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Abstract

This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis methods. It then highlights reports featuring multivariate analysis of multidimensional measurements of photoluminescent probes published between June 2015 and June 2022, emphasizing work in the last 5 years. Important trends include the development of probe arrays, which provide fingerprint responses to the analyte(s) of interest and facilitate the analysis of complex samples; the application of neural networks and deep learning to pattern recognition and feature selection in photoluminescence images; and the application of multiway multivariate analysis to mining matrices, three-way arrays, and higher-order measurements, including hyperspectral intensity and lifetime images. These examples illustrate the increase in information extraction provided by the combination of multidimensional measurements and multivariate analysis.

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2023-06-14
2024-04-25
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Literature Cited

  1. 1.
    Schäferling M. 2012. The art of fluorescence imaging with chemical sensors. Angew. Chem. Int. Ed. 51:153532–54
    [Google Scholar]
  2. 2.
    Terai T, Nagano T. 2013. Small-molecule fluorophores and fluorescent probes for bioimaging. Pflügers Arch 465:3347–59
    [Google Scholar]
  3. 3.
    Specht E, Braselmann E, Palmer AE. 2017. A critical and comparative review of fluorescent tools for live-cell imaging. Annu. Rev. Physiol. 79:93–117
    [Google Scholar]
  4. 4.
    Wu D, Sedgwick AC, Gunnlaugsson T, Akkaya EU, Yoon J, James TD. 2017. Fluorescent chemosensors: the past, present and future. Chem. Soc. Rev. 46:7105–23
    [Google Scholar]
  5. 5.
    Jing X, Yu F, Chen L 2014. Fluorescent probes for reactive nitrogen species. Prog. Chem. 26:5866–78
    [Google Scholar]
  6. 6.
    Li Y, Chen Q, Pan X, Lu W, Zhang J. 2022. Development and challenge of fluorescent probes for bioimaging applications: from visualization to diagnosis. Top. Curr. Chem. 380:422
    [Google Scholar]
  7. 7.
    Chen SY, Li Z, Li K, Yu XQ. 2021. Small molecular fluorescent probes for the detection of lead, cadmium and mercury ions. Coord. Chem. Rev. 429:213691
    [Google Scholar]
  8. 8.
    Gründler P. 2007. Chemical Sensors: An Introduction for Scientists and Engineers Berlin: Springer. , 1st ed..
  9. 9.
    Warner IM, Patonay G, Thomas MP. 1985. Multidimensional luminescence measurements. Anal. Chem. 57:3463A–83A
    [Google Scholar]
  10. 10.
    Lu Y, Saeys W, Kim M, Peng Y, Lu R. 2020. Hyperspectral imaging technology for quality and safety evaluation of horticultural products: a review and celebration of the past 20-year progress. Postharvest Biol. Technol. 170:111318
    [Google Scholar]
  11. 11.
    Lu G, Fei B. 2014. Medical hyperspectral imaging: a review. J. Biomed. Opt. 19:1010901
    [Google Scholar]
  12. 12.
    Feng YZ, Sun DW. 2012. Application of hyperspectral imaging in food safety inspection and control: a review. Crit. Rev. Food Sci. Nutr. 52:111039–58
    [Google Scholar]
  13. 13.
    Becker W. 2012. Fluorescence lifetime imaging—techniques and applications. J. Microsc. 247:2119–36
    [Google Scholar]
  14. 14.
    Petryayeva E, Algar WR, Medintz IL. 2013. Quantum dots in bioanalysis: a review of applications across various platforms for fluorescence spectroscopy and imaging. Appl. Spectrosc. 67:3215–52
    [Google Scholar]
  15. 15.
    Datta R, Heaster TM, Sharick JT, Gillette AA, Skala MC. 2020. Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications. J. Biomed. Opt. 25:7071203
    [Google Scholar]
  16. 16.
    Wu Z, Liu M, Liu Z, Tian Y. 2020. Real-time imaging and simultaneous quantification of mitochondrial H2O2 and ATP in neurons with a single two-photon fluorescence-lifetime-based probe. J. Am. Chem. Soc. 142:167532–41
    [Google Scholar]
  17. 17.
    Roach CA, Neal SL. 2010. Numerical correction of detector channel cross-talk using full-spectrum fluorescence correlation spectroscopy. Appl. Spectrosc. 64:101145–53
    [Google Scholar]
  18. 18.
    Nolan JP. 2022. The evolution of spectral flow cytometry. . Cytometry 101:10812–17
    [Google Scholar]
  19. 19.
