Journal articles: 'Data processing and Chemometrics'

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EL-Gindy, Alaa, and Ghada M. Hadad. "Chemometrics in Pharmaceutical Analysis: An Introduction, Review, and Future Perspectives." Journal of AOAC INTERNATIONAL 95, no. 3 (May 1, 2012): 609–23. http://dx.doi.org/10.5740/jaoacint.sge_el-gindy.

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Abstract Chemometrics is the application of statistical and mathematical methods to analytical data to permit maximum collection and extraction of useful information. The utility of chemometric techniques as tools enabling multidimensional calibration of selected spectroscopic, electrochemical, and chromatographic methods is demonstrated. Application of this approach mainly for interpretation of UV-Vis and near-IR (NIR) spectra, as well as for data obtained by other instrumental methods, makes identification and quantitative analysis of active substances in complex mixtures possible, especially in the analysis of pharmaceutical preparations present in the market. Such analytical work is carried out by the use of advanced chemical instruments and data processing, which has led to a need for advanced methods to design experiments, calibrate instruments, and analyze the resulting data. The purpose of this review is to describe various chemometric methods in combination with UV-Vis spectrophotometry, NIR spectroscopy, fluorescence spectroscopy, electroanalysis, chromatographic separation, and flow-injection analysis for the analysis of drugs in pharmaceutical preparations. Theoretical and practical aspects are described with pharmaceutical examples of chemometric applications. This review will concentrate on gaining an understanding of how chemometrics can be useful in the modern analytical laboratory. A selection of the most challenging problems faced in pharmaceutical analysis is presented, the potential for chemometrics is considered, and some consequent implications for utilization are discussed. The reader can refer to the citations wherever appropriate.

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Hibbert, David B. "Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)." Pure and Applied Chemistry 88, no. 4 (April 1, 2016): 407–43. http://dx.doi.org/10.1515/pac-2015-0605.

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AbstractRecommendations are given concerning the terminology relating to chemometrics. Building on ISO definitions of terms for basic concepts in statistics the vocabulary is concerned with mainstream chemometric methods. Where methods are used widely in science, definitions are given that are most useful to chemical applications. Vocabularies are given for general data processing, experimental design, classification, calibration and general multivariate methods.

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Dumancas, Gerard G., Ghalib Bello, Jeff Hughes, Renita Murimi, Lakshmi Viswanath, Casey O. Orndorff, Glenda Fe G. Dumancas, Jacy O'Dell, Prakash Ghimire, and Catherine Setijadi. "Chemometrics." International Journal of Fog Computing 2, no. 1 (January 2019): 1–42. http://dx.doi.org/10.4018/ijfc.2019010101.

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The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.

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Whitfield, Matthew B., and Mari S. Chinn. "Near infrared spectroscopic data handling and chemometric analysis with the R statistical programming language: A practical tutorial." Journal of Near Infrared Spectroscopy 25, no. 6 (November 14, 2017): 363–80. http://dx.doi.org/10.1177/0967033517740768.

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Near infrared spectroscopy is widely used for compositional analysis of bulk materials because it is inexpensive, fast, and non-destructive. However, the chemometric techniques required to produce near infrared calibrations are varied and complex. While there are a number of commercial applications capable of implementing these techniques, there has also been a recent proliferation of R packages for chemometrics. The R programming language has greater capabilities for data processing, automation of multiple analyses, and user development of new techniques than many of the closed-source, graphical user interface-based commercial chemometrics applications do. The R project is thus a powerful, open-source option for generating and testing near infrared calibrations, albeit with a longer learning curve than many of the commercial chemometric applications. The calibration techniques available in R have been widely demonstrated in both the primary literature and introductory texts, but less so the steps between the acquisition of the data and the calibration. This tutorial seeks to bridge that gap by demonstrating a practical approach to data transfer and handling, using R and several packages available on the Comprehensive R Archive Network ( https://cran.r-project.org/ ), and then illustrates the use of the resulting data framework in the generation of near infrared calibrations.

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Artemenko, Vladuslav, and Volodymyr Petrovych. "NEW CONCEPT OF CHEMOMETRICS." Automobile Roads and Road Construction, no. 113.2 (2023): 117–32. http://dx.doi.org/10.33744/0365-8171-2023-113.2-117-132.

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The parametric statistical methods of the research experimental data most often use presently in chemometrics. If experimental data do not correspond to the normal probabilistic distribution that in this case it is impossible prodice adequate data processing. At recently in chemometrics more often started to use the classical nonparametric methods. The classical nonparametric methods do not handle the raw datas probabilistic distribution. But these methods when undertakind the real calculations don’t care use that or other types of the distributions. The classical nonparametric methods under its realization usually requare referencing to corresponding statistical tables. Its own table is used for each such method. Chemometrics to presently presents itself many not bound between itself statistical methods of the investigations. The huge defect of the classical methods of chemometrics (parametric and nonparametric) in absence of the united approach to processing the raw datas. In this article is offered in fundamentally new approach to statistical investigations of the datas. If use the method of nonparametric bootstrap that possible replace many unbound between itself methods of classical chemometrics whole one ore two universal methods. And these universal methods have not what or essential defect. With standpoint of the program realization this means presence one or two universal procedures for decision nearly all practical problems of chemometrics. One of the primary tasks of nonparametric bootstrap as follows problem of the duplication of the sample is considered in article. Herewith simulated that was organized not one series of experiment but well over (for example 1000 … 10000). On example is shown use the method of nonparametric bootstrap with finding confidential interval for average and median for ecological time series. On programming language MATLAB is brought code corresponding bootstrap procedure.

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Pereira da Cunha, Pedro Henrique, Gabriely Silveira Folli, Sara Joaquina Inocencio Dionisio, Amanda Guedes Caldeira, and Paulo Roberto Filgueiras. "Tutorial para aplicação didática de quimiometria em software gratuito – Parte II: Regressão por Mínimos Quadrados Parciais (PLS) em dados de infravermelho médio e próximo para determinação de teor de adulterantes e propriedades físico-químicas." Revista Ifes Ciência 10, no. 4 (June 20, 2024): 01–16. http://dx.doi.org/10.36524/ric.v10i4.2535.

