Abstract

Food is a complex system that requires holistic quality assessment. Chemometrics uses mathematical, statistical, and computer science methods to analyze and interpret chemical data, which means good prospects for food quality evaluation. This review covered Russian and international publications indexed in Scopus, PubMed, MEDLINE, Web of Knowledge, Google Scholar, IEEE Xplore, Science Direct, and eLIBRARY.RU (RSCI). The search queries included such keywords as chemometrics; chemometric methods; principal component analysis; PLS (projection to latent structures); artificial neural network (ANN); multivariate classification; multivariate data analysis. The main chemometric tools applied to food systems included hierarchical cluster analysis (HCA), principal component analysis (PCA), latent structures-discriminant analysis (PLS-DA), projections to latent structures (PLS), quadratic projection to latent structures (QPLS), multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and ensemble model prediction (RF, XGBoost). The PCA proved to be the most popular chemometric method applied in the food industry. However, combinations of methods were always more effective than a single one. The KNN methods appeared to be quite unreliable. Combinations of chemometric methods demonstrate the best prospects, e.g., PCA + PLS-DA + ANN or PCA + PLS-DA +KNN. If combined with instrumental tools, they are able to improve analytical accuracy and provide effective management approaches, thus ensuring sustainable food industry.

Keywords

Chemometrics, food, principal component analysis, projections to latent structures, classification, regression

REFERENCES

1. Wold S. Spline functions, a new tool in data analysis. Kemisk Tidskrift. 1972;84(3):34–37.
2. Wold S. Chemometrics; What do we mean with it, and what do we want from it? Chemometrics and Intelligent Laboratory Systems. 1995;30(1):109–115. https://doi.org/10.1016/0169-7439(95)00042-9
3. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems. 1987;2(1–3):37–52. https://doi.org/10.1016/0169-7439(87)80084-9
4. Wold S, Sjöström M, Eriksson L. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems. 2001;58(2):109–130. https://doi.org/10.1016/s0169-7439(01)00155-1
5. Wold S. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics. 1978;20(4):397–405. https://doi.org/10.2307/1267639
6. Geladi P, Kowalski BR. Partial least-squares regression: A tutorial. Analytica Chimica Acta. 1986;185:1–17. https://doi.org/10.1016/0003-2670(86)80028-9
7. Chemometrics: Mathematics and Statistics. In: Kowalski BR, editor. NATO ASI series. Series C, mathematical and physical sciences. Dordrecht: Reidel Publ.Co.; 1984; vol. 138. 485 p.
8. Massart DL, Vandeginste BGM, Buydens LMC, de Jong S, Lewi PJ, et al. Handbook of Chemometrics and Qualimetrics. Amsterdam: Elsevier Science; vol. 20A. 867 p.
9. Rodionova OYe, Pomerantsev AL. Chemometrics: Achievements and prospects. Uspekhi khimii. 2006;75(4):302–321. (In Russ.) https://elibrary.ru/HSQIRD
10. Rodionova OYe. Chemometric approach to big data in chemistry. Russian Journal of General Chemistry. 2006;50(2):128–144. (In R uss.) https://elibrary.ru/HTUUSZ
11. Peris-Díaz MD, Krężel A. A guide to good practice in chemometric methods for vibrational spectroscopy, electrochemistry, and hyphenated mass spectrometry. Trends in Analytical Chemistry. 2021;135:116157. https://doi.org/10.1016/j.trac.2020.116157
12. Paula AM, Conti-Silva AC. Texture profile and correlation between sensory and instrumental analyses on extruded snacks. Journal of Food Engineering. 2014;121:9–14. https://doi.org/10.1016/j.jfoodeng.2013.08.007
