Proceedings of the 18th International Conference on Near Infrared Spectroscopy最新文献

{"title":"Monitoring of the application of laminating adhesives to polyurethane foam by near infrared chemical imaging","authors":"G. Mirschel, Olesya Daikos, C. Steckert, T. Scherzer","doi":"10.1255/nir2017.163","DOIUrl":"https://doi.org/10.1255/nir2017.163","url":null,"abstract":"Author Summary: Near infrared chemical imaging was used for the quantitative determination of the application weight of hot-melt adhesive layers laminated to polyether-based polyurethane (PUR) foam as well as for monitoring of its spatial distribution across the surface of the foam. Adhesive layers were applied as melt-spun fibrous webs of a thermoplastic polyester with application weights between about 20 g m–2 and 130 g m–2. In spite of the coarsely porous surface structure of the foam, RMSEP was found to be 3.8 g m–2 only. Chemical images of the adhesive layers revealed that a minimum of two adhesive webs corresponding to an application weight of about 40 g m–2 are required for the formation of a continuous layer without holes. Average application weights derived from the chemical images have shown an excellent correlation with gravimetric reference values.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89801629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First trial with an all-fibre near infrared spectrometer evaluated by multivariate curve resolution","authors":"T. Ringsted, B. Robotham, J. Buron, K. Sørensen, T. Nikolajsen, S. Engelsen","doi":"10.1255/nir2017.169","DOIUrl":"https://doi.org/10.1255/nir2017.169","url":null,"abstract":"Author Summary: A novel all-fibre spectrometer with theoretical advantages for industrial process measurements was tested on simulated process samples of water with sucrose. The spectrometer uses the dispersive Fourier transform method and combines a supercontinuum laser with a time-wavelength separating optical fibre. Multivariate curve resolution was used to resolve the pure spectra and concentration profiles of sucrose and water from spectra of solutions with both constituents. The new spectrometer has great potential for rapid and vibration-robust measurements, but further studies are needed to determine its performance on more complex and real process samples.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79593181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance comparison of bench-top, hyperspectral imaging and pocket near infrared spectrometers: the example of protein quantification in wheat flour","authors":"V. Baeten, J. Pierna, P. Vermeulen, B. Lecler, O. Minet, D. Zió, P. Dardenne","doi":"10.1255/NIR2017.151","DOIUrl":"https://doi.org/10.1255/NIR2017.151","url":null,"abstract":"Author Summary: There have been a number of recent developments in NIR technology which may prove to be significant in the near future. The overall objective of this work is to compare the performance of different NIR devices (i.e. bench-top, hyperspectral imaging and pocket NIR spectrometers) for the determination of a classical quality parameter, the protein content. In order to achieve this goal, several studies have been carried out with the different instrumentation on wheat flours (selected as a “homogeneous” product). These studies should determine if NIR hyperspectral imaging and NIR pocket instruments are as efficient as classical NIR bench-top instruments for predicting protein content.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81312240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotypic classification of sugarcane from near infrared spectra obtained directly from stalk using ordered predictors selection and partial least squares-discriminant analysis","authors":"L. Peternelli, M. H. Barbosa, J. Roque, R. Teófilo","doi":"10.1255/NIR2017.157","DOIUrl":"https://doi.org/10.1255/NIR2017.157","url":null,"abstract":"Author Summary: A new method was developed for the early selection of sugarcane genotypes using near infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA) and a variable selection method named ordered predictors selection (OPS). The use of the OPS method improved the predictive capacity of PLS-DA models to classify the sugarcane samples correctly according to fiber content (FC) and pol percent (PP).","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78855829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate data analysis of near-infrared spectra of cultivation medium powders for mammalian cells","authors":"É. Szabó, S. Gergely, A. Salgó","doi":"10.1255/NIR2017.143","DOIUrl":"https://doi.org/10.1255/NIR2017.143","url":null,"abstract":"Author Summary: Nowadays, qualification and control of medium formulations is performed based on simple methods (e.g. pH and osmolality measurement of media solutions), expensive and time-consuming cell culture tests, and quantification of some critical compounds by liquid chromatography. Besides the traditional medium qualification tools, relatively new spectroscopic techniques such as fluorescence spectroscopy, nuclear magnetic resonance, Raman and NIR spectroscopies or a combination of these techniques are increasingly being applied for cultivation medium powder investigation. A chemically defined cultivation medium powder for Chinese hamster ovary (CHO) cell cultivation was investigated in this study, regarding its response to heat treatments with different temperatures (30n°C, 50n°C and 70n°C). The heat treatments were performed according to a design of experiments (DoE) approach. Spectra of the control and the treated powders were collected to compare the sample groups using a dispersive near-infrared (NIR) and a Fourier-transform near-infrared (FT-NIR) spectrometer. Multivariate data analysis including unsupervised (cluster analysis, principal component analysis, polar qualification system) and supervised classification methods (linear discriminant analysis, soft