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IR/Raman Spectroscopy in Biological and Environmental Studies: Comparison
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Please note this is a comparison between Version 2 by Beatrix Zheng and Version 1 by Mikhail Shank.

Various types of vibrational spectroscopy (generally, it includes various variants of Raman and infrared spectroscopy) have been used for a long time to evaluate a variety of biological objects. Moreover, using vibrational spectroscopy, it is possible to evaluate individual compounds, cells, tissues, multicellular organisms (both living and fixed), and the products of their vital activity. These techniques are used for the assessment of the qualitative and quantitative composition of substances in studied biological objects and the conformations of compounds composing them. Among the advantages of these methods, one can mention their relative non-invasiveness, their significant experience in the subsequent analysis of results, and the possibility to perform in situ and in vivo measurements. 

  • infrared spectroscopy
  • Raman spectroscopy
  • vibrational spectroscopy in environmental studies

1. Application of Raman Spectroscopy

Since the second half of the 20th century laser-excited Raman spectroscopy as fast non-invasive and slightly damaging method has been widely used in investigation of different biological objects. During this time, in general, two main problems can be formulated for the use of Raman spectroscopy to study biological samples: (1) very low signal intensity of the Raman spectrum bands that requires enhanced laser power and a well-developed mathematical apparatus for treatment of spectra; and (2) rapid damage of radiated samples that requires a very careful selection of the radiation power and duration for each biological object. Unfortunately, due to these peculiarities, not all types of Raman spectroscopy are effective when working with biological objects. The solution of these problems is critical for the successful use of Raman spectroscopy. However, the aforementioned problems do not prevent the use of different methods of Raman spectroscopy for various biological and environmental studies.
TableTable 1 3 shows some examples of substances and objects that were detected using different methods of Raman spectroscopy.
Table 31.
Substances in cells observed using Raman spectroscopy.
Substance Objects Methods Bands, cm−1 Used for Estimation Ref.
Carotenoids and other polyenes (e.g., β-carotene, lutein, lycopene, astaxanthin) Microalgae, extremophiles, lichens;

Watermelon, hot pepper, skin		 Resonance Raman spectroscopy,

Raman microscopy;

SERS		 ~1530, ~1120–1200, and ~1000 Carotenoids detection;

Cell detection;

Stress detection

Concentration of carotenoids;

Maturity degree		 [42,43,44,45,46,47,48,2][3][4]49,[50,51,5][52,6][[9][1053,7][][11][54,55][1][8]12][13][14]
Chlorophyll Microorganisms (algae, bacteria, lichens, and endoliths) Raman spectroscopy,

Resonance Raman spectroscopy,

Raman microscopy		 Wide range e.g., ~1640, ~1554, ~1437, ~1325, ~1289, ~1233, ~1186, ~1173, ~1020, ~986, ~915 Organisms’ condition Monitoring of atmospheric CO2 [103][51]Reviewed in [43,Reviewed in [56]2][15]
Quinones, flavonoids (polyphenols), pheophytin, cytochromes, hemoglobin et al.;

Flexirubins, scytonemin (specific for cyanobacteria)		 Microorganisms (algae, bacteria, lichens, and endoliths) Raman spectroscopy,

Resonance Raman spectroscopy,

Raman microscopy		 Wide range Organisms’ condition

Cell detection;		 Reviewed in [43,57,58]Reviewed in [2][16][17]
Triglycerides (oil) and fatty acids
Wood Cellulose, lignine, phenolic compounds, and other wood components Mainly NIR, DRIFT-MIR Evaluation chemical structure and physical properties (including in vivo);

changes in composition;

density;

degradation level;

humidity		 [104,[105,106,52107,108]][53][54][55][56] Algae and microalgae
Control for:

Raw materials;

transesterification process in bioreactor		 Oils, fats, free fatty acids;

Raman spectroscopy,

Resonance Raman spectroscopy,

Raman microscopy;

SERS		 1650 (unsaturated) and 1440 (saturated) bonds, respectively methyl esters; NIR and IR spectroscopy Biodiesel quality control:

choice of the raw materials;

reduction of cost; control of trans-esterification reaction;

Biofuel production;

