Lime juice adulteration detection by spectroscopy and machine learning

Adeli, H., Khorasani, M. T., & Parvazinia, M. (2019). Wound dressing based
on electrospun PVA/chitosan/starch nanofibrous mats: Fabrication,
antibacterial and cytocompatibility evaluation and in vitro healing
assay. International Journal of Biological Macromolecules, 122, 238-
254.
AIJN. (2016). Cod of practice for evaluation of fruit and vegetable juices
6.26. reference guideline for lime juice.AliAbadi, M. H. S., KaramiOsboo, R., Kobarfard, F., Jahani, R., Nabi, M., Yazdanpanah, H., . . .
Faizi, M. (2022). Detection of lime juice adulteration by simultaneous
determination of main organic acids using liquid chromatographytandem mass spectrometry. Journal of Food Composition and Analysis,
105, 104223.
Barbosa, R. M., Batista, B. L., Varrique, R. M., Coelho, V. A., Campiglia, A.
D., & Barbosa, F. (2014). The use of advanced chemometric techniques
and trace element levels for controlling the authenticity of organic
coffee. Food Research International, 61, 246-251.
doi:https://doi.org/10.1016/j.foodres.2013.07.060
Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020), Basic Tenets of
Classification Algorithms K-Nearest-Neighbor, Support Vector
Machine, Random Forest and Neural Network: A Review. Journal of
Data Analysis and Information Processing, 08(04), 341-357.
Bizzani, M., William Menezes Flores, D., Alberto Colnago, L., & David
Ferreira, M. (2020). Monitoring of soluble pectin content in orange juice
by means of MIR and TD-NMR spectroscopy combined with machine
learning. Food Chemistry, 332, 127383.
doi:https://doi.org/10.1016/j.foodchem.2020.127383

Boggia, R., Casolino, M. C., Hysenaj, V., Oliveri, P., & Zunin, P. (2013). A
screening method based on UV–Visible spectroscopy and multivariate
analysis to assess addition of filler juices and water to pomegranate
juices. Food Chemistry, 140(4), 735-741.
Callao, M. P., & Ruisánchez, I. (2018). An overview of multivariate
qualitative methods for food fraud detection. Food Control, 86, 283-
293.
Chang, J. D., Zheng, H., Mantri, N., Xu, L., Jiang, Z., Zhang, J., . . . Lu, H.
(2016). Chemometrics coupled with ultraviolet spectroscopy: a tool for
the analysis of variety, adulteration, quality and ageing of apple juices.
International Journal of Food Science & Technology, 51(11), 2474-
2484.
Chudzinska, M., & Baralkiewicz, D. (2011). Application of ICP-MS method
of determination of 15 elements in honey with chemometric approach
for the verification of their authenticity. Food and Chemical Toxicology,
49(11), 2741-2749. doi:https://doi.org/10.1016/j.fct.2011.08.014
Dankowska, A., & Kowalewski, W. (2019). Comparison of different
classification methods for analyzing fluorescence spectra to
characterize type and freshness of olive oils. European Food Research
and Technology, 245(3), 745-752. doi:10.1007/s00217-018-3196-z
Dasenaki, M. E., & Thomaidis, N. S. (2019). Quality and authenticity control
of fruit juices-A review. Molecules, 24(6), 1014.
FAO. (2020). The Citrus Bulletin.
Fidelis, M., Santos, J. S., Coelho, A. L. K., Rodionova, O. Y., Pomerantsev,
A., & Granato, D. (2017). Authentication of juices from antioxidant and
chemical perspectives: A feasibility quality control study using
chemometrics. Food Control, 73, 796-805.
Gaiad, J. E., Hidalgo, M. J., Villafañe, R. N., Marchevsky, E. J., & Pellerano,
R. G. (2016). Tracing the geographical origin of Argentinean lemon
juices based on trace element profiles using advanced chemometric
techniques. Microchemical Journal, 129, 243-248.
doi:https://doi.org/10.1016/j.microc.2016.07.002
González-Molina, E., Domínguez-Perles, R., Moreno, D. A., & GarcíaViguera, C. (2010). Natural bioactive compounds of Citrus limon for
food and health. Journal of pharmaceutical and biomedical analysis,
51(2), 327-345. doi:https://doi.org/10.1016/j.jpba.2009.07.027
Guyon, F., Auberger, P., Gaillard, L., Loublanches, C., Viateau, M., Sabathié,
N., . . . Médina, B. (2014). 13C/12C isotope ratios of organic acids,
glucose and fructose determined by HPLC-co-IRMS for lemon juices
authenticity. Food Chemistry, 146, 36-40.
Jandrić, Z., & Cannavan, A. (2017). An investigative study on differentiation
of citrus fruit/fruit juices by UPLC-QToF MS and chemometrics. Food
Control, 72, 173-180.