    Lakowicz JR. 2006. Principles of Fluorescence Spectroscopy New York: Springer. , 3rd ed..
  20. 20.
    Valeur B, Berberan-Santos M. 2012. Molecular Fluorescence: Principles and Applications Weinheim, Ger: Wiley-VCH. , 2nd ed..
  21. 21.
    Furukawa H, Cordova KE, O'Keeffe M, Yaghi OM. 2013. The chemistry and applications of metal-organic frameworks. Science 341:61491230444
    [Google Scholar]
  22. 22.
    Allendorf MD, Bauer CA, Bhakta RK, Houk RJT. 2009. Luminescent metal-organic frameworks. Chem. Soc. Rev. 38:51330–52
    [Google Scholar]
  23. 23.
    Dong J, Zhao D, Lu Y, Sun WY. 2019. Photoluminescent metal-organic frameworks and their application for sensing biomolecules. J. Mater. Chem. A 7:22744–67
    [Google Scholar]
  24. 24.
    Chalfie M. 1995. Green fluorescent protein. J. Photochem. Photobiol. 62:4651–56
    [Google Scholar]
  25. 25.
    Shaner NC, Patterson GH, Davidson MW. 2007. Advances in fluorescent protein technology. J. Cell Sci. 120:244247–60
    [Google Scholar]
  26. 26.
    Thomas SW, Joly GD, Swager TM. 2007. Chemical sensors based on amplifying fluorescent conjugated polymers. Chem. Rev. 107:41339–86
    [Google Scholar]
  27. 27.
    Murphy CJ. 2002. Optical sensing with quantum dots. Anal. Chem. 74:19520A–26A
    [Google Scholar]
  28. 28.
    Permatasari FA, Irham MA, Bisri SZ, Iskandar F. 2021. Carbon-based quantum dots for supercapacitors: recent advances and future challenges. Nanomaterials 11:191
    [Google Scholar]
  29. 29.
    Resch-Genger U, Grabolle M, Cavaliere-Jaricot S, Nitschke R, Nann T. 2008. Quantum dots versus organic dyes as fluorescent labels. Nat. Methods 5:9763–75
    [Google Scholar]
  30. 30.
    Zheng J, Nicovich PR, Dickson RM. 2007. Highly fluorescent noble-metal quantum dots. Annu. Rev. Phys. Chem. 58:409–31
    [Google Scholar]
  31. 31.
    Azharuddin M, Zhu GH, Das D, Ozgur E, Uzun L et al. 2019. A repertoire of biomedical applications of noble metal nanoparticles. Chem. Commun. 55:496964–96
    [Google Scholar]
  32. 32.
    Rumi M, Barlow S, Wang J, Perry JW, Marder SR 2008. Two-photon absorbing materials and two-photon-induced chemistry. Photoresponsive Polymers I SR Marder, KS Lee 1–95. Berlin: Springer
    [Google Scholar]
  33. 33.
    Siraj N, El-Zahab B, Hamdan S, Karam TE, Haber LH et al. 2016. Fluorescence, phosphorescence, and chemiluminescence. Anal. Chem. 88:1170–202
    [Google Scholar]
  34. 34.
    Zipfel WR, Williams RM, Webb WW. 2003. Nonlinear magic: multiphoton microscopy in the biosciences. Nat. Biotechnol. 21:111368–77
    [Google Scholar]
  35. 35.
    Bigdeli A, Ghasemi F, Abbasi-Moayed S, Shahrajabian M, Fahimi-Kashani N et al. 2019. Ratiometric fluorescent nanoprobes for visual detection: design principles and recent advances—a review. Anal. Chim. Acta 1079:30–58
    [Google Scholar]
  36. 36.
    Kim D, Ryu HG, Ahn KH. 2014. Recent development of two-photon fluorescent probes for bioimaging. Org. Biomol. Chem. 12:4550–66
    [Google Scholar]
  37. 37.
    Dongare PR, Gore AH. 2021. Recent advances in colorimetric and fluorescent chemosensors for ionic species: design, principle and optical signaling mechanism. Chem. Select 6:235657–69
    [Google Scholar]
  38. 38.
    Valeur B, Leray I. 2000. Design principles of fluorescent molecular sensors for cation recognition. Coord. Chem. Rev. 205:3–40
    [Google Scholar]
  39. 39.
    Jung HS, Verwilst P, Kim WY, Kim JS. 2016. Fluorescent and colorimetric sensors for the detection of humidity or water content. Chem. Soc. Rev. 45:1242–56
    [Google Scholar]
  40. 40.
