Skip to main content

Advertisement

SpringerLink
Log in

Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

One of the most important aspects of people's everyday diet is edible oils. Good quality cooking oil plays a key role in one's health. Due to the increased demand for oil in both the international and domestic markets, vendors often mix the high-quality oil with low-quality ones causing adulteration which is a serious issue to be solved. Thus, qualified (authentic or pure) edible oils are expensive. Gall bladder cancer is mainly caused when the oil is adulterated with butter yellow, argemone oil, mixing good quality oil with low-quality oils, and wrong ingredients with fraudulent labeling. In the past decades, spectrophotometric methods and machine learning techniques are utilized for adulteration and authenticity identification of sunflower oil, olive oil, corn oil, coconut oil, mustard oil, soybean oils. Nevertheless, the performance of these methods is decreased due to data imbalance, overfitting, higher cost, more execution time, computational complexity, and inaccurate classification. To tackle these issues, we have proposed Deep Long Short-Term Memory (LSTM) neural network with a Seagull Optimization Algorithm (SOA) for the authenticity and adulteration of edible oils classification. In this study, 5 kinds of edible oils such as coconut oil, rice oil, sesame oil, sunflower oil, and Olive oil are used. Each of the oil samples was kept in the refrigerator at 4 °C. During data acquisition, the proton resonance frequency was 19.91 MHz and the magnetic field strength was 0.467 T. The obtained signals are applied for edible oil classification, which is handled using a deep LSTM neural network with SOA. Based on the experimental investigation, the proposed method accomplished superior performances than existing methods including LFNMR-CNN, LFNMR-SVM, DLC, Pre-trained CNN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of data and material

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  1. Time, Alexandra Sifferlin (2018) The 10 Best and Worst Oils For Your Health, July 23, https://time.com/5342337/best-worst-cooking-oils-for-your-health/. Access 14 Mar 2019

  2. Chien HY, Shih AT, Tseng YM (2019) Exploration of fast edible oil classification using infrared spectrum, machine learning, and chemometrics. In: 2019 IEEE 10th international conference on awareness science and technology (iCAST), IEEE, pp 1–6

  3. Zielińska A, Wójcicki K, Klensporf-Pawlik D, Dias-Ferreira J, Lucarini M, Durazzo A, Lucariello G et al (2020) Chemical and physical properties of meadowfoam seed oil and extra virgin olive oil: focus on vibrational spectroscopy. J Spectroscopy

  4. Wu X, Zhao Z, Tian R, Gao S, Niu Y, Liu H (2020) Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil. Food Chem 335:127640

  5. Sun Yu, Dou X, Yue X, Li Yu, Zhang L, Li Ji, Li P (2020) Optimization of headspace SPME GC× GC-TOF/MS analysis of volatile organic compounds in edible oils by central composite design for adulteration detection of edible oil. Food Anal Methods 13(6):1328–1336

    Article  Google Scholar 

  6. Bellou E, Gyftokostas N, Stefas D, Gazeli O, Couris S (2020) Laser-induced breakdown spectroscopy assisted by machine learning for olive oils classification: the effect of the experimental parameters. Spectrochim Acta Part B Atom Spectroscopy, 163:105746

  7. Karami H, Rasekh M, Mirzaee-Ghaleh E (2020) Comparison of chemometrics and AOCS official methods for predicting the shelf life of edible oil. Chemometrics Intell Lab Syst 206:104165

  8. Kwofie F, Lavine BK, Ottaway J, Booksh K (2020) Incorporating brand variability into classification of edible oils by Raman spectroscopy. J Chemometrics 34(7): e3173

  9. Han J, Sun R, Zeng X, Zhang J, Xing R, Sun C, Chen Y (2020) Rapid classification and quantification of camellia (Camellia oleifera Abel.) oil blended with rapeseed oil using FTIR-ATR spectroscopy. Molecules 25(9):2036

  10. Karami H, Rasekh M, Mirzaee‐Ghaleh E (2020) Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. J Food Process Preserv 44(9):e14696

  11. Hou X, Wang G, Wang X, Ge X, Fan Y, Nie S (2020) Convolutional neural network based approach for classification of edible oils using low-field nuclear magnetic resonance. J Food Composit Anal 92:03566

  12. Wang X, Wang G, Hou X, Nie S (2020) A rapid screening approach for authentication of olive oil and classification of binary blends of olive oils using low-field nuclear magnetic resonance spectra and support vector machine. Food Anal Methods 13(10):1894–1905

