[1]

Zhang L, Henson MJ, Sekulic SS. 2005. Multivariate data analysis for Raman imaging of a model pharmaceutical tablet. Analytica Chimica Acta 545:262−78

doi: 10.1016/j.aca.2005.04.080
[2]

Khandasammy SR, Fikiet MA, Mistek E, Ahmed Y, Halámková L, et al. 2018. Bloodstains, paintings, and drugs: Raman spectroscopy applications in forensic science. Forensic Chemistry 8:111−33

doi: 10.1016/j.forc.2018.02.002
[3]

Mcgoverin CM, Clark ASS, Holroyd SE, Gordon KC. 2010. Raman spectroscopic quantification of milk powder constituents. Analytica Chimica Acta 673:26−32

doi: 10.1016/j.aca.2010.05.014
[4]

Beć KB, Grabska J, Bonn GK, Popp M, Huck CW. 2020. Principles and applications of vibrational spectroscopic imaging in plant science: A review. Frontiers in Plant Science 11:1226

doi: 10.3389/fpls.2020.01226
[5]

Barnes RJ, Dhanoa MS, Lister SJ. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 43:772−77

doi: 10.1366/0003702894202201
[6]

Rinnan Å, Berg FVD, Engelsen SB. 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28:1201−22

doi: 10.1016/j.trac.2009.07.007
[7]

Karoui R, Downey G, Blecker C. 2010. Mid-infrared spectroscopy coupled with chemometrics: A tool for the analysis of intact food systems and the exploration of their molecular structure−Quality relationships − A review. Chemical Reviews 110:6144−68

doi: 10.1021/cr100090k
[8]

Lv Z, Zhang P, Sun W, Lei T, Benediktsson JA, et al. 2023. Sample iterative enhancement approach for improving classification performance of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters 21:2500605

doi: 10.1109/LGRS.2023.3348093
[9]

Hruschka WR. 1987. Data analysis: wavelength selection methods. In Near-infrared technology in the agricultural and food industries, eds. Williams P, Norris K. St. Paul, MN, USA: American Association of Cereal Chemists. pp. 35–55.

[10]

Zhao N, Wu ZS, Zhang Q, Shi XY, Ma Q, et al. 2015. Optimization of Parameter Selection for Partial Least Squares Model Development. Scientific Reports 5:11647

doi: 10.1038/srep11647
[11]

R Core Team. 2022. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

[12]

Tuszynski J. 2021. caTools: Tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc. https://CRAN.R-project.org/package=caTools

[13]

Dhanoa MS, Lister SJ, Sanderson R, Barnes RJ. 1994. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy 2:43−47

doi: 10.1255/jnirs.30
[14]

Martens H, Stark E. 1991. Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 9:625−35

doi: 10.1016/0731-7085(91)80188-F
[15]

Newey WK, Powell JL. 1987. Asymmetric least squares estimation and testing. Econometrica 55(4):819−47

doi: 10.2307/1911031
[16]

Chicco D, Jurman G. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:6

doi: 10.1186/s12864-019-6413-7
[17]

Powers DMW. 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technology 2(1):37−63

[18]

Lee LC, Liong CY, Jemain AA. 2017. A contemporary review on data preprocessing (DP) practice strategy in ATR-FTIR spectrum. Chemometrics and Intelligent Laboratory Systems 163:64−75

doi: 10.1016/j.chemolab.2017.02.008
[19]

Norris KH, Williams PC. 1984. Optimisation of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat I. influence of particle. Cereal Chemistry 61(2):158−65

[20]

Keidel A, Von Stetten D, Rodrigues C, Máguas C, Hildebrandt P. 2010. Discrimination of green arabica and robusta coffee beans by Raman spectroscopy. Journal of Agricultural and Food Chemistry 58:11187−92

doi: 10.1021/jf101999c
[21]

Wermelinger T, D'Ambrosio L, Klopprogge B, Yeretzian C. 2011. Quantification of the robusta fraction in a coffee blend via Raman spectroscopy: Proof of principle. Journal of Agricultural and Food Chemistry 59:9074−79

doi: 10.1021/jf201918a
[22]

