The DOI matches its Crossref record; 189 of the 189 references it cites were found in a registry or an index.
Near infrared spectroscopy: A mature analytical technique with new perspectives – A review
Abstract
Last decade's advances and modern aspects of near infrared spectroscopy are critically examined and reviewed. Innovative instrumentation, highlighted by portable and imaging instruments, chemometrics data multivariate processing, and new and valuable applications are presented and discussed. Because of these advances, this mature analytical technique is continually experiencing renewed interest. The drawbacks and misuses of the technique and its supporting mathematical tools are also addressed. The principal achievements in the field are shown in a critical manner, in order to understand why the technique has found intensive application in the most diverse and modern areas of analytical importance during the last ten years.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra
Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989) · Applied Spectroscopy · doi:10.1366/0003702894202201 · Cited by 3,867
Computer Aided Design of Experiments
Kennard, R. W., & Stone, L. A. (1969) · Technometrics · doi:10.1080/00401706.1969.10490666 · Cited by 3,648
Review of the most common pre-processing techniques for near-infrared spectra
Rinnan, Å., Berg, F. V. D., & Engelsen, S. B. (2009) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2009.07.007 · Cited by 3,057
Bro, R., & Smilde, A. K. (2014) · Anal. Methods · doi:10.1039/c3ay41907j · Cited by 3,038
Near-Infrared Technology: In the Agricultural and Food Industries
Williams, P. C., & Norris, K. (2001) · Andalas University Repository (Andalas University) · Cited by 2,749
Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review
Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2007.06.024 · Cited by 2,252
Li, H., Liang, Y., Xu, Q., & Cao, D. (2009) · Analytica Chimica Acta · doi:10.1016/j.aca.2009.06.046 · Cited by 1,763
The Mineral Nutrition of Higher Plants
Clarkson, D. T., & Hanson, J. B. (1980) · Annual Review of Plant Physiology · doi:10.1146/annurev.pp.31.060180.001323 · Cited by 1,551
Visible and near infrared spectroscopy in soil science
Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010) · Advances in Agronomy · doi:10.1016/s0065-2113(10)07005-7 · Cited by 1,549
Hahn, D. W., & Omenetto, N. (2012) · Applied Spectroscopy · doi:10.1366/11-06574 · Cited by 1,497
Research works printed but not linked: 245
- Herschel FW: Experiments on the Refrangibility of the Invisible Rays of the Sun, Philosophical Transactions of the Royal Academy 1800, 284–292. (This paper can also be found in: https://archive.org/stream/jstor-107057/107057#page/n1/mode/2up, consulted in 23/08/2017).
- Ellis, 1938, Alterations in the infrared absorption spectrum of water in gelatin, J. Chem. Phys., 6
- Spakowski, 1950, Determination of aromatics and olefins in wide petroleum fractions, Anal. Chem., 22, 1419, 10.1021/ac60047a020 · doi:10.1021/ac60047a020
- Evans, 1951, Determination of carbon-hydrogen groups in high molecular weight hydrocarbons by near-infrared absorption, Anal. Chem., 23, 1604, 10.1021/ac60059a022 · doi:10.1021/ac60059a022
- Hart, 1965, Direct spectrophotometric determination of moisture content of grain and seeds, vol. 4, 19
- Hart, 1996, Direct spectrophotometric determination of moisture content of grain and seeds, J. Near Infrared Spectrosc., 4, 23, 10.1255/jnirs.940 · doi:10.1255/jnirs.940
- Bengera, 1968, Direct spectrophotometric determination of fat and moisture in meat products, J. Food Sci., 33, 64, 10.1111/j.1365-2621.1968.tb00885.x · doi:10.1111/j.1365-2621.1968.tb00885.x
- Shapiro, 1986, In memoriam: tomas hirschfeld (1939-1986), Cytometry, 7, 399, 10.1002/cyto.990070502 · doi:10.1002/cyto.990070502
- Olinger, 1988, Quantitative effects of an absorbing matrix on near-infrared diffuse reflectance spectra, Anal. Chem., 60, 2427, 10.1021/ac00172a022 · doi:10.1021/ac00172a022
- Qin, 2017, Line-scan hyperspectral imaging techniques for food safety and quality applications, Appl. Sci. Basel, 7, 125, 10.3390/app7020125 · doi:10.3390/app7020125
- Dale, 2013, Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review, Appl. Spectrosc. Rev., 48, 142, 10.1080/05704928.2012.705800 · doi:10.1080/05704928.2012.705800
- He, 2015, Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products, Trends Food Sci. Technol., 46, 99, 10.1016/j.tifs.2015.08.001 · doi:10.1016/j.tifs.2015.08.001
- Hossain, 2014, Near- and mid-infrared spectroscopy as efficient tools for detection of fungal and mycotoxin contamination in agricultural commodities, World Mycotoxin J., 7, 507, 10.3920/WMJ2013.1679 · doi:10.3920/wmj2013.1679
- Levasseur-Garcia, 2013, Infrared spectroscopy used as a decision-making support for the determination of fungal and mycotoxic risk, Cah. Agric., 22, 216, 10.1684/agr.2013.0622 · doi:10.1684/agr.2013.0622
- Rasch, 2010, Sensing of mycotoxin producing fungi in the processing of grains, Food Bioprocess Technol., 3, 908, 10.1007/s11947-010-0364-y · doi:10.1007/s11947-010-0364-y