    Yuan L, Lin W, Zheng K, Zhu S. 2013. FRET-based small-molecule fluorescent probes: rational design and bioimaging applications. Acc. Chem. Res. 46:71462–73
    [Google Scholar]
  41. 41.
    Zhang H, Zhao Z, McGonigal PR, Ye R, Liu S et al. 2020. Clusterization-triggered emission: uncommon luminescence from common materials. Mater. Today 32:275–92
    [Google Scholar]
  42. 42.
    Liao P, Huang J, Yan Y, Tang BZ. 2021. Clusterization-triggered emission (CTE): one for all, all for one. Mater. Chem. Front. 5:6693–717
    [Google Scholar]
  43. 43.
    Neal SL, Patonay G, Thomas MP, Warner IM. 1986. Data analysis for multidimensional luminescence. Spectroscopy 1:322–28
    [Google Scholar]
  44. 44.
    Wamsley M, Nawalage S, Hu J, Collier WE, Zhang D. 2022. Back to the drawing board: a unifying first-principle model for correlating sample UV-vis absorption and fluorescence emission. Anal. Chem. 94:197123–31
    [Google Scholar]
  45. 45.
    Cehelnik ED, Mielenz KD, Velapoldi RA. 1975. Polarization effects of fluorescence measurements. J. Res. Nat. Bur. Stand. Sec. A 79A:11–15
    [Google Scholar]
  46. 46.
    Pian Q, Yao R, Sinsuebphon N, Intes X. 2017. Compressive hyperspectral time-resolved wide-field fluorescence lifetime imaging. Nat. Photonics 11:7411–14
    [Google Scholar]
  47. 47.
    Chang Y-Y, Wu H-L, Fang H, Wang T, Liu Z et al. 2018. Rapid, simultaneous and interference-free determination of three rhodamine dyes illegally added into chili samples using excitation-emission matrix fluorescence coupled with second-order calibration method. Spectrochim. Acta A Mol. Biomol. Spectrosc. 204:141–49
    [Google Scholar]
  48. 48.
    Lloyd WR, Wilson RH, Chang C-W, Gillispie GD, Mycek M-A. 2010. Instrumentation to rapidly acquire fluorescence wavelength-time matrices of biological tissues. Biomed. Opt. Express 1:2574–86
    [Google Scholar]
  49. 49.
    Weber G. 1961. Enumeration of components in complex systems by fluorescence spectrophotometry. Nature 190:27–29
    [Google Scholar]
  50. 50.
    Lemos M, Sárniková K, Bot F, Anese M, Hungerford G. 2015. Use of time-resolved fluorescence to monitor bioactive compounds in plant based foodstuffs. Biosensors 5:3367–97
    [Google Scholar]
  51. 51.
    Kulzer F, Orrit M. 2004. Single-molecule optics. Annu. Rev. Phys. Chem. 55:585–611
    [Google Scholar]
  52. 52.
    Mannam V, Zhang Y, Yuan X, Ravasio C, Howard SS. 2020. Machine learning for faster and smarter fluorescence lifetime imaging microscopy. J. Phys. Photonics 2:4042005
    [Google Scholar]
  53. 53.
    Rowe BA, Roach CA, Lin J, Asiago V, Dmitrenko O, Neal SL. 2008. Spectral heterogeneity of PRODAN fluorescence in isotropic solvents revealed by multivariate photokinetic analysis. J. Phys. Chem. A 112:5113402–12
    [Google Scholar]
  54. 54.
    Ozawa T, Yoshimura H, Kim SB 2013. Advances in fluorescence and bioluminescence imaging. Anal. Chem. 85:2590–609
    [Google Scholar]
  55. 55.
    Pak YL, Swamy KMK, Yoon J. 2015. Recent progress in fluorescent imaging probes. Sensors 15:924374–96
    [Google Scholar]
  56. 56.
    Chorvat D Jr., Chorvatova A. 2009. Multi-wavelength fluorescence lifetime spectroscopy: a new approach to the study of endogenous fluorescence in living cells and tissues. Laser Phys. Lett. 6:175–93
    [Google Scholar]
  57. 57.
    Rodrigues EM, Hemmer E. 2022. Trends in hyperspectral imaging: from environmental and health sensing to structure-property and nano-bio interaction studies. Anal. Bioanal. Chem. 414:4269–79
    [Google Scholar]
  58. 58.
    Peltier C, Winckler P, Dujourdy L, Bechoula S, Perrier-Cornet JM. 2019. Analysis of multivariate images in fluorescence microscopy. Methods Appl. Fluoresc. 7:3035004
    [Google Scholar]
  59. 59.
