Pub Date : 2021-03-01DOI: 10.1177/09603360211004069
C. Huck
Nutrient-poor, serpentinitic soils in the San Francisco Bay area sustain a native grassland that supports many rare species, including the Bay checkerspot butterfly (Euphydryas editha bayensis). Nitrogen (N) deposition from air pollution threatens biodiversity in these grasslands because N is the primary limiting nutrient for plant growth on serpentinitic soils. I investigated the role of N deposition through surveys of butterfly and plant populations across different grazing regimes by literature review, and with estimates of N deposition in the region. Several populations of the butterfly in south San Jose crashed following the cessation of cattle grazing. Nearby populations under continued grazing did not suffer similar declines. The immediate cause of the population crashes was rapid invasion by introduced annual grasses that crowded out the larval host plants of the butterfly. Ungrazed serpentinitic grasslands on the San Francisco Peninsula have largely resisted grass invasion for nearly four decades. Several lines of evidence indicate that dry N deposition from smog is responsible for the observed grass invasion. Fertilization experiments have shown that soil N limits grass invasion in serpentinitic soils. Estimated N deposition rates in south San Jose grasslands are 10-15 kg N/ha/year; Peninsula sites have lower deposition, 4-6 kg N/ha/year. Grazing cattle select grasses over forbs, and grazing leads to a net export of N as cattle are removed fro slaughter. Although poorly managed cattle grazing can significantly disrupt native ecosystems, in this case moderate, well-managed grazing is essential for maintaining native biodiversity in the face of invasive species and exogenous inputs of N from nearby urban areas.
旧金山海湾地区营养贫乏的蛇纹石土壤维持着原生草原，支持着许多稀有物种，包括海湾斑纹蝴蝶(Euphydryas editha bayensis)。空气污染造成的氮沉降威胁着这些草原的生物多样性，因为氮是蛇纹石土壤中植物生长的主要限制养分。我通过对不同放牧制度下的蝴蝶和植物种群的调查，以及对该地区N沉积的估计，研究了N沉积的作用。在停止放牧之后，圣何塞南部的几个蝴蝶种群崩溃了。附近持续放牧的种群数量没有出现类似的下降。导致蝴蝶数量锐减的直接原因是一年生草的迅速入侵，挤占了蝴蝶的幼虫寄主植物。近四十年来，旧金山半岛上未被放牧的蛇形草地在很大程度上抵御了草的入侵。一些证据表明，来自雾霾的干氮沉降是观测到的草入侵的原因。施肥试验表明，土壤氮限制了蛇纹岩土壤中草的入侵。估计南圣何塞草原的N沉积速率为10 ~ 15 kg N/ha/年;半岛的沉积物较少，为4-6千克N/公顷/年。放牧的牛选择草而不是草，放牧导致氮的净输出，因为牛被移出屠宰场。尽管管理不善的放牧会严重破坏本地生态系统，但在这种情况下，面对入侵物种和来自附近城市地区的外源氮输入，适度、管理良好的放牧对于维持本地生物多样性至关重要。
{"title":"Selected References","authors":"C. Huck","doi":"10.1177/09603360211004069","DOIUrl":"https://doi.org/10.1177/09603360211004069","url":null,"abstract":"Nutrient-poor, serpentinitic soils in the San Francisco Bay area sustain a native grassland that supports many rare species, including the Bay checkerspot butterfly (Euphydryas editha bayensis). Nitrogen (N) deposition from air pollution threatens biodiversity in these grasslands because N is the primary limiting nutrient for plant growth on serpentinitic soils. I investigated the role of N deposition through surveys of butterfly and plant populations across different grazing regimes by literature review, and with estimates of N deposition in the region. Several populations of the butterfly in south San Jose crashed following the cessation of cattle grazing. Nearby populations under continued grazing did not suffer similar declines. The immediate cause of the population crashes was rapid invasion by introduced annual grasses that crowded out the larval host plants of the butterfly. Ungrazed serpentinitic grasslands on the San Francisco Peninsula have largely resisted grass invasion for nearly four decades. Several lines of evidence indicate that dry N deposition from smog is responsible for the observed grass invasion. Fertilization experiments have shown that soil N limits grass invasion in serpentinitic soils. Estimated N deposition rates in south San Jose grasslands are 10-15 kg N/ha/year; Peninsula sites have lower deposition, 4-6 kg N/ha/year. Grazing cattle select grasses over forbs, and grazing leads to a net export of N as cattle are removed fro slaughter. Although poorly managed cattle grazing can significantly disrupt native ecosystems, in this case moderate, well-managed grazing is essential for maintaining native biodiversity in the face of invasive species and exogenous inputs of N from nearby urban areas.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114560599","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}