- Jha, 2010, Quality parameters of mango and potential of non-destructive techniques for their measurement - a review, J. Food Sci. Technol.-Mysore, 47, 1, 10.1007/s13197-010-0004-6 · doi:10.1007/s13197-010-0004-6
- Liu, 2010, Review of portable NIR instruments for detecting fruit interior quality, Spectrosc. Spectr. Anal., 30, 2874
- Poonia, 2017, Detection of adulteration in milk: a review, International Journal of Dairy Technology, 70, 23, 10.1111/1471-0307.12274 · doi:10.1111/1471-0307.12274
- Cattaneo, 2013, The use of near infrared spectroscopy for determination of adulteration and contamination in milk and milk powder: updating knowledge, J. Near Infrared Spectrosc., 21, 341, 10.1255/jnirs.1077 · doi:10.1255/jnirs.1077
- Holroyd, 2013, The use of near infrared spectroscopy on milk and milk products, J. Near Infrared Spectrosc., 21, 311, 10.1255/jnirs.1055 · doi:10.1255/jnirs.1055
- Wang, 2009, Application of near-infrared spectroscopy to quality detection of milk and its products, Spectrosc. Spectr. Anal., 29, 1281
- Schmitt, 2014, Determination of the mineral composition of foods by infrared spectroscopy: a review of a Green alternative, Crit. Rev. Anal. Chem., 44, 186, 10.1080/10408347.2013.835695 · doi:10.1080/10408347.2013.835695
- Lohumi, 2015, A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration, Trends Food Sci. Technol., 46, 85, 10.1016/j.tifs.2015.08.003 · doi:10.1016/j.tifs.2015.08.003
- Mlcek, 2010, Potentials of NIR spectroscopy in meat industry, Chem. Listy, 104, 855
- Tao, 2013, Application of near infrared reflectance spectroscopy to predict meat chemical compositions: a review, Spectrosc. Spectr. Anal., 33, 3008
- Zabrodska, 2014, Adulteration of honey and available methods for detection - a review, Acta Vet., 83, S85, 10.2754/avb201483S10S85 · doi:10.2754/avb201483s10s85
- Tu, 2010, Progress in quality analysis of honey by infrared spectroscopy, Spectrosc. Spectr. Anal., 30, 2971
- Zhou, 2016, Applications of near infrared spectroscopy in cotton impurity and fiber quality detection: a review, Appl. Spectrosc. Rev., 51, 298, 10.1080/05704928.2015.1131710 · doi:10.1080/05704928.2015.1131710
- Alishahi, 2010, Identification of transgenic foods using NIR spectroscopy: a review, Spectrochim. Acta Mol. Biomol. Spectrosc., 75, 1, 10.1016/j.saa.2009.10.001 · doi:10.1016/j.saa.2009.10.001
- Woodcock, 2008, Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity, Food Bioprocess Technol., 1, 117, 10.1007/s11947-007-0033-y · doi:10.1007/s11947-007-0033-y
- Liu, 2013, NIR spectroscopy and imaging techniques for evaluation of fish quality-a review, Appl. Spectrosc. Rev., 48, 609, 10.1080/05704928.2013.775579 · doi:10.1080/05704928.2013.775579
- Yang, 2011, The potential of near infrared microscopy to detect, identify and quantify processed animal by-products, J. Near Infrared Spectrosc., 19, 211, 10.1255/jnirs.935 · doi:10.1255/jnirs.935
- Sun, 2009, Progress in application of near infrared spectroscopy to nondestructive on-line detection of products/food quality, Spectrosc. Spectr. Anal., 29, 122
- Singh, 2010, Near-infrared hyperspectral imaging for quality analysis of agricultural and food products, vol. 7676
- Lin, 2008, Review of progress in application visible/near-infrared spectroscopy in liquid food detection, Spectrosc. Spectr. Anal., 28, 285
- Yan, 2015, Potential applicability of fecal NIRs: a review, Spectrosc. Spectr. Anal., 35, 3382
- Chen, 2015, Applications of on-line near infrared spectroscopy monitoring technology in polymer processing, Spectrosc. Spectr. Anal., 35, 1512
- Watari, 2014, A review of online real-time process analyses of melt-state polymer using the near-infrared spectroscopy and chemometrics, Appl. Spectrosc. Rev., 49, 462, 10.1080/05704928.2013.855637 · doi:10.1080/05704928.2013.855637
- Muselik, 2012, Pharmaceutical applications of near-infrared spectroscopy, Chem. Listy, 106, 10
- Chadha, 2015, Near-infrared spectroscopy: effective tool for screening of polymorphs in pharmaceuticals, Appl. Spectrosc. Rev., 50, 565, 10.1080/05704928.2015.1044663 · doi:10.1080/05704928.2015.1044663
- Swarbrick, 2014, Advances in instrumental technology, industry guidance and data management systems enabling the widespread use of near infrared spectroscopy in the pharmaceutical/biopharmaceutical sector, J. Near Infrared Spectrosc., 22, 157, 10.1255/jnirs.1121 · doi:10.1255/jnirs.1121
- Ritchie, 2008, The US pharmacopeia near infrared system suitability reference standard, J. Near Infrared Spectrosc., 16, 205, 10.1255/jnirs.779 · doi:10.1255/jnirs.779
- Hu, 2015, Review of the characteristics and prospects of near infrared spectroscopy for rapid drug-screening systems in China, J. Near Infrared Spectrosc., 23, 271, 10.1255/jnirs.1154 · doi:10.1255/jnirs.1154
- Chung, 2007, Applications of near-infrared spectroscopy in refineries and important issues to address, Appl. Spectrosc. Rev., 42, 251, 10.1080/05704920701293778 · doi:10.1080/05704920701293778
- Maia, 2013, Soil organic matter: chemical and physical characteristics and analytical methods. A review, Curr. Org. Chem., 17, 2985, 10.2174/13852728113179990123 · doi:10.2174/13852728113179990123