    Clow KE, Hall GJ, Chen H, Kenny JE. 2004. Spectral fingerprinting and classification by location of origin of natural waters by multidimensional fluorescence. Proc. SPIE 5586:107–15
    [Google Scholar]
  60. 60.
    Goicoechea H, Yu S, Moore AFT, Campiglia AD. 2012. Four-way modeling of 4.2 K time-resolved excitation emission fluorescence data for the quantitation of polycyclic aromatic hydrocarbons in soil samples. Talanta 101:330–36
    [Google Scholar]
  61. 61.
    Kim YC, Jordan JA, Nahorniak ML, Booksh KS. 2005. Photocatalytic degradation-excitation–emission matrix fluorescence for increasing the selectivity of polycyclic aromatic hydrocarbon analyses. Anal. Chem. 77:237679–86
    [Google Scholar]
  62. 62.
    Damiani PC, Durán-Merás I, García-Reiriz A, Jimenèz-Girón A. 2007. Multiway partial least-squares coupled to residual trilinearization: a genuine multidimensional tool for the study of third-order data. Simultaneous analysis of procaine and its metabolite p-aminobenzoic acid in equine serum. Anal. Chem. 79:186949–58
    [Google Scholar]
  63. 63.
    Qing XD, Wu HL, Yan XF, Li Y, Ouyang LQ et al. 2014. Development of a novel alternating quadrilinear decomposition algorithm for the kinetic analysis of four-way room-temperature phosphorescence data. Chemom. Intell. Lab. Syst. 132:8–17
    [Google Scholar]
  64. 64.
    Maggio RM, Muñoz de la Peña A, Olivieri AC 2011. Unfolded partial least-squares with residual quadrilinearization: a new multivariate algorithm for processing five-way data achieving the second-order advantage. Application to fourth-order excitation-emission-kinetic-pH fluorescence analytical data. Chemom. Intell. Lab. Syst. 109:2178–85
    [Google Scholar]
  65. 65.
    Kang C, Wu HL, Xie LX, Xiang SX, Yu RQ. 2014. Direct quantitative analysis of aromatic amino acids in human plasma by four-way calibration using intrinsic fluorescence: exploration of third-order advantages. Talanta 122:293–301
    [Google Scholar]
  66. 66.
    Helfer GA, Bock F, Marder L, Furtado JC, da Costa AB, Ferrão MF 2015. Chemostat: exploratory multivariate data analysis software. Quim. Nova 38:4575–79
    [Google Scholar]
  67. 67.
    Bro R, Smilde A. 2014. Principal component analysis. Anal. Methods 6:2812–31
    [Google Scholar]
  68. 68.
    Jolliffe IT, Cadima J. 2016. Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A 374:206520150202
    [Google Scholar]
  69. 69.
    Faber K, Kowalski BR. 1997. Critical evaluation of two F-tests for selecting the number of factors in abstract factor analysis. Anal. Chim. Acta 337:157–71
    [Google Scholar]
  70. 70.
    Rossi TM, Warner IM. 1986. Rank estimation of excitation-emission matrices using frequency analysis of eigenvectors. Anal. Chem. 58:4810–15
    [Google Scholar]
  71. 71.
    Wold S. 1978. Cross-validatory estimation of the number of components in factor and principal components models. Technometrics 20:4397–405
    [Google Scholar]
  72. 72.
    Danzer K, Currie LA. 1998. Guidelines for calibration in analytical chemistry. Part I. Fundamentals and single components calibration. Pure Appl. Chem. 70:4993–1014
    [Google Scholar]
  73. 73.
    Kumar N, Bansal A, Sarma GS, Rawal RK. 2014. Chemometrics tools used in analytical chemistry: an overview. Talenta 123:186–99
    [Google Scholar]
  74. 74.
    Oliveri AC, Escandar G. 2014. Practical Three-Way Calibration-Calibration Scenarios Oxford, UK: Elsevier. , 1st ed..
  75. 75.
    Adams MJ. 2004. Calibration and regression analysis. Chemometrics in Analytical Spectroscopy161–210. Cambridge, UK: R. Soc. Chem. , 2nd ed..
    [Google Scholar]
  76. 76.
    Bro R. 2003. Multivariate calibration: What is in chemometrics for the analytical chemist?. Anal. Chim. Acta 500:1–2185–94
    [Google Scholar]
  77. 77.
