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

Yıl 2021, Cilt: 7 Sayı: 3, 437 - 449, 25.09.2021

Mehmet Şerment Fatih Kahrıman

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

Öz

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

Anahtar Kelimeler

Dalga boyu seçimi, regresyon, sekonder metabolitler, Zea mays

Destekleyen Kurum

Çanakkale Onsekiz Mart Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Proje Numarası

FYL-2018-2754

Teşekkür

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

Öz

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

Anahtar Kelimeler

Regression, seconder metabolites, wavelength selection, Zea mays

Proje Numarası

FYL-2018-2754

Kaynakça

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