Chemistry › Analytical Chemistry

Spectroscopy and Chemometric Analyses

Works Count: 98284

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This cluster of papers covers a wide range of topics in chemometrics, with a focus on applications in analytical chemistry and food technology. It includes methods such as near-infrared spectroscopy, multivariate calibration, hyperspectral imaging, variable selection, and machine vision for quality assessment and food authentication.

Keywords

Near-Infrared Spectroscopy; Multivariate Calibration; Hyperspectral Imaging; Variable Selection; Chemometric Tools; Quality Assessment; Machine Vision; Spectral Analysis; Food Authentication; Principal Component Analysis

Adaptive control stability, convergence, and robustness

1993-05-01

Bo Egardt

Handbook of vibrational spectroscopy

2002-01-01

John M. Chalmers , Peter R. Griffiths

VOLUME 1: THEORY AND INSTRUMENTATION Introduction to the Theory and Practice of Vibrational Spectroscopy Instrumentation for Mid- and Far-infrared Spectroscopy Instrumentation for Near-infrared Spectroscopy Instrumentation for Raman Spectroscopy Time-resolved Spectroscopy … VOLUME 1: THEORY AND INSTRUMENTATION Introduction to the Theory and Practice of Vibrational Spectroscopy Instrumentation for Mid- and Far-infrared Spectroscopy Instrumentation for Near-infrared Spectroscopy Instrumentation for Raman Spectroscopy Time-resolved Spectroscopy Dichroism and Optical Activity in Vibrational Spectroscopy Surface-enhanced Vibrational Spectroscopy Other Instrumental Approaches for Vibrational Spectroscopy Calibration Procedures and Standards for Vibrational Spectroscopy VOLUME 2: SAMPLING TECHNIQUES Mid- and Near-infrared Transmission Spectroscopy Mid-infrared External Reflection Spectroscopy Mid-infrared Internal Reflection Spectroscopy Diffuse Reflection Spectroscopy Other IR Sampling Techniques Raman Spectroscopy Low Temperature and High Pressure Sampling Techniques Microscopy Depth profiling by Vibrational Spectroscopy Optical Conduits for Vibrational Specroscopy Hyphenated Techniques Atmospheric VOLUME 3: SAMPLE CHARACTERIZATION AND SPECTRAL DATA PROCESSING Spectra-Structure Correlations Group Theoretical and Numerical Approaches to the Calculation of Vibrational Spectra Discrimant Analysis Two-dimensional (2D) Analysis Spectral Enhancement and Band Resolution Techniques Quantitative Analysis Anomalies, Atifacts and Common Errors in Using Vibrational Spectroscopy Techniques Glossary VOLUME 4: APPLICATIONS IN INDUSTRY, MATERIALS AND THE PHYSICAL SCIENCES Analysis and Characterization of Polymers and Rubbers Rheo-optical Measurements of Polymers and Rubbers Materials Science Spectoelectrochemistry Process Vibrational Spectroscopy Atmospheric and Astronomical Vibrational Spectroscopy Industrial Applications of Vibrational Spectroscopy Forensic Applications of Vibrational Spectroscopy Catalysis Other Applications of Vibrational Spectroscopy Vibrational Spectroscopy in Education VOLUME 5: APPLICATIONS IN LIFE, PHARMACEUTICAL AND NATURAL SCIENCES Biomedical Applications Biochemical Applications Pharmaceutical Applications Food Science Agricultural Applications Abbreviations and Acronyms, Glossary, List of Contributors and Subject Index

A NEW COUPLING COMPONENT FOR SULFANILAMIDE DETERMINATION

1939-05-01

A. C. Bratton , E. K. Marshall

Introduction to Infrared and Raman Spectroscopy

1975-01-01

Norman B. Colthup , Lawrence H. Daly , Stephen E. Wiberley

Molecular Spectroscopy: Modern Research

1985-01-01

K. Narahari Rao , C. W. Mathews

PLS regression methods

1988-06-01

Agnar Höskuldsson

Abstract In this paper we develop the mathematical and statistical structure of PLS regression. We show the PLS regression algorithm and how it can be interpreted in model building. The … Abstract In this paper we develop the mathematical and statistical structure of PLS regression. We show the PLS regression algorithm and how it can be interpreted in model building. The basic mathematical principles that lie behind two block PLS are depicted. We also show the statistical aspects of the PLS method when it is used for model building. Finally we show the structure of the PLS decompositions of the data matrices involved.

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

2007-09-20

Bart Nicolaı̈ , Katrien Beullens , Els Bobelyn +4 more

SIMPLS: An alternative approach to partial least squares regression

1993-03-01

Sijmen de Jong

Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy

1993-09-01

Isao Noda

A two-dimensional (2D) correlation method generally applicable to various types of spectroscopy, including IR and Raman spectroscopy, is introduced. In the proposed 2D correlation scheme, an external perturbation is applied … A two-dimensional (2D) correlation method generally applicable to various types of spectroscopy, including IR and Raman spectroscopy, is introduced. In the proposed 2D correlation scheme, an external perturbation is applied to a system while being monitored by an electromagnetic probe. With the application of a correlation analysis to spectral intensity fluctuations induced by the perturbation, new types of spectra defined by two independent spectral variable axes are obtained. Such two-dimensional correlation spectra emphasize spectral features not readily observable in conventional one-dimensional spectra. While a similar 2D correlation formalism has already been developed in the past for analysis of simple sinusoidally varying IR signals, the newly proposed formalism is designed to handle signals fluctuating as an arbitrary function of time, or any other physical variable. This development makes the 2D correlation approach a universal spectroscopic tool, generally applicable to a very wide range of applications. The basic property of 2D correlation spectra obtained by the new method is described first, and several spectral data sets are analyzed by the proposed scheme to demonstrate the utility of generalized 2D correlation spectra. Potential applications of this 2D correlation approach are then explored.

Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data

1982-12-01

J. D. Scargle

view Abstract Citations (5172) References (28) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly … view Abstract Citations (5172) References (28) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data. Scargle, J. D. Abstract Detection of a periodic signal hidden in noise is frequently a goal in astronomical data analysis. This paper does not introduce a new detection technique, but instead studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced. This choice was made because, of the methods in current use, it appears to have the simplest statistical behavior. A modification of the classical definition of the periodogram is necessary in order to retain the simple statistical behavior of the evenly spaced case. With this modification, periodogram analysis and least-squares fitting of sine waves to the data are exactly equivalent. Certain difficulties with the use of the periodogram are less important than commonly believed in the case of detection of strictly periodic signals. In addition, the standard method for mitigating these difficulties (tapering) can be used just as well if the sampling is uneven. An analysis of the statistical significance of signal detections is presented, with examples Publication: The Astrophysical Journal Pub Date: December 1982 DOI: 10.1086/160554 Bibcode: 1982ApJ...263..835S Keywords: Astronomy; Signal Detection; Spectrum Analysis; Statistical Distributions; Time Series Analysis; Fourier Transformation; Frequency Response; Power Spectra; Signal To Noise Ratios; Astronomy full text sources ADS | Related Materials (5) Part 1: 1981ApJS...45....1S Part 3: 1989ApJ...343..874S Part 4: 1990ApJ...359..469S Part 5: 1998ApJ...504..405S Part 6: 2013ApJ...764..167S

The Mahalanobis distance

2000-01-01

Roy De Maesschalck , D. Jouan-Rimbaud , D.L. Massart

Some distance properties of latent root and vector methods used in multivariate analysis

1966-01-01

J. C. Gower

This paper is concerned with the representation of a multivariate sample of size n as points P1, P2, …, Pn in a Euclidean space. The interpretation of the distance Δ(Pi, … This paper is concerned with the representation of a multivariate sample of size n as points P1, P2, …, Pn in a Euclidean space. The interpretation of the distance Δ(Pi, Pj) between the ith and jth members of the sample is discussed for some commonly used types of analysis, including both Q and R techniques. When all the distances between n points are known a method is derived which finds their co-ordinates referred to principal axes. A set of necessary and sufficient conditions for a solution to exist in real Euclidean sapce is found. Q and R techniques are defined as being dual to one another when they both lead to a set of n points with the same inter-point distances. Pairs of dual techniques are derived. In factor analysis the distances between points whose co-ordinrates are the estimated factor scores can be interpreted as D2 with a singular dispersion matrix.