Pub Date : 2021-03-01DOI: 10.1177/09603360211003757
J. Grabska, K. Beć, C. Huck
Modern forensics encounters new challenges and demands new analytical methods that would meet variety of prerequisites regarding their accuracy, rapidness, flexibility, and reliability. Vibrational spectroscopic methods, in particular near-infrared spectroscopy, offer such potential and meet an increasing interest in forensics for authentication of various documents. Pittcon 2020 Conference, which took place in Chicago, Illinois, included a Session dedicated to the role of novel tools of investigation in the forensics of tomorrow. This article summarizes and complements the presentation upon how the current state-of-the-art and future prospects of vibrational spectroscopic techniques fits into this role. The application of near-infrared spectroscopy, including the benefits stemming from using novel miniaturized portable instruments, Raman and surface-enhanced Raman scattering techniques, is discussed in detail in the present article.
{"title":"Novel near-infrared and Raman spectroscopic technologies for print and photography identification, classification, and authentication","authors":"J. Grabska, K. Beć, C. Huck","doi":"10.1177/09603360211003757","DOIUrl":"https://doi.org/10.1177/09603360211003757","url":null,"abstract":"Modern forensics encounters new challenges and demands new analytical methods that would meet variety of prerequisites regarding their accuracy, rapidness, flexibility, and reliability. Vibrational spectroscopic methods, in particular near-infrared spectroscopy, offer such potential and meet an increasing interest in forensics for authentication of various documents. Pittcon 2020 Conference, which took place in Chicago, Illinois, included a Session dedicated to the role of novel tools of investigation in the forensics of tomorrow. This article summarizes and complements the presentation upon how the current state-of-the-art and future prospects of vibrational spectroscopic techniques fits into this role. The application of near-infrared spectroscopy, including the benefits stemming from using novel miniaturized portable instruments, Raman and surface-enhanced Raman scattering techniques, is discussed in detail in the present article.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115046521","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}
Pub Date : 2021-03-01DOI: 10.1177/09603360211003817
M. Saranwong, T. Fearn
{"title":"Announcement from ICNIRS for the International Conference of NIRS 2021, Beijing, China","authors":"M. Saranwong, T. Fearn","doi":"10.1177/09603360211003817","DOIUrl":"https://doi.org/10.1177/09603360211003817","url":null,"abstract":"","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121940477","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}
Pub Date : 2021-03-01DOI: 10.1177/09603360211003752
K. Beć, J. Grabska, C. Huck
The instrumentation, methods and applications of near-infrared spectroscopy has advanced remarkably in the last decade, in which near-infrared spectroscopy has successfully progressed at multiple directions and faced new challenges. Thus, gaps inevitably appeared in the coverage provided by renowned and handy cornerstone textbooks focused on near-infrared spectroscopy that were published in the past. A demand grew in near-infrared spectroscopy community for a new state-of-the-art textbook. With aim to satisfy such need, a go-to-book for background theory, applications and tutorial “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications” was prepared. That full-scale project, edited by Yukihiro Ozaki, Christian Huck, Satoru Tsuchikawa and Søren B. Engelsen, comprises of 23 chapters contributed by scholars and practitioners pushing the frontier of near-infrared spectroscopy. The chapters scope on newly opened pathways, major breakthroughs in basic science and applications as well as revisit several other topics. The sourcebook is intended for a wide range of readers from graduate students to scientists and engineers in both academia and industry. In this article, we sketch the main features of the newly released sourcebook with aim to help the community members in deciding whether this book should find its place in their library.
