Fast honey classification using infrared spectrum and machine learning

Hung-Yu Chien; An-Tong Shih; Bo-Shuen Yang; Vincent K. S. Hsiao; Hung-Yu Chien; An-Tong Shih; Bo-Shuen Yang; Vincent K. S. Hsiao

doi:10.3934/mbe.2019344

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 6874-6891. doi: 10.3934/mbe.2019344

Previous Article Next Article

Research article Special Issues

Fast honey classification using infrared spectrum and machine learning

1.
Department of Information Management, National ChiNan University, Taiwan, R.O.C.
2.
Department of Applied Materials and Optoelectronic Engineering, National ChiNan University, Taiwan, R.O.C.

Received: 22 May 2019 Accepted: 07 July 2019 Published: 29 July 2019

Honey has been one previous natural food in human history. However, as the supply cannot satisfy the market demand, many incidents of adulterated and fraudulent honey have been reported. In Taiwan, some common adulterated honey and fraudulent honey incidents include (1) mixing honey with fructose, (2) importing cheap honey abroad but labeling them as domestic honey, and (3) labeling cheaper honey (for example, nectar and lychee honey) as high-price honey (for example, longan honey). It is very difficult for consumers to tell the genuineness of the labeling of honey. To protect consumers and honest honey producers, we aim at exploring and developing an efficient and convenient technology that can effectively classify honey. We analyze the infrared spectra of honey samples and apply machine learning technologies to classify honey. The experimental results confirm that this technology can effectively distinguish several main honey types in Taiwan. This technology has the advantages of non-destruction, immediacy, and low manpower. It can serve as an effective tool to fast screen honey products.
- honey,
- infra-red,
- spectrum,
- machine learning,
- adulteration,
- fraud
Citation: Hung-Yu Chien, An-Tong Shih, Bo-Shuen Yang, Vincent K. S. Hsiao. Fast honey classification using infrared spectrum and machine learning[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6874-6891. doi: 10.3934/mbe.2019344

Related Papers:

Abstract

Honey has been one previous natural food in human history. However, as the supply cannot satisfy the market demand, many incidents of adulterated and fraudulent honey have been reported. In Taiwan, some common adulterated honey and fraudulent honey incidents include (1) mixing honey with fructose, (2) importing cheap honey abroad but labeling them as domestic honey, and (3) labeling cheaper honey (for example, nectar and lychee honey) as high-price honey (for example, longan honey). It is very difficult for consumers to tell the genuineness of the labeling of honey. To protect consumers and honest honey producers, we aim at exploring and developing an efficient and convenient technology that can effectively classify honey. We analyze the infrared spectra of honey samples and apply machine learning technologies to classify honey. The experimental results confirm that this technology can effectively distinguish several main honey types in Taiwan. This technology has the advantages of non-destruction, immediacy, and low manpower. It can serve as an effective tool to fast screen honey products.