Review of the most common pre-processing techniques for near-infrared spectra

2009-08-04

Åsmund Rinnan , Frans van den Berg , Søren Balling Engelsen

Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra

1989-07-01

Richard Barnes , M.S. Dhanoa , S.J. Lister

Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. … Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.

Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information

1988-06-01

David M. Haaland , Edward V. Thomas

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTPartial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative informationDavid M. Haaland and Edward V. ThomasCite this: Anal. … ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTPartial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative informationDavid M. Haaland and Edward V. ThomasCite this: Anal. Chem. 1988, 60, 11, 1193–1202Publication Date (Print):June 1, 1988Publication History Published online1 May 2002Published inissue 1 June 1988https://pubs.acs.org/doi/10.1021/ac00162a020https://doi.org/10.1021/ac00162a020research-articleACS PublicationsRequest reuse permissionsArticle Views5298Altmetric-Citations2001LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts

A general approach to confirmatory maximum likelihood factor analysis

1969-06-01

Karl G. Jöreskog

Least squares formulation of robust non-negative factor analysis

1997-05-01

Pentti Paatero

Trimmed Spearman-Karber method for estimating median lethal concentrations in toxicity bioassays

1977-07-01

Martin A. Hamilton , Rosemarie C. Russo , Robert V. Thurston

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTTrimmed Spearman-Karber method for estimating median lethal concentrations in toxicity bioassaysMartin A. Hamilton, Rosemarie C. Russo, and Robert V. ThurstonCite this: Environ. Sci. Technol. 1977, 11, 7, … ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTTrimmed Spearman-Karber method for estimating median lethal concentrations in toxicity bioassaysMartin A. Hamilton, Rosemarie C. Russo, and Robert V. ThurstonCite this: Environ. Sci. Technol. 1977, 11, 7, 714–719Publication Date (Print):July 1, 1977Publication History Published online1 May 2002Published inissue 1 July 1977https://pubs.acs.org/doi/10.1021/es60130a004https://doi.org/10.1021/es60130a004research-articleACS PublicationsRequest reuse permissionsArticle Views5420Altmetric-Citations1851LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts

Statistics and Chemometrics for Analytical Chemistry

2004-11-01

Eric R. Ziegel

(2004). Statistics and Chemometrics for Analytical Chemistry. Technometrics: Vol. 46, No. 4, pp. 498-499. (2004). Statistics and Chemometrics for Analytical Chemistry. Technometrics: Vol. 46, No. 4, pp. 498-499.

PLS-regression: a basic tool of chemometrics

2001-10-01

Svante Wold , Michael Sjöstróm , Lennart Eriksson

Principal Components Analysis

1989-01-01

George H. Dunteman

Introduction Basic Concepts of Principal Components Geometrical Properties of Principal Components Decomposition Properties of Principal Components Principal Components of Patterned Correlation Matrices Rotation of Principal Components Using Principal Components to … Introduction Basic Concepts of Principal Components Geometrical Properties of Principal Components Decomposition Properties of Principal Components Principal Components of Patterned Correlation Matrices Rotation of Principal Components Using Principal Components to Select a Subset of Variables Principal Components Versus Factor Analysis Uses of Principal Components in Regression Analysis Using Principal Components to Detect Outlying and Influential Observations Use of Principal Components in Cluster Analysis Use of Principal Components Analysis in Conjunction with Other Multivariate Analysis Procedures Other Techniques Related to Principal Components Summary and Conclusions

A Statistical View of Some Chemometrics Regression Tools

1993-05-01

lldiko E. Frank , Jerome H. Friedman

Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics and engineering literatures, chemometrics … Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics and engineering literatures, chemometrics itself has given rise to several new data-analytical methods. This article examines two methods commonly used in chemometrics for predictive modeling—partial least squares and principal components regression—from a statistical perspective. The goal is to try to understand their apparent successes and in what situations they can be expected to work well and to compare them with other statistical methods intended for those situations. These methods include ordinary least squares, variable subset selection, and ridge regression.

An Algorithm for Least-Squares Estimation of Nonlinear Parameters

1963-06-01

Donald W. Marquardt

Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] G. W. Booth, , G. E. P. … Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] G. W. Booth, , G. E. P. Box, , M. E. Muller and , T. I. Peterson, Forecasting by Generalized Regression Methods, Nonlinear Estimation (Princeton—IBM), Mimeo. (IBM Share Program No. 687 WL NL1), International Business Machines Corp., 1959 Google Scholar[2] G. E. P. Box and , G. A. Coutie, Application of digital computers in the exploration of functional relationships, Proc. Inst. Elec. Engrs. B, 103 (1957), 100–107 MR0094906 Google Scholar[3] Haskell B. Curry, The method of steepest descent for non-linear minimization problems, Quart. Appl. Math., 2 (1944), 258–261 MR0010667 0061.26801 CrossrefGoogle Scholar[4] H. O. Hartley, The modified Gauss-Newton method for the fitting of non-linear regression functions by least squares, Technometrics, 3 (1961), 269–280 MR0124117 0096.34603 CrossrefISIGoogle Scholar[5] D. W. Marquardt, , Chem. Eng. Progr., 55 (1959), 65–70 Google Scholar[6] D. W. Marquardt, , R. G. Bennett and , E. J. Burrell, , Jour. Molec. Spectroscopy, 7 (1961), 269–279 10.1016/0022-2852(61)90360-5 CrossrefISIGoogle Scholar[7] D. D. Morrison, Methods for nonlinear least squares problems and convergence proofs, Tracking Programs and Orbit Determination, Proc. Jet Propulsion Laboratory Seminar, 1960, 1–9 Google Scholar[8] Kenneth Levenberg, A method for the solution of certain non-linear problems in least squares, Quart. Appl. Math., 2 (1944), 164–168 MR0010666 0063.03501 CrossrefGoogle Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails Shadowing-Based Data Assimilation Method for Partially Observed ModelsBart M. de Leeuw and Svetlana DubinkinaSIAM Journal on Applied Dynamical Systems, Vol. 21, No. 2 | 11 April 2022AbstractPDF (2268 KB)Localized Sensitivity Analysis at High-Curvature Boundary Points of Reconstructing Inclusions in Transmission ProblemsHabib Ammari, Yat Tin Chow, and Hongyu LiuSIAM Journal on Mathematical Analysis, Vol. 54, No. 2 | 9 March 2022AbstractPDF (829 KB)A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity ResultsEl Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, and Clément W. RoyerSIAM/ASA Journal on Uncertainty Quantification, Vol. 10, No. 1 | 29 March 2022AbstractPDF (616 KB)Derivative-Free Bayesian Inversion Using Multiscale DynamicsG. A. Pavliotis, A. M. Stuart, and U. 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range:pp. 431-441ISSN (print):0368-4245ISSN (online):2168-3484Publisher:Society for Industrial and Applied Mathematics

Principal component analysis

1987-08-01

Svante Wold , Kim H. Esbensen , Paul Geladi

PLS path modeling

2004-04-22

Michel Tenenhaus , Vincenzo Esposito Vinzi , Yves-Marie Chatelin +1 more

Determining the number of components from the matrix of partial correlations

1976-09-01

Wayne F. Velicer

Performance of some variable selection methods when multicollinearity is present

2005-03-11

Il-Gyo Chong , Chi‐Hyuck Jun

The biplot graphic display of matrices with application to principal component analysis

1971-01-01

K. R. Gabriel

Any matrix of rank two can be displayed as a biplot which consists of a vector for each row and a vector for each column, chosen so that any element … Any matrix of rank two can be displayed as a biplot which consists of a vector for each row and a vector for each column, chosen so that any element of the matrix is exactly the inner product of the vectors corresponding to its row and to its column. If a matrix is of higher rank, one may display it approximately by a biplot of a matrix of rank two which approximates the original matrix. The biplot provides a useful tool of data analysis and allows the visual appraisal of the structure of large data matrices. It is especially revealing in principal component analysis, where the biplot can show inter-unit distances and indicate clustering of units as well as display variances and correlations of the variables.