    Lawson CL, Hanson RJ. 1995. The pseudoinverse. Solving Least Squares Problems36–40. Philadelphia, PA: SIAM
    [Google Scholar]
  78. 78.
    Kramer R. 1998. Chemometric Techniques for Quantitative Analysis Boca Raton, FL: CRC Press. , 1st ed..
  79. 79.
    Kowalski BR, Seasholtz MB. 1991. Recent developments in multivariate calibration. J. Chemom. 5:3129–45
    [Google Scholar]
  80. 80.
    Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58:2109–30
    [Google Scholar]
  81. 81.
    Lavine B. 2006. Pattern recognition. Crit. Rev. Anal. Chem. 36:3153–61
    [Google Scholar]
  82. 82.
    Brereton RG. 2015. Pattern recognition in chemometrics. Chemom. Intell. Lab. Syst. 149:90–96
    [Google Scholar]
  83. 83.
    Fisher RA. 1936. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7:179–88
    [Google Scholar]
  84. 84.
    Jolliffe IT. 2002. Principal Component Analysis New York: Springer. , 2nd ed..
  85. 85.
    Barker M, Williams R. 2003. Partial least squares for discrimination. J. Chemom. 17:3166–73
    [Google Scholar]
  86. 86.
    Brereton RG, Lloyd GR. 2014. Partial least squares discriminant analysis: taking the magic away. J. Chemom. 28:4213–25
    [Google Scholar]
  87. 87.
    Todeschini R. 1989. k-Nearest neighbor method. the influence of data transformations and metrics. Chemom. Intell. Lab. Syst. 6:3213–20
    [Google Scholar]
  88. 88.
    Luts J, Ojeda F, Van de Plas R, De Moor B, Van Huffel S, Suykens JAK. 2010. A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal. Chim. Acta 665:2129–45
    [Google Scholar]
  89. 89.
    Overall J, Magee KN. 1992. Replication as a rule for determining the number of clusters in hierarchical cluster analysis. Appl. Psychol. Meas. 16:2119–28
    [Google Scholar]
  90. 90.
    Yang MS, Liu HH. 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets Syst 106:2189–200
    [Google Scholar]
  91. 91.
    Marini F, Bucci R, Magrì AL, Magrì AD. 2008. Artificial neural networks in chemometrics: history, examples and perspectives. Microchem. J. 88:2178–85
    [Google Scholar]
  92. 92.
    Xiao D, Zang Z, Xie W, Sapermsap N, Chen Y, Uei-Li DD. 2022. Spatial resolution improved fluorescence lifetime imaging via deep learning. Opt. Express 30:711479–94
    [Google Scholar]
  93. 93.
    Lawton WH, Sylvestre EA. 1971. Self modeling curve resolution. Technometrics 13:617–33
    [Google Scholar]
  94. 94.
    de Juan A, Tauler R. 2021. Multivariate Curve Resolution: 50 years addressing the mixture analysis problem—a review. Anal. Chim. Acta 1145:59–78
    [Google Scholar]
  95. 95.
    Pal M, Roy R, Basu J, Milton BS. 2013. Blind source separation: a review and analysis. 2013 International Conference Oriental COCOSDA Held Jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE)1–5. New York: IEEE
    [Google Scholar]
  96. 96.
    Shinzawa H, Jiang JH, Iwahashi M, Noda I, Ozaki Y. 2007. Self-modeling curve resolution (SMCR) by particle swarm optimization (PSO). Anal. Chim. Acta 595:275–81
    [Google Scholar]
  97. 97.
    Rahimdoust MN, Sawall M, Naymeyr K, Abdollahi H. 2016. Investigating the effect of flexible constraints on the accuracy of self-modeling curve resolution methods in the presence of perturbations. J. Chemom. 30:5252–67
    [Google Scholar]
  98. 98.
    Maeder M. 1987. Evolving factor analysis for the resolution of overlapping chromatographic peaks. Anal. Chem. 59:3527–30
    [Google Scholar]
  99. 99.
    Malinowski ER. 1996. Automatic window factor analysis—a more efficient method for determining concentration profiles from evolutionary spectra. J. Chemom. 10:4273–79
    [Google Scholar]
  100. 100.
    Kvalheim OM, Liang YZ. 1992. Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution. Anal. Chem. 64:8936–46
    [Google Scholar]
  101. 101.
    Manne R, Shen H, Liang Y. 1999. Subwindow factor analysis. Chemom. Intell. Lab. Syst. 45:1–2171–76
    [Google Scholar]
  102. 102.