Figueiredo LP, Borém FM, Almeida MR, Oliveira LFC, Alves APDC, et al. 2019. Raman spectroscopy for the differentiation of arabic coffee genotypes. Food Chemistry 288:262−67

doi: 10.1016/j.foodchem.2019.02.093
[23]

Abreu GF, Borém FM, Oliveira LFC, Almeida MR, Alves APC. 2019. Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage. Food Chemistry 287:241−48

doi: 10.1016/j.foodchem.2019.02.019
[24]

Dias RCE, Yeretzian C. 2016. Investigating coffee samples by Raman spectroscopy for quality control- Preliminary study. International Journal of Experimental Spectroscopic Techniques 1:006

doi: 10.35840/2631-505x/8506
[25]

Marquetti I, Link JV, Lemes ALG, dos Santos Scholz MB, Valderrama P, et al. 2016. Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee. Computers and Electronics in Agriculture 121:313−19

doi: 10.1016/j.compag.2015.12.018
[26]

Moghimi A, Aghkhani MH, Sazgarnia A, Sarmad M. 2010. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering 106:295−302

doi: 10.1016/j.biosystemseng.2010.04.002
[27]

Lasch P. 2012. Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging. Chemometrics and Intelligent Laboratory Systems 117:100−14

doi: 10.1016/j.chemolab.2012.03.011
[28]

Liu Y, Huang J, Li M, Chen Y, Cui Q, et al. 2022. Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 267:120537

doi: 10.1016/j.saa.2021.120537
[29]

Downey G, Briandet R, Wilson RH, Kemsley EK. 1997. Near- and mid-infrared spectroscopies in food authentication: Coffee varietal identification. Journal of Agricultural and Food Chemistry 45:4357−61

doi: 10.1021/jf970337t
[30]

Obeidat SM, Hammoudeh AY, Alomary AA. 2018. Application of FTIR spectroscopy for assessment of green coffee beans according to their origin. Journal of Applied Spectroscopy 84:1051−55

doi: 10.1007/s10812-018-0585-9
[31]

Bona E, Marquetti I, Link JV, Makimori GYF, da Costa Arca V, et al. 2017. Support vector machines in tandem with infrared spectroscopy for geographical classification of green arabica coffee. LWT - Food Science and Technology 76:330−36

doi: 10.1016/j.lwt.2016.04.048
[32]

Medina J, Caro Rodríguez D, Arana VA, Bernal A, Esseiva P, et al. 2017. Comparison of attenuated total reflectance mid-infrared, near infrared, and 1H-nuclear magnetic resonance spectroscopies for the determination of coffee's geographical origin. International Journal of Analytical Chemistry 2017:7210463

doi: 10.1155/2017/7210463
[33]

Cebi N, Yilmaz MT, Sagdic O. 2017. A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses. Food Chemistry 229:517−26

doi: 10.1016/j.foodchem.2017.02.072
[34]

Rubayiza AB, Meurens M. 2005. Chemical discrimination of arabica and robusta Coffees by Fourier transform Raman spectroscopy. Journal of Agricultural and Food Chemistry 53:4654−59

doi: 10.1021/jf0478657
[35]

El-Abassy RM, Donfack P, Materny A. 2011. Discrimination between arabica and robusta green coffee using visible micro Raman spectroscopy and chemometric analysis. Food Chemistry 126:1443−48

doi: 10.1016/j.foodchem.2010.11.132
[36]

Luna AS, Da Silva AP, Alves EA, Rocha RB, Lima ICA, De Gois JS. 2017. Evaluation of chemometric methodologies for the classification of coffea canephora cultivars via FT-NIR spectroscopy and direct sample analysis. Analytical Methods 9:4255−60

doi: 10.1039/C7AY01167A
[37]

Giraudo A, Grassi S, Savorani F, Gavoci G, Casiraghi E, Geobaldo F. 2019. Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis. Food Control 99:137−45

doi: 10.1016/j.foodcont.2018.12.033