References

[1]	Agence France-Presse, South China Morning Post, Taiwan's beekeepers battle to cash in on rapidly growing market for pure honey, South China Morning Post, Oct 23, 2016. Available from: https://www.scmp.com/news/china/society/article/2039345/taiwanese-beekeepers-battle-cash-pu re-honey-buzz.
[2]	TRIDGE, Taiwan Honey suppliers, wholesale prices, and market information. Available from: https://www.tridge.com/intelligences/honey/TW.
[3]	United States Department of Agriculture, NATIONAL HONEY REPORT, United States Department of Agriculture, April 24, 2019. Available from: https://www.ams.usda.gov/mnreports/fvmhoney.pdf.
[4]	F. E. Murphy, IoT Applications for Honey Bee Colony Condition: What's the Buzz All About? IEEE IoT Newsletter–(May 2019).
[5]	HealthyWithHoney.com, Longan Honey benefits? It's a powerful antimicrobial honey, March 15th, 2018. Available from: https://healthywithhoney.com/longan-honey-benefits-its-a-powerful-antimicrobial-honey/.
[6]	Taipei Times, Researchers say 75% of Taiwan's honey not pure, Sep. 14, 2013. Available from: http://www.taipeitimes.com/News/lang/archives/2013/09/14/2003572064.
[7]	The Economist, The scourge of honey fraud, Aug. 30, 2018, Available from: https://www.economist.com/united-states/2018/08/30/the-scourge-of-honey-fraud.
[8]	Eurofins Scientific, Use SNIF-NMR and EA-IRMS to check whether honey is doped with C3/C4, Available from: https://www.eurofins.tw/%E9%A3%9F%E5%93%81%E6% AA%A2%E9%A9%97/%E8%9C%82%E8%9C%9C%E6%AA%A2%E9%A9%97/..
[9]	J. B. Paine III, Y. B. Pithawalla and J. D. Naworal, Carbohydrate pyrolysis mechanisms from isotopic labeling: Part 3. The Pyrolysis of d-glucose: Formation of C3 and C4 carbonyl compounds and a cyclopentenedione isomer by electrocyclic fragmentation mechanisms, J. Anal. Appl. Pyrol., 82 (2008), 42–69.
[10]	Council of Agriculture, Executive Yuan R.O.C., Origin Authentication of Taiwan Longan Honey, April 2009. Available from: https://www.coa.gov.tw/ws.php?id=19303&print=Y.
[11]	Wikipedia, Fractionation of carbon isotopes in oxygenic photosynthesis, Available from: https://en.wikipedia.org/wiki/Fractionation_of_carbon_isotopes_in_oxygenic_photosynthesis.
[12]	KKnews, The developments for the inspection methods of honey (use Aoac to distinguish high fructose corn syrup and honey), July 7, 2017. Available from: https://kknews.cc/zh-tw/news/zx6lkkp.html.
[13]	AOAC (Association of Official Analytical Communities) Taiwan Section, Introduction to the standards of an international organization. Available from: http://www.aoac.org.tw/.
[14]	KKnews, New instruments for identifying honey, May 31, 2016. Available from: https://kknews.cc/zh-tw/news/3v9y3.html.
[15]	S. Riddle, The chemistry of honey. "honey convert to monosaccharides, Bee Culture, July 25, 2016. Available from: https://www.beeculture.com/the-chemistry-of-honey/.
[16]	S. Chen, W. H. Chang and K. W. Hsieh, The study on prediction models for determination of sugar content in fruit juice, J. Agr. Mach., 7 (1998), 41–60.
[17]	C. Pasquini, Near infrared spectroscopy: A mature analytical technique with new perspectives – A review, Anal. Chim. Acta, 1026 (2018), 8–36.
[18]	M. Ferreiro-González, E. Espada-Bellido, L. Guillén-Cueto, et al., Rapid quantification of honey adulteration by visible-near infrared spectroscopy combined with chemometrics, Talanta, 188 (2018), 288–292.
[19]	L. Pan, Q. Zhu, R. Lu, et al., Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy, Food Chem., 167 (2015), 264–271.
[20]	L. Pan, R. Lu, Q. Zhu, et al., Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy, Postharvest Biol. Technol., 102 (2015), 42–50.
[21]	X. Fu, X. Wang, and X. Rao, An LED-based spectrally tuneable light source for visible and near-infrared spectroscopy analysis: A case study for sugar content estimation of citrus, Biosyst. Eng., 163 (2017), 87–93.
[22]	Y. Yang, H. Zhuang, S. C. Yoon, et al., Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy, Food Chem., 244 (2018), 184–189.
[23]	Wikipedia, Multilayer perceptron, Available from: https://en.wikipedia.org/wiki/Multilayer_perceptron.
[24]	Wikipedia, Convolutional neural network, Available from: https://en.wikipedia.org/ wiki/Convolutional_neural_network.
[25]	Wikipedia, Recurrent neural network, Available from: https://en.wikipedia.org/ wiki/Recurrent_neural_network.
[26]	Wikipedia, Principal component analysis. Available from: https://en.wikipedia.org/ wiki/Principal_component_analysis.
[27]	Malvern Panalytical Products, ASD range, Available from: https://www.malvernpanalytical.com/ en/products/product-range/asd-range.
[28]	A. M. C. Davies and T. Fearn, Something has happened to my data: potential problems with standard normal variate and multiplicative scatter correction spectral pre‐treatments, Spectroscopy Europe, 21 (2009), 15–19.
[29]	D. Pelliccia, Instruments & Data Tools, Two scatter correction techniques for NIR spectroscopy in Python, July 21, 2018. Available from: https://www.idtools.com.au/two-scatter-correction-techniques-nir-spectroscopy-python/.
[30]	RIP Tutorial, Using a Savitzky–Golay filter to smooth spectrum, Available from: https://riptutorial.com/scipy/example/15878/using-a-savitzky-golay-filter.
[31]	L. Svecnjak, N. Biliskov, D. Bubalo, et al., Application of Infrared Spectroscopy in Honey Analysis, Agr. Conspectus Sci., 76 (2011), 191–195.
[32]	Glorybee, HONEY FACTS & NUTRITION, Available from: https://glorybee.com/content/honey-facts-nutrition.
[33]	Tensorflow, Available from: https://www.tensorflow.org/.
[34]	U. Malik, Implementing SVM and Kernel SVM with Python's Scikit-Learn, April 17, 2018. Available from: https://stackabuse.com/implementing-svm-and-kernel-svm-with-pythons-scikit-learn/.
[35]	G. Seif, Principal Component Analysis: Your Tutorial and Code, Nov 9, 2018. Available from: https://towardsdatascience.com/principal-component-analysis-your-tutorial-and-code-9719d3d3 f376.
[36]	scikit-learn, Precision-Recall, Available from: https://scikit-learn.org/stable/auto_examples/model_ selection /plot_precision_recall.html.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)