Oil concentration		 determination of the properties, quality, and contaminants of biodiesel [61,109,110][20][57][58][56,1559,][1860,][1961,]62,63][[20][21][22]
Ethanol Lignin and cellulose conversion to bioethanol in bioreactor
Fungal strains, cyanobacteria, other microorganisms and plants;

single cells and biofilms		Raman spectroscopy 883 Biofuel production Compounds containing C=O groups IR spectroscopy Evaluating biosorption of copper, lead, and cadmium ions;

control of bioremediation, biodegradation, and biomineralization		 [92,111,112,113,114,115][38][59][60][61][62][63][59,64][18][23]
Glucose and Glucose oligomers and polysaccharides Lignin and cellulose conversion to bioethanol in bioreactor Raman spectroscopy 1120

Typical glycosidic bond peak at 920–960		 Biofuel production [59][18]
Inorganic polyphosphate inclusions Microalgae Raman microscopy Peak at ~1160 Substance and concentration detection [65,[2466]][25]
Nitrogen (crystalline guanine) Microalgae Raman microscopy Maximum at 651 Substance and concentration detection [67,68][26][27]
Starch Microalgae Raman microscopy Maximum ~479 Substance and concentration detection [68][27]
Lipids Microalgae Raman microscopy Maximum ~2854 Substance and concentration detection [68][27]
Calcite and aragonite

magnesite		 Shells (exoskeletons) Raman spectroscopy,

Raman microscopy		 Peaks at 156, 282, and 1087 (calcite); Peaks at 155, 206, and 1086 (aragonite); Band at 1089 (magnesite) Assessment of the size and mineralization level [69,70,71,72,73,74][28][29][30][31][32][33]
Microorganisms, strains of micro-organisms Specific binding cell sites SERS with special particles containing biorecognition molecules The peak of particles Ultrasensitive bacteria (pathogen) detection [75,76][34][35]

2. Application of Different Types of IR Spectroscopy

As wthe researchers have already mentioned, the advantages of IR spectroscopy include noninvasiveness, non-damaging or slightly damaging effects (even compared to the Raman spectroscopy), relatively rapid obtaining of results, and (unlike the Raman spectroscopy) the possibility to evaluate polar molecules (alcohols, phenolic compounds, etc.) [4][36]. As a result, different types of IR spectroscopy are quite actively used for the monitoring of environmental pollution as well as for solving different biomedical and other tasks, such as tumor diagnostics (reviewed in [4,5,92,93][36][37][38][39]). Moreover, IR spectroscopy is a cheap method compared to its analogues [5][37]. A study of biological samples usually requires the use of near-infrared (1000–2500 nm, 10,000–4000 cm−1) and mid-infrared (2500–25,000 nm, 4000–400 cm−1) spectroscopy [4][36]. Unfortunately, the low sensitivity of near-infrared (NIR) spectroscopy and the limitations of mid-IR spectroscopy in the case of dealing with aqueous solutions, as well as the lower resolution of the method compared to Raman spectroscopy [3][40], result in significant problems with microscopic studies [4][36] that limit the potential of the method in relation to living objects and environmental studies.
The result of measuring biological samples is an IR spectrum, which, like in the case of Raman spectroscopy, represents a superposition of numerous overlapping peaks, whose presence and intensity depend on the chemical bonds in the studied compounds and their conformation. Based on the presence of typical chemical groups, it is possible to determine the presence of proteins, amino acids, fatty acids, polysaccharides, phenolic compounds, etc. However, such a large number of peaks significantly complicates their interpretation and requires the use of special mathematical apparatus, which (in contrast to Raman spectroscopy) has already been developed. The obtained spectral data are treated using various multiparametric analysis methods, such as principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), hierarchical clustering analysis (HCA), canonical variation analysis (CVA), factor discriminant analysis (FDA), partial least squares discriminant analysis (PLSDA), and various regression models, including multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), etc. [33][41]. When applying these methods, there is no need to use the values of individual peaks; in these cases, whole (or selected sections of) IR spectra can be used without isolating individual peaks.
TableTable 2 4 shows some examples of using different methods of IR spectroscopy for environmental studies.
Table 42.
Substances in cells observed using IR spectroscopy.
Objects Substances Methods * Used for Estimation Ref.
Plants, fruits Antioxidants (e.g., phenolic compounds, carotenoids, triterpenoids); organic acids; polysaccharides; essential oils; fatty acids NIR and IR spectroscopy Qualitative and quantitative detection [94,95,101,102][42][43]96,[44]97,[45]98,[46]99,[47]100,[48][49][50]
Leaves of C. arabica Compounds of leaves NIR spectroscopy
Microorganisms, strains of microorganisms
Compounds (molecules) containing in microorganisms IR spectroscopy Rapid identification and classification of microorganisms Reviewed in [33,116]Reviewed in [41][64]
Agriculture:

faeces and manure;

agricultural products (including grains and milk)		 Compounds (molecules) containing in studied substances NIR (mainly) and IR spectroscopy Control the quality of agricultural products;

control the quality of grain and food rations (including their composition, humidity, homogeneity, etc.);

evaluate the composition of faeces and manure for the assessment of the digestion quality in animals and analysis of a stool composition;

control of atmospheric emissions of ammonia, nitric oxide, methane, and other volatile organic compounds;

control the environmental contamination (ammonia and greenhouse gases)		 [92,38117,][65118,][66119,]120,121][[67][68][69]
* Also include the special mathematical apparatus for the interpretation of the results of measurement.
All the aforesaid advantages of IR (especially NIR) spectroscopy, as well as the availability of relatively inexpensive and cheap equipment, make it possible to perform environmental studies using IR spectroscopy (reviewed in [6][70]). Since IR and especially NIR spectroscopy provide the possibility of working with aqueous solutions, this method allows for the in vivo qualitative and quantitative assessment of the composition of animals and plants and the evaluation of changes occurring in them. The objects of IR spectroscopy may include single-celled fungi, lichens, microalgae, plant and animal tissues (membranes, bones, plant organs), multicellular plants and animals, and products of their vital activities (consumed food, excretions, feces, etc.) [6][70].