    Jiang JH, Šaašić S, Yu RQ, Ozaki Y. 2003. Resolution of two-way data from spectroscopic monitoring of reaction or process systems by parallel vector analysis (PVA) and window factor analysis (WFA): inspection of the effect of mass balance, methods and simulations. J. Chemom. 17:3186–97
    [Google Scholar]
  103. 103.
    Sánchez FC, Toft J, Van den Bogaert B, Massart DL. 1996. Orthogonal projection approach applied to peak purity assessment. Anal. Chem. 68:179–85
    [Google Scholar]
  104. 104.
    Tauler R, Kowalski B, Fleming S. 1993. Multivariate curve resolution applied to spectral data from multiple runs of an industrial process. Anal. Chem. 65:152040–47
    [Google Scholar]
  105. 105.
    Windig W, Guilment J. 1991. Interactive self-modeling mixture analysis. Anal. Chem. 63:141425–32
    [Google Scholar]
  106. 106.
    Gemperline PJ. 1984. A priori estimates of elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis. J. Chem. Inf. Compt. Sci. 24:206–12
    [Google Scholar]
  107. 107.
    Vandeginste B, Esser R, Bosman T, Reijen J, Katman G. 1985. Three-component curve resolution in liquid chromatography with multiwavelength diode array detection. Anal. Chem. 57:6971–85
    [Google Scholar]
  108. 108.
    Paatero P, Tapper U. 1994. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5:2111–26
    [Google Scholar]
  109. 109.
    Lee D, Seung H. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401:788–91
    [Google Scholar]
  110. 110.
    Lee D, Seung H. 2001. Algorithms for non-negative matrix factorization. Adv. Neural Inf. Process. Syst. 13:556–62
    [Google Scholar]
  111. 111.
    Li SX, Wang YQ, Chen ZP, Chen Y. 2019. Probe technique-based generalized multivariate standard addition strategy for the analysis of fluorescence signals with matrix effects. Chemom. Intell. Lab. Syst. 190:41–47
    [Google Scholar]
  112. 112.
    Hong SC, Murale DP, Jang SY, Haque M, Seo M et al. 2018. Discrimination of avian influenza virus subtypes using host-cell infection fingerprinting by a sulfinate-based fluorescence superoxide probe. Angew. Chem. Int. Ed. 57:39716–21
    [Google Scholar]
  113. 113.
    Zhu J, Zhu F, Li L, Cheng L, Zhang L et al. 2019. Highly discriminant rate of Dianhong black tea grades based on fluorescent probes combined with chemometric methods. Food Chem 298:125046
    [Google Scholar]
  114. 114.
    Qiu H, Pu F, Ran X, Ren J, Qu X. 2017. A DNA-based label-free artificial tongue for pattern recognition of metal ions. Chemistry 23:399258–61
    [Google Scholar]
  115. 115.
    Mei Y, Zhang QW, Gu Q, Liu Z, He X, Tian Y. 2022. Pillar[5]arene-based fluorescent sensor array for biosensing of intracellular multi-neurotransmitters through host–guest recognitions. J. Am. Chem. Soc. 144:52351–59
    [Google Scholar]
  116. 116.
    Booksh KS, Kowalski BR. 1994. Theory of analytical chemistry. Anal. Chem. 66:15782A–91A
    [Google Scholar]
  117. 117.
    Duan N, Yang S. 2022. Research progress on multifunctional fluorescent probes for biological imaging, food and environmental detection. Crit. Rev. Anal. Chem. In press. https://doi.org/10.1080/10408347.2022.2098670
    [Google Scholar]
  118. 118.
    Yang X, Zhang D, Ye Y, Zhao Y. 2022. Recent advances in multifunctional fluorescent probes for viscosity and analytes. Coord. Chem. Rev. 453:214336
    [Google Scholar]
  119. 119.
    Chen S, Pang C, Chen X, Yan Z, Huang S et al. 2019. Research progress in design, synthesis and application of multifunctional fluorescent probes. Chin. J. Org. Chem. 39:71846–57
    [Google Scholar]
  120. 120.
    Nagy M, Kovács SL, Nagy T, Rácz D, Zsuga M, Kéki S. 2019. Isocyanonaphthalenes as extremely low molecular weight, selective, ratiometric fluorescent probes for Mercury(II). Talanta 201:165–73
    [Google Scholar]
  121. 121.