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The constant advance in technology, instrumentation, and the increase in data generation capacity has resulted in a new challenge for chemists: how to deal with complex, extensive, and multidimensional data sets. In this scenario, chemometrics emerged as a branch of chemistry specialized in the application of statistical and mathematical techniques for the analysis of multivariate analytical data, resulting in more precise and reliable scientific conclusions. As it is a relatively new area, there is still a lack of educational resources on the subject, and chemometric approaches are mostly confined to postgraduate courses. Therefore, this article provides a tutorial that can be used in various academic instances, with a focus on undergraduate education, to teach partial least squares regression (PLS). PLS is considered one of the most essential and common methods in chemometrics, and stands out for its ability to efficiently handle complex and highly correlated data sets, which is particularly important in regression analysis when there is significant multicollinearity among independent variables. In addition, PLS is known for its ease of use, making it a valuable tool for professionals seeking an effective approach to complex statistical modeling. We detail all the steps to create a complete PLS model, from installing the free GNU Octave software to producing the final figures. We also provide all the algorithms developed for data reading and processing. Finally, we interpret the obtained results so that the discussions and conclusions are easily understood by everyone.

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Vandeerstraeten, F., C. Wojciechowski, N. Dupuy, and J. P. Huvenne. "Recognition of starch origin and modifications by chemometrics spectral data processing (in French)." Analusis 26, no. 8 (October 1998): 57–62. http://dx.doi.org/10.1051/analusis:199826080057.

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Musa, Ayoko, Ward, Rösch, Brown, and Rainey. "Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity." Cells 8, no. 8 (August 7, 2019): 851. http://dx.doi.org/10.3390/cells8080851.

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Microalgae are swift replicating photosynthetic microorganisms with several applications for food, chemicals, medicine and fuel. Microalgae have been identified to be suitable for biofuels production, due to their high lipid contents. Microalgae-based biofuels have the potential to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. However, the present state of technology does not economically support sustainable large-scale production. The biofuel production process comprises the upstream and downstream processing phases, with several uncertainties involved. This review examines the various production and processing stages, and considers the use of chemometric methods in identifying and understanding relationships from measured study parameters via statistical methods, across microalgae production stages. This approach enables collection of relevant information for system performance assessment. The principal benefit of such analysis is the identification of the key contributing factors, useful for decision makers to improve system design, operation and process economics. Chemometrics proffers options for time saving in data analysis, as well as efficient process optimization, which could be relevant for the continuous growth of the microalgae industry.

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Kebede, Biniam, Pui Lee, Sze Leong, Vidya Kethireddy, Qianli Ma, Kemal Aganovic, Graham Eyres, Nazimah Hamid, and Indrawati Oey. "A Chemometrics Approach Comparing Volatile Changes during the Shelf Life of Apple Juice Processed by Pulsed Electric Fields, High Pressure and Thermal Pasteurization." Foods 7, no. 10 (October 17, 2018): 169. http://dx.doi.org/10.3390/foods7100169.

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High-Pressure Processing (HPP) and Pulsed Electric Fields (PEF) processing technologies are being used increasingly on a commercial basis, with high-quality labelled fruit juices being one of the most important promotion strategies. Quality-related enzymes, which might still be active after HPP and PEF pasteurization, can cause undesirable aroma changes during storage. This study investigated volatile changes during the shelf life of PEF (15.5 kV/cm and specific energy of 158 kJ/L), HPP (600 MPa for 3 min), and thermally (72 °C for 15 s) pasteurized Jazz apple juices—up to five weeks. To have an increased insight into the volatile changes, an integrated instrumental (GC-MS) and data analysis (chemometrics) approach was implemented. Immediately after pasteurization, PEF processing resulted a better retention of odor-active volatiles, such as (E)-2-hexenal and hexyl acetate, whereas thermal processing lowered their amount. During refrigerated storage, these volatiles have gradually decreased in all processed juices. By the end of storage, the amount of these aroma relevant volatiles appears to still be higher in PEF and HPP pasteurized juices compared to their conventional counterparts. This study demonstrated the potential of advanced chemometric approaches to obtain increased insight into complex shelf life changes.

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Stone, David C. "Application of median filtering to noisy data." Canadian Journal of Chemistry 73, no. 10 (October 1, 1995): 1573–81. http://dx.doi.org/10.1139/v95-195.

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The properties and application of the median filter are investigated using both simulated and real data for signals evolving with time. Comparison is made with existing numerical techniques for drift compensation and noise reduction in analytical measurements such as the moving average and Savitzky–Golay digital filters. The median filter provides a means for dealing with "spiky" noise and separating peaks from a slowly changing baseline, even when the exact nature of the drift and noise distribution is not known. Median filtering is a useful and complementary addition to existing digital filtering techniques, being mathematically robust and readily implemented on any computer platform. Keywords: median filtering, signal noise, signal processing, digital filters, chemometrics, data smoothing.

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Pacheco-Londoño, Leonardo C., John R. Castro-Suarez, and Samuel P. Hernández-Rivera. "Detection of Nitroaromatic and Peroxide Explosives in Air Using Infrared Spectroscopy: QCL and FTIR." Advances in Optical Technologies 2013 (May 7, 2013): 1–8. http://dx.doi.org/10.1155/2013/532670.

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A methodology for processing spectroscopic information using a chemometrics-based analysis was designed and implemented in the detection of highly energetic materials (HEMs) in the gas phase at trace levels. The presence of the nitroaromatic HEM 2,4-dinitrotoluene (2,4-DNT) and the cyclic organic peroxide triacetone triperoxide (TATP) in air was detected by chemometrics-enhanced vibrational spectroscopy. Several infrared experimental setups were tested using traditional heated sources (globar), modulated and nonmodulated FT-IR, and quantum cascade laser- (QCL-) based dispersive IR spectroscopy. The data obtained from the gas phase absorption experiments in the midinfrared (MIR) region were used for building the chemometrics models. Partial least-squares discriminant analysis (PLS-DA) was used to generate pattern recognition schemes for trace amounts of explosives in air. The QCL-based methodology exhibited a better capacity of discrimination for the detected presence of HEM in air compared to other methodologies.