近十年来，近红外光谱的仪器、方法和应用都有了长足的发展，近红外光谱在多个方向上取得了成功，也面临着新的挑战。因此，过去出版的以近红外光谱学为重点的著名和方便的基石教科书所提供的覆盖范围不可避免地出现了空白。近红外光谱学界对新教科书的需求日益增长。为了满足这一需求，我们编写了《近红外光谱理论、光谱分析、仪器和应用》的背景理论、应用和教程。这个全面的项目由尾崎行弘、克里斯蒂安·哈克、土川Satoru和Søren B. Engelsen编辑，由推动近红外光谱前沿的学者和实践者贡献的23章组成。章节范围在新开辟的途径，在基础科学和应用的重大突破，以及重新审视其他几个主题。该源手册的目的是为广泛的读者从研究生科学家和工程师在学术界和工业界。在本文中，我们概述了新发布的源代码的主要特性，目的是帮助社区成员决定是否应该在他们的图书馆中找到这本书的位置。
{"title":"The comprehensive sourcebook for modern NIR spectroscopy: A commentary on “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications”","authors":"K. Beć, J. Grabska, C. Huck","doi":"10.1177/09603360211003752","DOIUrl":"https://doi.org/10.1177/09603360211003752","url":null,"abstract":"The instrumentation, methods and applications of near-infrared spectroscopy has advanced remarkably in the last decade, in which near-infrared spectroscopy has successfully progressed at multiple directions and faced new challenges. Thus, gaps inevitably appeared in the coverage provided by renowned and handy cornerstone textbooks focused on near-infrared spectroscopy that were published in the past. A demand grew in near-infrared spectroscopy community for a new state-of-the-art textbook. With aim to satisfy such need, a go-to-book for background theory, applications and tutorial “Near-Infrared Spectroscopy Theory, Spectral Analysis, Instrumentation, and Applications” was prepared. That full-scale project, edited by Yukihiro Ozaki, Christian Huck, Satoru Tsuchikawa and Søren B. Engelsen, comprises of 23 chapters contributed by scholars and practitioners pushing the frontier of near-infrared spectroscopy. The chapters scope on newly opened pathways, major breakthroughs in basic science and applications as well as revisit several other topics. The sourcebook is intended for a wide range of readers from graduate students to scientists and engineers in both academia and industry. In this article, we sketch the main features of the newly released sourcebook with aim to help the community members in deciding whether this book should find its place in their library.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176551","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}
Pub Date : 2021-03-01DOI: 10.1177/09603360211003814
J. Riu, Giulia Gorla, B. Giussani
Near-infrared spectroscopy using benchtop instrumentation is widely used in the analysis of dairy products or in the dairy industry. In this paper, we review the use of miniaturized near-infrared instrumentation in dairy products or in the dairy industry, highlighting some strengths and limitations of current devices.
{"title":"Miniaturized near-infrared instruments in dairy products or dairy industry: First steps in a long-distance race?","authors":"J. Riu, Giulia Gorla, B. Giussani","doi":"10.1177/09603360211003814","DOIUrl":"https://doi.org/10.1177/09603360211003814","url":null,"abstract":"Near-infrared spectroscopy using benchtop instrumentation is widely used in the analysis of dairy products or in the dairy industry. In this paper, we review the use of miniaturized near-infrared instrumentation in dairy products or in the dairy industry, highlighting some strengths and limitations of current devices.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128737343","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}
Pub Date : 2021-03-01DOI: 10.1177/09603360211003758
H. Martens
NIR process monitoring and NIR hyperspectral video generates a deluge of non-selective spectral data, information-rich but per se useless. This paper demonstrates how interpretable data modelling can lead to simpler and better use of such NIR Big Data: A set of simple powder mixtures of the main constituents in wheat flour were measured by NIR transmission under different measurement conditions. Their absorbance spectra were submitted to multivariate calibration for predicting the protein content, by standard chemometric calibration by PLS regression. A reasonable calibration model was obtained, but it was unexpectedly complex and not robust. However, closer inspection the PLS regression subspace showed a surprising structure. This allowed us to identify the problem: Non-additive, strongly overlapping light scattering and light absorption effects in the NIR absorbance spectra. Based on this insight, a pragmatic, but causal preprocessing model was set up and iteratively optimized for predictive ability. This nonlinear optimized extended signal correction (OEMSC) separated and quantified the main physical and chemical sources of variation in the spectra. The preprocessing greatly simplified the NIR spectra and their quantitative calibration and prediction.
