Mısırda Toplam Fenolik ve Antosiyanin İçeriğinin Belirlenmesi için Geliştirilmiş NIRS (Yakın Kızıl Ötesi Spektroskopisi) Kalibrasyon Modelleri Üzerine Kemometrik Yöntemlerin Etkisi

Year 2021, Volume: 7 Issue: 3, 437 - 449, 25.09.2021

Mehmet Şerment , Fatih Kahrıman

https://doi.org/10.28979/jarnas.883418

Abstract

Bu çalışmada öğütülmüş mısır örneklerinde toplam antosiyanin ve fenolik içeriklerinin yakın kızıl ötesi (NIR) spektroskopisi ile tespitine yönelik geliştirilmiş kalibrasyon modellerinde, spektral ön işlem ve dalga boyu seçim yöntemlerinin tahmin başarısına etkisi araştırılmıştır. Araştırma materyali olarak 200 farklı mısır örneği kullanılmıştır. Çalışmada ön işlem olarak; birinci türev (FD), ikinci türev (SD), standart normal değişim (SNV) ve bu yöntemlerin birlikte kullanıldığı 2 farklı kombinasyon karşılaştırılmıştır. Spektral veriler 2 farklı dalga boyu seçim yöntemi (VIP ve SR) ile ayrı ayrı işleme alınmıştır. Ön işlem ve dalga boyu seçim yöntemlerinin uygulanmasından sonra hedef değişkenlerin tahmini için iki farklı model oluşturma tekniğinden (PLS ve SVMR) faydalanılmıştır. Böylelikle, çalışmada toplam fenolik ve toplam antosiyanin içeriği için 36’ar model kıyaslanmıştır. Oluşturulan modeller dış doğrulama işlemine tabi tutularak model güvenilirlikleri test edilmiştir. Çalışma sonucunda mısır tanesinde antosiyanin ve fenolik bileşen içeriğinin tespitinde kullanılan kemometrik yöntemin, model başarısını arttırdığı görülmüştür. Çalışmada karşılaştırılan modellerden antosiyanin içeriği için FD-SNV-SR kombinasyonu ile oluşturulan modelin (RMSECal=0.02, R2Cal=0.96, RPDCal=5.36, RMSEVal=0.03, R2Val=0.90, RPDVal=3.14) tahmin başarısı yüksek bulunmuştur. Fenolik bileşen içeriği için ise PLS modelleme tekniği daha başarılı (RMSECal =0.06, R2Cal =0.66, RPDCal=1.71, RMSEVal=0.07, R2Val=0.46, RPDVal=1.38) bulunmuştur. Sonuç olarak, uygulanan kemometrik yöntemlerin NIR ile sekonder bileşen tespitine katkı sunduğu belirlenmiştir

Keywords

Dalga boyu seçimi, regresyon, sekonder metabolitler, Zea mays

Supporting Institution

Çanakkale Onsekiz Mart Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

FYL-2018-2754

Thanks

Bu çalışma Çanakkale Onsekiz Mart Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimince desteklenmiştir

The Effect of Chemometric Methods on NIRS (Near Infrared Reflectance Spectroscopy) Calibration Models for Determination of Total Phenolic and Anthocyanin Contents in Maize

Abstract

In this study, the effect of spectral pretreatment and wavelength selection method combinations for determination of total anthocyanin and phenolic contents by near infrared (NIR) spectroscopy on prediction success was investigated. Two hundred maize samples were used as experimental material. As a pre-treatment in the study; First derivative (FD), second derivative (SD), standard normal variate (SNV) were compared with 2 different combinations using these methods together. Spectral data were processed separately with the combination of 2 different wavelength selection methods (VIP and SR). After applying pretreatment and wavelength selection methods, two different model generation techniques (PLS and SVMR) were used to estimate the target variables. Thus, 36 different models each were compared for total anthocyanin and phenolic contents in the study. The model reliability was tested by subjecting the created models to external validation. Results showed that the chemometric method used to determine the anthocyanin and phenolic component content in corn grain increased the model success. Estimation of the model (RMSECal = 0.02, R2Cal = 0.96, RPDCal = 5.36, RMSEVal = 0.03, R2Val = 0.90, RPDVal = 3.14) for anthocyanin content its success was found to be high. For the phenolic content, PLS modeling technique was found to be more successful (RMSECal = 0.06, R2Cal = 0.66, RPDCal = 1.71, RMSEVal = 0.07, R2Val = 0.46, RPDVal = 1.38). As a result, it has been determined that chemometric method applications contribute to secondary component detection with NIR