Jiménez-Carvelo, A. M., González-Casado, A., Bagur-González, M. G., &
Cuadros-Rodríguez, L. (2019). Alternative data mining/machine
learning methods for the analytical evaluation of food quality and
authenticity – A review. Food Research International, 122, 25-39.
doi:https://doi.org/10.1016/j.foodres.2019.03.063
Jittanit, W., Suriyapornchaikul, N., & Nithisopha, S. (2013). The comparison
between the quality of lime juices produced by different preservation
techniques. Procedia-Social and Behavioral Sciences, 91, 691-696.
L. Kaijanen, M. P., S. Pietarinen, E. Jernström, S. Reinikainen. (2015).
Ultraviolet Detection of Monosaccharides: Multiple Wavelength
Strategy to Evaluate Results after Capillary Zone
Electrophoretic Separation. Int. J. Electrochem. Sci, 10, 2950 - 2961.
Lorente, J., Vegara, S., Martí, N., Ibarz, A., Coll, L., Hernández, J., . . . Saura,
D. (2014). Chemical guide parameters for Spanish lemon (Citrus limon
(L.) Burm.) juices. Food Chemistry, 162, 186-191.
Lubinska-Szczygieł, M., Różańska, A., Namieśnik, J., Dymerski, T.,
Shafreen, R. B., Weisz, M., . . . Gorinstein, S. (2018). Quality of limes
juices based on the aroma and antioxidant properties. Food Control, 89,
270-279.
Lyu, W., Yuan, B., Liu, S., Simon, J. E., & Wu, Q. (2022). Assessment of
lemon juice adulteration by targeted screening using LC-UV-MS and
untargeted screening using UHPLC-QTOF/MS with machine learning.
Food chemistry, 373, 131424.
Maione, C., de Paula, E. S., Gallimberti, M., Batista, B. L., Campiglia, A. D.,
Jr, F. B., & Barbosa, R. M. (2016). Comparative study of data mining
techniques for the authentication of organic grape juice based on ICPMS analysis. Expert Systems with Applications, 49, 60-73.
doi:https://doi.org/10.1016/j.eswa.2015.11.024
Natick. (2019). MATLAB. MA, USA: The Mathworks, Inc.
Pérez-Caballero, G., Andrade, J., Olmos, P., Molina, Y., Jiménez, I., Durán,
J., . . . Miguel-Cruz, F. (2017). Authentication of tequilas using pattern
recognition and supervised classification. TrAC Trends in Analytical
Chemistry, 94, 117-129.
Qiu, S., & Wang, J. (2017). The prediction of food additives in the fruit juice
based on electronic nose with chemometrics. Food Chemistry, 230, 208-
214. doi:https://doi.org/10.1016/j.foodchem.2017.03.011
Ríos-Reina, R., Azcarate, S. M., Camiña, J., Callejón, R. M., & Amigo, J. M.
(2019). Application of hierarchical classification models and reliability
estimation by bootstrapping, for authentication and discrimination of
wine vinegars by UV–vis spectroscopy. Chemometrics and Intelligent
Laboratory Systems, 191, 42-53.
Rivera-Cabrera, F., Ponce-Valadez, M., Sanchez, F., Villegas-Monter, A., &
Perez-Flores, L. (2010). Acid limes. A review. Fresh produce, 4(1),
116-122.
Ropodi, A., Panagou, E., & Nychas, G.-J. (2016). Data mining derived from
food analyses using non-invasive/non-destructive analytical techniques;
determination of food authenticity, quality & safety in tandem with
computer science disciplines. Trends in Food Science & Technology,
50, 11-25.
Saha, D., & Manickavasagan, A. (2021). Machine learning techniques for
analysis of hyperspectral images to determine quality of food products:
A review. Current Research in Food Science, 4, 28-44.
doi:https://doi.org/10.1016/j.crfs.2021.01.002
Sanches, V. L., Cunha, T. A., Viganó, J., de Souza Mesquita, L. M., Faccioli,
L. H., Breitkreitz, M. C., & Rostagno, M. A. (2022). Comprehensive
analysis of phenolics compounds in citrus fruits peels by UPLC-PDA
and UPLC-Q/TOF MS using a fused-core column. Food Chemistry: X,
14, 100262. doi:https://doi.org/10.1016/j.fochx.2022.100262
Shafiee, S., & Minaei, S. (2018). Combined data mining/NIR spectroscopy
for purity assessment of lime juice. Infrared Physics & Technology, 91,
193-199.
Yu, C., Wang, Y., Cao, H., Zhao, Y., Li, Z., Wang, H., . . . Tang, Q. (2020).
Simultaneous Determination of 13 Organic Acids in Liquid Culture
Media of Edible Fungi Using High-Performance Liquid
Chromatography. BioMed Research International, 2020, 2817979.
doi:10.1155/2020/2817979