Supervised Pattern Recognition and its Applications in Food Analyses

Yıl 2019, , 429 - 438, 18.11.2019

Bahar Demircan Yeşim Elmacı

https://doi.org/10.24323/akademik-gida.647734

Öz

Supervised pattern recognition is a technique for
classification that the prior knowledge is used regarding member of sample
category. Classification model is improved by using samples separated into
category as training set. Supervised pattern recognition is getting more
important for chemistry, biology, pharmacology and food science. There are many
supervised pattern recognition methods. The main part is to select the most
appropriate method. There are implementations to different inputs for various
purposes such as food quality assessment and data interpretation. Wine, oil,
honey, dairy products, meat, fruits, beverages, cereals and fish could be given
as examples analyzed by supervised pattern recognition techniques. Also by
using this techniques, texture and aroma analyses, food verification, food
quality assessment, multiple element analysis, classification based on
geographical and botanical origins can be performed. In this review, supervised
pattern recognition is defined, its application techniques are summarized, and
information is provided by exemplifying studies on pattern recognition
techniques used in food analysis.

Anahtar Kelimeler

Supervised pattern recognition, Food analysis, Multivariable data analysis, Image processing, Classification

Denetimli Örüntü Tanıma ve Gıda Analizlerinde Uygulamaları

Öz

Denetimli örüntü tanıma, sınıflandırma için örnek
kategorisi üyeliği hakkında bir ön bilginin kullanıldığı teknikleri ifade etmektedir.
Sınıflandırma modeli, kategorileri olan örneklerin bir eğitim seti üzerinde
geliştirilmektedir. Kimya, biyoloji, ilaç ve gıda bilimi içinde denetimli
örüntü tanıma uygulaması giderek daha önemli hale gelmektedir. Denetimli örüntü
tanıma yöntemleri çok çeşitlidir ve asıl önemli nokta en uygun yöntemi
seçmektir. Gıda analizlerinde gıda kalite değerlendirmesi, veri yorumlama gibi
çeşitli amaçlarla farklı verilere uygulamaları bulunmaktadır. Denetimli örüntü
tanıma teknikleriyle incelenen gıdalara örnek olarak şarap, yağ, bal, süt
ürünleri, et, meyveler, içecekler, tahıllar ve balık verilebilir. Bu teknikler
kullanılarak gıdalarda doku analizi, aroma analizi, gıda doğrulaması, gıda
kalitesinin değerlendirilmesi, çoklu element analizi, coğrafi ve botanik kökene
göre sınıflandırma gerçekleştirilebilmektedir. Bu derlemede, denetimli örüntü
tanıma tanımlanmış, uygulama teknikleri özetlenmiş ve gıda analizlerinde
kullanılan örüntü tanıma teknikleri konusunda yapılan çalışmalar ile örneklendirilerek
bilgi verilmiştir. 

Anahtar Kelimeler

Denetimli örüntü tanıma, Gıda analizi, Çok değişkenli veri analizi, Görüntü işleme, Sınıflandırma