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Gudjónsdóttir, María, Gudrún Svana Hilmarsdóttir, Ólafur Ögmundarson, and Sigurjón Arason. "Near-Infrared Spectroscopy and Chemometrics for Effective Online Quality Monitoring and Process Control during Pelagic Fishmeal and Oil Processing." Foods 13, no. 8 (April 13, 2024): 1186. http://dx.doi.org/10.3390/foods13081186.

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Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at RCV2 = 0.9938, RMSECV = 2.41 for water; RCV2 = 0.9773, RMSECV = 3.94 for lipids; and RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (RCV2=0.9928, RMSECV=0.24), MUFA (RCV2=0.8291, RMSECV=1.49), PUFA (RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL RCV2=0.8617, RMSECV=0.11, and DHA (RCV2=0.8785, RMSECV=0.89) and EPA content RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.

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Covaciu, Florina-Dorina, Camelia Berghian-Grosan, Ioana Feher, and Dana Alina Magdas. "Edible Oils Differentiation Based on the Determination of Fatty Acids Profile and Raman Spectroscopy—A Case Study." Applied Sciences 10, no. 23 (November 24, 2020): 8347. http://dx.doi.org/10.3390/app10238347.

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This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil.

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Ivask, K. "CHEMOMETRIC METHODS IN PROCESSING THERMOCHROMATOGRAPHIC DATA." Proceedings of the Estonian Academy of Sciences. Chemistry 44, no. 2/3 (1995): 132. http://dx.doi.org/10.3176/chem.1995.2/3.07.

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Dubrovkin, Joseph. "A Novel Compression Method of Spectral Data Matrix Based on the Low-Rank Approximation and the Fast Fourier Transform of the Singular Vectors." Applied Spectroscopy 76, no. 3 (October 1, 2021): 369–78. http://dx.doi.org/10.1177/00037028211044759.

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Storage, processing, and transfer of huge matrices are becoming challenging tasks in the process analytical technology and scientific research. Matrix compression can solve these problems successfully. We developed a novel compression method of spectral data matrix based on its low-rank approximation and the fast Fourier transform of the singular vectors. This method differs from the known ones in that it does not require restoring the low-rank approximated matrix for further Fourier processing. Therefore, the compression ratio increases. A compromise between the losses of the accuracy of the data matrix restoring and the compression ratio was achieved by selecting the processing parameters. The method was applied to multivariate chemometrics analysis of the cow milk for determining fat and protein content using two data matrices (the file sizes were 5.7 and 12.0 MB) restored from their compressed form. The corresponding compression ratios were about 52 and 114, while the loss of accuracy of the analysis was less than 1% compared with processing of the non-compressed matrix. A huge, simulated matrix, compressed from 400 MB to 1.9 MB, was successfully used for multivariate calibration and segment cross-validation. The data set simulated a large matrix of 10 000 low-noise infrared spectra, measured in the range 4000–400 cm−1 with a resolution of 0.5 cm−1. The corresponding file was compressed from 262.8 MB to 19.8 MB. The discrepancies between original and restored spectra were less than the standard deviation of the noise. The method developed in the article clearly demonstrated its potential for future applications to chemometrics-enhanced spectrometric analysis with limited options of memory size and data transfer rate. The algorithm used the standard routines of Matlab software.

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Rasouli, Zolaikha, and Raouf Ghavami. "Simultaneously detection of calcium and magnesium in various samples by calmagite and chemometrics data processing." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 169 (December 2016): 72–81. http://dx.doi.org/10.1016/j.saa.2016.06.027.

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Dia, Syahril Maulid, Anggita Rosiana Putri, and Luthfi Ahmad Muchlashi. "Detection of Adulterants Metanil Yellow in Turmeric Powder Using Fourier Transform Infrared (FTIR) Spectroscopy Combined with Chemometrics OPLS-DA and PLS." IJCA (Indonesian Journal of Chemical Analysis) 7, no. 1 (March 31, 2024): 64–71. http://dx.doi.org/10.20885/ijca.vol7.iss1.art7.

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Turmeric powder (Curcuma longa L.) is often used for food coloring and flavoring. The high demand for turmeric powder has led to adulteration, one of which is metanil yellow. This study aims to detect metanil yellow dye in turmeric powder using Infrared Spectroscopy (FT-IR) and Chemometrics. The chemometrics model was built by preparing turmeric powder and metanil yellow in the concentration range of 0-50% (w/w). Data was processed using multivariate calibration using OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) and PLS (Partial Last Square). The results obtained from OPLS-DA processing of market samples 1 and 2 are estimated to be pure turmeric powder, and samples 3 to 10 are adulterated. From the PLS results, the best calibration model was obtained at wave numbers 1199-539 cm-1 with R2 values for calibration and validation of 0.9967 and 0.9970, respectively, with RMSEC of 2.48 and RMSEP of 6.35. The PLS results showed that in sample 3 containing metanil yellow, in samples 4 to 10, no metanil yellow was detected. The ingredients added were not metanil yellow. It was concluded that FTIR combined with chemometrics could detect the metanil yellow in turmeric powder.

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Liu, Ziyi, Luning Li, Weiming Xu, Xuesen Xu, Zhicheng Cui, Liangchen Jia, Wenhao Lv, Zhihui Shen, and Rong Shu. "Investigation into the Affect of Chemometrics and Spectral Data Preprocessing Approaches upon Laser-Induced Breakdown Spectroscopy Quantification Accuracy Based on MarSCoDe Laboratory Model and MarSDEEP Equipment." Remote Sensing 15, no. 13 (June 28, 2023): 3311. http://dx.doi.org/10.3390/rs15133311.