13. Dhanapal L, Erkinbaev C. Portable hyperspectral imaging coupled with multivariate analysis for real-time prediction of plant-based meat analogues quality. Journal of Food Composition and Analysis. 2024;126:105840. https://doi.org/10.1016/j.jfca.2023.105840
14. Xiao L, Qi L, Fu R, Nie Q, Zhang X, et al. A large-scale comparison of the meat quality characteristics of different chicken breeds in South China. Poultry Science. 2024;103(6):103740. https://doi.org/10.1016/j.psj.2024.103740
15. Toscas PJ, Shaw FD, Beilken SL. Partial least squares (PLS) regression for the analysis of instrument measurements and sensory meat quality data. Meat Science. 1999;52(2):173–178. https://doi.org/10.1016/S0309-1740(98)00165-X
16. Araújo JC, Lima ACS, Nunes MPM, Sousa MAP, Serrão GX, et al. Relationships among carcass shape, tissue composition, primal cuts and meat quality traits in lambs: A PLS path modeling approach. Small Ruminant Research. 2020;182:52–66. https://doi.org/10.1016/j.smallrumres.2019.106024
17. Huang W, Fan D, Li W, Meng Y, Liu TC. Rapid evaluation of milk acidity and identification of milk adulteration by Raman spectroscopy combined with chemometrics analysis. Vibrational Spectroscopy. 2022;123:103440. https://doi.org/10.1016/j.vibspec.2022.103440
18. Zhilinskaya NV, Sarkisyan VA, Vorobieva VM, Vorobieva IS, Kochetkova AA, et al. Development of a marmalade for patients with type 2 diabetes: Sensory characteristics and acceptability. Food Science and Technology International. 2018;24(7):617–626. https://doi.org/10.1177/1082013218779748
19. Deegan KC, Holopainen U, McSweeney PLH, Alatossava T, Tuorila H. Characterisation of the sensory properties and market positioning of novel reduced-fat cheese. Innovative Food Science & Emerging Technologies. 2014;21:169–178. https://doi.org/10.1016/j.ifset.2013.10.003
20. Li F, Lu Y, He Z, Yu D, Zhou J, et al. Analysis of carcass traits, meat quality, amino acid and fatty acid profiles between different duck lines. Poultry Science. 2024;103(7):103791. https://doi.org/10.1016/j.psj.2024.103791
21. Liu Y, Yang X, Wu S, Degen A, Huan X, et al. Identifying meat from grazing or feedlot yaks using visible and near-infrared spectroscopy with chemometrics. Journal of Food Protection. 2024;87(7):100295. https://doi.org/10.1016/j.jfp.2024.100295
22. Maritha V, Harlina PW, Musfiroh I, Muchtaridi M, Rafi M, et al. Lipidomics analysis for halal authentication of Triceps brachii, Longissimus dorsi, and Biceps femoris meats: Profiling the lipid composition. LWT. 2023;185:115187. https://doi.org/10.1016/j.lwt.2023.115187
23. Amelin VG, Emelyanov OE, Tretyakov AV, Kish LK. Identification and detection of adulterated butter by colorimetry and near-IR-spectroscopy. Journal of Applied Spectroscopy. 2024;91:826–834. https://doi.org/10.1007/s10812-024-01790-0
24. Amelin VG, Shogah ZACh, Bolshakov DS. Identification and authentication of cow’s milk powder using a smartphone and chemometric analysis. Moscow University Chemistry Bulletin. Series 2: Chemistry. 2023;64(1):49–59. (In Russ.) https://doi.org/10.55959/MSU0579-9384-2-2023-64-1-49-59
25. Platov YuT, Bobozhonova GA, Metlenkin DA. Spectrocolorimetry in buckwheat identification. Bakery of Russia. 2020;(1):24–28. (In Russ.) https://elibrary.ru/MXAOOU
26. Platov YuT, Metlenkin DA, Platova RA, Rassulov VA, Vereshchagin AI, et al. Buckwheat identification by combined UV-VIS-NIR spectroscopy and multivariate analysis. Journal of Applied Spectroscopy. 2021;88(4):723–730. https://doi.org/10.1007/s10812-021-01231-2
27. Metlenkin DA, Platov YuT, Platova RA, Rubtzov AE, Mikhaylova AM. Identification of coffee beans using ftir-spectroscopy and multivariate analysis. Izvestiya vuzov. Food Technology. 2021;(5–6):92–97. (In Russ.) https://doi.org/10.26297/0579-3009.2021.5-6.17