Kaynakça

• [1] Lavine, B.K. (2000). Encyclopedia of Analytical Chemistry. John Wiley & Sons Ltd., Chichester, New York.
• [2] Massart, D.L., Vandeginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., Smeyers-Verbeke, J. (1997). Handbook of Chemometrics and Qualimetrics: Part A, Elsevier, Amsterdam, 207p.
• [3] Brereton, R.G. (2003). Chemometrics: Data Analysis for the Laboratory and Chemical Plant. Wiley, Chichester, 119p.
• [4] Mazzatorta, P., Benfenati, E., Lorenzini, P., Vighi, M. (2004). QSAR in ecotoxicity: an overview of modern classification techniques. Journal of Chemical Information and Computer Sciences, 44(1), 105-112.
• [5] Todeschini, R., Ballabio, D., Consonni, V., Mauri, A. Pavan, M. (2007). CAIMAN (Classification And Influence Matrix Analysis): A new approach to the classification based on leverage-scaled functions. Chemometrics and Intelligent Laboratory Systems, 87(1), 3-17.
• [6] Berrueta, L.A., Alonso-Salces, R.M., Héberger, K. (2007). Supervised pattern recognition in food analysis. Journal of Chromatography A, 1158(1-2), 196-214.
• [7] Koyuncu, İ. (2016). İleri Örüntü Tanıma Teknikleri ve Uygulamaları. http://docplayer.biz.tr/3182643-Ileri-oruntu-tanima-teknikleri-ve-uygulamalari-icerik.html.
• [8] Anonim (2014). Örüntü Tanıma. http://ehm.kocaeli.edu.tr/dersnotlari_data/kgullu/Oruntu%20Tanima/Sunu1_2.pdf.
• [9] Samtaş, G., Gülesin, M. (2011). Sayısal Görüntü İşleme ve Farklı Alanlardaki Uygulamaları. Electronic Journal of Vocational Colleges, 2(1), 85-97.
• [10] Solak, S., Altınışık, S. (2018). Görüntü işleme teknikleri ve kümeleme yöntemleri kullanılarak fındık meyvesinin tespit ve sınıflandırılması. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 56-65.
• [11] Viola, P., Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1). IEEE.
• [12] Hussin, R., Juhari, M.R., Kang, N.W., Ismail, R.C., Kamarudin, A. (2012). Digital image processing techniques for object detection from complex background image. Procedia Engineering, 41, 340-344.
• [13] Sonka, M., Hlavac, V., Boyle, R. (2014). Image Processing, Analysis, and Machine Vision. Cengage Learning, Stamford, USA, 37p.
• [14] Wu, D., Sun, D.W. (2013). Colour measurements by computer vision for food quality control–A review. Trends in Food Science & Technology, 29(1), 5-20.
• [15] Hof, A., Wolf, N. (2014). Estimating potential outdoor water consumption in private urban landscapes by coupling high-resolution image analysis, irrigation water needs and evaporation estimation in Spain. Landscape and Urban Planning, 123, 61-72.
• [16] Latha, M., Poojith, A., Reddy, B.A., Kumar, G.V. (2014). Image processing in agriculture. International Journal of Innovative Research In Electrical, Electronics, Instrumentation and Control Engineering, 2(6), 1562-1565.
• [17] Kurtulmuş, F., Vardar, A., Kavdır, İ. (2013). Bahçe Koşullarında Alınmış Renkli Görüntülerde Doku ve Şekil Öznitelikleriyle Genç Şeftali Meyvelerinin Saptanması. Tarım Makinaları Bilimi Dergisi, 9(2), 141-148.
• [18] Sert, E., Taşkın, D., Suçsuz, N. (2010). Görüntü İşleme teknikleri ile şeftali ve elma sınıflandırma. Trakya Üniversitesi Fen Bilimleri Dergisi, 11(2), 82-88.
• [19] Sofu, M., Er, O., Kayacan, M.C., Çetişli, B. (2013). Elmaların görüntü işleme yöntemi ile sınıflandırılması ve leke tespiti. Gıda Teknolojileri Elektronik Dergisi, 8(1), 12-25.
• [20] Demirbaş, H.Y., Dursun, İ. (2007). Buğday tanelerinin bazı fiziksel özelliklerinin görüntü işleme tekniğiyle belirlenmesi. Ankara Üniversitesi Ziraat Fakültesi Tarım Bilimleri Dergisi, 13(3), 176-185.
• [21] Bayrakdar, S., Çomak, B., Başol, D., Yücedağ, İ. (2015). Determination of type and quality of hazelnut using image processing techniques. In Signal Processing and Communications Applications Conference (SIU), May 2015, 616-619p.
• [22] Balcı, M., Altun, A.A., Taşdemir, Ş. (20169. Görüntü işleme teknikleri kullanılarak Napolyon tipi kirazların sınıflandırılması. Selçuk-Teknik Dergisi, 15(3), 221-237.
• [23] Beyer, M., Hahn, R., Peschel, S., Harz, M., Knoche, M. (2002). Analysing fruit shape in sweet cherry (Prunus avium L.). Scientia Horticulturae, 96(1), 139-150.
• [24] Ercisli, S., Sayinci, B., Kara, M., Yildiz, C., Ozturk, I. (2012). Determination of size and shape features of walnut (Juglans regia L.) cultivars using image processing. Scientia horticulturae, 133, 47-55.
• [25] Antonucci, F., Costa, C., Pallottino, F., Paglia, G., Rimatori, V., De Giorgio, D., Menessati, P. (2012). Quantitative method for shape description of almond cultivars (Prunus amygdalus Batsch). Food and bioprocess technology, 5(2), 768-785.
• [26] Lavine, B.K. (2006). Pattern recognition. Critical Reviews in Analytical Chemistry, 36, 153–161.
• [27] Héberger, K., Csomós, E., Simon-Sarkadi, L. (2003). Principal component and linear discriminant analyses of free amino acids and biogenic amines in hungarian wines. Journal of Agricultural and Food Chemistry, 51(27), 8055-8060.
• [28] Hörchner, U., Kalivas, J.H. (1995). Simulated annealing type optimization algorithms: fundamentals and wavelength selection applications. Journal of Chemometrics, 9, 283-308.
• [29] Rogers, D., Hopfinger, A.J. (1994). Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. Journal of Chemical Information and Computer Sciences, 34(4), 854-866.
• [30] Derde, M.P., Massart, D.L. (1986). UNEQ: a disjoint modelling technique for pattern recognition based on normal distribution. Analytica Chimica Acta, 184, 33-51.