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As part of China’s Tianwen-1 Mars mission, the Mars Surface Composition Detector (MarSCoDe) instrument on the Zhurong rover adopts laser-induced breakdown spectroscopy (LIBS) to perform chemical component detection of the materials on the Martian surface. However, it has always been a challenging issue to achieve high accuracy in LIBS quantification. This study investigated the effect of chemometrics and spectral data preprocessing approaches on LIBS quantification accuracy based on different chemometrics algorithms and diverse preprocessing methods. A total of 2340 LIBS spectra were collected from 39 kinds of geochemical samples by a laboratory duplicate model of the MarSCoDe instrument. The samples and the MarSCoDe laboratory model were placed in a simulated Martian atmosphere environment based on equipment called the Mars-Simulated Detection Environment Experiment Platform (MarSDEEP). To quantify the concentration of MgO in the samples, we employed two common LIBS chemometrics; i.e., partial least squares (PLS) and a back-propagation neural network (BPNN). Meanwhile, in addition to necessary routine preprocessing such as dark subtraction, we used five specific preprocessing approaches, namely intensity normalization, baseline removal, Mg-peak wavelength correction, Mg-peak feature engineering, and concentration range reduction. The results indicated that the performance of the BPNN was better than that of the PLS and that the preprocessing of Mg-peak wavelength correction had the most prominent effect to improve the quantification accuracy. The results of this study are expected to provide inspiration for the processing and analysis of the in situ LIBS data acquired by MarSCoDe on Mars.

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de Oliveira, Elcio. "Highlighting the Importance of Data Treatment in Analytical Chemistry." Brazilian Journal of Analytical Chemistry 11, no. 45 (October 3, 2024): 1–2. http://dx.doi.org/10.30744/brjac.2179-3425.editorial.n45.

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In recent decades, statistical analysis and data processing have become essential tools in Analytical Chemistry, ensuring reliable results from experimental data treatment. Some of the main statistical techniques used in this field include descriptive statistics, hypothesis tests, quality control of analytical measurements, calibration methods in instrumental analysis with a focus on regression lines, nonparametric and robust methods, experimental design, response surface methodology associated with the optimization of analytical methods, uncertainty evaluation and its application for compliance assessment, and chemometrics for interpreting chemical data. These transversal approaches are widely used for solving problems in medical scenarios, environmental research, food security, pharmaceuticals, and more. It is crucial to emphasize that proper data processing requires a solid knowledge of statistics that remains grounded in the core principles of Analytical Chemistry to ensure the correct interpretation and reliability of the results. This issue of BrJAC includes one letter, four articles, and one technical note, each featuring at least one statistical analysis and data processing method mentioned earlier in this editorial. Professor Tarley from the University of Londrina believes that chemometrics practiced in Brazil is on par with world-leading research centers. Professor Edenir Rodrigues Pereira-Filho from Federal University of São Carlos discusses the practical application of academic training to professionals in Brazilian industry. Renowned Brazilian researchers shed light on the importance of correctly expressing units of measurement in Analytical Chemistry, particularly considering the updated definition of the mole based on Avogadro's constant, following the International System of Units guidelines. Additionally, Italian and Rwandan researchers have investigated the toxicity of methanol and ethanol, seeking to identify the origin and the frequency of methanol contamination in illegal alcoholic beverages in Rwanda, supported by performance figures in method validation. Indian researchers have developed a voltammetric method for measuring acetaminophen in oral suspensions, in which the accuracy and precision parameters were essential for establishing the study’s reliability. This issue also features two optimization studies: the first validates a new method for isotopic determinations in beverages, while the second utilizes Box-Behnken design and Response Surface Methodology to optimize a method that uses hollow fiber microextraction and gas chromatography coupled to mass spectrometry for determining the presence of pesticides in postmortem whole blood samples. Finally, by using parametric and nonparametric tests and Principal Component Analysis, it was possible to infer that consuming diacetyl as a flavoring agent may pose health risks. BrJAC reaffirms the enduring relationship between data treatment and Analytical Chemistry in its publications. This focus may have contributed to the recent increase in our impact factor from 0.7 to 1.1, indicating that we are on the right track!

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Huang, Rui, Shuangcheng Ma, Shengyun Dai, and Jian Zheng. "Application of Data Fusion in Traditional Chinese Medicine: A Review." Sensors 24, no. 1 (December 25, 2023): 106. http://dx.doi.org/10.3390/s24010106.

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Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

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Khokha, Yurii, and Myroslava Yakovenko. "Use of chemometric methods and regression models in processing NIR spectra of peat for quantitative determination of its chemical and technological indicators." Geology and Geochemistry of Combustible Minerals 3-4, no. 195-196 (2024): 100–125. https://doi.org/10.15407/ggcm2024.195-196.100.

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The article discusses theoretical and practical aspects of the use of near infrared (NIR) spectroscopy combined with chemometrics for express analysis of peat. Near infrared spectroscopy provides a significant amount of information about complex organic systems, including irregular polymers such as peat. Compared to classical analytical methods, NIR spectrometry allows analysis without complex sample preparation with analysis time measured in minutes. Since the results represent the intensity of radiation reflection in the overtone range of fundamental frequencies, their processing requires the use of special mathematical and statistical methods. The use of the Chemoface software package modules (PLS method) for quantitative analysis of the technical and chemical properties of peat based on NIR spectroscopy data has demonstrated the possibility of obtaining calibration models that allow for the quick and reliable analysis of this raw material, including in field conditions. The conducted studies have shown that using a spectrometer that analyzes reflected (absorbed) radiation in the near-infrared spectrum and based on the averaged spectral characteristics of the reflected (absorbed) radiation and using chemometric software, it is possible to calculate the chemical and technological characteristics of peat. The analysis procedure consists of the following stages: selection of a sample representing the entire batch of raw materials; irradiation of the sample with radiation containing a significant proportion of energy in the near-infrared spectrum; analysis with a detector of reflected (absorbed) radiation and construction of an integral spectral characteristic of the sample; compilation of a calibration model using chemometric software; processing of the obtained spectrum using chemometric software with subsequent calculation of the qualitative and quantitative characteristics of the raw materials. The proposed method (express analysis) for rapid determination of qualitative and quantitative characteristics of fossil carbon raw materials of organic origin, namely lowland and highland peat of various degrees of decomposition, can be used to establish its compliance with current norms, standards and technical conditions for moisture content, ash (inorganic) residue content and acidity (pH).

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Palani, Bharani Kumar, Marta Siol, Sina Makouie, Joanna Bryś, Eliza Gruczyńska-Sękowska, and Piotr Koczoń. "Investigation of Oil Extracted from Roasted and Unroasted Oats with Use of Chemometrics." Applied Sciences 14, no. 24 (December 10, 2024): 11481. https://doi.org/10.3390/app142411481.

