DETECTION OF ADULTERATION IN COCONUT MILK USING CUCKOO SEARCH-OPTIMIZED XGBOOST ON HIGH-DIMENSIONAL FTIR SPECTRAL DATA

I Gusti Ngurah Sentana Putra
Kusman Sadik
Agus Mohamad Soleh
Cici Suhaeni


DOI: https://doi.org/10.29100/jipi.v10i3.8376

Abstract


Coconut milk adulteration is an important issue because it can reduce food quality and endanger consumers. This study aims to develop a rapid and accurate detection method for coconut milk adulteration using a combination of FTIR spectroscopy technology and the XGBoost machine learning algorithm optimized with the Cuckoo Search Algorithm (CSA). FTIR spectral data from traditional and instant coconut milk samples were analyzed using Standard Normal Variate (SNV) and Savitzky-Golay (SG) preprocessing to reduce noise and clarify spectral features. The XGBoost model was then optimized through CSA with hyperparameter tuning. The results showed that the combination of SNV+SG preprocessing increased the model accuracy by 84.44%, with a precision of 92.73% and an F1-score of 79.94%. In addition, CSA optimization provided a 19.7% increase in accuracy compared to the model without tuning. These findings prove the effectiveness of the CSA-XGBoost approach in analyzing high-dimensional spectral data and is a potential solution in efficiently detecting the authenticity of coconut milk. In conclusion, this approach has the potential to be widely applied to test the authenticity of other food products quickly, non-destructively and accurately.

Keywords


Adulteration, FTIR Spectroscopy, XGBoost, Cuckoo Search, Spectral preprocessing

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References


A. Rohman and Y. B. C. Man, “Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil,” Food Res. Int., vol. 45, no. 1, pp. 117–121, 2012.

M. Bevilacqua, R. Bro, F. Marini, Å. Rinnan, M. A. Rasmussen, and T. Skov, “Recent chemometrics advances for foodomics,” TrAC Trends Anal. Chem., vol. 52, pp. 1–11, 2013.

T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, 2016, pp. 785–794.

A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Front. Neurorobot., vol. 7, p. 21, 2013.

J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., vol. 13, no. 1, pp. 281–305, 2012.

X. S. Yang and S. Deb, “Cuckoo search: recent advances and applications,” Neural Comput. Appl., vol. 24, no. 1, pp. 169–174, 2014.

X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proc. World Congr. Nature Biol. Inspired Comput. (NaBIC), 2009, pp. 210–214.

A. H. Gandomi, X. S. Yang, and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,” Eng. Comput., vol. 29, no. 1, pp. 17–35, 2013.

Y. Zhang and X. S. Yang, “Cuckoo search for hyperparameter optimization in deep learning,” Appl. Soft Comput., vol. 94, p. 106450, 2020.

A. McDermott, N. Deighton, and J. L. Walsh, “Machine learning for mid-infrared spectroscopy-based protein prediction in milk,” J. Dairy Sci., vol. 104, no. 2, pp. 1848–1858, 2021.

Å. Rinnan, F. van den Berg, and S. B. Engelsen, “Review of the most common pre-processing techniques for near-infrared spectra,” TrAC Trends Anal. Chem., vol. 28, no. 10, pp. 1201–1222, 2009.

A. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Anal. Chem., vol. 36, no. 8, pp. 1627–1639, 1964.

Y. Zhang and X. S. Yang, “Cross-disciplinary applications of cuckoo search algorithm,” Arch. Comput. Methods Eng., vol. 25, no. 4, pp. 1055–1075, 2018.

P. Mishra and D. Passos, “A synergistic approach to FTIR spectra and machine learning for food authenticity,” Food Chem., vol. 343, p. 128485, 2021.

S. Lohumi, H. Lee, M. S. Kim, J. Qin, and B. K. Cho, “Raman spectroscopy coupled with chemometrics for food authentication: A review,” TrAC Trends Anal. Chem., vol. 107, pp. 196–208, 2018.