- Genot, 2014, Use of near-infrared reflectance spectroscopy in soil analysis, Biotechnologie Agronomie Societe Et Environnement, 18, 247
- Ding, 2012, Research progress and application prospect of near infrared spectroscopy in soil nutrition analysis, Spectrosc. Spectr. Anal., 32, 88
- Chodak, 2008, Application of near infrared spectroscopy for analysis of soils, litter and plant materials, Pol. J. Environ. Stud., 17, 631
- Roberts, 2016, Wet or dry? The effect of sample characteristics on the determination of soil properties by near infrared spectroscopy, Trac. Trends Anal. Chem., 83, 25, 10.1016/j.trac.2016.08.002 · doi:10.1016/j.trac.2016.08.002
- de Muniz, 2012, Background and state of the art of near infrared specyroscopy in the forest sector, Ciência Florest., 22, 865
- Yao, 2009, Application of near infrared spectroscopy in analysis of wood properties, Spectrosc. Spectr. Anal., 29, 974
- Leblon, 2013, A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood, For. Chron., 89, 595, 10.5558/tfc2013-111 · doi:10.5558/tfc2013-111
- Trung, 2015, Pulp mill and chemical recovery control with advanced analysers - from trees to final product, Appita, 68, 39
- Ishikawa, 2014, Recent progress of near-infrared (NIR) imaging -development of novel instruments and their applicability for practical situations, Anal. Sci., 30, 143, 10.2116/analsci.30.143 · doi:10.2116/analsci.30.143
- Ishikawa, 2015, Near-infrared imaging using a high-speed monitoring near infrared hyperspectral camera (compovision), Spectrosc. Spectr. Anal., 35, 865
- Zhang, 2011, A novel MOEMS NIR spectrometer, vol. 19
- Yu, 2013, Current status and prospects of portable NIR spectrometer, Spectrosc. Spectr. Anal., 33, 2983
- Kenda, 2014, Development, characterization and application of compact spectrometers based on MEMS with in-plane capacitive drives, vol. 9101
- Pruett, 2015, Latest developments in Texas Instruments DLPH near-infrared spectrometers enable the next generation of embedded compact, portable systems, vol. 9482
- Zhang, 2010, Research and development of AOTF based NIR spectrometer, vol. 7655
- Li, 2007, Study on the methods and applications of near-infrared spectroscopy chemical pattern recognition, Spectrosc. Spectr. Anal., 27, 1299
- Honorato, 2007, Calibration transfer in multivariate methods, Quim. Nova, 30, 1301, 10.1590/S0100-40422007000500044 · doi:10.1590/s0100-40422007000500044
- Shao, 2010, Multivariate calibration methods in near infrared spectroscopic analysis, Analytical Methods, 2, 1662, 10.1039/c0ay00421a · doi:10.1039/c0ay00421a
- Lima, 2009, Near infrared and mid infrared optical sensors, Quim. Nova, 32, 1635, 10.1590/S0100-40422009000600047 · doi:10.1590/s0100-40422009000600047
- Shao, 2012, Application of near-infrared spectroscopy in micro inorganic analysis, Hua Hsueh Hsueh Pao, 70, 2109
- Zhou, 2012, Spectral database systems: a review, Appl. Spectrosc. Rev., 47, 654, 10.1080/05704928.2012.700668 · doi:10.1080/05704928.2012.700668
- Chu, 2014, Research and application progress of near infrared spectroscopy analytical technology in China in the past five years, Spectrosc. Spectr. Anal., 34, 2595
- Richardson, 2008, State of the art: non-invasive interrogation of textiles in museum collections, Microchimica Acta, 162, 303, 10.1007/s00604-007-0885-x · doi:10.1007/s00604-007-0885-x
- Smith, 2015, Quo vadis regulated NIR analytical procedures?, Spectroscopy, 30, 10
- Rayment, 2012, Using interlaboratory proficiency data to guide NIR/MIR calibrations, Commun. Soil Sci. Plant Anal., 43, 399, 10.1080/00103624.2012.641799 · doi:10.1080/00103624.2012.641799
- Gonzaga, 2005, Near-infrared emission spectroscopy based on an acousto-optical tunable filter, Anal. Chem., 77, 1046, 10.1021/ac048656o · doi:10.1021/ac048656o
- Williams, 2017, Flinn P tutorial: items to be included in a report on a near infrared spectroscopy project, J. Near Infrared Spectrosc., 25, 85, 10.1177/0967033517702395 · doi:10.1177/0967033517702395
- Petisco, 2008, ash and mineral contents in leaves of woody species: analysis by near-infrared reflectance spectroscopy, Commun. Soil Sci. Plant Anal., 39, 905, 10.1080/00103620701881253 · doi:10.1080/00103620701881253
- Villatoro-Pulido, 2012, Characterization and prediction by near-infrared reflectance of mineral composition of rocket (Eruca vesicaria subsp. sativa and Eruca vesicaria subsp. vesicaria), J. Sci. Food Agric., 92, 10.1002/jsfa.4694 · doi:10.1002/jsfa.4694
- Gonzalez-Martin, 2007, Use of nirs technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa, Anal. Bioanal. Chem., 387, 2199, 10.1007/s00216-006-1039-4 · doi:10.1007/s00216-006-1039-4
- Ward, 2011, Rapid assessment of mineral concentration in meadow grasses by near infrared reflectance spectroscopy, Sensors, 11, 4830, 10.3390/s110504830 · doi:10.3390/s110504830
- Liao, 2012, Rapid diagnosis of nutrient elements in fingered citron leaf using near infrared reflectance spectroscopy, J. Plant Nutr., 35, 1725, 10.1080/01904167.2012.698352 · doi:10.1080/01904167.2012.698352
- Jia, 2014, Predicting soil nitrogen and organic carbon using near infrared spectroscopy coupled with variable selection, Appl. Eng. Agric., 30, 641