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One of the beneficial components of oats is oil that is rich in fatty acids and has excellent health and technological potential. During thermal processing, the oil is prone to chemical changes, both beneficial and adverse. One such processing method is roasting, which involves heating the product uniformly at a temperature below its melting point. The primary objective is to evaluate and report with statistical models how the chemical changes that occur during the roasting of whole oat grains affect the properties and quality of the oil found in the oat grains. To achieve this goal, a pressure differential scanning calorimeter, infrared spectroscopy calorimetric bomb, and gas chromatography were used. Using chemometric methods, the spectral data were combined with calorimetric data, automatic titration data, and chromatographic data. The discriminant and reference models of high statistical significance were calibrated and validated to rapidly and robustly evaluate the properties of oat oil and the changes that occurred due to the roasting process. It has been shown that roasting oat grains increases the oxidative stability of the oil contained in grains. The acid and peroxide values of oil extracted from roasted oat grains are lower than those determined for oil from fresh oat grains. The composition of fatty acids was not statistically significantly affected by the roasting process of oat grains.

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Ciaccheri, Leonardo, Barbara Adinolfi, Andrea Azelio Mencaglia, and Anna Grazia Mignani. "Bluetooth-Connected Pocket Spectrometer and Chemometrics for Olive Oil Applications." Foods 11, no. 15 (July 29, 2022): 2265. http://dx.doi.org/10.3390/foods11152265.

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Unsaturated fatty acids are renowned for their beneficial effects on the cardiovascular system. The high content of unsaturated fatty acids is a benefit of vegetable fats and an important nutraceutical indicator. The ability to quickly check fat composition of an edible oil could be advantageous for both consumers and retailers. A Bluetooth-connected pocket spectrometer operating in NIR band was used for analyzing olive oils of different qualities. Reference data for fatty acid composition were obtained from a certified analytical laboratory. Chemometrics was used for processing data, and predictive models were created for determining saturated and unsaturated fatty acid content. The NIR spectrum also demonstrated good capability in classifying extra virgin and non-extra virgin olive oils. The pocket spectrometer used in this study has a relatively low cost, which makes it affordable for a wide class of users. Therefore, it may open the opportunity for quick and non-destructive testing of edible oil, which can be of interest for consumer, retailers, and for small/medium-size producers, which lack easy access to conventional analytics.

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Arndt, Maike, Alissa Drees, Christian Ahlers, and Markus Fischer. "Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics." Foods 9, no. 12 (December 13, 2020): 1860. http://dx.doi.org/10.3390/foods9121860.

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The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017–2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.

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Dai, Linghao, Yang Guan, Bo Wang, and Linyan Wang. "Current role and future perspectives of chemometrics in spectroscopic and chromatographic analysis of traditional Chinese medicines." Materials Express 12, no. 2 (February 1, 2022): 202–13. http://dx.doi.org/10.1166/mex.2022.2156.

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Recently, Traditional Chinese Medicines (TCMs), having a rich history in China for their use in maintaining health and treating disease, have gained popularity worldwide. However, their modernization and globalization are impeded due to their “multi-component, multi-pathway, and multi-target” properties. Chemometrics, a comprehensive product of statistics, computers, and information, is an interfacial discipline that extracts practical information from large chemical and biochemical datasets, beneficial to overcome TCMs restrictions. This review summarized key research findings on the basis and application of TCMs according to their components, authenticity, processing conditions, geographical origin, pharmacological activity, and metabolomics based on recent studies. Here, we discussed the benefits and shortcomings of cluster analysis, principal component analysis, soft independent modeling of class analogy, artificial neural networks, support vector machine, partial least squares-discriminant analysis, data fusion and calibration, and the appropriate application of these methods in different fields of TCMs. This review aimed to provide a basic understanding of the role and perspectives of chemometrics in the spectroscopic and chromatographic analysis of TCMs.

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Suhandy, Diding, and Meinilwita Yulia. "Classification of Lampung robusta Specialty Coffee According to Differences in Cherry Processing Methods Using UV Spectroscopy and Chemometrics." Agriculture 11, no. 2 (February 1, 2021): 109. http://dx.doi.org/10.3390/agriculture11020109.

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The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

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Escuredo, Olga, María Shantal Rodríguez-Flores, Laura Meno, and María Carmen Seijo. "Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing." Foods 10, no. 2 (February 3, 2021): 317. http://dx.doi.org/10.3390/foods10020317.

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There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

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Wang, Lulu. "Terahertz Imaging for Breast Cancer Detection." Sensors 21, no. 19 (September 28, 2021): 6465. http://dx.doi.org/10.3390/s21196465.

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Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.

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Anzardi, Maria B., Juan A. Arancibia, and Alejandro C. Olivieri. "Processing multi-way chromatographic data for analytical calibration, classification and discrimination: A successful marriage between separation science and chemometrics." TrAC Trends in Analytical Chemistry 134 (January 2021): 116128. http://dx.doi.org/10.1016/j.trac.2020.116128.

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Lobanov, Alexei V., Ivan A. Borisov, Sherald H. Gordon, Richard V. Greene, Timothy D. Leathers, and Anatoly N. Reshetilov. "Analysis of ethanol–glucose mixtures by two microbial sensors: application of chemometrics and artificial neural networks for data processing." Biosensors and Bioelectronics 16, no. 9-12 (December 2001): 1001–7. http://dx.doi.org/10.1016/s0956-5663(01)00246-9.

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Lolli, Veronica, Animesh Acharjee, Donato Angelino, Michele Tassotti, Daniele Del Rio, Pedro Mena, and Augusta Caligiani. "Chemical Characterization of Capsule-Brewed Espresso Coffee Aroma from the Most Widespread Italian Brands by HS-SPME/GC-MS." Molecules 25, no. 5 (March 5, 2020): 1166. http://dx.doi.org/10.3390/molecules25051166.