H. Wijaya et al., “Application of FTIR spectroscopy for rapid and non-destructive analysis of adulteration in coconut milk,” J. Food Compos. Anal., vol. 97, p. 103768, 2021.

R. J. Barnes et al., “Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra,” Appl. Spectrosc., vol. 43, no. 5, pp. 772–777, 1989.

M. Kuhn and K. Johnson, Applied Predictive Modeling. New York: Springer, 2013.

D. M. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation,” J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37–63, 2020.

P. Probst et al., “Hyperparameters and tuning strategies for random forest,” WIREs Data Min. Knowl. Discov., vol. 9, no. 3, p. e1301, 2019.

X. S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd ed. Frome, UK: Luniver Press, 2010.

A. H. Gandomi and X. S. Yang, “Chaotic bat algorithm,” J. Comput. Sci., vol. 5, no. 2, pp. 224–232, 2014.

S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Adv. Neural Inf. Process. Syst., vol. 30, 2017.

J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” J. Mach. Learn. Res., vol. 7, pp. 1–30, 2006.

P. Virtanen et al., “SciPy 1.0: Fundamental algorithms for scientific computing in Python,” Nat. Methods, vol. 17, pp. 261–272, 2020.

A. Rohman and Y. B. Che Man, “FTIR spectroscopy combined with chemometrics for analysis of lard adulteration in virgin coconut oil,” Food Anal. Methods, vol. 5, no. 4, pp. 828–834, 2012.

A. M. Marina et al., “Monitoring virgin coconut oil adulteration by FTIR spectroscopy and chemometrics,” J. Food Sci. Technol., vol. 56, no. 4, pp. 2183–2192, 2019.

P. M. Santos et al., “Chemometrics and FTIR spectroscopy: A team to fight food fraud,” TrAC Trends Anal. Chem., vol. 127, p. 115877, 2020.

O. I. Mba et al., “Challenges in infrared spectroscopy for food authentication,” Crit. Rev. Food Sci. Nutr., vol. 61, no. 5, pp. 826–854, 2021.

X. Zhang et al., “Intelligent FTIR spectroscopy for food authentication: A tutorial review,” Anal. Chim. Acta, vol. 1193, p. 339405, 2022.

E. C. Y. Li-Chan, “The basics of infrared spectroscopy for food quality analysis,” Food Chem., vol. 217, pp. 342–353, 2017.

A. Rohman et al., “FTIR-based rapid detection of coconut milk adulteration: Recent advances,” J. Food Compos. Anal., vol. 108, p. 104411, 2022.

R. Ríos-Reina et al., “Preprocessing of FTIR spectra for food authentication: A comparative study,” Chemometr. Intell. Lab. Syst., vol. 212, p. 104287, 2021.

D. Suhandy et al., “Data fusion of FTIR and NIR spectroscopy for coconut products authentication,” Food Control, vol. 145, p. 109432, 2023.

J. Wang et al., “1D-CNN for FTIR spectral classification of edible oils,” Spectrochim. Acta A, vol. 285, p. 121891, 2023.

A. M. Marina et al., “FTIR spectral markers for traditional vs industrial coconut milk,” LWT-Food Sci. Technol., vol. 173, p. 114362, 2023.

Y. B. Che Man et al., “Chemometric analysis of coconut milk stabilizers using FTIR,” J. Food Sci., vol. 87, no. 5, pp. 1988–1999, 2022.

T. Pirak et al., “Geographical discrimination of Southeast Asian coconut milk by FTIR-chemometrics,” Food Chem., vol. 404, p. 134612, 2023.

E. Hatzakis et al., “NMR-FTIR combined approach for coconut lipid analysis,” Talanta, vol. 253, p. 123943, 2023.

X. Zhang et al., “Real-time quality control of coconut milk production using portable FTIR,” J. Food Eng., vol. 337, p. 111234, 2023.


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