independent modelling of class analogies and partial least squares discriminant analysis) were employed to investigate the monitoring capability of near-infrared spectroscopy for qualification of cultivation medium powder stored at different temperatures, to identify the treatment-induced variations in the samples, and compare the efficiency of spectrometers with distinct optical arrangements (i.e. dispersive and Fourier transformed spectrometers). During heat treatment, cultivation medium powders went through spectroscopically recognisable changes. Both NIR and FT-NIR analysis could separate samples according to the temperature set-points, irrespective of spectrometer attributes. In classification of samples cluster analysis and linear discriminant analysis shown the best results for both NIR and FT-NIR spectra","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81381773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The unreasonable effectiveness of near infrared spectroscopy explained. Introducing the era of NIRS integrated\u0000functional fingerprinting","authors":"L. Munck","doi":"10.1255/NIR2017.105","DOIUrl":"https://doi.org/10.1255/NIR2017.105","url":null,"abstract":"Author Summary: Near infrared (NIR) spectral patterns from seeds carry surprisingly consistent information on functional food quality that seems almost unreasonable. In the developing cereal seed, all genes interact with all other active genes. This is proven by manipulating the immense complexity of physiological expression by single seed mutants in a barley seed model at a constant gene background/environment. Miraculously, it is possible to get a causal relation between the mutated gene and its NIR spectral pattern. When combining under controlled conditions, Nature’s and the near infrared spectroscopy (NIRS) instrument’s combined ability to reproduce the physiochemical composition of a seed population of a DNA-specific genotype, we obtain at stunning absorption log 1/R 10–4–10–5 mean distance between two similar single seed spectra. The deterministic gene/genotype-specific NIRS patterns from single seeds/populations and the corresponding deterministic metabolite patterns are interpreted, in the light of the present achievements in molecular and quantum biology, as a discovery of a global decision mechanism by “set probability”. It involves a global cell/seed/plant communication resulting in individual specific NIRS and chemical metabolic patterns that prevail on all levels of the phenotype. This ensnaring phenomenon is here labelled as “biological entanglement” with a possible explanation in the quantum world of physics. The observed gene interaction leading to conserved physiochemical fingerprints has consequences with regard to the assessment and breeding for cereal food quality. Only by targeting the food functional complex by a spectral pattern from a certified quality cereal line, that includes an overview of gene interaction, is it possible to get the selection target right from the beginning. Selection by functional NIR spectral fingerprinting first is more effective than by quantitative trait locus (QTL) markers for single quantitative traits, because these DNA markers do not include information on the global gene interaction. The cost of using integrated NIRS breeding is less than one per cent of the costs for molecular QTL breeding. For a new NIRS fingerprinting culture to take on in industry, plant breeding and basic science—an inverse engineering strategy is needed with a library for functional fingerprint evaluation that builds a confidence into each user based on own experience, on how to read the meaning out of every single NIR spectrum.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89897466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending predictions of soil parameters for a national dataset","authors":"A. Robertson, E. Perez-Fernandez, N. Baggaley, B. McKenzie, I. J. Owen, A. Lilly","doi":"10.1255/NIR2017.123","DOIUrl":"https://doi.org/10.1255/NIR2017.123","url":null,"abstract":"Author Summary: The National Soil Inventory of Scotland (NSIS) is an objective dataset and represents the most common soil types across Scotland. The soils in this unique dataset have been extensively characterised, with a wide range of chemical and physical parameters measured, including the near infrared reflectance (NIR) spectra. Significantly, the parameters measured have often been carried using more than one analytical method. In this work, we are looking to build on previously developed NIR calibrations for prediction of soil parameters and extend the useful information that can be gained from the NIR soil spectral data. We have examined in more detail the differences in the NIR correlations between elemental concentrations for some of the soil nutrients (Al, Fe, K, Mg, Mn, Na and P) measured in different ways. In addition, we are increasing the range of parameters used for calibration development to ones more directly linked to soil function. Here results are reported for correlations to soil aggregate stability data, which we are looking to apply to improving prediction of erosion risk.