Portable Near-Infrared Spectroscopy and Support Vector Regression for Fast Quality Evaluation of Vanilla (Vanilla planifolia)
DOI:
https://doi.org/10.23960/jtep-l.v14i2.515-526
Abstract View: 183
Abstract
Vanilla (Vanilla planifolia) is a high-value agricultural product, with its quality influenced by essential factors such as moisture and vanillin content. Conventional techniques for evaluating these characteristics are inefficient, require sample destruction, and are impractical for swift assessments. This research explores the feasibility of using portable Near-Infrared (NIR) spectroscopy combined with Support Vector Regression (SVR) to enable quick and noninvasive property prediction. Spectral information was obtained from vanilla samples using two portable NIR instruments, SCiO (740–1070 nm) and Neospectra (1350 2550 nm). Preprocessing techniques such as normalization, SNV, MSC, first derivative, first derivative-SNV, and first derivative-MSC were applied. For moisture content prediction, SCiO achieved an R² of 0.768, an RMSE of 4.720%, an RPD of 2.075 and an RER 10.197 using Min-Max normalization, while Neospectra yielded an R² of 0.758, an RMSE of 5.161%, an RPD of 2.033 and an RER 9.325 with MSC preprocessing. In contrast, predicting vanillin concentration proved more challenging, with SCiO achieving moderate accuracy with an R² 0.406, an RMSE 0.379%, an RPD 1.297, an RER 5.039, and Neospectra demonstrating limited performance with an R² 0.172, an RMSE 0.576%, an RPD 1.098 and an RER 3.315. These findings highlight the potential of portable NIR spectroscopy as a practical tool for assessing vanilla quality, particularly for moisture content, in industrial and field applications.
Keywords: Moisture content, Portable NIR spectroscopy, Support vector regression, Vanilla planifolia, Vanillin content.
Downloads
References
Alasadi, S.A., & Bhaya, W.S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102-4107.
Anand, A., Khurana, R., Wahal, N., Mahajan, S., Mehta, M., Satija, S., Sharma, N., Vyas, M., & Khurana, N. (2019). Vanillin: A comprehensive review of pharmacological activities. Plant Archives, 19(2), 1000-1004.
Anyidoho, E.K., Teye, E., Agbemafle, R., Amuah, C.L.Y., & Boadu, V.G. (2021). Application of portable near infrared spectroscopy for classifying and quantifying cocoa bean quality parameters. Journal of Food Processing and Preservation, 45(5), e15445. https://doi.org/10.1111/jfpp.15445
Badan Standardisasi Nasional. (n.d.). SNI 01-0010-2002 Panili.
Baqueiro-Peña, I., & Guerrero-Beltrán, J.Á. (2017). Vanilla (Vanilla planifolia Andr.), its residues and other industrial by-products
for recovering high value flavor molecules: A review. Journal of Applied Research on Medicinal and Aromatic Plants, 6, 1–9. https://doi.org/10.1016/j.jarmap.2016.10.003
Beć, K.B., Grabska, J., & Huck, C.W. (2021). Principles and applications of miniaturized Near-Infrared (NIR) spectrometers. In Chemistry - A European Journal, 27(5), 1514-1532. https://doi.org/10.1002/chem.202002838
Beć, K.B., Grabska, J., & Huck, C.W. (2022). Miniaturized NIR spectroscopy in food analysis and quality control: Promises, challenges, and perspectives. Foods, 11(10), 1465. https://doi.org/10.3390/foods11101465
Bittner, L.K., Schönbichler, S.A., Bonn, G.K., & Huck, C.W. (2013). Near infrared spectroscopy (NIRS) as a tool to analyze phenolic compounds in plants. Current Analytical Chemistry, 9(3), 417–423. https://doi.org/10.2174/1573411011309030010
Chadalavada, K., Anbazhagan, K., Ndour, A., Choudhary, S., Palmer, W., Flynn, J.R., Mallayee, S., Pothu, S., Prasad, K.V.S.V., Varijakshapanikar, P., Jones, C.S., & Kholová, J. (2022). NIR instruments and prediction methods for rapid access to grain
protein content in multiple cereals. Sensors, 22(10), 3710. https://doi.org/10.3390/s22103710
Chen, C., Li, H., Lv, X., Tang, J., Chen, C., & Zheng, X. (2019). Application of near infrared spectroscopy combined with SVR algorithm in rapid detection of cAMP content in red jujube. Optik, 194, 163063. https://doi.org/10.1016/j.ijleo.2019.163063
Chicco, D., Warrens, M.J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/PEERJ-CS.623
Correia, R.M., Tosato, F., Domingos, E., Rodrigues, R.R.T., Aquino, L.F.M., Filgueiras, P.R., Lacerda, V., & Romão, W. (2018). Portable near infrared spectroscopy applied to quality control of Brazilian coffee. Talanta, 176, 59-68. https://doi.org/10.1016/j.talanta.2017.08.009
Cozzolino, D. (2016). Near infrared spectroscopy and food authenticity. In Advances in Food Traceability Techniques and Technologies: Improving Quality Throughout the Food Chain, 119-136. https://doi.org/10.1016/B978-0-08-100310-7.00007-7
Douglas, R.K., Nawar, S., Alamar, M.C., Mouazen, A.M., & Coulon, F. (2018). Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Science of the Total Environment, 616–617. https://doi.org/10.1016/j.scitotenv.2017.10.323
Drucker, H., Surges, C.J.C., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. Advances in Neural Information Processing Systems, 9, 155-161.