References

  1. Osterrothová, K.; Culka, A.; Němečková, K.; Kaftan, D.; Nedbalová, L.; Procházková, L.; Jehlička, J. Analyzing carotenoids of snow algae by Raman microspectroscopy and high-performance liquid chromatography. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2019, 212, 262–271.
  2. Jehlička, J.; Edwards, H.G.M.; Oren, A. Raman Spectroscopy of Microbial Pigments. Appl. Environ. Microbiol. 2014, 80, 3286–3295.
  3. Kaczor, A.; Baranska, M. Structural Changes of Carotenoid Astaxanthin in a Single Algal Cell Monitored in situ by Raman Spectroscopy. Anal. Chem. 2011, 83, 7763–7770.
  4. Darvin, M.E.; Gersonde, I.; Albrecht, H.; Gonchukov, S.A.; Sterry, W.; Lademann, J. Determination of Beta Carotene and Lycopene Concentrationsin Human Skin Using Resonance Raman Spectroscopy. Laser Phys. 2005, 15, 295–299.
  5. Mishra, K.B.; Vítek, P.; Barták, M. A correlative approach, combining chlorophyll a fluorescence, reflectance, and Raman spectroscopy, for monitoring hydration induced changes in Antarctic lichen Dermatocarpon polyphyllizum. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2019, 208, 13–23.
  6. Mishra, K.B.; Vítek, P.; Mishra, A.; Hájek, J.; Barták, M. Chlorophyll a fluorescence and Raman spectroscopy can monitor activation/deactivation of photosynthesis and carotenoids in Antarctic lichens. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2020, 239, 118458.
  7. Shutova, V.V.; Tyutyaev, E.V.; Churin, A.A.; Ponomarev, V.Y.; Belyakova, G.A.; Maksimov, G.V. IR and Raman spectroscopy in the study of carotenoids of Cladophora rivularis algae. Biophysics 2016, 61, 601–605.
  8. Jehlička, J.; Edwards, H.G.M.; Osterrothová, K.; Novotná, J.; Nedbalová, L.; Kopecký, J.; Němec, I.; Oren, A. Potential and limits of Raman spectroscopy for carotenoid detection in microorganisms: Implications for astrobiology. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2014, 372, 20140199.
  9. Todorenko, D.A.; Hao, J.; Slatinskaya, O.V.; Allakhverdiev, E.S.; Khabatova, V.V.; Ivanov, A.D.; Radenovic, C.N.; Matorin, D.N.; Alwasel, S.; Maksimov, G.V.; et al. Effect of thiamethoxam on photosynthetic pigments and primary photosynthetic reactions in two maize genotypes (Zea mays). Funct. Plant Biol. 2021, 48, 994–1004.
  10. Todorenko, D.A.; Slatinskaya, O.V.; Hao, J.; Seifullina, N.K.; Radenović, Č.; Matorin, D.N.; Maksimov, G.V. Photosynthetic pigments and phytochemical activity of photosynthetic apparatus of maize (Zea mays L.) leaves under the effect of thiamethoxam. Agric. Biol. 2020, 55, 66–76.
  11. Pilát, Z.; Bernatová, S.; Ježek, J.; Šerý, M.; Samek, O.; Zemánek, P.; Nedbal, L.; Trtílek, M. Raman microspectroscopy of algal lipid bodies: β-carotene quantification. J. Appl. Phycol. 2012, 24, 541–546.
  12. Zhong, J.-Z.; Liao, Y.-Q.; She, Q.-T.; Xue, T.; You, R.-Y.; Chen, Y.-Q.; Huang, L.-Q. Rapid and sensitive label-free SERS determination of fucoxanthin in algae using gold nanoparticles. Opt. Mater. Express 2021, 11, 240–249.
  13. Dhanani, T.; Dou, T.; Biradar, K.; Jifon, J.; Kurouski, D.; Patil, B.S. Raman Spectroscopy Detects Changes in Carotenoids on the Surface of Watermelon Fruits during Maturation. Front. Plant Sci. 2022, 13, 832522.
  14. Legner, R.; Voigt, M.; Servatius, C.; Klein, J.; Hambitzer, A.; Jaeger, M. A Four-Level Maturity Index for Hot Peppers (Capsicum annum) Using Non-Invasive Automated Mobile Raman Spectroscopy for On-Site Testing. Appl. Sci. 2021, 11, 1614.
  15. Wei, X.; Jie, D.; Cuello, J.J.; Johnson, D.J.; Qiu, Z.; He, Y. Microalgal detection by Raman microspectroscopy. TrAC Trends Anal. Chem. 2014, 53, 33–40.
  16. Němečková, K.; Culka, A.; Němec, I.; Edwards, H.G.M.; Mareš, J.; Jehlička, J. Raman spectroscopic search for scytonemin and gloeocapsin in endolithic colonizations in large gypsum crystals. J. Raman Spectrosc. 2021, 52, 2633–2647.