- Norris, 2009, Hazards with near infrared spectroscopy in detecting contamination, Journal of Near Infrared Sopectroscopy, 17, 165, 10.1255/jnirs.844 · doi:10.1255/jnirs.844
- Silva, 2015, Determination of naphtha composition by near infrared spectroscopy and multivariate regression to control steam cracker processes, Fuel Process. Technol., 131, 230, 10.1016/j.fuproc.2014.10.035 · doi:10.1016/j.fuproc.2014.10.035
- Tsenkova, 2016, Aquaphotomics tenth anniversary, NIR News, 27, 45, 10.1255/nirn.1586 · doi:10.1255/nirn.1586
- Grant, 1989, Simultaneous determination of sodium-hydroxide, sodium-carbonate and sodium-chloride concentrations in aqueous solitions by near-infrared spectrometry, Analyst, 114, 819, 10.1039/an9891400819 · doi:10.1039/an9891400819
- Lin, 1996, Identification of electrolytes in aqueous solutions from near-IR spectra, Appl. Spectrosc., 50, 444, 10.1366/0003702963906096 · doi:10.1366/0003702963906096
- Williams, 2009, Influence of water on prediction of composition and quality factors: the Aquaphotomics of low moisture agricultural materials, J. Near Infrared Spectrosc., 17, 315, 10.1255/jnirs.862 · doi:10.1255/jnirs.862
- Putra, 2017, An efficient near infrared spectroscopy based on aquaphotomics technique for rapid determining the level of Cadmium in aqueous solution, vol. 210
- Gowen, 2015, On the feasibility of near infrared spectroscopy to detect contaminants in water using single salt solutions as model systems, Talanta, 131, 609, 10.1016/j.talanta.2014.08.049 · doi:10.1016/j.talanta.2014.08.049
- Bazar, 2015, Water revealed as molecular mirror when measuring low concentrations of sugar with near infrared light, Anal. Chim. Acta, 896, 52, 10.1016/j.aca.2015.09.014 · doi:10.1016/j.aca.2015.09.014
- Takemura, 2015, Aquagrams of raw milk for oestrus detection in dairy cows, Reprod. Domest. Anim., 50, 522, 10.1111/rda.12504 · doi:10.1111/rda.12504
- Kinoshita, 2010, Near infrared spectroscopy of urine proves useful for estimating ovulation in giant panda (Ailuropoda melanoleuca), Analytical Methods, 2, 1671, 10.1039/c0ay00333f · doi:10.1039/c0ay00333f
- Nakakimura, 2012, Extracellular metabolites play a dominant role in near-infrared spectroscopic quantification of bacteria at food-safety level concentrations, Analytical Methods, 4, 1389, 10.1039/c2ay05771a · doi:10.1039/c2ay05771a
- Bazar, 2016, Evaluating spectral signals to identify spectral error, PLoS One, 11
- Kojic, 2017, Improving accuracy and reproducibility of vibrational spectra for diluted solutions, Anal. Chim. Acta, 955, 86, 10.1016/j.aca.2016.12.019 · doi:10.1016/j.aca.2016.12.019
- Norris, 2001, Applying Norris derivatives - understanding and correcting the factors which affect diffuse transmittance spectra, NIR News, 12, 6, 10.1255/nirn.613 · doi:10.1255/nirn.613
- Hopkins, 2001, What is a Norris derivative?, NIR News, 12, 3
- Hibbert, 2016, Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016), Pure Appl. Chem., 88, 407, 10.1515/pac-2015-0605 · doi:10.1515/pac-2015-0605
- Genkawa, 2015, Baseline correction of diffuse reflection near-infrared spectra using searching region standard normal variate (SRSNV), Appl. Spectrosc., 69, 1432, 10.1366/15-07905 · doi:10.1366/15-07905
- Hopkins, 2016, Revisiting the Norris derivative quotient math in regression, NIR News, 27, 23, 10.1255/nirn.1643 · doi:10.1255/nirn.1643
- Antonov, 2017, An alternative for the calculation of derivative spectra in the near-infrared spectroscopy, J. Near Infrared Spectrosc., 25, 145, 10.1177/0967033516688222 · doi:10.1177/0967033516688222
- Feudale, 2002, Piecewise orthogonal signal correction, Chemometr. Intell. Lab. Syst., 63, 129, 10.1016/S0169-7439(02)00020-5 · doi:10.1016/s0169-7439(02)00020-5
- Tang, 2014, A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm, Analyst, 139, 4894, 10.1039/C4AN00837E · doi:10.1039/c4an00837e
- Xu, 2014, Nondestructive determination of bamboo shoots lignification using FT-NIR with efficient variables selection algorithms, Analytical Methods, 6, 1090, 10.1039/c3ay41777h · doi:10.1039/c3ay41777h
- Yang, 2016, Comparison of chemometric approaches for near-infrared spectroscopic data, Analytical Methods, 8, 1914, 10.1039/C5AY01304F · doi:10.1039/c5ay01304f
- Chen, 2012, Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools, Food Chem., 135, 590, 10.1016/j.foodchem.2012.05.011 · doi:10.1016/j.foodchem.2012.05.011
- Fujiwara, 2012, Input variable selection for PLS modeling using nearest correlation spectral clustering, Chemometr. Intell. Lab. Syst., 118, 109, 10.1016/j.chemolab.2012.08.007 · doi:10.1016/j.chemolab.2012.08.007
- Zheng, 2012, Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra, Chemometr. Intell. Lab. Syst., 112, 48, 10.1016/j.chemolab.2012.01.002 · doi:10.1016/j.chemolab.2012.01.002
- Leardi, 2004, Sequential application of backward interval PLS and genetic algorithms for the selection of relevant spectral regions, J. Chemometr., 18, 486, 10.1002/cem.893 · doi:10.1002/cem.893
- Xiaobo, 2007, Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of ‘Fuji’ apple based on different PLS models, Chemometr. Intell. Lab. Syst., 87, 43, 10.1016/j.chemolab.2006.09.003 · doi:10.1016/j.chemolab.2006.09.003