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in Energy Research, 9, Article 652801. https://doi.org/10.3389/fenrg.2021.652801
Havkin-Frenkel, D., & Frenkel, C. (2008). Postharvest handling and storage of cured vanilla beans. Stewart Postharvest Review, 2(4), 1-9. https://doi.org/10.2212/spr.2006.4.6
Hayati, R., Munawar, A.A., & Fachruddin, F. (2020). Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango. Data in Brief, 30, 105571. https://doi.org/10.1016/j.dib.2020.105571
Huck, C.W. (2020). New trend in instrumentation of NIR spectroscopy-miniaturization. In Near-Infrared Spectroscopy: Theory, Spectral Analysis, Instrumentation, and Applications, 193-210. https://doi.org/10.1007/978-981-15-8648-4_8
Malvandi, A., Feng, H., & Kamruzzaman, M. (2022). Application of NIR spectroscopy and multivariate analysis for Nondestructive evaluation of apple moisture content during ultrasonic drying. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 269, 120733. https://doi.org/10.1016/j.saa.2021.120733
Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116(44), 22071-22080. https://doi.org/10.1073/pnas.1900654116
Osborne, B.G., Fearn, T., & Hindle, P.H. (1993). Practical NIR Spectroscopy with Applications in Food and Beverage Analysis. Longman Scientific and Technical, Harlow, UK: 227 pp.
Pandiselvam, R., Prithviraj, V., Manikantan, M.R., Kothakota, A., Rusu, A.V., Trif, M., & Mousavi Khaneghah, A. (2022). Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review. In Frontiers in Nutrition, 9, Article 973457. https://doi.org/10.3389/fnut.2022.973457
Pereira, C.G., Leite, A.I.N., Andrade, J., Bell, M.J.V., & Anjos, V. (2019). Evaluation of butter oil adulteration with soybean oil by FT-MIR and FT-NIR spectroscopies and multivariate analyses. LWT, 107, 1-8. https://doi.org/10.1016/j.lwt.2019.02.072
Pu, Y., Pérez-Marín, D., O’shea, N., & Garrido-Varo, A. (2021). Recent advances in portable and handheld NIR spectrometers and applications in milk, cheese and dairy powders. Foods, 10(10), 2377. https://doi.org/10.3390/foods10102377
Raju, V.N.G., Lakshmi, K.P., Jain, V.M., Kalidindi, A., & Padma, V. (2020). Study the influence of normalization/transformation process on the accuracy of supervised classification. Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020. https://doi.org/10.1109/ICSSIT48917.2020.9214160
Ranadive, A.S. (2019). Quality control of vanilla beans and extracts. In Havkin-Frenkel, D., & Belanger, F.C. (Eds.), Handbook of Vanilla Science and Technology (2nd Ed., pp. 239–259). Wiley. https://doi.org/10.1002/9781119377320.ch15
Rodríguez-Pérez, R., & Bajorath, J. (2022). Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery. Journal of Computer-Aided Molecular Design, 36(5), 355–362. https://doi.org/10.1007/s10822-022-00442-9
Sachindra, D.A., & Kanae, S. (2019). Machine learning for downscaling: the use of parallel multiple populations in genetic programming. Stochastic Environmental Research and Risk Assessment, 33(8–9), 1497–1533. https://doi.org/10.1007/s00477-
-01721-y
Schwanninger, M., Rodrigues, J.C., & Fackler, K. (2011). A review of band assignments in near infrared spectra of wood and wood components. In Journal of Near Infrared Spectroscopy, 19(5), 287–308. https://doi.org/10.1255/jnirs.955