Keywords

Regression, seconder metabolites, wavelength selection, Zea mays

Project Number

FYL-2018-2754

References

• Abdel-Aal, E.S.M. ve Hucl, P. (1999). A rapid method for quantifying total anthocyanins in blue aleurone and purple pericarp wheats. Cereal Chemistry, 76, 350-354.
• Adom, K.K. ve Liu, R.H. (2002). Antioxidant activity of grains. Journal of Agricultural and Food Chemistry, 50: 6182-6187.
• Agelet, L.E., Hurburgh Jr., C.R. (2010). A tutorial on near ınfrared spectroscopy and ıts calibration. Critical Reviews in Analytical Chemistry, 40:4, 246-260
• Alfieri, M., Cabassi, G., Habyarimana, E., Quaranta, F., Balconi, C. ve Redaelli, R. (2019). Discrimination and prediction of polyphenolic compounds and total antioxidant capacity in sorghum grains. Journal of Near Infrared Spectroscopy, 27(1):46–53.
• Baye, T.M., Pearson T.C. ve Settles A.M. (2006). Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy. Journal of Cereal Science, 43(2): 236-243.
• Egesel C.Ö. ve Kahriman F. (2012). Determination of quality parameters in maize by nir reflectance spectroscopy. Journal of Agricultural Sicences, 18:43-53.
• Egesel C.Ö. Kahriman F. Ekinci N. Kavdir İ.ve Büyükcan M.B. (2016). Analysis of fatty acids in kernel, flour and oil samples of maize by NIR spectroscopy using conventional regression methods.Cereal Chemistry, 93:487-492.
• Galicia, L., Nurit, E., Rosales, A.ve Palacios-Rojas, N. (2009). Laboratory protocols (2008): Maize nutrition quality and plant tissue analysis laboratory. CIMMYT, 42.
• Ji, H.C., Lee, H.B. ve Takeo, Y. (2010). Major agricultural characteristics and antioxidants analysis of the new developed colored waxy corn hybrids. Journal of the Faculty of Agriculture, Kyushu University, 55(1): 55-59.
• Jing, P. ve Giusti, M. M. (2005). Characterization of anthocyanin-rich waste from purple corn cobs (Zea mays L.) and its application to color milk. Journal of Agricultural and Food Chemistry, 53: 8775-8781.
• Kahriman F., (2017). Mısır yağında yağ asitlerinin uv-vis spektroskopisi ve kemometrik yöntemler yardımıyla tespit edilmesi. Journal of Food And Health Science, 3:82-89.
• Kahriman, F., Öner, F., Türk, F., Gökçe, A., Düzen, E., Onaç, İ. ve Egesel C.Ö. (2017). Efficiency of different chemometric methods for determination of oil content in maize by nir spectroscopy, AGROSYM (2017), Jahorina, Bosnia and Herzegovina, 5-8 October 2017.
• Kahrıman, F. ve Egesel C.Ö. (2018). Using near infrared (NIR) spectroscopy in the analysis of cereal products: the example of maize, In: Recent researches in science and landscape management, Ed: Prof. Dr. Recep Efe, (pp. 560-574).
• Kahriman, F., Onaç, İ., Mert F., Öner F.ve Egesel, C.Ö., (2019). Determination of carotenoid and tocopherol content in maize flour and oil samples using near-infrared spectroscopy. Spectroscopy Letters, 52, 473-481.
• Kahriman, F., Onaç, İ., Öner, F., Mert-Turk, F. ve Egesel C.Ö. (2020). Analysis of secondary biochemical components in maize flour samples by NIR (near infrared reflectance) spectroscopy. Journal of Food Measurement and Characterization, 14:2320–2332.
• Kahrıman, F. ve Liland, K-H. (2021). SelectWave: a graphical user interface for spectral data analysis, Chemometrics and Integillent Laboratry Systems, 212:104275.
• Keleş, Y. (2015). Antosiyanin pigmentlerin biyokimyası ve analizi, Türk Bilimsel Derlemeler Dergisi, 8 (1): 19-25. Lago, C., Cassani, E., Zanzi, C., ve Pilu R. (2014). Development and study of a maize cultivar rich in anthocyanins: coloured polenta, a new functional food. Plant Breeding, 133(2):210–217.