28. Platova RA, Zhirkova EV, Metlenkin DA, Lysenkova AA, Platov YuT, et al. Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods. Food Systems. 2024;7(1):22–30. (In Russ.) https://doi .org/10.21323/2618-9771-2024-7-1-22-30
29. Zhu Z, Bassey AP, Cao Y, Du X, Huang T, et al. Meat quality and flavor evaluation of Nanjing water boiled salted duck (NWSD) produced by different Muscovy duck (Cairina moschata) ingredients. Food Chemistry. 2022;397:133833. https://doi.org/10.1016/j.foodchem.2022.133833
30. Coombs CE, Liddle RR, González LA. Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef. Journal of Near Infrared Spectroscopy. 2021;29(6):321–329. https://doi.org/10.1177/09670335211049506
31. Pu K, Qiu J, Li J, Huang W, Lai X, et al. MALDI-TOF MS protein profiling combined with multivariate analysis for identification and quantitation of beef adulteration. Food Analytical Methods. 2023;16:132–142. https://doi.org/10.1007/s12161-022-02403-2
32. Metlenkin DA, Platov YuT, Rubtzov AE. Identification of butter using IR spectroscopy and multivariate analysis. Food Industry. 2020;(3):58–61. (In Russ.) https://doi.org/10.24411/0235-2486-2020-10033
33. Cristea G, Voica C, Feher I, Puscas R, Magdas DA. Isotopic and elemental characterization of Romanian pork meat in corroboration with advanced chemometric methods: A first exploratory study. Meat Science. 2022;189:108825. https://doi.org/10.1016/j.meatsci.2022.108825
34. Tian S, Zhang J, Zhang Z, Zhao J, Zhang Z, et al. Effective modification through transmission Vis/NIR spectraaffected by fruit size to improve the prediction of moldy apple core. Infrared Physics and Technology. 2019;100:117–124. https://doi.org/10.1016/j.infrared.2019.05.015
35. Rohman A, Ghazali MAB, Windarsih A, Irnawati, Riyanto S, et al. Comprehensive review on application of FTIR spectroscopy coupled with chemometrics for authentication analysis of fats and oils in the food products. Molecules. 2020;25(22):5485. https://doi.org/10.3390/molecules25225485
36. Dib OH, Assaf A, Grangé E, Morin JF, Cordella CBY, et al. Automatic recognition of food bacteria using Raman spectroscopy and chemometrics: A comparative study of multivariate models. Vibrational Spectroscopy. 2023;126:103535. https://doi.org/10.1016/j.vibspec.2023.103535
37. Hu X, Ma P, He Y, Guo J, Li Z, et al. Nondestructive and rapid detection of foreign materials in wolfberry by hyperspectral imaging combing with chemometrics. Vibrational Spectroscopy. 2023;128:103578. https://doi.org/10.1016/j.vibspec.2023.103578
38. Logan BG, Hopkins DL, Schmidtke LM, Fowler SM. Assessing chemometric models developed using Raman spectroscopy and fatty acid data for Northern and Southern Australian beef production systems. Meat Science. 2022;187:108753. https://doi.org/10.1016/j.meatsci.2022.108753
39. Amelin VG, Emelyanov OE, Shaoka ZACh, Tratyakov AV. Determination of the mass fraction of milk fat in bottled milk by non-contact colorimetric method. Zhurnal analiticheskoi khimii. 2024;79(11):1147–1153. (In Russ.) https://elibrary.ru/SXOHRV
40. Feng S, Qi S, Chen C, Khan IM, Gu Q, et al. Classification and quantification of adulterants in Alaska pollock based on fourier transform near-infrared spectroscopy with chemometrics. Food Bioscience. 2024;59:104003. https://doi.org/10.1016/j.fbio.2024.104003
41. Özbalci B, Boyaci İH, Topcu A, Kadılar C, Tamer U. Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and arti-ficial neural networks. Food Chemistry. 2013;136(3–4):1444–1452. https://doi.org/10.1016/j.foodchem.2012.09.064