Orthogonal projections to latent structures (O‐PLS)

2002-01-18

Johan Trygg , Svante Wold

Abstract A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O‐PLS), is described. O‐PLS removes variation from X (descriptor variables) that is not correlated to Y … Abstract A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O‐PLS), is described. O‐PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y . In an earlier paper, Wold et al. ( Chemometrics Intell. Lab. Syst . 1998; 44 : 175–185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O‐PLS method analyzes the variation explained in each PLS component. The non‐correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non‐correlated variation itself can be analyzed further. As an example, near‐infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O‐PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non‐correlated variation in X and, not least, improved interpretational ability of both correlated and non‐correlated variation in the NIR spectra. Copyright © 2002 John Wiley & Sons, Ltd.

Spectral Analysis and its Applications.

1969-12-01

Walter Freiberger , Gwilym M. Jenkins , Donald G. Watts

(1970). Spectral Analysis and its Applications. Technometrics: Vol. 12, No. 1, pp. 174-175. (1970). Spectral Analysis and its Applications. Technometrics: Vol. 12, No. 1, pp. 174-175.

Nonlinear principal component analysis using autoassociative neural networks

1991-02-01

Mark Kramer

Abstract Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well‐known method of principal component analysis. NLPCA, like PCA, is used to identify and … Abstract Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well‐known method of principal component analysis. NLPCA, like PCA, is used to identify and remove correlations among problem variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA identifies only linear correlations between variables, NLPCA uncovers both linear and nonlinear correlations, without restriction on the character of the nonlinearities present in the data. NLPCA operates by training a feedforward neural network to perform the identity mapping, where the network inputs are reproduced at the output layer. The network contains an internal “bottleneck” layer (containing fewer nodes than input or output layers), which forces the network to develop a compact representation of the input data, and two additional hidden layers. The NLPCA method is demonstrated using time‐dependent, simulated batch reaction data. Results show that NLPCA successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.

Probabilistic Principal Component Analysis

1999-09-01

Michael E. Tipping , Chris Bishop

Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes … Summary Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach to PCA.

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Principal Component Analysis

1988-08-01

Colin Goodall , Ian T. Jolliffe

Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation of Data Using Principal Components * Choosing a Subset … Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation of Data Using Principal Components * Choosing a Subset of Principal Components or Variables * Principal Component Analysis and Factor Analysis * Principal Components in Regression Analysis * Principal Components Used with Other Multivariate Techniques * Outlier Detection, Influential Observations and Robust Estimation * Rotation and Interpretation of Principal Components * Principal Component Analysis for Time Series and Other Non-Independent Data * Principal Component Analysis for Special Types of Data * Generalizations and Adaptations of Principal Component Analysis

Partial least-squares regression: a tutorial

1986-01-01

Paul Geladi , Bruce R. Kowalski

Spectrum analysis—A modern perspective

1981-01-01

Steven Kay , S.L. Marple

A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the … A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.

Time-frequency distributions-a review

1989-07-01

Leon Cohen

A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a … A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

The Problem of Overfitting

2003-12-02

Douglas M. Hawkins

ADVERTISEMENT RETURN TO ISSUEArticleNEXTThe Problem of OverfittingDouglas M. HawkinsView Author Information School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455 Cite this: J. Chem. Inf. Comput. Sci. 2004, 44, 1, … ADVERTISEMENT RETURN TO ISSUEArticleNEXTThe Problem of OverfittingDouglas M. HawkinsView Author Information School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455 Cite this: J. Chem. Inf. Comput. Sci. 2004, 44, 1, 1–12Publication Date (Web):December 2, 2003Publication History Received30 October 2003Published online2 December 2003Published inissue 1 January 2004https://pubs.acs.org/doi/10.1021/ci0342472https://doi.org/10.1021/ci0342472research-articleACS PublicationsCopyright © 2004 American Chemical SocietyRequest reuse permissionsArticle Views19627Altmetric-Citations1642LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options SUBJECTS:Calibration,Hydrocarbons,Quality management,Testing and assessment,Transition temperature Get e-Alerts

Principal component analysis: a review and recent developments

2016-03-07

Ian T. Jolliffe , Jorge Cadima

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the … Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori , hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.

Handbook of Vibrational Spectroscopy

2001-12-20

Principal component analysis

2014-01-01

Rasmus Bro , Age K. Smilde

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how … Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

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Multivariate Data Analysis

2008-11-21

Principal Component Analysis

2005-04-15

Ian T. Jolliffe

Abstract When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables … Abstract When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q , of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p . Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined.

Principal Component Analysis

1986-01-01

Ian T. Jolliffe

Hyperspectral imaging for rapid impurity detection in power system liquids

2025-06-25

Xue Liang , Liang Zhang , Zhuoyue Yang +3 more | Engineering Applications of Artificial Intelligence

Análise quantitativa de misturas binárias usando a Espectroscopia de Absorção Molecular

2025-06-24

João Carlos de Andrade | Revista Chemkeys

A absorção molecular na região espectral entre 160 e 780 nm é causada pela excitação eletrônica das espécies absorventes. Em comprimentos de onda abaixo de 400 nm a energia da … A absorção molecular na região espectral entre 160 e 780 nm é causada pela excitação eletrônica das espécies absorventes. Em comprimentos de onda abaixo de 400 nm a energia da radiação incidente é suficiente para quebrar ligações químicas, enquanto na região infravermelha (acima de 780 nm) são observadas principalmente vibrações moleculares. Como as espécies químicas poliatômicas que absorvem na região UV-Vis do espectro eletromagnético têm bandas de absorção largas, as determinações simultâneas de múltiplas espécies são mais complicadas, dada a dificuldade em encontrar comprimentos de onda apropriados que permitam a medida direta da concentração individual de cada componente na mistura. Considerando a situação mais simples, onde a amostra consiste em apenas duas espécies absorventes em solução, os procedimentos mais diretos e eficientes que podem ser usados para resolver esse problema são o método algébrico, o método de espectroscopia de comprimento de onda duplo e o método de adição padrão no ponto H (HPSAM). Destes, o último parece ser mais eficiente, pois permite eliminar ou reduzir erros sistemáticos. Procedimentos experimentais e cálculos associados a esses métodos serão apresentados neste trabalho, tomando como exemplo a determinação simultânea das concentrações de dois íons distintos em uma solução aquosa ácida.