  17. Jehlička, J.; Osterrothová, K.; Oren, A.; Edwards, H.G.M. Raman spectrometric discrimination of flexirubin pigments from two genera of Bacteroidetes. FEMS Microbiol. Lett. 2013, 348, 97–102.
  18. Ewanick, S.; Schmitt, E.; Gustafson, R.; Bura, R. Use of Raman spectroscopy for continuous monitoring and control of lignocellulosic biorefinery processes. Pure Appl. Chem. 2014, 86, 867–879.
  19. Pořízka, P.; Prochazková, P.; Prochazka, D.; Sládková, L.; Novotný, J.; Petrilak, M.; Brada, M.; Samek, O.; Pilát, Z.; Zemánek, P.; et al. Algal Biomass Analysis by Laser-Based Analytical Techniques—A Review. Sensors 2014, 14, 17725–17752.
  20. Zhang, W.-B. Review on analysis of biodiesel with infrared spectroscopy. Renew. Sustain. Energy Rev. 2012, 16, 6048–6058.
  21. Sharma, S.K.; Nelson, D.R.; Abdrabu, R.; Khraiwesh, B.; Jijakli, K.; Arnoux, M.; O’Connor, M.J.; Bahmani, T.; Cai, H.; Khapli, S.; et al. An integrative Raman microscopy-based workflow for rapid in situ analysis of microalgal lipid bodies. Biotechnol. Biofuels 2015, 8, 164.
  22. Ramya, A.N.; Ambily, P.S.; Sujitha, B.S.; Arumugam, M.; Maiti, K.K. Single cell lipid profiling of Scenedesmus quadricauda CASA-CC202 under nitrogen starved condition by surface enhanced Raman scattering (SERS) fingerprinting. Algal Res. 2017, 25, 200–206.
  23. Mollaeian, K.; Wei, S.; Islam, M.R.; Dickerson, B.; Holmes, W.E.; Benson, T.J. Development of an Online Raman Analysis Technique for Monitoring the Production of Biofuels. Energy Fuels 2016, 30, 4112–4117.
  24. Moudříková, Š.; Sadowsky, A.; Metzger, S.; Nedbal, L.; Mettler-Altmann, T.; Mojzeš, P. Quantification of Polyphosphate in Microalgae by Raman Microscopy and by a Reference Enzymatic Assay. Anal. Chem. 2017, 89, 12006–12013.
  25. Solovchenko, A.; Khozin-Goldberg, I.; Selyakh, I.; Semenova, L.; Ismagulova, T.; Lukyanov, A.; Mamedov, I.; Vinogradova, E.; Karpova, O.; Konyukhov, I.; et al. Phosphorus starvation and luxury uptake in green microalgae revisited. Algal Res. 2019, 43, 101651.
  26. Mojzeš, P.; Gao, L.; Ismagulova, T.; Pilátová, J.; Moudříková, Š.; Gorelova, O.; Solovchenko, A.; Nedbal, L.; Salih, A. Guanine, a high-capacity and rapid-turnover nitrogen reserve in microalgal cells. Proc. Natl. Acad. Sci. USA 2020, 117, 32722–32730.
  27. Moudříková, Š.; Ivanov, I.N.; Vítová, M.; Nedbal, L.; Zachleder, V.; Mojzeš, P.; Bišová, K. Comparing Biochemical and Raman Microscopy Analyses of Starch, Lipids, Polyphosphate, and Guanine Pools during the Cell Cycle of Desmodesmus quadricauda. Cells 2021, 10, 62.
  28. Komura, T.; Kagi, H.; Ishikawa, M.; Yasui, M.; Sasaki, T. Spectroscopic Investigation of Shell Pigments from the Family Neritidae (Mollusca: Gastropoda); Springer: Singapore, 2018; pp. 73–82.
  29. Langer, G.; Nehrke, G.; Baggini, C.; Rodolfo-Metalpa, R.; Hall-Spencer, J.M.; Bijma, J. Limpets counteract ocean acidification induced shell corrosion by thickening of aragonitic shell layers. Biogeosciences 2014, 11, 7363–7368.
  30. Lu, Y.; Li, Y.; Li, Y.; Wang, Y.; Wang, S.; Bao, Z.; Zheng, R. Micro spatial analysis of seashell surface using laser-induced breakdown spectroscopy and Raman spectroscopy. Spectrochim. Acta Part B At. Spectrosc. 2015, 110, 63–69.
  31. DeCarlo, T.M.; Comeau, S.; Cornwall, C.E.; Gajdzik, L.; Guagliardo, P.; Sadekov, A.; Thillainath, E.C.; Trotter, J.; McCulloch, M.T. Investigating marine bio-calcification mechanisms in a changing ocean with in vivo and high-resolution ex vivo Raman spectroscopy. Glob. Change Biol. 2019, 25, 1877–1888.
  32. Ramesh, K.; Melzner, F.; Griffith, A.W.; Gobler, C.J.; Rouger, C.; Tasdemir, D.; Nehrke, G. In vivo characterization of bivalve larval shells: A confocal Raman microscopy study. J. R. Soc. Interface 2018, 15, 20170723.
  33. Bergamonti, L.; Bersani, D.; Csermely, D.; Lottici, P.P. The Nature of the Pigments in Corals and Pearls: A Contribution from Raman Spectroscopy. Spectrosc. Lett. 2011, 44, 453–458.
