Implementation of an olfactory machine system for the classification of different types of black pepper based on geographical origin and detection of cheating in Indian black pepper

Document Type : Full Research Paper

Authors

• Faezeh Jamalizadeh ¹
• Mahdi Ghasemi-Varnamkhasti ¹
• Mehdi Ghasemi Nafchi ²
• Mojtaba Tohidi ¹
• Majid Dowlati ³

¹ Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University.

² Department of Horticultural Science Engineering, Faculty of Agriculture, Shahrekord University.

³ Tuyserkan Faculty of Engineering and Natural Resources, Bu- Ali Sina University.

https://doi.org/10.22067/ifstrj.v16i4.76455

Abstract

Introduction: Spices are the most valuable medicinal plants used in food and medical science industries and due to quality and price diversity between various species, distinction, classification and separation of them based on purity and quality degree have great importance. Spices are produced in different countries, including India, Pakistan, China, and East and South Asian countries. The difference in the percentage of aromatic compounds in various types of spices from different regions has led to a distinction between spices. Also, profitable individuals for economic purposes and more profit without regard to the general health of the community will lead to the creation of adulteration in different types of spices. The most important of these adulterations is the addition of volatile ingredients such as cubeb pepper and palm kernel powder in black pepper.
 
Materials and Methods: In this study, an olfactory machine system based on eight metal oxide semiconductor sensors in combination with pattern recognition methods were used to classify and separate of black pepper samples based on geographic origin and also to detect cubeb pepper adulteration and palm kernel powder. The adulterated black pepper samples were tested with different adulteration levels (10, 20 and 30%).The fractional method was used to improve and optimize the electronic nose output signals before entering diagnostic methods. In order to analyze the extracted data from the sensor response signal, the principal component analysis method (PCA) was used. Based on the results, PCA with two main components of 96% for black pepper and 95% of cubeb pepper and palm kernel adulteration can be described from the variance of data. Also, three methods of linear separation analysis (LDA), Support vector machine (SVM) and decision tree (DT) were used to classify the samples. The use of the LDA method for black pepper showed a classification precision of 100%, and for adulterations, accuracy was 97.14%.
 
Results and Discussion: The results showed that SVM with Gaussian function has the highest accuracy in classifying black pepper samples, cubeb pepper, and palm kernel adulteration Also, the success rate of the DT method in separating and categorizing black pepper, cubeb pepper, and palm kernel was 96.66% and 88.5%  respectively.
According to the results obtained, the machine olfaction system in combination with pattern recognition methods has the ability to detect and classify different black pepper samples from different geographical origin and the lowest level of adulteration.

Keywords

• Nose electronics
• Black pepper
• Cubeb pepper
• Palm kernel

References

امیدبیگی، ر.، 1384، تولید و فرآوری گیاهان دارویی، انتشارات آستان قدس رضوی.

 

توحیدی، م.؛ قاسمی ورنامخواستی، م.؛ غفاری نیا، و.؛ محتسبی، س.س. و بنیادیان، م.، 1395، ساخت و توسعه یک سامانه ماشین بویایی در ترکیب با روش‌های شناسایی الگو برای تشخیص تقلب فرمالین در شیر خام، مهندسی بیوسیستم ایران، دوره 47 ، شماره 4، ص 1-10.

 

حاجی نژاد، م.، محتسبی، س. س.، قاسمی ورنامخواستی، م. و آغباشلو، م.، 1396، تشخیص تقلب در نمونه عسل کنار با استفاده از یک سامانه ماشین بویایی، ماشین‌های کشاورزی، جلد 7، شماره 2، ص 450-439.

 

سیدی، ع. و عابدینی، م.، 1390، شیمی عمومی با نگرش کاربردی جلد 2، ویرایش اول، چاپ هفتم.

 

طلوعی اشلقی، ع.؛ پورابراهیمی، ع.؛ ابراهیمی، م. و قاسم احمد، ل.، 1391، پیش‌بینی عود مجدد سرطان پستان به کمک سه تکنیک داده کاوی، بیماری‌های پستان ایران، سال 5، شمارع 4، ص 23-34.

 

قاسمی ورنامخواستی، م.، 2011، طراحی و توسعه و پیاده‌سازی یک سیستم زبان الکترونیک بر پایه حسگرهای نیمه‌هادی اکسید فلزی (MOS) در ترکیب با روش‌های تجزیه و تحلیل تشخیص الگو برای بررسی تشخیص کیفیت و تغییرات آبجو، پایان‌نامه دکتری، دانشگاه تهران.

 

کیانی، س. و مینایی، س.، 1394، امکان سنجی استفاده از سامانه‌های هوشمند بر پایه ماشین بویایی و ماشین بینایی به منظور بررسی کیفیت و تعیین مواد موثر فرآورده‌های گیاهان داروئی (مطالعه موردی زعفران)، همایش ملی گیاهان دارویی و داروهای گیاهی.