Automation and Robotics in Food Processing

2025-06-24

Sakshi Gupta , Gargi Ghoshal | CRC Press eBooks

Food Waste Monitoring and Analytical Technologies

2025-06-24

Ramanpreet Kaur , Gargi Ghoshal | CRC Press eBooks

Near-Infrared Multi-spectral Imaging with Bayer-Filter Color Cameras: A Single-Exposure Approach for Soot and Temperature Diagnostics

2025-06-24

Ozan Oner , Francisco Cepeda , Seth Dworkin +1 more | Applied Optics

Origin Identification of Table Salt Using Flame Atomic Absorption and Portable Near-Infrared Spectrometries

2025-06-24

Luís Lima , Luana Dalagrana dos Santos , Isabella Taglieri +5 more | Chemosensors

The mineral composition of table salt can be indicative of its origin. This work evaluated the possibility of identifying the origin of salt from four countries: Brazil, Spain, France, and … The mineral composition of table salt can be indicative of its origin. This work evaluated the possibility of identifying the origin of salt from four countries: Brazil, Spain, France, and Portugal. Eight metals were quantified through flame atomic absorption/emission spectroscopy (FAAS). The possibility of using portable near-infrared spectroscopy (NIR) as a faster and lower-cost alternative for identifying salt provenance was also evaluated. The content of Ca, Mg, Fe, Mn, and Cu was identified as possible markers to differentiate the salt origin. One-class classifiers using FAAS data and DD-SIMCA could discriminate the salt origin with few misclassifications. For NIR spectroscopy, it was possible to highlight the importance of controlling the humidity and granulometry before the spectra acquisition. After drying and milling the samples, it was possible to discriminate between samples based on the interaction between the water of hydration and the presence of the cations in the sample. The Mg, Mn, and Cu are important in identifying the origin of salt using NIR spectra. The DD-SIMCA model using NIR spectra could classify the origin with the same performance as observed in FAAS. However, it is important to emphasize that NIR spectroscopy requires less sample preparation, is faster, and has low-cost instrumentation.

Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks

2025-06-24

Mustafa Ahmed Jalal Al-Sammarraie , Łukasz Gierz , Ghaith H. Jihad +3 more | Food and Bioprocess Technology

Abstract Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can … Abstract Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality.

Codebook-constrained spectral synthesis: A vector quantized-augmented generative algorithm framework for predicting corn starch content

2025-06-24

Jiarui Cui , Shi Cao , Jiali Men +4 more | Industrial Crops and Products

Hyperspectral imaging combined with a universal hybrid deep network for identifying early chilling injury in kiwifruit across varieties

2025-06-24

Sunli Cong , Jun Sun , Lei Shi +4 more | Postharvest Biology and Technology

Euclid Quick Data Release (Q1). From spectrograms to spectra: The SIR spectroscopic processing function

2025-06-23

Y. Copin , M. Fumana , C. Mancini +7 more | Astronomy and Astrophysics

The Euclid space mission aims to investigate the nature of dark energy and dark matter by mapping the large-scale structure of the Universe. A key component of Euclid's observational strategy … The Euclid space mission aims to investigate the nature of dark energy and dark matter by mapping the large-scale structure of the Universe. A key component of Euclid's observational strategy is slitless spectroscopy, which is conducted using the Near Infrared Spectrometer and Photometer (NISP). This technique enables the acquisition of large-scale spectroscopic data without the need for targeted apertures, thus allowing for precise redshift measurements of millions of galaxies. These data are essential for Euclid's core science objectives, including the study of cosmic acceleration and the evolution of galaxy clustering, and will enable many non-cosmological investigations. This study presents the SIR processing function, which is responsible for processing slitless spectroscopic data from Euclid's NISP instrument. The objective is to generate fully calibrated science-grade one-dimensional spectra in order to ensure high-quality spectroscopic data for cosmological or astrophysical analyses. The processing function relies on a source catalogue generated from photometric data, effectively corrects detector effects, subtracts cross-contaminations, minimises self-contamination, calibrates wavelength and flux, and produces reliable spectra for later scientific use. The first Quick Data Release (Q1) of Euclid's spectroscopic data provides approximately three million validated spectra for sources observed in the red-grism mode from a selected portion of the Euclid Wide Survey. We find that the wavelength accuracy and measured resolving power are within top-level mission requirements, thanks to the excellent optical quality of the instrument. The SIR processing function represents a significant step in processing slitless spectroscopic data for the Euclid mission. As the survey progresses, continued refinements and additional features will enhance its capabilities, thus further supporting high-precision cosmological and astrophysical measurements.

Phenotyping of navel orange based on hyperspectral imaging technology

2025-06-23

Qi Wang , Jinzhu Lu , Yuanhong Wang +2 more | Computers and Electronics in Agriculture

Near-Infrared Spectroscopy and Multivariate Analysis as an Effective Method to Discriminate <i>Escherichia coli</i> in Clinical Samples

2025-06-23

Liliane Desgualdo Pereira , Ayrton L. F. Nascimento , Loriane Gratão de Mesquita +2 more | ACS Omega

Smart Quality Control in Logistics for Fresh Edible Nuts/Fruits with Rich Lipid: Research Progress, Challenges and Potential Applications

2025-06-23

Zichen Zhou , Min Zhang , Arun S. Mujumdar +1 more | Food Reviews International

Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis

2025-06-23

A. Flanagan , Dhairya Dalal , Frank G. Glavin | Chemical Reviews

Generative artificial intelligence (AI) techniques are advancing rapidly and are becoming increasingly challenging to implement. Researchers, practitioners, and enthusiasts alike now require an understanding of complex concepts far beyond the … Generative artificial intelligence (AI) techniques are advancing rapidly and are becoming increasingly challenging to implement. Researchers, practitioners, and enthusiasts alike now require an understanding of complex concepts far beyond the scope of simple feed-forward neural networks to implement the current state-of-the-art methods for their research interests. In contrast, while data augmentation methods may not perform at the same level, they are easier to understand and implement, and are well demonstrated. For these reasons, this review aims to bridge the knowledge gap between the sciences of chemometrics and generative AI and provide a starting point for new researchers. In the context of spectroscopy, this work collects, categorizes, and describes the most popular preprocessing techniques and the state-of-the-art in generative AI and data augmentation, spanning over 104 peer-reviewed journals and proceedings across 32 publishers and organisations. We provide intuitive explanations of the methods, highlighting their strengths and weaknesses, and we include graphical and practical examples of their applications.

Integração de Sistemas Óptico e Microfluídico para caracterização e monitoramento em tempo real de emulsões de água em óleo

2025-06-23

Rômulo Ferreira dos Santos | Seven Editora eBooks

Advancements in Plant Diagnostic and Sensing Technologies

2025-06-23

Shalini Krishnamoorthi , Sally Shuxian Koh , Mervin Chun‐Yi Ang +5 more | Advanced Sensor Research

Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer … Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved.

A Synergistic Approach to Forecasting Sesamum Prices in Andhra Pradesh: Hybrid, Machine Learning and Wavelet Decomposition Models

2025-06-23

Shaik Shameem , Lavanya Kumari. P , R. B. +1 more | Journal of Scientific Research and Reports

Sesamum (Sesamum indicum L.) is an important oilseed crop in Andhra Pradesh, playing a vital role in India's agricultural economy through both domestic consumption and export. Despite its long history … Sesamum (Sesamum indicum L.) is an important oilseed crop in Andhra Pradesh, playing a vital role in India's agricultural economy through both domestic consumption and export. Despite its long history of cultivation, sesamum constitutes only a small fraction of global vegetable oil production. Its oil is highly valued for its nutritional quality, antioxidant properties, and stability, making it suitable for culinary, medicinal, and industrial uses. Accurate forecasting of wholesale sesamum prices is essential for stakeholders to make informed decisions and manage market risks efficiently. In this context, secondary data on sesamum prices in Andhra Pradesh from 2008 to 2024 was analysed using autocorrelation with the Box-Pierce test. A range of models were developed, including the ARIMA model and machine learning models such as ANN, SVR, ELM, as along with hybrid and wavelet-based models. Among these, the ARIMA+SVR hybrid model exhibited the highest predictive accuracy, thereby enhancing the reliability of price forecasts and contributing to improved planning, market stability, and economic efficiency in the agricultural sector.

