from the culture medium. On each day, aliquots of the fermentation were taken, and the cell-free supernatant was analyzed by ATR-FTIR. The main objective of this step was to evaluate whether FTIR would be able to detect the appearance of specific peaks related to the production of SPG. The results of the PCA analysis showed that there was a reasonable separation of the days through the FTIR spectra. Then PCA-LDA was applied to the same dataset, which confirmed the formation of groups for each day of fermentation, after which, a calibration and test set was developed. Through a matrix generated by an experimental design with 2 factors and 5 levels, 25 samples were created with variations in the concentration of the culture medium and SPG. The ATR-FTIR spectra of this data set were modeled using PLS regression with backward selection of predictors. The results revealed that the amount of SPG produced can be quantified directly in the culture medium with excellent precision with R2CV = 0.951, R2P = 0.970, RMECV = 0.205 g, RMSEP = 0.170 g, RPDcv = 4.53 and RPDp = 5.88. The traditional method to quantify SPG is time consuming, requires several steps and uses solvents. In contrast, the method proposed in this work is a viable, faster, and a simpler alternative, which does not use reagents and does not require extensive pre-treatment of the samples.
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Affiliation(s)
- Felipe Raposo Passos Mansoldo
- Federal University of Rio de Janeiro (UFRJ), Institute of Microbiology Paulo de Góes, BIOINOVAR - Biocatalysis, Bioproducts and Bioenergy, Rio de Janeiro, Brazil.
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12
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Xu L, Shi Q, Lu D, Wei L, Fu HY, She Y, Xie S. Simultaneous detection of multiple frauds in kiwifruit juice by fusion of traditional and double-quantum-dots enhanced fluorescent spectroscopic techniques and chemometrics. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105105] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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