42. Berhe DT, Eskildsen CE, Lametsch R, Hviid MS, van den Berg F. Prediction of total fatty acid parameters and individual fatty acids in pork back fat using Raman spectroscopy and chemometrics: Understanding the cage of covariance between highly correlated fat parameters. Meat Science. 2016;111:18–26. https://doi.org/10.1016/j.meatsci.2015.08.009
43. Berhe DT, Lawaetz AJ, Engelsen SB, Hviid MS, Lametsch R. Accurate determination of endpoint temperature of cooked meat after storage by Raman spectroscopy and chemometrics. Food Control. 2015;52:119–125. https://doi.org/10.1016/j.foodcont.2014.12.011
44. Nache M, Hinrichs J, Scheier R, Schmidt H, Hitzmann B. Prediction of the pH as indicator of porcine meat quality using Raman spectroscopy and metaheuristics. Chemometrics and Intelligent Laboratory Systems. 2016;154:45–51. https://doi.org/10.1016/j.chemolab.2016.03.011
45. Andersen PV, Afseth NK, Gjerlaug-Enger E, Wold JP. Prediction of water holding capacity and pH in porcine longissimus lumborum using Raman spectroscopy. Meat Science. 2021;172:108357. https://doi.org/10.1016/j.meatsci.2020.108357
46. Bauer A, Scheier R, Eberle T, Schmidt H. 46 Meat Science. 2016;115:27–33. https://doi.org/10.1016/j.meatsci.2015.12.020
47. Nunes KM, Andrade MVO, Almeida MR, Fantini C, Sena MM. Raman spectroscopy and discriminant analysis applied to the detection of frauds in bovine meat by the addition of salts and carrageenan. Microchemical Journal. 2019;147:582–589. https://doi.org/10.1016/j.microc.2019.03.076
48. Chen Q, Zhang Y, Guo Y, Cheng Y, Qian H, et al. Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy. Journal of Food Engineering. 2020;266:109693. https://doi.org/10.1016/j.jfoodeng.2019.109693
49. Zhao M, Nian Y, Allen P, Downey G, Kerry JP, et al. Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef. Food Research International. 2018;107:27–40. https://doi.org/10.1016/j.foodres.2018.02.007
50. Logan BG, Hopkins DL, Schmidtke L, Morris S, Fowler SM. Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef. Meat Science. 2020;160:107970. https://doi.org/10.1016/j.meatsci.2019.107970
51. Nian Y, Zhao M, O’Donnell CP, Downey G, Kerry JP, et al. Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics. Food Research International. 2017;99(Part 1):778–789. https://doi.org/10.1016/j.foodres.2017.06.056
52. Fowler SM, Schmidt H, van de Ven R, Hopkins DL. Preliminary investigation of the use of Raman spectroscopy to predict meat and eating quality traits of beef loins. Meat Science. 2018;138:53–58. https://doi.org/10.1016/j.meatsci.2018.01.002
53. Robert C, Fraser-Miller SJ, Jessep WT, Bain WE, Hicks TM, et al. Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy. Food Chemistry. 2021;343:128441. https://doi.org/10.1016/j.foodchem.2020.128441
54. Fowler SM, Ponnampalam EN, Schmidt H, Wynn P, Hopkins DL. Prediction of intramuscular fat content and major fatty acid groups of lamb M. longissimus lumborum using Raman spectroscopy. Meat Science. 2015;110:70–75. https://doi.org/10.1016/j.meatsci.2015.06.016
55. Schmidt H, Scheier R, Hopkins DL. Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss. Meat Science. 2013;93(1):138–143. https://doi.org/10.1016/j.meatsci.2012.08.019
56. Fowler SM, Schmidt H, van de Ven R, Wynn P, Hopkins DL. Raman spectroscopy compared against traditional predictors of shear force in lamb m. longissimus lumborum. Meat Science. 2014;98(4):652–656. https://doi.org/10.1016/j.meatsci.2014.06.042
57. Fowler SM, Schmidt H, van de Ven R, Wynn P, Hopkins DL. Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy. Meat Science. 2014;97(4):597–601. https://doi.org/10.1016/j.meatsci.2014.02.018
58. Fowler SM, Schmidt H, van de Ven R, Wynn P, Hopkins DL. Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device. Meat Science. 2015;108:138–144. https://doi.org/10.1016/j.meatsci.2015.06.010