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Coffee capsules market is on the rise as it allows access to a wide selection of coffee, differing in taste and brand. However, few data about the chemical characterization of the capsule-brewed coffee aroma are available. In this work, an untargeted approach using headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography–mass spectrometry (GC-MS) and combined to chemometrics was performed to study and compare aroma profile from 65 capsule-brewed espresso coffees (ECs) commercialized by five of the most representative brands in Italy. Volatile profiles obtained from ECs were subjected to multivariate statistical analysis, which generally did not show a significant variability among coffees belonging to the same brand, except for those modified after the addition of specific flavor additives or aromatic substances (such as caramel, chocolate, etc.). Similarities may be related to the starting coffee brew or the processing method, which is likely the same for each individual brand. Additionally, partial least squares discriminant analysis (PLS-DA) showed that capsules from a specific brand contain the highest concentration of pyrazines, thus characterized by an intense and characteristic aroma, and a stronger note than those from the other brands. This study supports that the chemical analysis in conjunction with chemometric tools is a useful approach for assessing flavor quality, even if the need remains to identify volatile markers of high-quality beverages.

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Antonova, Olga, Javier Calvo, and Andreas Seifert. "Rapid Detection of Thermal Treatment of Honey by Chemometrics-Assisted FTIR Spectroscopy." Foods 10, no. 11 (November 22, 2021): 2892. http://dx.doi.org/10.3390/foods10112892.

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Honey, as a nutritious natural sweetener produced by honeybees, offers a unique biochemical composition with great benefit to human health. Transportation and storage conditions as well as violations of processing can lead to decomposition of vitamins, destruction of the integrity of the antioxidant components and enzymes, and further biochemical changes with impact on nutritional quality. We developed a fast detection method of adulterations or changes of honey caused by thermal exposure, which does not require any sample pretreatment. By Fourier-transform infrared spectroscopy, supported by chemometrics methods, we investigated three types of raw honey before and after heat treatment for varying exposure times at different temperatures. Applying principal component analysis and linear discriminant analysis to the preprocessed spectroscopic data, allowed us to discriminate raw honey from thermally altered ones even at low temperatures of 40 °C with high accuracies ≥90%.

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Steidle Neto, Antonio José, João L. M. P. de Lima, Alexandre Maniçoba da Rosa Ferraz Jardim, Daniela de Carvalho Lopes, and Thieres George Freire da Silva. "Discrimination of Fungicide-Contaminated Lettuces Based on Maximum Residue Limits Using Spectroscopy and Chemometrics." Horticulturae 10, no. 8 (August 5, 2024): 828. http://dx.doi.org/10.3390/horticulturae10080828.

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The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making.

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Hu, Shiqi, and Le Wang. "Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics." Journal of Food Quality 2021 (September 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/5527826.

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Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.

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Siyukhova, N. T., Z. T. Tazova, L. V. Lunina, and Z. N. Blyagoz. "Analytical quality control of wines and wine materials." New Technologies 18, no. 4 (February 23, 2023): 78–94. http://dx.doi.org/10.47370/2072-0920-2022-18-4-78-94.

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Ensuring food safety of the quality of sold products is one of the urgent problems of the wine industry. The trend of falsification of well-known brands of domestic and imported wines is a characteristic feature of the modern market of wine products. Known methodological bases for assessing the quality of wine products in most cases do not have the potential to recognize many modern «technological solutions». To solve this problem an analytical method can be proposed on modern equipment "FTIR BACCHUS 3". This analyzer allows you to determine the chemical substances and the range characteristics of these substances. This method has been chosen for the identification of wine samples and not by chance, when using it, the analysis time is significantly reduced, and the range of chemical elements determined by the analyzer is quite wide, and reagents are not needed for this equipment. The method does not require preliminary preparation of samples; it is also quite new, not popular in Russia. The article presents a comparative review of classical wine identification methods. The presented material indicates the relevance of creating a database on the chemical composition of wines that determine their authenticity using new instrumental techniques. The proposed multi-parameter wine analyzer FTIR BACCHUS 3 is a system for the analysis of worts and wines using chemometric methods, based on the acquisition of the mid-IR spectrum (FTIR). The method is based on chemometrics, i.e. obtaining chemical data using mathematical methods of data processing and extraction, which will increase the reliability of assessing the authenticity and place of origin of wine products.

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Aouadi, Balkis, John-Lewis Zinia Zaukuu, Flora Vitális, Zsanett Bodor, Orsolya Fehér, Zoltan Gillay, George Bazar, and Zoltan Kovacs. "Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview." Sensors 20, no. 19 (September 24, 2020): 5479. http://dx.doi.org/10.3390/s20195479.

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Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

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Guo, Mei, Zhicong Wu, Qi An, Hengyang Li, Lei Wang, Yuguang Zheng, Long Guo, and Dan Zhang. "Comparison of Volatile Oils and Primary Metabolites of Raw and Honey-Processed Ephedrae Herba by GC–MS and Chemometrics." Journal of AOAC INTERNATIONAL 105, no. 2 (October 9, 2021): 576–86. http://dx.doi.org/10.1093/jaoacint/qsab139.

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Abstract Background Ephedrae Herba (EH) is the terrestrial stem of Ephedra sinica Stapf, E. intermedia Schrenk et C. A. Mey., or E. equisetina Bge, which has been used as a diaphoretic, antiasthmatic, and diuretic. Honey-processed EH (HEH) is a widely used traditional Chinese medicine, and has a better effect of dispersing lung qi and relieving asthma and a lower effect of dispelling cold than raw EH (REH). Objective To understand the differences in volatile oils and primary metabolites between REH and honey-processed HEH, and to provide a reference for elucidating the mechanism behind the effect of this processing. Methods A system data acquisition and mining strategy was designed to investigate the differences in volatiles and primary metabolites between the REH and HEH, based on GC–MS coupled with multivariate statistical analysis, including principal component analysis and orthogonal partial least squares discriminant analysis. Results Overall, 15 volatile oils and 14 primary metabolites were shown to be potential characteristic markers differentiating REH and HEH. Conclusion The results may provide a scientific foundation for comprehensively revealing the honey-processing mechanism of EH. Highlight Volatile oils and primary metabolites were used to distinguish REH and HEH and elucidate the processing mechanism of EH for the first time.