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77548480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian and partial least square global forage calibrations models developed by an iterative procedure using\u0000R","authors":"A. Ferragina, F. Benozzo, P. Berzaghi","doi":"10.1255/NIR2017.051","DOIUrl":"https://doi.org/10.1255/NIR2017.051","url":null,"abstract":"Author Summary: The aim of our study was to test an iterative process of validation implemented in the R software, assessing the accuracy of the best selected equations, developed using two different regression algorithms Partial Least Square (PLS) and Bayesian. A data set (Seta) with 3187 records of 6 different types of forages was used. The calibrations were tested for Protein, Neutral Detergent Fiber and Acid Detergent Fiber. For each sample a spectrum was collected using a FOSS NIRSystem (1100–2498 nm). A subset composed of 20 samples for each type of forage (Setext;120 samples) was randomly selected for a final validation of the best selected equations. The remaining samples (Setb = Seta – Setext) were used for the iterative calibration process. For each iteration the Setb was randomly divided in a testing set (Settst; 10 % of Setb) and a training set (Settrn = Setb – Settst); 300 iterations were done. All of the computations were done in the R environment. The packages used were “pls” for the PLS, “BGLR” for the Bayesian, “prospectr” for the spectral treatments. In each iteration we used three spectral treatments (raw, 1 derivative, standard normal variate and detrend), two approaches for selection of the optimal number of PLS components and the Bayesian model. Nine types of equations were developed and tested in each iteration [(2 PLS techniques + 1 Bayesian) × 3 spectral treatments]. Among the 300 iterations, for each one of the 9 equation types, the best one (lowest RMSE) and the average of the best 25 % (RMSE < 1 quartile) were selected and validated by forage type. R has demonstrated its potential when used for the chemiometric process on big data set and with complex statistical procedures. R2 higher than 0.9 was obtained for almost all the calibrations. In the external validation the Bayesian models in many cases outperform the commonly used PLS, demonstrating that an alternative for the improvement of the prediction accuracy exists. The present work has demonstrated that iterative validation subsampling on big data can lead to the selection of proper equations, and it can be done using R.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90561768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-destructive determination of silymarin in Silybum marianum extracts with a handheld near infrared\u0000spectrometer","authors":"Hui Yan, M. Liang, Ming-jian Jianga, H. Siesler","doi":"10.1255/NIR2017.135","DOIUrl":"https://doi.org/10.1255/NIR2017.135","url":null,"abstract":"Author Summary: The main objective of this study is to provide a potential method of detecting the silymarin in Silybum marianum extracts. Near infrared (NIR) spectroscopy and a handheld NIR spectrometer was used for the first time to detect the Silybum marianum extracts. A total of 80 samples were collected. The content of silymarin was determined in all samples by high-performance liquid chromatography. Then 56 samples were selected for the development of a calibration model, and the other 24 samples were used as test set. The diffuse reflection spectra acquired by the handheld MicroNIR 1700 spectrometer were pretreated by standard normal variate, and then a partial least squares regression model was developed. The results showed that the optimum number of factors was four. For calibration, cross validation (CV) and test set, the slopes were all larger than 0.98, the R2 values were 0.9874, 0.9839 and 0.9888, respectively, the root mean square error of calibration, root mean square error of CV and root mean square error of prediction were 1.06 %, 1.25 % and 0.98 %, respectively, and the residual predictive deviation was 7.56, which indicated that the calibration model was perfect and has high precision predictive ability. The overall results of this study revealed the feasibility for use of handheld NIR spectroscopy as a non-destructive method for the quantitative on-site determination of the silymarin content in Silybum marianum extracts.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74640013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Fourier transform near infrared spectroscopy for the detection of residues from wood processing industry in\u0000the pellet sector","authors":"M. Mancini, Å. Rinnan, A. Pizzi, G. Toscano","doi":"10.1255/NIR2017.077","DOIUrl":"https://doi.org/10.1255/NIR2017.077","url":null,"abstract":"Author Summary: With the aim to reduce the dependence on fossil fuels and mitigate climate change, biomass for energy use is becoming more and more important. In particular, wood pellets are gaining greater attention because of the easy logistics and their high energy density in comparison to other solid biomasses. This is also demonstrated by the rapid growth of its demand in Europe. For pellets, traceability is a very important and complex issue, since the feedstock employed is de-structured by grinding and densification and thus losing qualitative information. As a consequence, a multitude of wood sources can participate to their blend in a concealed way, modifying the quality. The international standard EN ISO 17225–2 defines different quality classes for woody pellets taking into consideration chemical-physical parameters and the provenance traceability and composition of the material. In particular, the European standard considers the possibility of using by-products and residues from the wood processing industry, i.e. wood containing glue residues, for pellet production, but Italian national legislation considered these materials like waste. This work aimed at verifying the ability of Fourier Transform–Near-Infrared (FT-NIR) spectroscopy to discriminate between treated and virgin wood. For this purpose, more than one hundred samples of virgin and treated wood deriving from the wood processing industry were collected and analyzed by FT-NIR. The results obtained showed that this technique is able to provide qualitative information about pellet traceability. Therefore, the methodology should be considered as a valid tool for pellet quality control, because it allows to obtain information about the origin of the material used for its production.","PeriodicalId":20429,"journal":{"name":"Proceedings of the 18th International Conference on Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83371467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}