Monitoring of the Physicochemical Properties and Aflatoxin of Aspergillus flavus-Contaminated Peanut Kernels Based on Near-Infrared Spectroscopy Combined with Machine Learning

2025-06-22

Yingge Wang , Mengke Li , Xu Li +6 more | Foods

This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by Aspergillus flavus (A. flavus). The key innovation … This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by Aspergillus flavus (A. flavus). The key innovation lies in the development of an optimized spectral processing pipeline that effectively overcomes moisture interference while maintaining high sensitivity to low aflatoxin concentrations. NIR spectra were collected from peanut samples at different incubation times within the spectral range of 950 to 1650 nm. Spectral data were preprocessed, and Competitive Adaptive Reweighted Sampling (CARS) selected ten characteristic bands. Correlation analysis was performed to examine the relationships between physicochemical properties, characteristic bands, and aflatoxin content. Three machine learning models—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were used to predict aflatoxin levels. The SNV-SVM model demonstrated superior performance, achieving calibration metrics (R2C = 0.9945, RMSEC = 9.92, RPDC = 14.59) and prediction metrics (R2P = 0.9528, RMSEP = 19.58, RPDP = 7.01), along with leave-one-out cross-validation (LOOCV) results (R2 = 0.9834, RMSE = 11.20). The results demonstrate that NIR spectroscopy combined with machine learning offers a rapid, non-destructive approach for aflatoxin detection in peanuts, with significant implications for food safety and agricultural quality control.

Application of Hyperspectral Imaging for Early Detection of Pathogen-Induced Stress in Cabbage as Case Study

2025-06-22

Magdalena Szechyńska‐Hebda , R. Hołownicki , G. Doruchowski +5 more | Agronomy

Cabbage (Brassica oleracea L.) is a globally significant vegetable crop that faces productivity challenges due to fungal and bacterial pathogens. This review highlights the potential of spectral imaging techniques, specifically … Cabbage (Brassica oleracea L.) is a globally significant vegetable crop that faces productivity challenges due to fungal and bacterial pathogens. This review highlights the potential of spectral imaging techniques, specifically multispectral and hyperspectral methods, in detecting biotic stress in cabbage, with a particular emphasis on pathogen-induced responses. These non-invasive approaches enable real-time assessment of plant physiological and biochemical changes, providing detailed spectral data to identify pathogens before visible symptoms appear. Hyperspectral imaging, with its high spectral resolution, allows for distinctions among different pathogens and the evaluation of stress responses, whereas multispectral imaging offers broad-scale monitoring suitable for field-level applications. The work synthesizes research in the existing literature while presenting novel experimental findings that validate and extend current knowledge. Significant spectral changes are reported in cabbage leaves infected by Alternaria brassicae and Botrytis cinerea. Early-stage detection was facilitated by alterations in flavonoids (400–450 nm), chlorophyll (430–450, 680–700 nm), carotenoids (470–520 nm), xanthophyll (520–600 nm), anthocyanin (550–560 nm, 700–710 nm, 780–790 nm), phenols/mycotoxins (700–750 nm, 718–722), water/pigments content (800–900 nm), and polyphenols/lignin (900–1000). The findings underscore the importance of targeting specific spectral ranges for early pathogen detection. By integrating these techniques with machine learning, this research demonstrates their applicability in advancing precision agriculture, improving disease management, and promoting sustainable production systems.

Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds

2025-06-22

Peng Zhang , Jiangping Liu | Agriculture

Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and … Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and non-destructive prediction of this key quality parameter, this study presents a modeling framework integrating hyperspectral imaging (HSI) technology with a dual-optimization machine learning strategy. Seven spectral preprocessing techniques—standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD), and combinations such as SNV + FD, SNV + SD, and SNV + MSC—were systematically evaluated. Among them, SNV combined with FD was identified as the optimal preprocessing scheme, effectively enhancing spectral feature expression. To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. PCA exhibited superior performance in information compression and modeling stability. Subsequently, a dual-optimized neural network model, termed Bayes-ASFSSA-BP, was developed by incorporating Bayesian optimization and the Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA). Bayesian optimization was used for global tuning of network structural parameters, while ASFSSA was applied to fine-tune the initial weights and thresholds, improving convergence efficiency and predictive accuracy. The proposed Bayes-ASFSSA-BP model achieved determination coefficients (R2) of 0.982 and 0.963, and root mean square errors (RMSEs) of 0.173 and 0.188 on the training and test sets, respectively. The corresponding mean absolute error (MAE) on the test set was 0.170, indicating excellent average prediction accuracy. These results significantly outperformed benchmark models such as SSA-BP, ASFSSA-BP, and Bayes-BP. Compared to the conventional BP model, the proposed approach increased the test R2 by 0.046 and reduced the RMSE by 0.157. Moreover, the model produced the narrowest 95% confidence intervals for test set performance (Rp2: [0.961, 0.971]; RMSE: [0.185, 0.193]), demonstrating outstanding robustness and generalization capability. Although the model incurred a slightly higher computational cost (480.9 s), the accuracy gain was deemed worthwhile. In conclusion, the proposed Bayes-ASFSSA-BP framework shows strong potential for accurate and stable non-destructive prediction of oat seed moisture content. This work provides a practical and efficient solution for quality assessment in agricultural products and highlights the promise of integrating Bayesian optimization with ASFSSA in modeling high-dimensional spectral data.

Analysis of the enrichment characteristics of trace element in soil-wheat systems and hyperspectral quantitative inversion

2025-06-22

Jing Zhang , Daming Wang , Surong Zhang +3 more | Computers and Electronics in Agriculture

Classification of Cashew Plant Diseases Using Hyperspectral Imaging

2025-06-22

Mitali .M. Sawant | International Scientific Journal of Engineering and Management

ABSTRACT: Cashew plant Diseases can reduce crop yields and quality.Diseases can hurt the plants and reduce the amount of cashews they produce.To help farmers, we are detect diseases in cashew … ABSTRACT: Cashew plant Diseases can reduce crop yields and quality.Diseases can hurt the plants and reduce the amount of cashews they produce.To help farmers, we are detect diseases in cashew plants using hyper- spectral imaging. This is a new and exciting way to keep plants healthy.Hyperspectral imaging capture images that can see things that are not visible to the human eye. We used different models, including ResNet 50, ResNet 18, and AlexNet, to compare their performance.We also used LabelMe to segment images of cashew plants, which helped us to focus on specific areas of the plant affected by disease.We tested our method on many cashew plants images and it worked very well. We were able to detect diseases accurately and quickly. With our method, they can detect diseases early and take action to stop them from spreading. This means they can grow healthier plants and produce more cashews.Our method is also good for the environment. By detecting diseases early, farmers can reduce the amount of chemicals they use. This helps to keep the soil and water clean.This helps farmers and the planet. Keywords: in this paper, in this paper, in this paper.

Identifikasi Glibenklamid Pada Jamu Kencing Manis Yang Beredar di Medan Johor

2025-06-22

Vina Andriani , Novitaria Br Sembiring , Roy Indrianto Bangar | Journal of Pharmaceutical And Sciences