59. Szykuła KM, Offermans T, Lischtschenko O, Meurs J, Guenther D, et al. Predicting animal welfare labels from pork fat using raman spectroscopy and chemometrics. AppliedChem. 2023;3(2):279–289. https://doi.org/10.3390/appliedchem3020017
60. Saleem M, Amin A, Irfan M. Raman spectroscopy based characterization of cow, goat and buffalo fats. Journal of Food Science and Technology. 2021;58(1):234–243. https://doi.org/10.1007/s13197-020-04535-x
61. Berhe DT, Eskildsen CE, Lametsch R, Hviid MS, van den Berg F, et al. Prediction of total fatty acid parameters and individual fatty acids in pork backfat using Raman spectroscopy and chemometrics: Understanding the cage of covariance between highly correlated fat parameters. Meat Science. 2016;111:18–26. https://doi.org/10.1016/j.meatsci.2015.08.009
62. Logan BG, Hopkins DL, Schmidtke L, Morris S, Fowler SM. Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef. Meat Science. 2020;160:107970. https://doi.org/10.1016/j.meatsci.2019.107970
63. Ostovar pour S, Fowler SM, Hopkins DL, Torley PJ, Gil H, et al. Investigation of chemical composition of meat using spatially off-set Raman spectroscopy. Analyst. 2019;144(8):2618–2627. https://doi.org/10.1039/c8an01958d
64. Abbas O, Fernández Pierna JA, Codony R, von Holst C, Baeten V. Assessment of the discrimination of animal fat by FT-Raman spectroscopy. Journal of Molecular Structure. 2009;924–926:294–300. https://doi.org/10.1016/j.molstruc.2009.01.027
65. Boyaci IH, Uysal RS, Temiz T, Shendi EG, Yadegari RJ, et al. A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method. European Food Research and Technology. 2014;238:845–852. https://doi.org/10.1007/s00217-014-2168-1
66. Lyndgaard LB, Sørensen KM, van der Berg F, Engelsen SB. Depth profiling of porcine adipose tissue by Raman spectroscopy. Journal of Raman Spectroscopy. 2012;43(4):482–489. https://doi.org/10.1002/jrs.3067
67. Gao F, Ben-Amotz D, Zhou S, Yang Z, Han L, et al. Comparison and chemical structure-related basis of species discrimination of animal fats by Raman spectroscopy using near-infrared and visible excitation lasers. LWT. 2020;134:110105. https://doi.org/10.1016/j.lwt.2020.110105
68. Li H, Haruna SA, Wang Y, Hassan M, Geng W, et al. Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration. Food Chemistry. 2022;372:131146. https://doi.org/10.1016/j.foodchem.2021.131146
69. Zhao M, Nian Y, Allen P, Downey G, Kerry JP, et al. Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef. Food Research International. 2018;107:27–40. https://doi.org/10.1016/j.foodres.2018.02.007
70. Andersen PV, Afseth NK, Gjerlaug-Enger E, Wold JP. Prediction of water holding capacity and pH in porcine longissimus lumborum using Raman spectroscopy. Meat Science. 2020;172:108357. https://doi.org/10.1016/j.meatsci.2020.108357
71. Reiner J, Protte K, Hinrichs J. Investigation of the applicability of raman spectroscopy as online process control during consumer milk production. ChemEngineering. 2020;4(3):45. https://doi.org/10.3390/chemengineering4030045
72. Acar-Soykut E, Tayyarcan EK, Boyaci IH. A simple and fast method for discrimination of phage and antibiotic contaminants in raw milk by using Raman spectroscopy. Journal of Food Science and Technology. 2018;55(1):82–89. https://doi.org/10.1007/s13197-017-2798-y
73. Rodrigues Júnior PH, de Sá Oliveira K, de Almeida CE, de Oliveira LF, Stephani R, et al. FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: Classification of samples for the presence of lactose and fraud detection by addition of maltodextrin. Food Chemistry. 2016;196:584–588. https://doi.org/10.1016/j.foodchem.2015.09.055