- Shao, 2012, Application of latent projective graph in variable selection for near infrared spectral analysis, Chemometr. Intell. Lab. Syst., 114, 44, 10.1016/j.chemolab.2012.03.003 · doi:10.1016/j.chemolab.2012.03.003
- Todeschini, 1999, Kohonen artificial neural networks as a tool for wavelength selection in multicomponent spectrofluorimetric PLS modelling: application to phenol, o-cresol, m-cresol and p-cresol mixtures, Trac. Trends Anal. Chem., 18, 93, 10.1016/S0165-9936(98)00097-1 · doi:10.1016/s0165-9936(98)00097-1
- Teófilo, 2009, Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression, J. Chemometr., 23, 32, 10.1002/cem.1192 · doi:10.1002/cem.1192
- Tan, 2010, Ensemble multivariate calibration based on mutual information for food analysis using near-infrared spectroscopy, Anal. Lett., 43, 2640, 10.1080/00032711003731373 · doi:10.1080/00032711003731373
- Galvão, 2007, Cross-validation for the selection of spectral variables using the successive projections algorithm, J. Braz. Chem. Soc., 18, 1580, 10.1590/S0103-50532007000800021 · doi:10.1590/s0103-50532007000800021
- Gomes, 2014, The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization, Anal. Chim. Acta, 811, 13, 10.1016/j.aca.2013.12.022 · doi:10.1016/j.aca.2013.12.022
- Rinnan, 2014, Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS, J. Chemometr., 28, 439, 10.1002/cem.2582 · DOI
- Jiang, 2015, Variance constrained partial least squares, Chemometr. Intell. Lab. Syst., 145, 60, 10.1016/j.chemolab.2015.04.014 · doi:10.1016/j.chemolab.2015.04.014
- Song, 2016, A novel algorithm for spectral interval combination optimization, Anal. Chim. Acta, 948, 19, 10.1016/j.aca.2016.10.041 · doi:10.1016/j.aca.2016.10.041
- Zheng, 2017, Similar offspring voting genetic algorithm for spectral variable selection, J. Chemometr., 31, e2893, 10.1002/cem.2893 · doi:10.1002/cem.2893
- R, 2005, A new method for multivariate calibration, J. Near Infrared Spectrosc., 13, 241, 10.1255/jnirs.555 · doi:10.1255/jnirs.555
- Shi, 2012, Novel sample selection strategy by Near-Infrared spectroscopy-based sigh throughput tablet tester for content uniformity in early-phase. pharmaceutical product development, J. Pharmaceut. Sci., 101, 2502, 10.1002/jps.23153 · doi:10.1002/jps.23153
- Anzanello, 2016, Wavelength selection framework for classifying food and pharmaceutical samples into multiple classes, J. Chemometr., 30, 346, 10.1002/cem.2799 · doi:10.1002/cem.2799
- Soares, 2017, A non-equidistant wavenumber interval selection approach for classifying diesel/biodiesel samples, Chemometr. Intell. Lab. Syst., 167, 171, 10.1016/j.chemolab.2017.06.005 · doi:10.1016/j.chemolab.2017.06.005
- Cao, 2017, Double outlyingness analysis in quantitative spectral calibration: implicit detection and intuitive categorization of outliers. Chemometrics and Intelligent Laboratory, Systems, 166, 23
- Baeten, 2014, From targeted to untargeted detection of contaminants and foreign bodies in food and feed using NIR spectroscopy, New Food, 17, 16
- Pierna, 2016, Use of a multivariate moving window PCA for the untargeted detection of contaminants in agro-food products, as exemplified by the detection of melamine levels in milk using vibrational spectroscopy, Chemometr. Intell. Lab. Syst., 152, 157, 10.1016/j.chemolab.2015.10.016 · doi:10.1016/j.chemolab.2015.10.016
- Mora, 2008, On the selection of samples for multivariate regression analysis: application to near-infrared (NIR) calibration models for the prediction of pulp yield in Eucalyptus nitens, Can. J. For. Res., 38, 2626, 10.1139/X08-099 · doi:10.1139/x08-099
- Liu, 2014, An optimal selection method of samples of calibration set and validation set for spectral multivariate analysis, Spectrosc. Spectr. Anal., 34, 947
- Li, 2014, A training set sample selection method based on SIMPLISMA for robust calibration in Near-Infrared spectral analysis, Spectroscopy, 29, 62
- He, 2015, Design of a reference value-based sample-selection method and evaluation of its prediction capability, Chemometr. Intell. Lab. Syst., 148, 72, 10.1016/j.chemolab.2015.09.001 · doi:10.1016/j.chemolab.2015.09.001
- Dantas, 2004, A strategy for selecting calibration samples for multivariate modelling, Chemometr. Intell. Lab. Syst., 72, 83, 10.1016/j.chemolab.2004.02.008 · doi:10.1016/j.chemolab.2004.02.008
- Allegrini, 2016, Generalized error-dependent prediction uncertainty in multivariate calibration, Anal. Chim. Acta, 903, 51, 10.1016/j.aca.2015.11.028 · doi:10.1016/j.aca.2015.11.028
- Allegrini, 2017, Recent advances in analytical figures of merit: heteroscedasticity strikes back, Analytical Methods, 9, 739, 10.1039/C6AY02916G · doi:10.1039/c6ay02916g
- Hernandez, 2009, Support vector regression for functional data in multivariate calibration problems, Anal. Chim. Acta, 642, 110, 10.1016/j.aca.2008.10.063 · doi:10.1016/j.aca.2008.10.063
- Feng, 2011, Gaussian process regression and its application in near-infrared spectroscopy analysis, Spectrosc. Spectr. Anal., 31, 1514