 

Arshak, K.E., Moore, G. M., Lyons, J., Harris, S. & Clifford., 2004, A review of gas sensors employed in electronic nose applications, Sensor review, 24, 181-198.

 

Baldwin, E.A., Bai, J., Plotto, A. & Dea, S., 2011, Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries, Sensors, 11(5), 4744-4766.

 

Banach, U., Tiebe, C. & Hubert, T, 2012, Multigas Sensors for the Quality Control of Spice Mixtures, Food Control, 26, 23-27.

 

Banerjee, D., Chowdhary, S., Chakraborty, S. & Bhattacharyya, R., 2017, Recent Advances in Detection of Food Adulteration. Academic Press, 145-160.

 

Bhattacharyya, N., Bandyopadhyay, R., Bhuyan, M., Tudu, B., Ghosh, D. & Jana, A., 2008, Electronic nose for black tea classification and correlation of measurements with “Tea Taster” marks, IEEE transactions on instrumentation and measurement, 57(7), 1313-1321.

 

Carmona, M., Martinez, J., Zalacain, A., Rodriguez-Mendez, M. L., de Saja, J. A. & Alonso, G. L., 2006, Analysis of saffron volatile fraction by TD-GC–MS and e-nose, European Food Research and Technology, 223, 96

 

Gliszczyńska-Świgło, A. & Chmielewski, J., 2017, Electronic nose as a tool for monitoring the authenticity of food, Food Analytical Methods, 10(6), 1800-1816.

 

Heidarbeigi, K., Mohtasebi, S.S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S. & Rezaei, K, 2015, Detection of adulteration in saffron samples using electronic nose, International Journal of Food Properties, 18(7), 1391-1401.

 

Kiani, S., Minaei, S. & Ghasemi-Varnamkhasti., M., 2016a, Application of Electronic Nose Systems for Assessing Quality of Medicinal and Aromatic Plant Products: A review, Journal of Applied Research on Medicinal and Aromatic Plants, 3(1), 1-9.

 

Kiani, S., Minaei, S. & Ghasemi-Varnamkhasti, M., 2016b, Portable Electronic Nose as an Expert System for Aroma-based Classification of Saffron, Chemometrics and Intelligent Laboratory Systems, 156, 148-156.

 

Li, C.P., Heinemann, R. & Sherry., 2007, Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection, Sensors and Actuators B: Chemical, 125, 301-310.

 

Li, Ch., Xu, F., Cao, Ch., Shang, M.Y., Zhang, C.Y., Yu, J., Liu, G.X., Wang, X. & Cai, SH.C., 2013, Comparative analysis of two species of Asari Radix et Rhizoma by electronic nose, headspace GC–MS and chemometrics, Journal of Pharmaceutical and Biomedical Analysis, 85, 231-238.

 

Liu, H., Zeng, F.K., Wang, Q.H. & Wu, H. S., 2013, Studies on the chemical and flavor qualities of white pepper (Piper nigrum L.) derived from five new genotypes, European Food Research and Technology, 237(2), 245-251.

 

Lozano, J., Santos, J.P. & Horrillo, M.C., 2005, Classification of white wine aromas with an electronic nose, Talanta., 67, 610–616.

 

Mahmoudi, E, 2009, Electronic nose technology and its applications, Sensors & Transducers, 107, 17.

 

Patil, T.R. & Sherekar, S.S., 2013, Performance analysis of Naive Bayes and J48 classification algorithm for data classification, International Journal of Computer Science and Applications, 6(2), 256-261.

 

Pearce T.C., Gardner J.W., Friel S., Barlett P.N. & Blair., N., 2003, Electronic nose for monitoring the flavor of beers, Analyst, 118, 371–377.

 

Peter, K.V., (Ed.), 2012, Handbook of herbs and spices, Elsevier.

 

Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M., Ahmadi, H. & Lozano, J., 2014, Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM), Czech Journal of Food Sciences, 32(6), 538– 548.

 

Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M.. & Ahmadi, H., 2015b, Design, development and implementation of a metal oxide semiconductor (MOS) based machine olfaction system for monitoring of banana ripeness, .Journal of Agricultural Machinery, 5(1), 111-121.

 

Shafiqul Islam, A.K.M., Ismail, Z., Saad, B., Othman, A.R., Ahmad, M.N. & Shakaff, A.Y.Md., 2006, Correlation studies between electronic nose response and headspace volatiles of Eurycoma longifolia extracts, Sensors and Actuators B, 120, 245-251.

 

Tahri, K., Tiebe, C., El Bari, N., Hübert, T. & Bouchikhi, B., 2016, Geographical provenience differentiation and adulteration detection of cumin by means of electronic sensing systems and SPME-GC-MS in combination with different chemometric approaches, Analytical Methods, 8(42), 7638-7649.

 

Tudu, B., Kow, B., Bhattacharyya, N. & Bandyopadhyay, R., 2008, November, Comparison of multivariate normalization techniques as applied to electronic nose based pattern classification for black tea, In Sensing Technology, 2008. ICST 2008. 3rd International Conference on (pp. 254-258). IEEE.