• Lao, C., Zhang, Z., Chen, J., Chen, H., Yao, Z., Xing, Z., Tai, X., Ning, J., Chen, Y. (2020). Determination of in-situ salinized soil moisture content from visible-near infrared (VIS–NIR) spectroscopy by fractional order derivative and spectral variable selection algorithms. International Journal of Precision Agricultural Aviation, 3(3):21-34.
• Lopez-Martinez, L.X., Oliart-Ros, R.M., Valerio-Alfaro, G., Lee, C.H., Parkin, K.L. ve Garcia, H.S. (2009). Antioxidant activity, phenolic compounds and anthocyanins content of eighteen strains of Mexican maize. LWT-Food Science and Technology, 42:1187–92.
• Mangalvedhe, A.A., Danao, M.C., Paulsmeyer, M., Rausch, K.D., Singh, V.ve Juvik, J.A. (2015). Anthocyanin determination in different corn hybrids using near infrared spectroscopy. ASABE Annual International Meeting, New Orleans. Paper Number: 152181716.
• Mariani, N.C.T., Teixeira, G.H.A., Lima, K.M.G., Morgenstern, T.B., Nardini, V. ve Cunha, L.C. (2015). NIRS and iSPA-PLS for predicting total anthocyanin content in jaboticaba fruit. Journal of Food Chemistry, 174: 643–648.
• Meng, Q., Murray, S.C., Mahan, A., Collison, A., Yang, L. ve Awika, J. (2015), Rapid estimation of phenolic content in colored maize by near‐infrared reflectance spectroscopy and its use in breeding. Crop Science, 55: 2234-2243.
• Miller C.E. (2001). Chemical principles of near-infrared technology. In: Williams P.C., Norris K.H., editors. Near-Infrared Technology in the Agricultural and Food Industries. 2nd ed. American Association of Cereal Chemists; St. Paul, MN, USA.
• Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I. ve Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology, 46:99–118.
• Noah, L., Robert, P., Millar, S. ve Champ, M. (1997). Near-infrared spectroscopy as applied to starch analysis of digestive contents. Journal of Agricultural and Food Chemistry, 45: 2593–2597.
• Osborne B.G., Fearn T. ve Hindle P. (1993). Practical nır spectroscopy with applications in food and beverage analysis. Longman Scientific and Technical; London, UK: 1993.
• Pasquini, C., (2003). Near infrared spectroscopy:Fundamentals, practical aspects and analytical applications. Journal of the Brazilian Chemical Society,14(2):198-219.
• Paulsen, M. R., Mbuvi, S. W., Haken, A. E., Ye, B. ve Stewart, R. K. (2003). Extractable starch as a quality measurement of dried corn. Applied Engineering in Agriculture 19: 211–217.
• R Core Team, (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/
• Redaelli R., Alfieri M.ve Cabassi G. (2016). Development of a NIRS calibration for total antioxidant capacity in maize germplasm. Talanta, 154:164-168.
• Sabancı, S. (2016). Ege bölgesinde yetiştirilen bazı mısır (Zea mays L.) çeşitlerinin verim, kalite ve antioksidan aktivitelerinin belirlenmesi. Yüksek Lisans Tezi, Adnan Menderes Üniversitesi Fen Bilimleri Enstitüsü, Aydın.
• Sans, S., Ferré, J., Boqué, R., Sabaté, J., Casals, J., Simó, J. (2020). Estimating sensory properties with near-ınfrared spectroscopy: a tool for quality control and breeding of ‘Calçots’ (Allium cepa L.). Agronomy, 10:828.
• Williams, P. ve Norris, K.H. (1987). Near-infrared technology in the agricultural and food Industries. 2nd Edn., American Association of Cereal Chemists, Inc., St. Paul, MN., ISBN-13: 9780913250495, p 330.
• Yi, L., Dong, N., Yun, Y., Deng, B., Ren, D., Liu, S.ve Liang, Y.(2016). Chemometric methods in data processing of mass spectrometry-based metabolomics: A review.Analytica Chimica Acta, 914:17-34.