    Article  Google Scholar 

  13. Huang W, Zeng J, Wang Z, Liang J (2017) Partial noise assisted multivariate EMD: an improved noise assisted method for multivariate signals decomposition. Biomed Signal Process Control 36:205–220

    Article  Google Scholar 

  14. Jia H, Xing Z, Song W (2019) A new hybrid seagull optimization algorithm for feature selection. IEEE access 7:49614–49631

    Article  Google Scholar 

  15. Godoy AC, dos Santos PDS, Nakano AY, Bini RA, Siepmann DAB, Schneider R, Santos OO (2020) Analysis of vegetable oil from different suppliers by chemometric techniques to ensure correct classification of oil sources to deal with counterfeiting. Food Anal Methods 13(5):1138–1147

    Article  Google Scholar 

  16. Borghi FT, Santos PC, Santos FD, Nascimento MHC, CorrêaT, Cesconetto M, Pires AA et al (2020) Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-infrared spectrometer associated with chemometrics. Microchem J 159:105544

  17. Valli E, Panni F, Casadei E, Barbieri S, Cevoli C, Bendini A, García-González DL, Gallina Toschi T (2020) An HS-GC-IMS method for the quality classification of virgin olive oils as screening support for the panel test. Foods 9(5):657

    Article  Google Scholar 

  18. Vinu S, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288

    Article  Google Scholar 

  19. Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  20. Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325

    Article  Google Scholar 

  21. Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, BhambriRejeeshSundararaj PMRR (2020) CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovoltaics Res Appl 28(11):1128–1145

    Article  Google Scholar 

  22. Ravikumar S, Kavitha D (2021) CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system. J Field Robot

  23. Ravikumar S, Kavitha D (2020) IoT based home monitoring system with secure data storage by Keccak–Chaotic sequence in cloud server. J Ambient Intell Human Comput, pp1–13

  24. Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78(16):22691–22710

    Article  Google Scholar 

  25. Kavitha D, Ravikumar S (2021) IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring. Trans Emerg Telecommun Technol 32(1):e4132

  26. Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V. and Rejeesh, M.R., 2021. An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control, 66, p.102480.

  27. Hassan BA (2020) CSCF: a chaotic sine cosine firefly algorithm for practical application problems. Neural Comput Appl, pp 1–20

  28. Nisha, S. and Madheswari, A.N., 2016, February. Prevention of phishing attacks in voting system using visual cryptography. In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS) (pp. 1–4). IEEE

  29. Haseena KS, Anees S, Madheswari N (2014) Power optimization using EPAR protocol in MANET. Int J Innov Sci Eng Technol 6:430–436

    Google Scholar 

  30. Gowthul Alam MM, Baulkani S (2019) Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowl Inf Syst 60(2):971–1000

    Article  Google Scholar 

  31. Nirmal Kumar SJ, Ravimaran S, Alam MM (2020) An effective non-commutative encryption approach with optimized genetic algorithm for ensuring data protection in cloud computing. Comput Model Eng Sci 125(2):671–697

    Google Scholar 

  32. Nirmal Kumar, S.J., Ravimaran, S. and Alam, M.M., 2020. An Effective Non-Commutative Encryption Approach with Optimized Genetic Algorithm for Ensuring Data Protection in Cloud Computing. Computer Modeling in Engineering & Sciences, 125(2), pp.671-697

    Article  Google Scholar 

  33. Nisha S, Madheswari AN (2016) Secured authentication for internet voting in corporate companies to prevent phishing attacks. Int J Emerg Technol Comput Sci Electron IJETCSE 22(1):45–49

    Google Scholar 

  34. Surya V, Senthilselvi A (2020) A qualitative analysis of the machine learning methods in food adultery: a focus on Milk adulteration detection. J Adv Res Dyn Control Syst 12(7):543–551

    Article  Google Scholar 

  35. Renith G, Senthilselvi A (2020) Accuracy improvement in diabetic retinopathy detection using DLIA. J Adv Res Dyn Control Syst 12(4):133–149

    Google Scholar 

  36. Mohammed Thaha M, Mohan Pradeep kumar K, Murugan BS, Dhanasekeren S, Vijay Karthick P, Senthilselvi A (2019) Brain tumor segmentation using convolutional neural networks in MRI images. J Med Syst 43(9):294