{"title":"Understanding the root cause(s) of nonlinearities in near infrared spectroscopy","authors":"H. Martens","doi":"10.1177/09603360211003758","DOIUrl":"https://doi.org/10.1177/09603360211003758","url":null,"abstract":"NIR process monitoring and NIR hyperspectral video generates a deluge of non-selective spectral data, information-rich but per se useless. This paper demonstrates how interpretable data modelling can lead to simpler and better use of such NIR Big Data: A set of simple powder mixtures of the main constituents in wheat flour were measured by NIR transmission under different measurement conditions. Their absorbance spectra were submitted to multivariate calibration for predicting the protein content, by standard chemometric calibration by PLS regression. A reasonable calibration model was obtained, but it was unexpectedly complex and not robust. However, closer inspection the PLS regression subspace showed a surprising structure. This allowed us to identify the problem: Non-additive, strongly overlapping light scattering and light absorption effects in the NIR absorbance spectra. Based on this insight, a pragmatic, but causal preprocessing model was set up and iteratively optimized for predictive ability. This nonlinear optimized extended signal correction (OEMSC) separated and quantified the main physical and chemical sources of variation in the spectra. The preprocessing greatly simplified the NIR spectra and their quantitative calibration and prediction.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128428059","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}
Pub Date : 2021-03-01DOI: 10.1177/09603360211003755
R. Calvini, G. Foca, A. Ulrici
Following the previous papers of our colleagues from the University of Genova and from the University of Rome “La Sapienza” in the series of articles presenting the Italian research groups active in the field of NIR spectroscopy, this paper aims at introducing the main activities of the Chemometrics, Imaging and Spectroscopy Laboratory (CHIMSLAB) of the University of Modena and Reggio Emilia. The group is headed by Prof. Alessandro Ulrici, associate professor in Analytical Chemistry and Coordinator of the Research Doctorate School in Food and Agricultural Science, Technology and Biotechnology (STEBA) of the University of Modena and Reggio Emilia. CHIMSLAB team also includes Dr. Giorgia Foca, as assistant professor, and Dr. Rosalba Calvini, as post-doc researcher. We would also like to mention our former PhD students, Dr. Carlotta Ferrari and Dr. Giorgia Orlandi, who gave a fundamental contribution to our recent activities. In addition, in 2017, we had the pleasure to host Prof. Sylvio Barbon Junior (Computer Science Department, Londrina State University) and Dr. Ana Paula A. C. Barbon (Animal Science Department, Londrina State University) as visiting researchers. CHIMSLAB research group is also affiliated to BIOGEST-SITEIA, the interdepartmental research centre of the University of Reggio Emilia working on the improvement and valorisation of agri-food biological resources. The keywords in the group name recall our main research activities: the application and development of chemometric methods for data modelling with a specific interest in spectroscopic and imaging data. In particular, the application of near infrared (NIR) spectroscopy, computer vision and NIR hyperspectral imaging (NIR-HSI) in the agri-food sector represents a considerable part of our expertise. However, thanks to collaborations with other research groups, we had the possibility of applying chemometric modelling to a wide range of research fields, including cultural heritage, electrochemical sensing, microbiology and entomology, among others. Concerning our research topics of main interest for the readers of NIR News, in the past years, we focused on two key aspects of spectroscopic and imaging data analysis: variable selection and data dimensionality reduction. Feature selection is a crucial aspect in the analysis of spectroscopic signals, since the selection of the spectral regions of interest for the problem at hand usually allows to discard noise and to obtain calibration or classification models with higher performances. For these reasons, starting from the beginning of our research activities, the application of state-of-art variable selection methods and the development of new selection strategies have represented key topic of our work. The importance of variable selection methods is even more relevant when dealing with NIR hyperspectral images. Indeed, usually, industrial applications require sorting technologies meeting the requirements of fast time of analysis and low
