Mısır Danesinde Kalite Özelliklerinin NIR Yansıma Spektroskopisi ile Belirlenmesi

Bu çalışmada mısır ununda protein, yağ, karbonhidrat ve kül oranının NIRS ile tespitinde kullanılabilecek farklı kalibrasyon modellerinin karşılaştırılması amaçlanmıştır. Çalışmada 115 hibrit genotip ve 23 adet saf hatta ait toplam 138 örnek materyal olarak kullanılmıştır. Referans analizlerden elde edilen sonuçlara göre Kısmi En Küçük Kareler Regresyonu (PLSR) ve Çoklu Doğrusal Regresyon (MLR) yöntemleri kullanılarak farklı tahmin modelleri oluşturulmuştur. Oluşturulan modellerin (n=110) validasyon işlemi farklı genotipler (n=28) kullanılarak gerçekleştirilmiştir. Oluşturulan modellerin her ikisinde de en yüksek doğruluk protein oranında (rMLR=0.990 ve rPLSR=0.987) tespit edilmiştir. Diğer özellikler için MLR modeli PLSR modelinden (karbonhidrat için rMLR=0.801, rPLSR=0.755; yağ için rMLR=0.823, rPLSR=0.723; kül için rMLR=0.926, rPLSR=0.810) matematiksel modellere göre daha iyi sonuç vermiş olmasına karşın, dış validasyon işleminde PLSR modelinde yapılan tahminlerin MLR modeline göre hata payının düşük olduğu görülmüştür. Sonuçlar, NIR yöntemi ile protein oranının başarılı şekilde tahminlenebileceğini, karbonhidrat ve yağ gibi diğer özellikler için ise daha fazla çalışmalara ihtiyaç olduğunu ortaya koymuştur. Modellerde etkili olan dalga boylarına ait profil analizi, modele dahil edilen dalga boylarının regresyon katsıyaları düşük olduğunda tahmin gücünün de zayıf olduğunu göstermiştir. Ayrıca, kül ve yağ oranının, protein ve karbonhidrat oranına göre tarama yapılan bölgede daha fazla sayıda spektral bölge ile ilişkili olduğu belirlenmiştir.

Anahtar Kelimeler:

Protein, Yağ, Zea mays, Karbonhidrat, Regresyon

Determination of Quality Parameters in Maize Grain by NIR Reflectance Spectroscopy

The objective of this study is to compare different calibration models that could be used in the analysis of protein, oil, carbohydrate and ash contents in maize flour by NIRS. A total of 138 samples were used from 115 hybrids and 23 inbreds in the study as material. Based on reference analysis results, different estimation models were developed using Partial Least Squares Regression (PLSR) and Multiple Linear Regression (MLR) methods. Validation procedure of these models (n=110) were accomplished using samples from different genotypes (n=28). In both of the developed models, the highest accuracy was attained for protein content (r=0.990 for MLR and r=0.987 for PLSR). For the other traits analyzed, although MLR model yielded better results based on mathematical evaluations (rMLR=0.801, rPLSR=0.755 for carbohydrate, rMLR=0.823, rPLSR=0.723 for oil, rMLR=0.926 and rPLSR=0.810 for ash), external validation suggested PLSR model provide a lower error rate than MLR. Results suggested that protein content could be successfully estimated, whereas, for some other traits, such as carbohydrate and oil ratios, it seems that there is still need for more studies before getting accurate measurements using NIR methods. Profile analysis regarding the wavelengths potent in the models showed that the estimation power declined when the regression coefficients of the wavelengths included in the model were low. Among the analyzed traits, ash and oil contents seemed to be related with more spectral regions within the scanned spectra than protein and carbohydrate.

Keywords:

Protein, Oil, Zea mays, Carbohydrate, Regression,

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