    Yan YH, He XY, Su L, Miao JY, Zhao BX. 2019. A new FRET-based ratiometric fluorescence probe for hypochlorous acid and its imaging in living cells. Talanta 201:330–34
    [Google Scholar]
  122. 122.
    de Castro CS, Cova T, Pais A, Pinheiro D, Nuñez C et al. 2016. Probing metal cations with two new Schiff base bischromophoric pyrene based chemosensors: synthesis, photophysics and interactions patterns. Dyes Pigments 134:601–12
    [Google Scholar]
  123. 123.
    Castro RC, Ribeiro DSM, Páscoa RNMJ, Soares JX, Mazivila SJ, Santos JLM. 2020. Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II). Anal. Chim. Acta 1114:29–41
    [Google Scholar]
  124. 124.
    Ye C, Chen S, Li F, Ge J, Yong P et al. 2018. Research progress of high-performance multi-analyte recognitions and multivariate analysis. Acta Chim. Sin. 76:4237–45
    [Google Scholar]
  125. 125.
    Li Z, Askim JR, Suslick KS. 2019. The optoelectronic nose: colorimetric and fluorometric sensor arrays. Chem. Rev. 119:1231–92
    [Google Scholar]
  126. 126.
    Hizir MS, Nandu N, Yigit MV. 2018. Homologous miRNA analyses using a combinatorial nanosensor array with two-dimensional nanoparticles. Anal. Chem. 90:106300–6
    [Google Scholar]
  127. 127.
    Shik AV, Stepanova IA, Doroshenko IA, Podrugina TA, Beklemishev MK. 2022. Carbocyanine-based fluorescent and colorimetric sensor array for the discrimination of medicinal compounds. Chemosensors 10:288
    [Google Scholar]
  128. 128.
    Okada H, Mimura M, Tomita S, Kurita R. 2021. Affinity diversification of a polymer probe for pattern-recognition-based biosensing using chemical additives. Anal. Sci. 37:5713–19
    [Google Scholar]
  129. 129.
    Xu S, Li W, Zhao X, Wu T, Cui Y et al. 2019. Ultrahighly efficient and stable fluorescent gold nanoclusters coated with screened peptides of unique sequences for effective protein and serum discrimination. Anal. Chem. 91:2113947–52
    [Google Scholar]
  130. 130.
    Yang P, Li J, Li P, Hou C, Huo D et al. 2019. Quantum dot-based Baijiu fluorescent identification sensor array jointly verified by multivariate analysis and radial basis function neural network. Anal. Methods 11:4842–50
    [Google Scholar]
  131. 131.
    Wu Y, Liu X, Wu Q, Yi J, Zhang G. 2017. Carbon nanodots-based fluorescent turn-on sensor array for biothiols. Anal. Chem. 89:137084–89
    [Google Scholar]
  132. 132.
    Jambari NN, Wang X, Alcocer M. 2017. Protein microarray-based IgE immunoassay for allergy diagnosis. Methods Mol. Biol. 1592:129–37
    [Google Scholar]
  133. 133.
    Xue SF, Han XY, Chen ZH, Yan Q, Lin Z-Y et al. 2018. The chemistry of europium(III) encountering DNA: sprouting unique sequence-dependent performances for multifunctional time-resolved luminescent assays. Anal. Chem. 90:1710614–20
    [Google Scholar]
  134. 134.
    Sun Z, Fan YZ, Du SZ, Yang YZ, Ling Y et al. 2020. Conversion of fluorescence signals into optical fingerprints realizing high-throughput discrimination of anionic sulfonate surfactants with similar structure based on a statistical strategy and luminescent metal–organic frameworks. Anal. Chem. 92:107273–81
    [Google Scholar]
  135. 135.
    Durkee MS, Abraham R, Ai J, Veselits M, Clark MR, Giger ML 2021. Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis. J. Biomed. Opt. 26:2022910
    [Google Scholar]
  136. 136.
    Cai C, Nishimura T, Hwang J, Hu XM, Kuroda A. 2021. Asbestos detection with fluorescence microscopy images and deep learning. Sensors 21:134582
    [Google Scholar]
  137. 137.
    Yang F, Gong X, Faulkner D, Gao S, Yao R et al. 2021. Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy. Biochem. Biophys. Res. Commun. 562:29–35
    [Google Scholar]
  138. 138.
    Hagen GM, Bendesky J, Machado R, Nguyen TA, Kumar T, Ventura J. 2021. Fluorescence microscopy datasets for training deep neural networks. GigaScience 10:5giab032
    [Google Scholar]
  139. 139.
    Ma Z, Wang F, Wang W, Zhong Y, Dai H. 2021. Deep learning for in vivo near-infrared imaging. PNAS 118:1e2021446118
    [Google Scholar]
  140. 140.