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Mohamad Rafi, Mohamad Rafi, Bayu Nurcahyo Bayu Nurcahyo, Wulan Tri Wahyuni, Zulhan Arif, Dewi Anggraini Septaningsih, Sastia Prama Putri Sastia Prama Putri, and Eiichiro Fukusaki. "Feasibility of UV-Vis Spectral Fingerprinting Combined with Chemometrics for Rapid Detection of Phyllanthus niruri Adulteration with Leucaena leucocephala." Sains Malaysiana 50, no. 4 (April 30, 2021): 997–1006. http://dx.doi.org/10.17576/jsm-2021-5004-10.

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Phyllanthus niruri is widely used in Indonesia as immunostimulant. The morphology of Leucaena leucocephala leaves is similar to that of P. niruri leaves. L. leucocephala is easy to find and collect because it is widely distributed in the world. Therefore, it is likely P. niruri could be adulterated with L. leucocephala. Therefore, identification and authentication of P. niruri is important to ensure the raw materials used are original without any substitution or mixture with other similar plants causing inconsistencies in their efficacy. In this paper, we described feasibility used of UV-Vis spectral fingerprinting and chemometrics for rapid method for the identification and detection of P. niruri leaves adulterated with L. leucocephala leaves. UV-Vis spectra of samples measured in the interval of 200-800 nm and signal smoothing followed by standard normal variate were used for pre-processing the spectral data. Principal component analysis (PCA)with the absorbance data from the pre-processed UV-Vis spectra in the range of 250-700 nm as variables could distinguish P. niruri from L. leucocephala. PCA followed by discriminant analysis (DA) could successfully classified P. niruri mixed with 5, 25, and 50% L. luecocephala into their respective groups (96.81%). We also employed soft independent modelling of class analogy (SIMCA) for authentication of P. niruri and found that 88.3% of the samples were also correctly classified into their respective groups. A combination of UV-Vis spectroscopy with chemometrics, such as PCA-DA and SIMCA, were used for the first time for the identification and detection of P. niruri adulterated with L. leucocephala.

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Cavdaroglu, Cagri, and Banu Ozen. "Prediction of vinegar processing parameters with chemometric modelling of spectroscopic data." Microchemical Journal 171 (December 2021): 106886. http://dx.doi.org/10.1016/j.microc.2021.106886.

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Xie, Yu-Long, Ji-Hong Wang, Yi-Zeng Liang, and Ru-Qin Yu. "Robust Kalman filter as a chemometric method for analytical data processing." Analytica Chimica Acta 269, no. 2 (November 1992): 307–16. http://dx.doi.org/10.1016/0003-2670(92)85417-5.

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Castro, Rafael C., Ricardo N. M. J. Páscoa, M. Lúcia M. F. S. Saraiva, João L. M. Santos, and David S. M. Ribeiro. "Chemometric models for data processing in quantum dots-based photoluminescence methodologies." Coordination Chemistry Reviews 502 (March 2024): 215605. http://dx.doi.org/10.1016/j.ccr.2023.215605.

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Segelke, Torben, Stefanie Schelm, Christian Ahlers, and Markus Fischer. "Food Authentication: Truffle (Tuber spp.) Species Differentiation by FT-NIR and Chemometrics." Foods 9, no. 7 (July 13, 2020): 922. http://dx.doi.org/10.3390/foods9070922.

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Truffles are certainly the most expensive mushrooms; the price depends primarily on the species and secondly on the origin. Because of the price differences for the truffle species, food fraud is likely to occur, and the visual differentiation is difficult within the group of white and within the group of black truffles. Thus, the aim of this study was to develop a reliable method for the authentication of five commercially relevant truffle species via Fourier transform near-infrared (FT-NIR) spectroscopy as an easy to handle approach combined with chemometrics. NIR-data from 75 freeze-dried fruiting bodies were recorded. Various spectra pre-processing techniques and classification methods were compared and validated using nested cross-validation. For the white truffle species, the most expensive Tuber magnatum could be differentiated with an accuracy of 100% from Tuber borchii. Regarding the black truffle species, the relatively expensive Tuber melanosporum could be distinguished from Tuber aestivum and the Chinese truffles with an accuracy of 99%. Since the most expensive Italian Tuber magnatum is highly prone to fraud, the origin was investigated and Italian T. magnatum truffles could be differentiated from non-Italian T. magnatum truffles by 83%. Our results demonstrate the potential of FT-NIR spectroscopy for the authentication of truffle species.

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Xu, Lu, Hai-Yan Fu, Chen-Bo Cai, and Yuan-Bin She. "Quality Degradation of Chinese White Lotus Seeds Caused by Dampening during Processing and Storage: Rapid and Nondestructive Discrimination Using Near-Infrared Spectroscopy." Journal of Analytical Methods in Chemistry 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/345352.

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Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n=167) and dampened (n=118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000–12000 cm−1) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively.

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Alzagameem, Abla, Michel Bergs, Xuan Tung Do, Stephanie Elisabeth Klein, Jessica Rumpf, Michael Larkins, Yulia Monakhova, Ralf Pude, and Margit Schulze. "Low-Input Crops as Lignocellulosic Feedstock for Second-Generation Biorefineries and the Potential of Chemometrics in Biomass Quality Control." Applied Sciences 9, no. 11 (May 31, 2019): 2252. http://dx.doi.org/10.3390/app9112252.

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Lignocellulose feedstock (LCF) provides a sustainable source of components to produce bioenergy, biofuel, and novel biomaterials. Besides hard and soft wood, so-called low-input plants such as Miscanthus are interesting crops to be investigated as potential feedstock for the second generation biorefinery. The status quo regarding the availability and composition of different plants, including grasses and fast-growing trees (i.e., Miscanthus, Paulownia), is reviewed here. The second focus of this review is the potential of multivariate data processing to be used for biomass analysis and quality control. Experimental data obtained by spectroscopic methods, such as nuclear magnetic resonance (NMR) and Fourier-transform infrared spectroscopy (FTIR), can be processed using computational techniques to characterize the 3D structure and energetic properties of the feedstock building blocks, including complex linkages. Here, we provide a brief summary of recently reported experimental data for structural analysis of LCF biomasses, and give our perspectives on the role of chemometrics in understanding and elucidating on LCF composition and lignin 3D structure.