继热那亚大学和罗马大学“La Sapienza”的同事在一系列介绍活跃在近红外光谱领域的意大利研究小组的文章之后，本文旨在介绍摩德纳大学和雷焦艾米利亚大学化学计量学，成像和光谱实验室(CHIMSLAB)的主要活动。该小组由Alessandro Ulrici教授领导，他是摩德纳大学和Reggio Emilia大学食品和农业科学、技术和生物技术研究博士学院(STEBA)的分析化学副教授和协调员。CHIMSLAB团队还包括助理教授Giorgia Foca博士和博士后研究员Rosalba Calvini博士。我们还要提到我们以前的博士生，Carlotta Ferrari博士和Giorgia Orlandi博士，他们为我们最近的活动作出了根本性的贡献。此外，在2017年，我们有幸接待了Sylvio Barbon Junior教授(Londrina State University计算机科学系)和Ana Paula A. C. Barbon博士(Londrina State University动物科学系)作为访问研究人员。CHIMSLAB研究小组还隶属于Reggio Emilia大学的跨部门研究中心BIOGEST-SITEIA，致力于农业食品生物资源的改善和增值。小组名称中的关键词回顾了我们的主要研究活动:化学计量学方法在数据建模中的应用和发展，对光谱和成像数据特别感兴趣。特别是，近红外(NIR)光谱，计算机视觉和近红外高光谱成像(NIR- hsi)在农业食品领域的应用代表了我们专业知识的相当一部分。然而，由于与其他研究小组的合作，我们有可能将化学计量学建模应用于广泛的研究领域，包括文化遗产，电化学传感，微生物学和昆虫学等。对于NIR新闻读者感兴趣的研究课题，在过去的几年里，我们专注于光谱和成像数据分析的两个关键方面:变量选择和数据降维。特征选择是光谱信号分析中的一个关键方面，因为对手头问题感兴趣的光谱区域的选择通常可以丢弃噪声并获得具有更高性能的校准或分类模型。因此，从我们的研究活动开始，应用最先进的变量选择方法和开发新的选择策略一直是我们工作的重点。在处理近红外高光谱图像时，变量选择方法的重要性更为重要。实际上，工业应用通常要求分选技术满足快速分析时间和低成本的要求。因此，变量选择通常应用于在实验室尺度上获得的高光谱数据，以便找到与手头问题相关的少数波长，以便在更快更便宜的多光谱成像系统中实现。在这方面，最近与Jose Amigo教授(巴斯克地区大学)和caffe Molinari S.p.A.的合作旨在促进基于仅四个波长的多光谱成像系统的实施，用于对阿拉比卡和罗布斯塔绿咖啡豆进行分类。通过将基于稀疏的变量选择方法应用于高光谱数据来选择四个波长，我们模拟的关键方面在于从四个波长处注册的反射率值中识别相关描述符，以获得与高光谱成像系统相似的分类性能。我们研究活动的第二个关键主题涉及数据维度方法的发展
{"title":"Chemometrics, imaging and spectroscopy laboratory – Department of Life Sciences, University of Modena and Reggio Emilia","authors":"R. Calvini, G. Foca, A. Ulrici","doi":"10.1177/09603360211003755","DOIUrl":"https://doi.org/10.1177/09603360211003755","url":null,"abstract":"Following the previous papers of our colleagues from the University of Genova and from the University of Rome “La Sapienza” in the series of articles presenting the Italian research groups active in the field of NIR spectroscopy, this paper aims at introducing the main activities of the Chemometrics, Imaging and Spectroscopy Laboratory (CHIMSLAB) of the University of Modena and Reggio Emilia. The group is headed by Prof. Alessandro Ulrici, associate professor in Analytical Chemistry and Coordinator of the Research Doctorate School in Food and Agricultural Science, Technology and Biotechnology (STEBA) of the University of Modena and Reggio Emilia. CHIMSLAB team also includes Dr. Giorgia Foca, as assistant professor, and Dr. Rosalba Calvini, as post-doc researcher. We would also like to mention our former PhD students, Dr. Carlotta Ferrari and Dr. Giorgia Orlandi, who gave a fundamental contribution to our recent activities. In addition, in 2017, we had the pleasure to host Prof. Sylvio Barbon Junior (Computer Science Department, Londrina State University) and Dr. Ana Paula A. C. Barbon (Animal Science Department, Londrina State University) as visiting researchers. CHIMSLAB research group is also affiliated to BIOGEST-SITEIA, the interdepartmental research centre of the University of Reggio Emilia working on the improvement and valorisation of agri-food biological resources. The keywords in the group name recall our main research activities: the application and development of chemometric methods for data modelling with a specific interest in spectroscopic and imaging data. In particular, the application of near infrared (NIR) spectroscopy, computer vision and NIR hyperspectral imaging (NIR-HSI) in the agri-food sector represents a considerable part of our expertise. However, thanks to collaborations with other research groups, we had the possibility of applying chemometric modelling to a wide range of research fields, including cultural heritage, electrochemical sensing, microbiology and entomology, among others. Concerning our research topics of main interest for the readers of NIR News, in the past years, we focused on two key aspects of spectroscopic and imaging data analysis: variable selection and data dimensionality reduction. Feature selection is a crucial aspect in the analysis of spectroscopic signals, since the selection of the spectral regions of interest for the problem at hand usually allows to discard noise and to obtain calibration or classification models with higher performances. For these reasons, starting from the beginning of our research activities, the application of state-of-art variable selection methods and the development of new selection strategies have represented key topic of our work. The importance of variable selection methods is even more relevant when dealing with NIR hyperspectral images. Indeed, usually, industrial applications require sorting technologies meeting the requirements of fast time of analysis and low","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129874406","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}