  34. Bi, L.; Wang, X.; Cao, X.; Liu, L.; Bai, C.; Zheng, Q.; Choo, J.; Chen, L. SERS-active core-shell nanorod () tags for ultrasensitive bacteria detection and antibiotic-susceptibility testing. Talanta 2020, 220, 121397.
  35. Li, Y.; Lu, C.; Zhou, S.; Fauconnier, M.-L.; Gao, F.; Fan, B.; Lin, J.; Wang, F.; Zheng, J. Sensitive and simultaneous detection of different pathogens by surface-enhanced Raman scattering based on aptamer and Raman reporter co-mediated gold tags. Sens. Actuators B Chem. 2020, 317, 128182.
  36. Beć, K.B.; Grabska, J.; Huck, C.W. Near-Infrared Spectroscopy in Bio-Applications. Molecules 2020, 25, 2948.
  37. Beć, K.B.; Grabska, J.; Huck, C.W. Biomolecular and bioanalytical applications of infrared spectroscopy—A review. Anal. Chim. Acta 2020, 1133, 150–177.
  38. Claudia Maria, S. Application of FTIR Spectroscopy in Environmental Studies; IntechOpen: Rijeka, Croatia, 2012; p. 49, Chapter 2.
  39. Huck, C. Miniaturized MIR and NIR sensors for medicinal plant quality control. Spectrosc. (St. Monica) 2017, 32, 15–32.
  40. Baranska, M.; Roman, M.; Majzner, K. General Overview on Vibrational Spectroscopy Applied in Biology and Medicine. In Optical Spectroscopy and Computational Methods in Biology and Medicine; Baranska, M., Ed.; Springer: Dordrecht, The Netherlands, 2014; pp. 3–14.
  41. Spitsyn, A.N.; Utkin, D.V.; Kuznetsov, O.S.; Erokhin, P.S.; Osina, N.A.; Kochubei, V.I. Application of Optical Techniques to Investigation and Identification of Microorganisms: A Review. Opt. Spectrosc. 2021, 129, 135–148.
  42. Agatonovic-Kustrin, S.; Balyklova, K.S.; Gegechkori, V.; Morton, D.W. HPTLC and ATR/FTIR Characterization of Antioxidants in Different Rosemary Extracts. Molecules 2021, 26, 6064.
  43. da Silva, C.; Prasniewski, A.; Calegari, M.A.; de Lima, V.A.; Oldoni, T.L.C. Determination of Total Phenolic Compounds and Antioxidant Activity of Ethanolic Extracts of Propolis Using ATR–FT-IR Spectroscopy and Chemometrics. Food Anal. Methods 2018, 11, 2013–2021.
  44. Zhang, Y.-C.; Deng, J.; Lin, X.-L.; Li, Y.-M.; Sheng, H.-X.; Xia, B.-H.; Lin, L.-M. Use of ATR-FTIR Spectroscopy and Chemometrics for the Variation of Active Components in Different Harvesting Periods of Lonicera japonica. Int. J. Anal. Chem. 2022, 2022, 8850914.
  45. Agatonovic-Kustrin, S.; Gegechkori, V.; Mohammed, E.U.R.; Ku, H.; Morton, D.W. Isolation of Bioactive Pentacyclic Triterpenoid Acids from Olive Tree Leaves with Flash Chromatography. Appl. Sci. 2022, 12, 996.
  46. Hong, T.; Yin, J.-Y.; Nie, S.-P.; Xie, M.-Y. Applications of infrared spectroscopy in polysaccharide structural analysis: Progress, challenge and perspective. Food Chem. X 2021, 12, 100168.
  47. de Oliveira, G.A.; de Castilhos, F.; Renard, C.M.-G.C.; Bureau, S. Comparison of NIR and MIR spectroscopic methods for determination of individual sugars, organic acids and carotenoids in passion fruit. Food Res. Int. 2014, 60, 154–162.
  48. de Oliveira, V.M.A.T.; Baqueta, M.R.; Março, P.H.; Valderrama, P. Authentication of organic sugars by NIR spectroscopy and partial least squares with discriminant analysis. Anal. Methods 2020, 12, 701–705.
  49. Heredia-Guerrero, J.A.; Benítez, J.J.; Domínguez, E.; Bayer, I.S.; Cingolani, R.; Athanassiou, A.; Heredia, A. Infrared and Raman spectroscopic features of plant cuticles: A review. Front. Plant Sci. 2014, 5, 305.
  50. Farber, C.; Li, J.; Hager, E.; Chemelewski, R.; Mullet, J.; Rogachev, A.Y.; Kurouski, D. Complementarity of Raman and Infrared Spectroscopy for Structural Characterization of Plant Epicuticular Waxes. ACS Omega 2019, 4, 3700–3707.
  51. Tormena, C.D.; Marcheafave, G.G.; Pauli, E.D.; Bruns, R.E.; Scarminio, I.S. Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis. Environ. Sci. Pollut. Res. 2019, 26, 30356–30364.