    Juntunen C, Woller IM, Abramczyk A, Sung Y. 2022. Deep-learning-assisted Fourier transform imaging spectroscopy for hyperspectral fluorescence imaging. Sci. Rep. 12:12477
    [Google Scholar]
  141. 141.
    Alcaraz M, Monago-Maraña O, Goicoechea H Muñoz de la Peña A. 2019. Four- and five-way excitation-emission luminescence-based data acquisition and modeling for analytical applications. A review. Anal. Chim. Acta 1083.41–57
    [Google Scholar]
  142. 142.
    Lazurko C, Radonjic I, Suchý M, Liu G, Rolland-Logan AG, Shuhendler A. 2019. Fingerprinting biogenic aldehydes through pattern recognition analyses of excitation–emission matrices. ChemBioChem 20:4543–54
    [Google Scholar]
  143. 143.
    Horochowska M, Stanimirova I, Czarnik-Matusewicz B. 2016. Studying the influence of enflurane, isoflurane, and sevoflurane on the DPPC lipid bilayer using the analysis of variance and parallel factor analysis. Chemom. Intell. Lab. Syst. 153:146–52
    [Google Scholar]
  144. 144.
    Naghashian-Haghighi A, Hemmateenejad B, Shamsipur M. 2018. Determination of enantiomeric excess of some amino acids by second-order calibration of kinetic-fluorescence data. Anal. Biochem. 550:15–26
    [Google Scholar]
  145. 145.
    Yoshioka HT, Liu C, Hayashi K. 2015. Multispectral fluorescence imaging for odorant discrimination and visualization. Sens. Actuators B Chem. 220:1297–304
    [Google Scholar]
  146. 146.
    Gu H, Huang X, Chen Q. 2020. Rapid assessment of total polar material in used frying oils using manganese tetraphenylporphyrin fluorescent sensor with enhanced sensitivity. Food Anal. Methods 13:2080–86
    [Google Scholar]
  147. 147.
    Ebrahimi S, Kompany-Zareh M. 2016. Investigation of kinetics and thermodynamics of DNA hybridization by means of 2-D fluorescence spectroscopy and soft/hard modeling techniques. Anal. Chim. Acta 906:58–71
    [Google Scholar]
  148. 148.
    Barati A, Shamsipur M, Adollahi H. 2016. Carbon dots with strong excitation-dependent fluorescence changes towards pH. Application as nanosensors for a broad range of pH. Anal. Chim. Acta 931:25–33
    [Google Scholar]
  149. 149.
    Sheikholeslami M, Hamidipanah Y, Salehnia F, Arshian S, Hosseini M, Ganjali M. 2022. Multiplex detection of antibiotic residues in milk: application of MCR-ALS on excitation–emission matrix fluorescence (EEMF) data sets. Anal. Chem. 94:166206–15
    [Google Scholar]
  150. 150.
    Zhuang Q, Cao W, Ni Y, Wang Y. 2018. Synthesis-identification integration: one-pot hydrothermal preparation of fluorescent nitrogen-doped carbon nanodots for differentiating nucleobases with the aid of multivariate chemometrics analysis. Talanta 185:491–98
    [Google Scholar]
  151. 151.
    Zheltikov AM. 2006. Let there be white light: supercontinuum generation by ultrashort laser pulses. Physics-Uspekhi 49:6605–28
    [Google Scholar]
  152. 152.
    Gustafsson MGL. 2005. Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution. PNAS 102:3713081–86
    [Google Scholar]
  153. 153.
    August Y, Stern A. 2013. Compressive sensing spectrometry based on liquid crystal devices. Opt Lett 38:234996–99
    [Google Scholar]
  154. 154.
    Andrews NLP, Ferguson T, Rangaswamy AMM, Bernicky AR, Henning N et al. 2017. Hadamard-transform fluorescence excitation-emission-matrix spectroscopy. Anal Chem 89:168554–64
    [Google Scholar]
  155. 155.
    Rae BR, Muir KR, Gong Z, McKendry J, Girkin JM et al. 2009. A CMOS time-resolved fluorescence lifetime analysis micro-system. Sensors 9:119255–74
    [Google Scholar]
  156. 156.
    Baraniuk RG. 2007. Compressive sensing. IEEE Signal Process. Mag. 24:4118–24
    [Google Scholar]
  157. 157.
    Shi Y-Y, Li J-Y, Chu X-L. 2019. Progress and applications of multivariate calibration model transfer methods. Chin. J. Anal. Chem. 47:4479–87
    [Google Scholar]
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