- Chakraborty, 2012, Bayesian multiple response kernel regression model for high dimensional data and its practical applications in near infrared spectroscopy, Comput. Stat. Data Anal., 56, 2742, 10.1016/j.csda.2012.02.019 · doi:10.1016/j.csda.2012.02.019
- Chakraborty, 2012, Bayesian nonlinear regression for large p small n problems, J. Multivariate Anal., 108, 28, 10.1016/j.jmva.2012.01.015 · doi:10.1016/j.jmva.2012.01.015
- Peng, 2017, Maximum correntropy criterion based regression for multivariate calibration, Chemometr. Intell. Lab. Syst., 161, 27, 10.1016/j.chemolab.2016.12.002 · doi:10.1016/j.chemolab.2016.12.002
- Willis, 2017, L0-constrained regression using mixed integer linear programming, Chemometr. Intell. Lab. Syst., 165, 29, 10.1016/j.chemolab.2016.12.016 · doi:10.1016/j.chemolab.2016.12.016
- Poerio, 2017, Stacked interval sparse partial least squares regression analysis, Chemometr. Intell. Lab. Syst., 166, 49, 10.1016/j.chemolab.2017.03.006 · doi:10.1016/j.chemolab.2017.03.006
- Zamora-Rojas, 2010, Evaluation of a new local modelling approach for large and heterogeneous NIRS data sets, Chemometr. Intell. Lab. Syst., 101, 87, 10.1016/j.chemolab.2010.01.004 · doi:10.1016/j.chemolab.2010.01.004
- Douha, 2012, Support vector regression in spectrophotometry: an experimental study, Crit. Rev. Anal. Chem., 42, 214, 10.1080/10408347.2011.651945 · doi:10.1080/10408347.2011.651945
- Forina, 2007, Stepwise orthogonalization of predictors in classification and regression techniques: an "old" technique revisited, Chemometr. Intell. Lab. Syst., 87, 252, 10.1016/j.chemolab.2007.03.003 · doi:10.1016/j.chemolab.2007.03.003
- Perez, 2009, Calculation of the reliability of classification in discriminant partial least-squares binary classification, Chemometr. Intell. Lab. Syst., 95, 122, 10.1016/j.chemolab.2008.09.005 · doi:10.1016/j.chemolab.2008.09.005
- Ottavian, 2012, Multispectral data classification using similarity factors, Chemometr. Intell. Lab. Syst., 118, 13, 10.1016/j.chemolab.2012.07.008 · doi:10.1016/j.chemolab.2012.07.008
- Soares, 2014, A new validation criterion for guiding the selection of variables by the successive projections algorithm in classification problems, J. Braz. Chem. Soc., 25, 176
- Lopez, 2014, Multivariate screening in food adulteration: untargeted versus targeted modelling, Food Chem., 147, 177, 10.1016/j.foodchem.2013.09.139 · doi:10.1016/j.foodchem.2013.09.139
- Shen, 2016, A feasibility study of non-targeted adulterant screening based on NIRM spectral library of soybean meal to guarantee quality: the example of non-protein nitrogen, Food Chem., 210, 35, 10.1016/j.foodchem.2016.04.101 · doi:10.1016/j.foodchem.2016.04.101
- Lima, 2002, Evaluation of standardisation methods of near infrared calibration models, J. Near Infrared Spectrosc., 10, 269, 10.1255/jnirs.343 · doi:10.1255/jnirs.343
- Guenard, 2007, Importance of prediction Outlier diagnostics in determining a successful inter-vendor multivariate calibration model transfer, Appl. Spectrosc., 61, 747, 10.1366/000370207781393280 · doi:10.1366/000370207781393280
- Bao, 2008, A simple and convenient standardization algorithm of NIR spectra, Spectrosc. Spectr. Anal., 28, 829
- Sulub, 2008, Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: a process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms, Anal. Chim. Acta, 611, 143, 10.1016/j.aca.2008.02.016 · doi:10.1016/j.aca.2008.02.016
- Martins, 2010, Multivariate calibration transfer employing variable selection and subagging, J. Braz. Chem. Soc., 21, 127, 10.1590/S0103-50532010000100019 · doi:10.1590/s0103-50532010000100019
- Lin, 2013, Application of orthogonal space regression to calibration transfer without standards, J. Chemometr., 27, 406, 10.1002/cem.2536 · doi:10.1002/cem.2536
- Ridder, 2014, Robust calibration transfer in noninvasive ethanol measurements, Part I: mathematical basis for spectral distortions in fourier transform near-infrared spectroscopy (FT-NIR), Appl. Spectrosc., 68, 852, 10.1366/13-07422 · doi:10.1366/13-07422
- Zhang, 2014, Calibration transfer without standards for spectral analysis based on stability competitive adaptive reweighted sampling, Spectrosc. Spectr. Anal., 34, 1429
- Zheng, 2014, Calibration transfer of near-infrared spectra for extraction of informative components from spectra with canonical correlation analysis, J. Chemometr., 28, 773, 10.1002/cem.2637 · doi:10.1002/cem.2637
- Li, 2015, Correcting multivariate calibration model for near infrared spectral analysis without using standard samples, J. Near Infrared Spectrosc., 23, 285, 10.1255/jnirs.1165 · doi:10.1255/jnirs.1165
- Mou, 2016, Robust calibration model transfer, Chemometr. Intell. Lab. Syst., 156, 62, 10.1016/j.chemolab.2016.05.014 · doi:10.1016/j.chemolab.2016.05.014
- Folch-Fortuny, 2017, Calibration transfer between NIR spectrometers: new proposals and a comparative study, J. Chemometr., 31
- Milanez, 2017, Selection of robust variables for transfer of classification models employing the successive projections algorithm, Anal. Chim. Acta, 984, 76, 10.1016/j.aca.2017.07.037 · doi:10.1016/j.aca.2017.07.037