74. Taylan O, Cebi N, Yilmaz MT, Sagdic O, Bakhsh AA. Detection of lard in butter using Raman spectroscopy combined with chemometrics. Food Chemistry. 2020;332:127344. https://doi.org/10.1016/j.foodchem.2020.127344
75. Lohumi S, Lee H, Kim MS, Qin J, Cho B-K. Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy. Analytical and Bioanalytical Chemistry. 2018;410(22):5663–5673. https://doi.org/10.1007/s00216-018-1189-1
76. Gómez-Mascaraque LG, Kilcawley K, Hennessy D, Tobin JT, O’Callaghan TF. Raman spectroscopy: A rapid method to assess the effects of pasture feeding on the nutritional quality of butter. Journal of Dairy Science. 2020;103(10):8721–8731. https://doi.org/10.3168/jds.2020-18716
77. Genis DO, Sezer B, Durna S, Boyaci IH. Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis. Food Chemistry. 2021;336:127699. https://doi.org/10.1016/j.foodchem.2020.127699
78. Czaja T, Baranowska M, Mazurek S, Szostak R. Determination of nutritional parameters of yoghurts by FT Raman spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2018;196:413–417. https://doi.org/10.1016/j.saa.2018.02.054
79. Karacaglar NNY, Bulat T, Boyaci IH, Topcu A. Raman spectroscopy coupled with chemometric methods for the discrimination of foreign fats and oils in cream and yogurt. Journal of Food and Drug Analysis. 2019;27(1):101–110. https://doi.org/10.1016/j.jfda.2018.06.008
80. Cheng Y, Dong Y, Wu J, Yang X, Bai H, et al. Screening melamine adulterant in milk powder with laser Raman spectrometry. Journal of Food Composition and Analysis. 2010;23(2):199–202. https://doi.org/10.1016/j.jfca.2009.08.006
81. Karunathilaka SR, Farris S, Mossoba MM, Moore JC, Yakes BJ. Non-targeted detection of milk powder adulteration using Raman spectroscopy and chemometrics: Melamine case study. Food Additives & Contaminants: Part A. 2017;34(2):170–182. https://doi.org/10.1080/19440049.2016.1260168
82. Yaman H. A rapid method for detection adulteration in goat milk by using vibrational spectroscopy in combination with chemometric methods. Journal of Food Science and Technology. 2020;57(8):3091–3098. https://doi.org/10.1007/s13197-020-04342-4
83. He L, Chen T, Labuza TP. Recovery and quantitative detection of thiabendazole on apples using a surface swab capture method followed by surface-enhanced Raman spectroscopy. Food Chemistry. 2014;148:42–46. https://doi.org/10.1016/j.foodchem.2013.10.023
84. Sebben JA, da Silveira Espindola J, Ranzan L, de Moura NF, Trierweiler LF, et al. Development of a quantitative approach using Raman spectroscopy for carotenoids determination in processed sweet potato. Food Chemistry. 2018;245:1224–1231. https://doi.org/10.1016/j.foodchem.2017.11.086
85. Hara R, Ishigaki M, Kitahama Y, Ozaki Y, Genkawa T. Excitation wavelength selection for quantitative analysis of carotenoids in tomatoes using Raman spectroscopy. Food Chemistry. 2018;258:308–313. https://doi.org/10.1016/j.foodchem.2018.03.089
86. Hara R, Ishigaki M, Ozaki Y, Ahamed T, Noguchi R, et al. Effect of Raman exposure time on the quantitative and discriminant analyses of carotenoid concentrations in intact tomatoes. Food Chemistry. 2021;360:129896. https://doi.org/10.1016/j.foodchem.2021.129896
87. Mandrile L, Rotunno S, Miozzi L, Vaira AM, Giovannozzi AM, et al. Nondestructive Raman spectroscopy as a tool for early detection and discrimination of the infection of tomato plants by two economically important viruses. Analytical Chemistry. 2019;91(14):9025–9031. https://doi.org/10.1021/acs.analchem.9b01323
88. Khodabakhshian R. Feasibility of using Raman spectroscopy for detection of tannin changes in pomegranate fruits during maturity. Scientia Horticulturae. 2019;257:108670. https://doi.org/10.1016/j.scienta.2019.108670