Latar Belakang: Meningkatnya penggunaan jamu antidiabetes di masyarakat diiringi dengan maraknya praktik pemalsuan dengan penambahan obat sintetik seperti glibenklamid (C₂₃H₂₈ClN₃O₅S) untuk meningkatkan khasiat dan penjualan. Hal ini berpotensi menimbulkan risiko … Latar Belakang: Meningkatnya penggunaan jamu antidiabetes di masyarakat diiringi dengan maraknya praktik pemalsuan dengan penambahan obat sintetik seperti glibenklamid (C₂₃H₂₈ClN₃O₅S) untuk meningkatkan khasiat dan penjualan. Hal ini berpotensi menimbulkan risiko kesehatan, sehingga perlu dilakukan pengawasan ketat terhadap produk jamu yang beredar. Tujuan: Penelitian ini bertujuan untuk mengidentifikasi keberadaan glibenklamid dalam tiga sampel jamu antidiabetes yang beredar di Medan Johor menggunakan Fourier-Transform Infrared Spectroscopy (FTIR). Metode: Identifikasi dilakukan dengan membandingkan spektrum FTIR sampel (bilangan gelombang 650-4000 cm⁻¹) terhadap standar glibenklamid BPFI. Gugus fungsi khas glibenklamid yang menjadi acuan adalah 3369,5 cm⁻¹ (Amida N-H Stretching), 1714,6 cm⁻¹ (C=O Stretching), dan 1155,5 cm⁻¹ (S=O Stretching). Hasil Penelitian: Hasil analisis menunjukkan bahwa ketiga sampel (A2, A3, A4) tidak mengandung puncak serapan karakteristik glibenklamid pada bilangan gelombang kritis tersebut. Dengan demikian, sampel yang diuji dinyatakan bebas dari bahan kimia obat (BKO) glibenklamid. Kesimpulan: Temuan ini mengindikasikan bahwa ketiga produk jamu aman untuk dikonsumsi sesuai aturan pakai. Namun, pengawasan rutin oleh BPOM tetap diperlukan untuk memastikan keamanan produk jamu yang beredar di pasaran. Penelitian lanjutan dengan metode yang lebih sensitif seperti spektrofotometri UV-Vis disarankan untuk memverifikasi hasil ini.

Artificial intelligence-enabled analysis methods and their applications in food chemistry

2025-06-21

Chunyan Gu , Gang Wang , Weihua Zhuang +7 more | Critical Reviews in Food Science and Nutrition

Food chemistry is a science that studies the composition, properties, and changes of food at the chemical and molecular levels, as well as their relationships to human health. With the … Food chemistry is a science that studies the composition, properties, and changes of food at the chemical and molecular levels, as well as their relationships to human health. With the rapid advancement of artificial intelligence (AI) technology, the field of food chemistry has undergone significant transformation, and new development opportunities have emerged. AI provides efficient, precise, and intelligent solutions for food analysis. This review examines the integration of AI technologies with conventional analytical methodologies in food chemistry, focusing on recent advancements in their applications. It elaborates on AI-driven approaches in spectroscopic analysis, chromatography, mass spectrometry, and sensor technology, highlighting their transformative potential in food quality control, identification of bioactive constituents, contaminant detection, nutritional analysis, and novel ingredient design. Through specific case studies, the review demonstrates how AI enhances analytical efficiency and accuracy, providing innovative solutions for future research and practical applications in food chemistry.

Recent Advances and Applications of Imaging and Spectroscopy Technologies for Tea Quality Assessment: A Review

2025-06-21

Shujun Zhi , Ting An , Han Zhang +3 more | Agronomy

Significant research has been carried out on the applications of imaging and spectroscopy technologies for a variety of foods and agricultural products, and the technical fundamentals and their feasibilities have … Significant research has been carried out on the applications of imaging and spectroscopy technologies for a variety of foods and agricultural products, and the technical fundamentals and their feasibilities have also been widely demonstrated in the past decade. Imaging technologies, including computer vision, Raman, X-ray, magnetic resonance (MR), fluorescence imaging, spectroscopy technology, as well as spectral imaging technologies, including hyperspectral or multi-spectral imaging, have found their applications in non-destructive tea quality assessment. Tea quality can be assessed by considering their external qualities (color, texture, shape, and defect), internal qualities (contents of polyphenols, amino acids, caffeine, theaflavin, etc.), and safety. In recent years, numerous studies have been published to advance non-destructive methods for assessing tea quality using imaging and spectroscopy technologies. This review aims to give a thorough overview of imaging and spectroscopy technologies, data processing and analyzing methods, as well as their applications in tea quality non-destructive assessment. The challenges and directions of tea quality inspection by using imaging and spectroscopy technologies for future research and development will also be reported and formulated in this review.

Canola Yield Estimation Using Remotely Sensed Images and M5P Model Tree Algorithm

2025-06-21

Ileana De los Ángeles Fallas Calderón , Muditha K. Heenkenda , T. S. Sahota +1 more | Remote Sensing

Northwestern Ontario has a shorter growing season but fertile soil, affordable land, opportunities for agricultural diversification, and a demand for canola production. Canola yield mainly varies with spatial heterogeneity of … Northwestern Ontario has a shorter growing season but fertile soil, affordable land, opportunities for agricultural diversification, and a demand for canola production. Canola yield mainly varies with spatial heterogeneity of soil properties, crop parameters, and meteorological conditions; thus, existing yield estimation models must be revised before being adopted in Northwestern Ontario to ensure accuracy. Region-specific canola cultivation guidelines are essential. This study utilized high spatial-resolution images to estimate flower coverage and yield in experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada. Spectral profiles were created for canola flowers and pods. During the peak flowering period, the reflectance of green and red bands was almost identical, allowing for the successful classification of yellow flower coverage using a recursive partitioning and regression tree algorithm. A notable decrease in reflectance in the RedEdge and NIR bands was observed during the transition from pod maturation to senescence, reflecting physiological changes. Canola yield was estimated using selected vegetation indices derived from images, the percent cover of flowers, and the M5P Model Tree algorithm. Field samples were used to calibrate and validate prediction models. The model’s prediction accuracy was high, with a correlation coefficient (r) of 0.78 and a mean squared error of 7.2 kg/ha compared to field samples. In conclusion, this study provided an important insight into canola growth using remote sensing. In the future, when modelling, it is recommended to consider other variables (soil nutrients and climate) that might affect crop development.

Exploring Formation and Control of Hazards in Thermal Processing for Food Safety

2025-06-21

Zeyan Liu , Shanshan Gao , Zhong‐Yong Yuan +6 more | Foods

Thermal-processed foods like baked, smoked, and fried products are popular for their unique aroma, taste, and color. However, thermal processing can generate various contaminants via Maillard reaction, lipid oxidation, and … Thermal-processed foods like baked, smoked, and fried products are popular for their unique aroma, taste, and color. However, thermal processing can generate various contaminants via Maillard reaction, lipid oxidation, and thermal degradation, negatively impacting human health. This review summarizes the formation pathways, influencing factors, and tracing approaches of potential hazards in thermally processed foods, such as polycyclic aromatic hydrocarbons (PAHs), heterocyclic aromatic amines (HAAs), furan, acrylamide (AA), trans fatty acids (TFAs), advanced glycation end-products (AGEs), sterol oxide. The formation pathways are explored through understanding high free radical activity and multiple active intermediates. Control patterns are uncovered by adjusting processing conditions and food composition and adding antioxidants, aiming to inhibit hazards and enhance the safety of thermal-processed foods.

Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy

2025-06-21

Chenxiao Li , Jian Yu , Shaofeng Wang +3 more | Agronomy

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical … This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D1–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D1–RF reached 5.9 for protein, and CARS–SG + D2–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications.

Hyperspectral assessment of bacterial blight disease in red kidney beans by feature selection and machine learning algorithms

2025-06-21

Xingxing Qiao , Jiachen Wang , Binghan Jing +7 more | Precision Agriculture

The Quality Prediction of Olive and Sunflower Oils Using NIR Spectroscopy and Chemometrics: A Sustainable Approach

2025-06-20

Taha Mehany , J.M. González-Sáiz , C. Pizarro | Foods

This study presents a novel approach combining near-infrared (NIR) spectroscopy with multivariate calibration to develop simplified yet robust regression models for evaluating the quality of various edible oils. Using a … This study presents a novel approach combining near-infrared (NIR) spectroscopy with multivariate calibration to develop simplified yet robust regression models for evaluating the quality of various edible oils. Using a reduced number of NIR wavelengths selected via the stepwise decorrelation method (SELECT) and ordinary least squares (OLS) regression, the models quantify pigments (carotenoids and chlorophyll), antioxidant activity, and key sensory attributes (rancid, fruity green, fruity ripe, bitter, and pungent) in nine extra virgin olive oil (EVOO) varieties. The dataset also includes low-quality olive oils (e.g., refined and pomace oils, supplemented or not with hydroxytyrosol) and sunflower oils, both before and after deep-frying. SELECT improves model performance by identifying key wavelengths—up to 30 out of 700—and achieves high correlation coefficients (R = 0.86–0.96) with low standard errors. The number of latent variables ranges from 26 to 30, demonstrating adaptability to different oil properties. The best models yield low leave-one-out (LOO) prediction errors, confirming their accuracy (e.g., 1.36 mg/kg for carotenoids and 0.88 for rancidity). These results demonstrate that SELECT–OLS regression combined with NIR spectroscopy provides a fast, cost-effective, and reliable method for assessing oil quality under diverse processing conditions, including deep-frying, making it highly suitable for quality control in the edible oils industry.