- Manley, 2006, Comparison of Fourier transform near infrared spectroscopy partial least square regression models for South African extra virgin olive oil using spectra collected on two spectrophotometers at different resolutions and path lengths, J. Near Infrared Spectrosc., 14, 11
- Chung, 2004, Investigation of partial least squares (PLS) calibration performance based on different resolutions of near infrared spectra, Bull. Kor. Chem. Soc., 25, 647, 10.5012/bkcs.2004.25.5.647 · doi:10.5012/bkcs.2004.25.5.647
- Wang, 2009, Comparison of the models of mixed liquid samples under different near-infrared spectral resolutions, Spectrosc. Spectr. Anal., 29, 1813
- Linga, 2009, Very high gain and low excess noise near infrared single photon avalanche detector: a NIR solid state photomultiplier. Advanced Photon Counting Techniques III, vol. 7320
- Armstrong, 2006, Comparison of dispersive and Fourier-transform NIR instruments for measuring grain and flour attributes, Appl. Eng. Agric., 22, 453, 10.13031/2013.20448 · doi:10.13031/2013.20448
- Schmidt, 2009, Comparison of different types of NIR instruments in ability to measure β-glucan content in naked barley, Cereal Chem., 86, 398, 10.1094/CCHEM-86-4-0398 · doi:10.1094/cchem-86-4-0398
- Gergely, 2015, Comparison of different types of near infrared (NIR) instruments in ability to measure alkaloids in capsule of poppy (P. Somniferum), Period. Polytech. - Chem. Eng., 59, 277, 10.3311/PPch.7725 · doi:10.3311/ppch.7725
- Gentilin, 2016, Development and testing of a hardware platform for measuring instruments based on near-infrared diffuse reflection, Measurement, 86, 14, 10.1016/j.measurement.2016.02.047 · doi:10.1016/j.measurement.2016.02.047
- Uemur, 2015, Takeda Y: development of near-infrared absorption spectrometry system by using NIR wideband glass phosphor LED. 6th international conference on optical, optoelectronic and photonic materials and applications (ICOOPMA) 2014, J. Phys. Conf., 619
- Lukovic, 2016, LED-based Vis-NIR spectrally tunable light source - the optimization algorithm, J. Eur. Opt. Soc. Rapid Publ., 12, 19, 10.1186/s41476-016-0021-9 · doi:10.1186/s41476-016-0021-9
- Fuchi, 2014, Ultrawide-band near-infrared light source over 1 mW by Sm3+,Pr3+-codoped glass phosphor combined with LED, APEX, 7
- Li, 2015, Filter design for molecular factor computing using wavelet functions, Anal. Chim. Acta, 880, 26, 10.1016/j.aca.2015.04.026 · doi:10.1016/j.aca.2015.04.026
- Watari, 2010, Applications of near-infrared spectroscopy to process analysis using Fourier transform spectrometer, Opt. Rev., 17, 317, 10.1007/s10043-010-0057-9 · doi:10.1007/s10043-010-0057-9
- Alam, 2017, In-line monitoring and optimization of powder flow in a simulated continuous process using transmission near infrared spectroscopy, International Journal of Phamaceutics, 526, 199, 10.1016/j.ijpharm.2017.04.054 · doi:10.1016/j.ijpharm.2017.04.054
- Naidu, 2017, PAT-based control of fluid bed coating process using NIR spectroscopy to monitor the cellulose coating on pharmaceutical pellets, AAPS PharmSciTech, 18, 2045, 10.1208/s12249-016-0680-2 · doi:10.1208/s12249-016-0680-2
- Reboucas, 2010, Near-infrared spectroscopic prediction of chemical composition of a series of petrochemical process streams for aromatics production, Vib. Spectrosc., 52, 97, 10.1016/j.vibspec.2009.09.006 · doi:10.1016/j.vibspec.2009.09.006
- Macho, 2002, Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry, Trac. Trends Anal. Chem., 21, 799, 10.1016/S0165-9936(02)01202-5 · doi:10.1016/s0165-9936(02)01202-5
- Analytical Method Committee - Royal Society of Chemistry, 2006, Report by the analytical methods committee: evaluation of analytical instrumentation - Part XXI - NIR instrumentation for process control, Accred Qual. Assur., 11, 236, 10.1007/s00769-005-0007-6 · doi:10.1007/s00769-005-0007-6
- Gonzaga, 2010, A low cost short wave near infrared spectrophotometer: application for determination of quality parameters of diesel fuel, Anal. Chim. Acta, 670, 92, 10.1016/j.aca.2010.04.060 · doi:10.1016/j.aca.2010.04.060
- Martinsen, 2010, Temporal sensitivity of the wavelength calibration of a photodiode array spectrometer, Appl. Spectrosc., 64, 1325, 10.1366/000370210793561727 · doi:10.1366/000370210793561727
- Ferstl, 2007, Inline analysis in microreaction technology: a suitable tool for process screening and optimization, Chem. Eng. Technol., 30, 370, 10.1002/ceat.200600404 · doi:10.1002/ceat.200600404
- Friedrich, 2014, Miniature near-infrared spectrometer for point-of-use chemical analysis, Photonic Instrumentation Engineering, 8992, 899203, 10.1117/12.2040669 · doi:10.1117/12.2040669
- Alcala, 2013, Qualitative and quantitative pharmaceutical analysis with a novel hand-held miniature near infrared spectrometer, J. Near Infrared Spectrosc., 21, 445, 10.1255/jnirs.1084 · doi:10.1255/jnirs.1084
- Paiva, 2015, Quantification of biodiesel and adulteration with vegetable oils in diesel/biodiesel blends using portable near-infrared spectrometer, Fuel, 160, 57, 10.1016/j.fuel.2015.07.067 · doi:10.1016/j.fuel.2015.07.067
- Ribessi, 2016, iHEART: a miniaturized near-infrared in-line gas sensor using heart-shaped substrate-integrated hollow waveguides, Analyst, 18, 5298, 10.1039/C6AN01027J · doi:10.1039/c6an01027j