    Article  Google Scholar 

  37. Senthilselvi A, Duela JS, Prabavathi R et al (2021) Performance evaluation of adaptive neuro fuzzy system (ANFIS) over fuzzy inference system (FIS) with optimization algorithm in de-noising of images from salt and pepper noise. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03024-z

    Article  Google Scholar 

  38. Senthilselvi A, Sellam V, Alahmari SA, Rajeyyagari S (2020) Accuracy enhancement in mobile phone recycling process using machine learning technique and MEPH process. Environ Tech Innovation 20:101137

    Article  Google Scholar 

  39. Yuan YY, Wang ST, Wang JZ, Cheng Q, Wu XJ, Kong DM (2020) Rapid detection of the authenticity and adulteration of sesame oil using excitation-emission matrix fluorescence and chemometric methods. Food control 112:107145

  40. Berghian-Grosan C, Magdas DA (2020) Raman spectroscopy and machine-learning for edible oils evaluation.Talanta 218:121176

  41. Amorim TL, Duarte LM, Mendes da Silva E, Augusto Leal de Oliveira M (2021) Capillary electromigration methods for fatty acids determination in vegetable and marine oils: a review. Electrophoresis 42(3):289–304

  42. Hu W, Zhang L, Li P, Wang X, Zhang Qi, Baocheng Xu, Sun X, Ma F, Ding X (2014) Characterization of volatile components in four vegetable oils by headspace two-dimensional comprehensive chromatography time-of-flight mass spectrometry. Talanta 129:629–635

    Article  Google Scholar 

  43. Yang H, Irudayaraj J, Paradkar MM (2005) Discriminant analysis of edible oils and fats by FTIR. FT-NIR FT-Raman Spectroscopy Food Chem 93(1):25–32

    Google Scholar 

  44. Dubois V, Breton S, Linder M, Fanni J, Parmentier M (2007) Fatty acid profiles of 80 vegetable oils with regard to their nutritional potential. Eur J Lipid Sci Technol 109(7):710–732

    Article  Google Scholar 

  45. Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232

    Article  MathSciNet  Google Scholar 

  46. Frankel AL, Jones RE, Alleman C, Templeton JA (2019) Predicting the mechanical response of oligocrystals with deep learning. Comput Mater Sci169:109099

  47. Dhiman G, Singh KK, Soni M, Nagar A, Dehghani M, Slowik A, Kaur A, Sharma A, Houssein EH, Cengiz K (2021) MOSOA: a new multi-objective seagull optimization algorithm. Exp Syst Appl 167:114150

  48. Kamsing P, Torteeka P, Yooyen S, (2020) An enhanced learning algorithm with a particle filter-based gradient descent optimizer method. Neural Comput Appl, pp 1–12

  49. Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning , pp 1310–1318. PMLR.

  50. Zhu X, Li N, Pan Y (2019) Optimization performance comparison of three different group intelligence algorithms on a SVM for hyperspectral imagery classification. Remote Sens 11(6):734

    Article  Google Scholar 

  51. Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. PloS ONE 10(5):e0122827

  52. Lv L, Kong W, Qi J, Zhang J (2018) An improved long short-term memory neural network for stock forecast. In: MATEC web of conferences, vol 232, p 01024. EDP Sciences

  53. Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl Based Syst 165:169–196

    Article  Google Scholar 

  54. Vigli G, Philippidis A, Spyros A, Dais P (2003) Classification of edible oils by employing 31P and 1H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils. J Agric Food Chem 51(19):5715–5722

    Article  Google Scholar 

  55. Brodnjak-Vončina D, Cencič Kodba Z, Novič M (2005) Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids. Chemometrics Intell Lab Syst 75(1):31–43

  56. Toh CM, Ewe HT, Tey SH, Tay YH (2017) A study on leaf area index and SAR image of oil palm with entropy decomposition and deep learning classification. In: 2017 Progress in electromagnetics research symposium-fall (PIERS-FALL), IEEE, pp 271–278

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India

    V. Surya & A. Senthilselvi

Authors
  1. V. Surya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Senthilselvi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Surya.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent does not apply as this was a retrospective review with no identifying patient information.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Surya, V., Senthilselvi, A. Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm. Neural Comput & Applic 34, 7611–7625 (2022). https://doi.org/10.1007/s00521-021-06829-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06829-3

Keywords

Search

Navigation