ENHANCING RICE QUALITY PREDICTION USING A HYBRID DEEP LEARNING MODEL WITH EXPLAINABLE AI TECHNIQUES

2025-06-20

Mohmad Mujtaba Nabi Chadroo , Gurvinder Singh | International Journal of Information Technology

Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines

2025-06-20

Yuting You , Lei Zhang , Zhuo Yu +3 more | Frontiers in Chemistry

Hyperspectral imaging (HSI) technology integrates spectral analysis and image recognition with non-destructive and efficient advantages, and is widely used in the agriculture, geological exploration, military sectors, among others. Traditional Chinese … Hyperspectral imaging (HSI) technology integrates spectral analysis and image recognition with non-destructive and efficient advantages, and is widely used in the agriculture, geological exploration, military sectors, among others. Traditional Chinese medicine (TCM) has a long history of use in China, and to ensure the quality of TCM herbs, it is necessary to perform accurate quality assessments. It is also crucial to evaluate the active ingredients and changes in cultivation strategies and processing parameters over time. The use of HSI technology for the investigation of Chinese medicines has grown in importance, and recent advances in HSI have enabled the multi-dimensional non-destructive analyses of various components, origins, and growth statuses, thereby providing innovative solutions for modernization. This paper systematically reviews the application of HSI for detecting active ingredients, evaluating their quality, and recognizing the authenticity and species of Chinese herbal medicines. It clearly describes the limitations of hyperspectral technology in terms of data processing, emphasizes the importance of textural information, and suggests the application of HSI for large-scale detection.

Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (Salvia officinalis L.) Components in Fresh Cheese Production

2025-06-20

Dajana Vukić , Biljana Lončar , Lato Pezo +1 more | Foods

Plant-derived materials from Salvia officinalis L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against Listeria … Plant-derived materials from Salvia officinalis L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against Listeria monocytogenes, Escherichia coli, and Staphylococcus aureus was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from Staphylococcus aureus, with a value of –33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)—were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R2 = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems.

Rapid evaluation of Farinograph and Extensograph characteristics in bread wheat using near-infrared spectroscopy and chemometrics

2025-06-20

Zihui Zhao , Lie Li , W. Li +7 more | Food Research International

Navigating AGN variability with self-organizing maps

2025-06-20

Y. Maruccia , D. De Cicco , S. Cavuoti +6 more | Astronomy and Astrophysics

The classification of active galactic nuclei (AGNs) is a challenge in astrophysics. Variability features extracted from light curves offer a promising avenue for distinguishing AGNs and their subclasses. This approach … The classification of active galactic nuclei (AGNs) is a challenge in astrophysics. Variability features extracted from light curves offer a promising avenue for distinguishing AGNs and their subclasses. This approach would be very valuable in sight of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Our goal is to utilize self-organizing maps (SOMs) to classify AGNs based on variability features and investigate how the use of different subsets of features impacts the purity and completeness of the resulting classifications. We derived a set of variability features from light curves, similar to those employed in previous studies, and applied SOMs to explore the distribution of AGNs subclasses. We conducted a comparative analysis of the classifications obtained with different subsets of features, focusing on the ability to identify different AGNs types. Our analysis demonstrates that using SOMs with variability features yields a relatively pure AGNs sample, though completeness remains a challenge. In particular, Type 2 AGNs are the hardest to identify, as can be expected. These results represent a promising step toward the development of tools that may support AGNs selection in future large-scale surveys such as LSST.

Mathematical Foundations and Comparative Analysis of the Least Squares Method

2025-06-20

Yanxi Li | Theoretical and Natural Science

The least squares method is a fundamental statistical approach used to determine the best-fitting function for a given set of data by minimizing the sum of squared residuals. Originating from … The least squares method is a fundamental statistical approach used to determine the best-fitting function for a given set of data by minimizing the sum of squared residuals. Originating from the work of Carl Friedrich Gauss and Adrien-Marie Legendre in the early 19th century, it has since become a cornerstone in regression analysis, data fitting, and various scientific applications. This paper aims to explore the mathematical principles underlying the least squares method, including its derivation, geometric interpretation, and theoretical comparisons between Ordinary Least Squares and Weighted Least Squares. We compare Ordinary Least Squares with Weighted Least Squares, highlighting their respective assumptions and use cases. Our findings demonstrate that while Ordinary Least Squares provide unbiased and efficient estimations under homoskedasticity, Weighted Least Squares are preferable when dealing with heteroskedastic data. By presenting a comprehensive analysis of the least squares method, we offer insights into its theoretical foundations, providing valuable guidance for researchers and practitioners in data analysis.

Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects

2025-06-19

Jingyi Liu , Jun Sun , Yasong Wang +3 more | Foods

Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing … Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing technology has become a research hotspot due to its outstanding advantages. In this paper, the principle, application, advantages and disadvantages of optical, acoustic, electromagnetics, dielectric properties research and electronic nose non-destructive testing technology in fruit quality testing are systematically reviewed. These technologies can detect a variety of chemical components of fruit, realize the assessment of maturity, damage degree, disease degree, and are suitable for orchard picking, quality grading, shelf life prediction and other fields. However, there are limitations to these techniques. The optical, acoustic and electronic nose technologies are susceptible to environmental factors, the electromagnetic technology has defects in the detection of complex molecules and fruit internal quality, and the dielectric characteristics are greatly affected by the shape and state of the sample surface. In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry.

Detection of degraded high-density polyethylene via near-infrared hyperspectral imaging

2025-06-19

Jutta Geier , Chiara Barretta , Mario Messiha +6 more | Waste Management

The quality of recycled plastics is crucial to make them competitive in more demanding applications and to extend their range of applications. However, there are many influencing factors that can … The quality of recycled plastics is crucial to make them competitive in more demanding applications and to extend their range of applications. However, there are many influencing factors that can reduce the quality and limit the use of recyclates. One of these factors is degradation, which can occur at different stages of a plastic's life cycle. Degraded material can affect the quality of recyclates. Therefore, it would be beneficial to sort out heavily aged plastics from the recycling stream before further processing. This work investigates the possibility of separating severely degraded polyethylene (PE) from unaged or less degraded PE using near-infrared (NIR) hyperspectral imaging. For this purpose, PE samples were artificially aged using two methods: (i) exposure to UV light, (ii) exposure to aqueous chlorine dioxide solution. The ageing state of the samples was assessed by means of Fourier Transform Infrared (FTIR) spectroscopy and tensile tests and their NIR spectra were recorded on a laboratory NIR sorter. The ability to separate highly degraded from non-/less-degraded samples, which was defined by their mechanical performance, was then analysed using multivariate data analysis and machine learning algorithms applied to the NIR data. These analyses showed promising results for separating highly degraded PE samples, with the classification between degraded and less degraded PE achieving accuracy and F1 scores exceeding 90%.

Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder

2025-06-19

Özcan Çataltaş | New Trends in Computer Sciences

In this study, a deep learning-based denoising autoencoder approach is proposed to increase the robustness of near-infrared spectroscopy data to random noise and improve quantitative modeling accuracy. Artificial Gaussian noise … In this study, a deep learning-based denoising autoencoder approach is proposed to increase the robustness of near-infrared spectroscopy data to random noise and improve quantitative modeling accuracy. Artificial Gaussian noise at four different levels (10, 15, 20, and 25 dB) was added to the near-infrared spectra obtained from milk samples to mimic the real measurement conditions. The noisy spectra were denoised by processing with an autoencoder architecture consisting of fully connected layers. The noise removal performance is quantitatively evaluated with both theoretical and measured signal-to-noise ratio values. The results show that the AE model significantly improves the spectral signal quality at all signal-to-noise ratio levels. In particular, at the lowest signal-to-noise ratio level (10 dB), the signal-to-noise ratio value nearly tripled to 29.6 dB with the autoencoder. At all other levels, an average increase of 18-20 dB was observed in the signal-to-noise ratio of the denoised spectra. In the second stage of the study, Partial Least Squares Regression models were built using both the noisy and cleaned spectra and evaluated on the test set with root mean square error and coefficient of determination. The Partial Least Squares Regression models built with the denoised spectra achieved lower root mean square error and higher coefficient of determination values at all signal-to-noise ratio levels. Especially at the 10 dB signal-to-noise ratio level, the coefficient of determination value of the model increased from 0.44 to 0.71, while the root means square error decreased from 0.60 to 0.43. The results show that the deep learning-based AE architecture can effectively reduce random noise in near-infrared spectral data and significantly improve both spectral signal quality and quantitative modeling performance. This approach provides an effective solution to improve model reliability and accuracy in near-infrared spectroscopy analysis.

Use of Internet of Things for Monitoring, Automation, and Performance in Industrial Production of Cheese

2025-06-19

Valério Junior Piana , Crivian Pelisser , Maristela Schleicher Silveira +3 more | Research Society and Development

This article describes a solution for monitoring, automation, and control in industrial production. The presented approach combines Internet of Things (IoT), cloud storage, and sensing technologies applied to a cheese … This article describes a solution for monitoring, automation, and control in industrial production. The presented approach combines Internet of Things (IoT), cloud storage, and sensing technologies applied to a cheese manufacturing tank. The sensors attached to the cheese manufacturing tank enable readings of temperature, pH, and level, contributing to process control, streamlining decision-making, and consequently improving product quality. The system can be accessed via the internet, allowing remote access and control, where users select the type of cheese to be produced, and based on the algorithm, the tank autonomously performs the process. The results demonstrate that through the digitization process, it is possible to monitor and act upon the tank, either manually or through autonomous programming, using a multi-technology interface of open-source circuits and code, providing a low investment compared to proprietary solutions in the industrial market. In this present work, in addition to controlling and digitizing equipment data that directly affects profitability, the benefits of the implemented system extend to increased productivity, remote access, cloud storage, and the possibility of adapting industrial equipment (retrofitting) from different scenario segments, which were configured within a framework.

Non-Destructive Prediction of Moisture Content in Cascara Using NIR Spectroscopy with PLS and PCR

2025-06-19

Andasuryani Andasuryani , Nadilla Alifia Safmi , Khandra Fahmy | Jurnal Keteknikan Pertanian

Coffee cherry pulp is a by-product of coffee processing that has not been optimally utilized. Coffee cherry pulp can be dried to produce a herbal tea product, known as cascara. … Coffee cherry pulp is a by-product of coffee processing that has not been optimally utilized. Coffee cherry pulp can be dried to produce a herbal tea product, known as cascara. As an herbal tea product, moisture content is one of the most important quality parameters for assessing the quality of cascara. Therefore, a method is required to measure the moisture content of cascara. One of the methods developed is NIR spectroscopy, which is non-destructive, fast, and does not require chemicals. The purpose of this research is to explore the application of NIR spectroscopy in predicting cascara moisture content using partial least squares (PLS) and principal component regression (PCR) methods and to evaluate the performance of each method in building an optimal calibration model. Pretreatment of the spectrum data was carried out with standard normal variate (SNV), gap-segment 2nd derivative (dg2), and a combination of SNV+dg2. The results showed that the best prediction of cascara moisture content used the PLS calibration technique with dg2 pretreatment and five factors 5. The values obtained were Rc2 = 0.96, RMSEC = 0.87 %, SEC = 0.87 %, Rp2 = 0.90, RMSEP = 1.22 %, SEP = 1.16 %, and RPD = 3.44. Meanwhile, the PCR method produced good predictions using SNV pretreatment, with a factor of 8. The prediction results were Rc2 = 0.89, RMSEC = 1.40 %, Rp2 = 0.90, RMSEP = 1.33 %, and RPD = 3.15. NIR spectroscopy can predict the moisture content of cascara nondestructively and rapidly.

Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model

2025-06-19

Zeqing Yang , Jiahui Zhang , Zhimeng Li +2 more | Agriculture

Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in … Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry.

Classification of Common Bean Landraces of Three Species Using a Neuroevolutionary Approach with Probabilistic Color Characterization

2025-06-19

José-Luis Morales-Reyes , Elia Nora Aquino-Bolaños , Héctor‐Gabriel Acosta‐Mesa +2 more | Mathematical and Computational Applications

The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and … The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and seed mixtures as part of agricultural practices. In this work, we propose a methodology for classifying bean landrace samples using three two-dimensional histograms with data in the CIE L*a*b* color space while additionally integrating chroma (C*) and hue (h°) to develop a new proposal from histograms, employing deep learning for the classification task. The results indicate that utilizing three histograms based on L*, C*, and h° brings an average accuracy of 85.74 ± 2.37 compared to three histograms using L*, a*, and b*, which reported an average accuracy of 82.22 ± 2.84. In conclusion, the new color characterization approach presents a viable solution for classifying common bean landraces of both homogeneous and heterogeneous colors.

In-line Monitoring Astaxanthin in Krill Meal Using Computer Vision

2025-06-19

Quantong Zhang , Yang Liu , Xin Wang +2 more | Journal of Food Composition and Analysis

Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion

2025-06-19

Li Guo , Qin Gao , Mengyi Zhang +7 more | Agriculture

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet … Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R2 of 0.87 and an RMSE of 1.68, a 23% improvement in R2 compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R2 improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management.

ATR-FTIR spectroscopy coupled with machine learning for the discrimination of luxury handbags: Authentication and counterfeit brand classification

2025-06-19

Haohan Yuan , Jian Zhang , Jinlong Zhang +4 more | Microchemical Journal

Prediction of PDO Kalamata Olive Oil Tasting Quality based on Statistical Machine Learning Techniques Utilizing Real Data from Synchronous Emission-Excitation Fluorescence Spectra and Tasting Analyses

2025-06-19

Theodoros Anagnostopoulos , Anna Milionis , Chara Kottara | WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT

Combining domain knowledge provided by analyses of Protected Designation of Origin (PDO) Kalamata olive oil data sources could input multiclass classification statistical machine learning models to output accurate predictions of … Combining domain knowledge provided by analyses of Protected Designation of Origin (PDO) Kalamata olive oil data sources could input multiclass classification statistical machine learning models to output accurate predictions of certain olive oil quality classes. Concretely, machine learning models are trained with both synchronous emission-excitation fluorescence spectra provided by certain spectroscopic techniques with tasting analyses of domain tasting panelists to infer the quality class of the input sample. Such a process enhances tasting panelists specific tasting knowledge by supporting their prediction accuracy with chemical data of certain real olive oil data samples. In this research effort, real PDO Kalamata olive oil data is used from fluorescence spectra and tasting processes to train multiclass classification statistical machine learning models to provide accurate predictions of the examined olive oil samples’ quality classes.