- Sun, 2016, Pharmaceutical raw material identification using miniature near-infrared (MicroNIR) spectroscopy and supervised pattern recognition using support vector machine, Appl. Spectrosc., 70, 816, 10.1177/0003702816638281 · doi:10.1177/0003702816638281
- Ribeiro, 2016, Non-destructive determination of quality traits of cashew apples (Anacardium occidentale, L.) using a portable near infrared spectrophotometer, J. Near Infrared Spectrosc., 24, 77, 10.1255/jnirs.1197 · doi:10.1255/jnirs.1197
- Silva, 2014, Recent developments towards low-cost MEMS spectrometers. Next-Generation Spectroscopic Technologies VII, vol. 9101
- Xiang, 2011, Solid-state digital micro-mirror array spectrometer for hadamard transform measurements of glucose and lactate in aqueous solutions, Appl. Spectrosc., 65, 1170, 10.1366/11-06340 · doi:10.1366/11-06340
- Wang, 2015, Review and recent progress of handheld spectrometry at Thermo Fisher scientific. Next-generation spectroscopic technologies VIII, vol. 9482
- Paz, 2009, Evaluating NIR instruments for quantitative and qualitative assessment of intact apple quality, J. Sci. Food Agric., 89, 781, 10.1002/jsfa.3512 · doi:10.1002/jsfa.3512
- Cayuela, 2010, Intact orange quality prediction with two portable NIR spectrometers, Pstharvest Biology and Technology, 58, 113, 10.1016/j.postharvbio.2010.06.001 · doi:10.1016/j.postharvbio.2010.06.001
- Pederson, 2014, Dreischor, m: pocket-size near-infrared spectrometer for narcotic materials identification. Next-generation spectroscopic technologies VII, vol. 9101, 910100
- Brouillette, 2016, Analysis of twenty-two performance properties of diesel, gasoline, and jet fuels using a field-portable near-infrared (NIR) analyzer, Appl. Spectrosc., 70, 746, 10.1177/0003702816638279 · doi:10.1177/0003702816638279
- Zumba, 2016, Cotton micronaire measurements using small portable near-infrared (NIR) analyzers, Appl. Spectrosc., 70, 794, 10.1177/0003702816638227 · doi:10.1177/0003702816638227
- Modrono, 2017, Handheld NIRS sensors for routine compound feed quality control: real time analysis and field monitoring, Talanta, 162, 597, 10.1016/j.talanta.2016.10.075 · doi:10.1016/j.talanta.2016.10.075
- Basri, 2017, Classification and quantification of palm oil adulteration via portable NIR spectroscopy, Spectrochim. Acta Mol. Biomol. Spectrosc., 173, 335, 10.1016/j.saa.2016.09.028 · doi:10.1016/j.saa.2016.09.028
- Tardaguila, 2017, Non-destructive assessment of grapevine water status in the field using a portable NIR spectrophotometer, Journal of the Science of |Food and Agriculture, 97, 3772, 10.1002/jsfa.8241 · doi:10.1002/jsfa.8241
- Soriano-Disla, 2017, Evaluation of the performance of portable visible-infrared instruments for the prediction of soil properties, Biosyst. Eng., 161, 24, 10.1016/j.biosystemseng.2017.06.017 · doi:10.1016/j.biosystemseng.2017.06.017
- Pierna, 2010, Calibration transfer from dispersive instruments to handheld spectrometers, Appl. Spectrosc., 64, 644, 10.1366/000370210791414353 · doi:10.1366/000370210791414353
- da Silva, 2017, Standardization from a benchtop to a handheld NIR spectrometer using mathematically mixed NIR spectra to determine fuel quality parameters, Anal. Chim. Acta, 954, 32, 10.1016/j.aca.2016.12.018 · doi:10.1016/j.aca.2016.12.018
200 of 245 shown
Cited by
Tutorial: multivariate classification for vibrational spectroscopy in biological samples
Morais, C. L. M., Lima, K. M. G., Singh, M., & Martin, F. L. (2020) · Nature Protocols · doi:10.1038/s41596-020-0322-8 · Cited by 355
Deep learning for vibrational spectral analysis: Recent progress and a practical guide
Yang, J., Xu, J., Zhang, X., Wu, C., Lin, T., & Ying, Y. (2019) · Analytica Chimica Acta · doi:10.1016/j.aca.2019.06.012 · Cited by 299
Beć, K. B., Grabska, J., & Huck, C. W. (2021) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2020.113686 · Cited by 81
Contieri, L. S., de Souza Mesquita, L. M., Sanches, V. L., Chaves, J., Pizani, R. S., da Silva, L. C., Viganó, J., Ventura, S. P. M., & Rostagno, M. A. (2022) · Separation and Purification Technology · doi:10.1016/j.seppur.2022.121640 · Cited by 50
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Erickson, Z., Xing, E., Srirangam, B., Chernova, S., & Kemp, C. C. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341165 · Cited by 50
Metadata
- Authors
- Pasquini, C.
- Venue
- Analytica Chimica Acta
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 1026
- Pages
- 8-36
- ISSN
- 0003-2670 (ISSN-L)
- OpenAlex
- W2799365888
- Cited by
- 1,153
- References
- 189
- FWCI
- 57.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (307541/2014, 465768/2014-8)
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2014/50951?)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Near infrared spectroscopy: A mature analytical technique with new perspectives – A review. https://app.cochat.ai/papers/10.1016/j.aca.2018.04.004
- The paper
- Pasquini, C. (2018). Near infrared spectroscopy: A mature analytical technique with new perspectives – A review. Analytica Chimica Acta. https://doi.org/10.1016/j.aca.2018.04.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.