• [31] Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, CA, 131-156p.
• [32] Zhang, M.H., Xu, Q.S., Daeyaert, F., Lewi, P.J., Massart, D.L. (2005). Application of boosting to classification problems in chemometrics. Analytica Chimica Acta, 544, 167-176.
• [33] Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
• [34] Cortes, C., Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273-297.
• [35] Xu, Y., Zomer, S., Brereton, R.G. (2006). Support Vector Machines: A Recent Method for Classification in Chemometrics. Critical Reviews in Analytical Chemistry, 36(3-4), 177-188.
• [36] Barile, D., Coisson, J.D., Arlorio, M., Rinaldi, M. (2006). Identification of production area of Ossolano Italian cheese with chemometric complex approach. Food Control, 17(3), 197-206.
• [37] Basheer, I.A., Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
• [38] Gonçalves, E.C., Minim, L.A., Coimbra, J.S.R., Minim, VPR. (2005). Modeling sterilization process of canned foods using artificial neural networks. Chemical Engineering and Processing: Process Intensification, 44(12), 1269-1276.
• [39] Jain, A.K., Mao, J., Mohiuddin, K.M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31-44.
• [40] Tzouros, N.E., Arvanitoyannis, I.S. (2001). Agricultural produces: synopsis of employed quality control methods for the authentication of foods and application of chemometrics for the classification of foods according to their variety or geographical origin. Critical Reviews in Food Science and Nutrition, 41(4), 287-319.
• [41] Arvanitoyannis, I.S., Tsitsika, E.V., Panagiotaki, P. (2005). Implementation of quality control methods (physicochemical, microbiological and sensory) in conjunction with multivariate analysis towards fish authenticity. International Journal of Food Science and Technology, 40, 237-263.
• [42] Arvanitoyannis, I.S., van Houwelingen-Koukaliaroglou, M. (2003). Implementation of chemometrics for quality control and authentication of meat and meat products. Critical Reviews in Food Science and Nutrition, 43(2), 173-218.
• [43] Arvanitoyannis, I.S., Katsota, M.N., Psarra, E.P., Soufleros, E.H., Kallithraka, S. (1999). Application of quality control methods for assessing wine authenticity: Use of multivariate analysis (chemometrics). Trends in Food Science & Technology, 10(10), 321-336.
• [44] Gishen, M., Dambergs, R.G., Cozzolino, D. (2005). Grape and wine analysis ‐ enhancing the power of spectroscopy with chemometrics. Australian Journal of Grape and Wine Research, 11(3), 296-305.
• [45] Siebert, K.J. (2001). Chemometrics in Brewing-A Review. Journal of the American Society of Brewing Chemists, 59(4), 147-156.
• [46] Arvanitoyannis, I.S., Chalhoub, C., Gotsiou, P., Lydakis-Simantiris, N., Kefalas, P. (2005). Novel quality control methods in conjunction with chemometrics (multivariate analysis) for detecting honey authenticity. Critical Reviews in Food Science and Nutrition, 45(3), 193-203.
• [47] Ampuero, S., Bosset, J.O. (2003). The electronic nose applied to dairy products: a review. Sensors and Actuators B: Chemical, 94(1), 1-12.
• [48] Vlasov, Y., Legin, A., Rudnitskaya, A., di Natale, C., Amico, A.D. (2005). Nonspecific sensor arrays (electronic tongue) for chemical analysis of liquids. Pure and Applied Chemistry, 77(11), 1965-1983.
• [49] Karoui, R., Mazerolles, G., Dufour, É. (2003). Spectroscopic techniques coupled with chemometric tools for structure and texture determinations in dairy products. International Dairy Journal, 13(8), 607-620.
• [50] Noble, A.C., Ebeler, S.E. (2002). Use of multivariate statistics in understanding wine flavor. Food Reviews International, 18(1), 1-20.
• [51] Downey, G. (1998). Food and food ingredient authentication by mid-infrared spectroscopy and chemometrics. Trends in Analytical Chemistry, 17(7), 418-424.
• [52] Sundberg, R. (2000). Aspects of statistical regression in sensometrics. Food Quality and Preference, 11, 17-26.
• [53] Du, J.C., Sun, D.W. (2006). Learning techniques used in computer vision for food quality evaluation: a review. Journal of Food Engineering, 72(1), 39-55.
• [54] Ellis, D.I., Goodacre, R. (2001). Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. Trends in Food Science & Technology, 12(11), 414-424.
• [55] Kelly, S., Heaton, K., Hoogewerff, J. (2005). Tracing the geographical origin of food: The application of multi-element and multi-isotope analysis. Trends in Food Science & Technology, 16(12), 555-567.
• [56] Perez-Magarino, S., Ortega-Heras, M., Gonzalez-San Jose, M.L., Boger, Z. (2004). Comparative study of artificial neural networj and multivariate methods to classify Spanish DO rose wines. Talanta, 62(5), 983-990.
• [57] Gómez, A.H., Wang, J., Hu, G., Pereira, A.G. (2006). Electronic nose technique potential monitoring mandarin maturity. Sensors and Actuators B: Chemical, 113(1), 347-353.
• [58] Cozzolino, D., Smyth, H.E., Gishen, M. (2003). Feasibility study on the use of visible and near-ınfrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. Journal of Agricultural and Food Chemistry, 51(26), 7703-7708.
• [59] Tewari, J.C., Irudayaraj, J.M.K. (2005). Floral classification of honey using mid-ınfrared spectroscopy and surface acoustic wave based z-nose sensor. Journal of Agricultural and Food Chemistry, 53(18), 6955-6966.