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Al Ktash, Mohammad, Mona Knoblich, Frank Wackenhut, and Marc Brecht. "UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality." Chemosensors 13, no. 1 (January 17, 2025): 21. https://doi.org/10.3390/chemosensors13010021.

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Cotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky fibers, thereby impeding textile processing. This study investigates ultraviolet (UV) hyperspectral imaging (230–380 nm) combined with multivariate data analysis to detect and quantify honeydew contaminations in real cotton samples. Reference cotton samples were sprayed multiple times with honey solutions to replicate the natural composition of honeydew. Comparisons were made with an alternative method where samples were soaked in sugar solutions of varying concentrations. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) effectively differentiated and classified samples based on honey spraying times. Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination R2 = 0.75 and root mean square error of RMSE = 0.8 for the honey model. By employing a realistic spraying method that closely mimics natural contamination, this study refines sample preparation techniques for improved evaluation of honeydew levels. In conclusion, the integration of hyperspectral imaging with multivariate analysis represents a robust, non-destructive, and rapid approach for real-time detection of honeydew contamination in cotton, offering significant potential for industrial applications.

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Yi, Lunzhao, Naiping Dong, Yonghuan Yun, Baichuan Deng, Dabing Ren, Shao Liu, and Yizeng Liang. "Chemometric methods in data processing of mass spectrometry-based metabolomics: A review." Analytica Chimica Acta 914 (March 2016): 17–34. http://dx.doi.org/10.1016/j.aca.2016.02.001.

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Cattaneo, Tiziana MP, Maurizio Cutini, Alessandro Cammerata, Annamaria Stellari, Laura Marinoni, Carlo Bisaglia, and Massimo Brambilla. "Near infrared spectroscopic and aquaphotomic evaluation of the efficiency of solar dehydration processes in pineapple slices." Journal of Near Infrared Spectroscopy 29, no. 6 (November 23, 2021): 352–58. http://dx.doi.org/10.1177/09670335211054303.

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Parallel transformation tests on pineapple slices using two micro drying plants (M1 and M2) operating with solar energy were carried out. Method M1 consisted of an active fan at the top, whose ventilation rate depended on the internal temperature. Method M2 had a continuously working fan at the bottom. The dehydration performance of these two micro-plants was compared by collecting spectra from pineapple slices in reflectance mode (900–1600 nm) at three different times: (0) process start, (1) during the process [48 h] and (2) process end [56 h]. Simultaneously, dry matter, titratable acidity (SH°), pH and aw (water activity) were measured. For these parameters, significant differences ( p < 0.05) were detected between the fresh (t = 0) and the dried product (t = 56). Near infrared (NIR) spectroscopic analysis was carried out according to previously published methods. Spectral data in the wavelength region from 1300 to 1550 nm underwent statistical processing to perform aquaphotomics evaluation and chemometric analysis using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The aquagrams highlighted differences among fresh, half-dried and dried slices where water molecules were highly organized between the water matrix coordinates C1 to C3 at t = 0 and C2 to C6 for the other evaluated times. The PCA could explain about 98% of the total variance in the PC1–PC3 scores plot. And the additional LDA classified the NIR spectra with an accuracy of 100, 98 and 83% for t = 0, t = 56-M1 and t = 56-M2, respectively. Such preliminary results suggest the applicability of Aquaphotomics and chemometrics for the continuous monitoring of fruit drying processes using an adequate NIR probe. Further experiments are already in progress.

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Azarenko, S. I., and I. O. Kuznetsov. "Expert studies of ethylene copolymers by IR-spectroscopy using chemometric algorithms." Аналитика и контроль 27, no. 4 (2023): 285–91. http://dx.doi.org/10.15826/analitika.2023.27.4.010.

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The article is devoted to the development of a technique for separation of low-density polyethylene, linear low-density polyethylene with the mass fraction of olefin link not exceeding 5 %, copolymers of ethylene with alpha-olefins containing more than 5 % but less than 50 % of the latter, as well as determining type of the alpha-olefin unit and its content in these products. Since the existing technique for determining type of the olefin unit and its content does not always allow determining the type and amount of the co-monomer olefin unit in ethylene copolymers using the spectroscopy method, it is necessary to analyze such products on NMR spectrometers, expensive instruments, measurements on which are laborious and take considerable time. A technique based on a combination of infrared spectroscopy and chemometrics was developed in order to reduce the time for customs examinations for verifying reliability of the declaration of ethylene polymers. This technique allows separating low-density polyethylene (LDPE), linear low-density polyethylene (LLDPE) (copolymers of ethylene with butene, hexene, octene with the mass fraction of olefin below 5 %), copolymers of ethylene with propylene, copolymers of ethylene with propylene and diene (EPDM), copolymers of ethylene with olefins: butene, hexene, octene with the mass fraction of the latter above 5 %, but below 50 %, as well as to determine type of the olefin unit and its content in these copolymers. The essence of this technique lies in the possibility of processing the obtained spectral data using chemometric algorithms: independent modeling of classes, discriminant analysis of projections on latent structures, discriminant analysis using regression on latent structures.

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Yulia, Meinilwita, and Diding Suhandy. "Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics." Molecules 26, no. 20 (October 9, 2021): 6091. http://dx.doi.org/10.3390/molecules26206091.

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In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

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Oravec, Michal, Lukáš Gál, and Michal Čeppan. "Pre-processing of inkjet prints NIR spectral data for principal component analysis." Acta Chimica Slovaca 8, no. 2 (October 1, 2015): 191–96. http://dx.doi.org/10.1515/acs-2015-0031.

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Abstract This paper presents a novel approach in non-destructive analysis of inkjet-printed documents. Our method is based on the combination of molecular spectroscopy in the Near Infrared Region (NIR) and a chemometric method - principal component analysis (PCA). The aim of this work was to prepare spectral data for the analysis of the interrelationships between 19 samples consisting of the same type of office paper on which black squares were full printed in black ink only. The spectra were obtained separately using the Ocean Optics System in two spectral regions, i.e., overtones: 1000-1600 nm and combination bands: 1600-2300 nm, with the paper base. Experimental results confirmed the high reliability of the proposed approach despite the sparse dataset.

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Journal articles on the topic 'Data processing and Chemometrics'

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