Pub Date : 2020-12-01DOI: 10.1177/0960336020978714
P. Williams, Erik Eising, D. Malley
The objective of the work was to use near-infrared spectroscopy (NIRS) to identify the pattern of distribution of the manure nutrients onto a field. The paper describes what is believed to be the first application of NIRS to continuous analysis of liquid manure on an industrial scale. The on-line analysis is accomplished without the need for a sample cell to scan the sample, or the need for sampling during manure application to the land, because the entire amount of the material is analyzed. Diode array instruments were used for continuous scanning of the manure as it was being applied. Because of differences among the instruments in spectral data, four instruments were set up in line for the development of the industrial-level calibration. Spectral data were recorded as direct reflectance over the range of 11,100 cm−1 to 5,900 cm−1. Calibrations for total solids, nitrogen, phosphorus, and potassium were developed using the Unscrambler, with a data pre-treatment of first derivative. No outliers were removed. Values of the RPD (ratio of SD of reference data to SEP) of between 3.1 and 4 were attained for all constituents. Interpolation of blind duplicates revealed that reproducibility of NIRS predictions for all constituents was significantly superior to that of laboratory analysis by certified laboratories. This was attributed to the enormous differences in the volume of manure scanned by the NIRS instruments, relative to the volumes used in laboratory analysis. Continuous scanning and analysis of liquid manures enable mapping of nutrients as they are being applied to the land, which provides valuable information to farmers as to the purchase, and efficiency of use of fertilizers.
{"title":"Industrial-scale continuous on-line analysis of liquid hog manure by NIRS","authors":"P. Williams, Erik Eising, D. Malley","doi":"10.1177/0960336020978714","DOIUrl":"https://doi.org/10.1177/0960336020978714","url":null,"abstract":"The objective of the work was to use near-infrared spectroscopy (NIRS) to identify the pattern of distribution of the manure nutrients onto a field. The paper describes what is believed to be the first application of NIRS to continuous analysis of liquid manure on an industrial scale. The on-line analysis is accomplished without the need for a sample cell to scan the sample, or the need for sampling during manure application to the land, because the entire amount of the material is analyzed. Diode array instruments were used for continuous scanning of the manure as it was being applied. Because of differences among the instruments in spectral data, four instruments were set up in line for the development of the industrial-level calibration. Spectral data were recorded as direct reflectance over the range of 11,100 cm−1 to 5,900 cm−1. Calibrations for total solids, nitrogen, phosphorus, and potassium were developed using the Unscrambler, with a data pre-treatment of first derivative. No outliers were removed. Values of the RPD (ratio of SD of reference data to SEP) of between 3.1 and 4 were attained for all constituents. Interpolation of blind duplicates revealed that reproducibility of NIRS predictions for all constituents was significantly superior to that of laboratory analysis by certified laboratories. This was attributed to the enormous differences in the volume of manure scanned by the NIRS instruments, relative to the volumes used in laboratory analysis. Continuous scanning and analysis of liquid manures enable mapping of nutrients as they are being applied to the land, which provides valuable information to farmers as to the purchase, and efficiency of use of fertilizers.","PeriodicalId":113081,"journal":{"name":"NIR News","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127808400","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：