Torniainen, J., Afara, I.O., Prakash, M., Sarin, J.K., Stenroth, L., & Töyräs, J. (2020). Open-source python module for automated preprocessing of near infrared spectroscopic data. Analytica Chimica Acta, 1108, 1-9. https://doi.org/10.1016/j.aca.2020.02.030
Wahyuningsih, R., Fitriasari, B., & Suwardji, S. (2022). Development of vanilla agribusiness and its export opportunities to support triple export program (Gratitude) on Lombok Island. Path of Science, 8(6), 5020–5024. https://doi.org/10.22178/pos.82-18
Wani, O.A., Mahdi, S.S., Yeasin, Md., Kumar, S.S., Gagnon, A.S., Danish, F., Al-Ansari, N., El-Hendawy, S., & Mattar, M.A. (2024). Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas. Scientific Reports, 14, Article number 27876. https://doi.org/10.1038/s41598-024-77687-x
Widyaningrum, W., Purwanto, Y.A., Widodo, S., Supijatno, & Iriani, E.S. (2024). Rapid assessment of vanilla (Vanilla planifolia) quality parameters using portable near-infrared spectroscopy combined with random forest. Journal of Food Composition and Analysis, 133(March), 106346. https://doi.org/10.1016/j.jfca.2024.106346
Williams, P., & Norris, K. (Eds.) (1987). Near-Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, St. Paul, Minnesota, USA: 330 p.
Wokadala, O.C., Human, C., Willemse, S., & Emmambux, N.M. (2020). Rapid non-destructive moisture content monitoring using a handheld portable Vis–NIR spectrophotometer during solar drying of mangoes (Mangifera indica L.). Journal of Food Measurement and Characterization, 14(2), 790–798. https://doi.org/10.1007/s11694-019-00327-w
Workman, J., & Weyer, L. (2007). Practical Guide to Interpretive Near-Infrared Spectroscopy (1st ed.). CRC Press. https://doi.org/10.1201/9781420018318
Wu, Y., Guo, J., Sun, R., & Min, J. (2020). Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells. Npj Computational Materials, 6(1), Article number 120. https://doi.org/10.1038/s41524-020-00388-2
Zareef, M., Chen, Q., Hassan, M. M., Arslan, M., Hashim, M. M., Ahmad, W., Kutsanedzie, F. Y. H., & Agyekum, A. A. (2020). An overview on the applications of typical non-linear algorithms coupled with NIR spectroscopy in food analysis. Food Engineering Reviews, 12(2), 173–190. https://doi.org/10.1007/s12393-020-09210-7
Zhang, F., & O'Donnell, L.J. (2020). Support vector regression. In Machine learning: Methods and applications to brain disorders (pp. 123–140). Elsevier. https://doi.org/10.1016/B978-0-12-815739-8.00007-9
Zhang, W., Kasun, L.C., Wang, Q.J., Zheng, Y., & Lin, Z. (2022). A review of machine learning for near-infrared spectroscopy. Sensors, 22(24), 9764. https://doi.org/10.3390/s22249764
Zhou, S.K., Greenspan, H., Davatzikos, C., Duncan, J.S., Van Ginneken, B., & Madabhushi, A. (2021). A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE, 109(5), 820 - 838. https://doi.org/10.1109/JPROC.2021.3054390
Zou, H., Shen, S., Lan, T., Sheng, X., Zan, J., Jiang, Y., Du, Q., & Yuan, H. (2022). Prediction method of the moisture content of black tea during processing based on the miniaturized near-infrared spectrometer. Horticulturae, 8(12), 1170. https://doi.org/10.3390/horticulturae8121170
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International Lice that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknik Pertanian Lampung
JTEPL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.jpg)