  52. Tsuchikawa, S.; Kobori, H. A review of recent application of near infrared spectroscopy to wood science and technology. J. Wood Sci. 2015, 61, 213–220.
  53. Fahey, L.M.; Nieuwoudt, M.K.; Harris, P.J. Using near infrared spectroscopy to predict the lignin content and monosaccharide compositions of Pinus radiata wood cell walls. Int. J. Biol. Macromol. 2018, 113, 507–514.
  54. Hwang, S.-W.; Hwang, U.T.; Jo, K.; Lee, T.; Park, J.; Kim, J.-C.; Kwak, H.W.; Choi, I.-G.; Yeo, H. NIR-chemometric approaches for evaluating carbonization characteristics of hydrothermally carbonized lignin. Sci. Rep. 2021, 11, 16979.
  55. dos Santos, L.M.; Amaral, E.A.; Nieri, E.M.; Costa, E.V.S.; Trugilho, P.F.; Calegário, N.; Hein, P.R.G. Estimating wood moisture by near infrared spectroscopy: Testing acquisition methods and wood surfaces qualities. Wood Mater. Sci. Eng. 2021, 16, 336–343.
  56. Nuopponen, M.H.; Birch, G.M.; Sykes, R.J.; Lee, S.J.; Stewart, D. Estimation of Wood Density and Chemical Composition by Means of Diffuse Reflectance Mid-Infrared Fourier Transform (DRIFT-MIR) Spectroscopy. J. Agric. Food Chem. 2006, 54, 34–40.
  57. Javidialesaadi, A.; Raeissi, S. Biodiesel Production from High Free Fatty Acid-Content Oils: Experimental Investigation of the Pretreatment Step. APCBEE Procedia 2013, 5, 474–478.
  58. de Lima, S.M.; Silva, B.F.A.; Pontes, D.V.; Pereira, C.F.; Stragevitch, L.; Pimentel, M.F. In-line monitoring of the transesterification reactions for biodiesel production using NIR spectroscopy. Fuel 2014, 115, 46–53.
  59. Usman, K.; Al-Ghouti, M.A.; Abu-Dieyeh, M.H. The assessment of cadmium, chromium, copper, and nickel tolerance and bioaccumulation by shrub plant Tetraena qataranse. Sci. Rep. 2019, 9, 5658.
  60. Gurbanov, R.; Simsek Ozek, N.; Gozen, A.G.; Severcan, F. Quick Discrimination of Heavy Metal Resistant Bacterial Populations Using Infrared Spectroscopy Coupled with Chemometrics. Anal. Chem. 2015, 87, 9653–9661.
  61. Kepenek, E.S.; Severcan, M.; Gozen, A.G.; Severcan, F. Discrimination of heavy metal acclimated environmental strains by chemometric analysis of FTIR spectra. Ecotoxicol. Environ. Saf. 2020, 202, 110953.
  62. Martínez, K.I.; González-Mota, R.; Soto-Bernal, J.J.; Rosales-Candelas, I. Evaluation by IR spectroscopy of the degradation of different types of commercial polyethylene exposed to UV radiation and domestic compost in ambient conditions. J. Appl. Polym. Sci. 2021, 138, 50158.
  63. Markowicz, F.; Szymańska-Pulikowska, A. Assessment of the Decomposition of Oxo- and Biodegradable Packaging Using FTIR Spectroscopy. Materials 2021, 14, 6449.
  64. Wenning, M.; Scherer, S. Identification of microorganisms by FTIR spectroscopy: Perspectives and limitations of the method. Appl. Microbiol. Biotechnol. 2013, 97, 7111–7120.
  65. Sujka, K.; Koczoń, P.; Ceglińska, A.; Reder, M.; Ciemniewska-Żytkiewicz, H. The Application of FT-IR Spectroscopy for Quality Control of Flours Obtained from Polish Producers. J. Anal. Methods Chem. 2017, 2017, 4315678.
  66. Evangelista, C.; Basiricò, L.; Bernabucci, U. An Overview on the Use of Near Infrared Spectroscopy (NIRS) on Farms for the Management of Dairy Cows. Agriculture 2021, 11, 296.
  67. Zhang, S.; Liu, S.; Shen, L.; Chen, S.; He, L.; Liu, A. Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review. Curr. Res. Food Sci. 2022, 5, 1305–1312.
  68. Chen, L.; Xing, L.; Han, L. Review of the Application of Near-Infrared Spectroscopy Technology to Determine the Chemical Composition of Animal Manure. J. Environ. Qual. 2013, 42, 1015–1028.
  69. Tsuchikawa, S.; Ma, T.; Inagaki, T. Application of near-infrared spectroscopy to agriculture and forestry. Anal. Sci. 2022, 38, 635–642.
  70. Counsell, K.R.; Vance, C.K. Recent Advances of near Infrared Spectroscopy in Wildlife and Ecology Studies. NIR News 2016, 27, 29–32.
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