89. Sanchez L, Pant S, Xing Z, Mandadi K, Kurouski D. Rapid and noninvasive diagnostics of Huanglongbing and nutrient deficits on citrus trees with a handheld Raman spectrometer. Analytical and Bioanalytical Chemistry. 2019;411:3125–3133. https://doi.org/10.1007/s00216-019-01776-4
90. César J-S, Ruiz JR. Use of Raman spectroscopy for analyzing edible vegetable oils. Applied Spectroscopy Reviews. 2016;51(5):417–430. https://doi.org/10.1080/05704928.2016.1141292
91. Covaciu F-D, Berghian-Grosan C, Feher I, Magdas DA. Edible oils differentiation based on the determination of fatty acids profile and Raman spectroscopy–A case study. Applied Sciences. 2020;10(23):8347. https://doi.org/10.3390/app10238347
92. Wang H, Xin Y, Ma H, Fang P, Li C, et al. Rapid detection of Chinese-specific peony seed oil by using confocal Raman spectroscopy and chemometrics. Food Chemistry. 2021;362:130041. https://doi.org/10.1016/j.foodchem.2021.130041
93. Castro RC, Ribeiro DSM, Santos JLM, Páscoa RNMJ. The use of in-situ Raman spectroscopy to monitor at real time the quality of different types of edible oils under frying conditions. Food Control. 2022;136:108879. https://doi.org/10.1016/j.foodcont.2022.108879
94. Zhao H, Zhan Y, Xu Z, Nduwamungu JJ, Zhou Y, et al. The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration. Food Chemistry. 2022;373(Part B):131471. https://doi.org/10.1016/j.foodchem.2021.131471
95. Samyn P, van Nieuwkerke D, Schoukens G, Vonck L, Stanssens D, et al. Quality and statistical classification of Brazilian vegetable oils using mid-infrared and Raman spectroscopy. Applied Spectroscopy. 2012;66(5):552–565. https://doi.org/10.1366/11-06484
96. Zou M-Q, Zhang X-F, Qi X-H, Ma H-L, Dong Y, et al. Rapid authentication of olive oil adulteration by Raman spectrometry. Journal of Agricultural and Food Chemistry. 2009;57(14):6001–6006. https://doi.org/10.1021/jf900217s
97. Tiryaki GY, Ayvaz H. Quantification of soybean oil adulteration in extra virgin olive oil using portable Raman spectroscopy. Journal of Food Measurement and Characterization. 2016;11(2):523–529. https://doi.org/10.1007/s11694-016-9419-8
98. Georgouli K, del Rincon JM, Koidis A. Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry. 2017;217:735–742. https://doi.org/10.1016/j.foodchem.2016.09.011
99. Barros HASI, Santos LP, Filgueiras PR, Romão W. Design experiments to detect and quantify soybean oil in extra virgin olive oil using portable Raman spectroscopy. Vibrational Spectroscopy. 2021;116:103294. https://doi.org/10.1016/j.vibspec.2021.103294
100. Sánchez-López E, Sánchez-Rodríguez MI, Marinas A, Marinas JM, Urbano FJ, et al. Chemometric study of Andalusian extra virgin olive oils Raman spectra: Qualitative and quantitative information. Talanta. 2016;156–157:180–190. https://doi.org/10.1016/j.talanta.2016.05.014
101. Berghian-Grosan C, Magdas DA. Raman spectroscopy and machine-learning for edible oils evaluation. Talanta. 2020;218:121176. https://doi.org/10.1016/j.talanta.2020.121176
102. Jiang H, He Y, Xu W, Chen Q. Quantitative detection of acid value during edible oil storage by Raman spectroscopy: Comparison of the optimization effects of BOSS and VCPA algorithms on the characteristic Raman spectra of edible oils. Food Analytical Methods. 2021;14:1826–1835. https://doi.org/10.1007/s12161-020-01939-5
103. Fang P, Wang H, Wan X. Olive oil authentication based on quantitative β-carotene Raman spectra detection. Food Chemistry. 2022;397:133763. https://doi.org/10.1016/j.foodchem.2022.133763
104. Zhao H, Zhan Y, Xu Z, Nduwamungu J, Zhou Y, et al. The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration. Food Chemistry. 2022;373(Part B):131471. https://doi.org/10.1016/j.foodchem.2021.131471