Kısmi En Küçük Kareler Regresyonu (KEKKR) ve Yapay Sinir Ağı (YSA) Modelleri Kullanarak, Kanopi Kızılötesi Spektroskopisi (KS) ile Kış Buğdayında Protein İçeriğinin Tahmini

Yıl 2019, Cilt: 29 Sayı: 1, 43 - 51, 29.03.2019

Vali Rasooli Sharabıanı , Araz Soltani Nazarloo Ebrahim Taghınezhad

https://doi.org/10.29133/yyutbd.447926

Cited By: 4

Öz

Bu çalışmada, buğdaydaki protein miktarını
tahmin etmek için, tahribatsız ve hızlı bir gözlem yöntemi olan yakın
kızılötesi spektroskopi (KS) tekniği kullanılmıştır. Sırasıyla spektral
bantları ve en iyi modelleri seçmek için Kısmi En Küçük Kareler Regresyonu
(KEKKR) ve Yapay Sinir Ağı (YSA) yöntemleri kullanılmıştır. Modellerin
verimliliğini karşılaştırmak için Kök-ortalama-kare hata (KOKH) ve R²
uygulanmıştır. Cascade ileri geri yayılımının (CİGY) en iyi sonucu,
Levenberg-Marquardt (LM) ile 8-8-1 ağ yapısı ve TANSIG-TANSIG-PURELIN
(TANSIG-TANSIG-PURELIN (R𝑀𝑆𝐸 = 0.0289 ve 𝑅2) 'nin işlevi ile ilgilidir. YSA modeli için
tahmin sonuçları (𝑅2 = 0.9881), KEKKR modelinden (𝑅² = 0.9783)
daha iyi bulunmuştur. Bu nedenle, sonuçlara göre, buğdaydaki protein miktarının
belirlenmesinde KS'nin tahmin etme potansiyeli yüksek olduğu söylenebilir. 

Anahtar Kelimeler

PLSR, NIRS, ANN, Buğday protein içeriği

Prediction of Protein Content of Winter Wheat by Canopy of Near Infrared Spectroscopy (NIRS), Using Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) Models

Öz

In
this study to predict amount of protein in wheat, near infrared spectroscopy
technique (NIRS) was used that is a non-destructive and fast observing method. Partial Least Squares Regression (PLSR) and
Artificial Neural Network (ANN) methods were used to choose the spectral bands
and the best models, respectively. To compare the efficiency of models
Root-mean-square error (RMSE) and R² were applied. The finest
consequence by cascade forward back propagation (CFBP) was related to network
structure of 8-8-1 with Levenberg-Marquardt (LM), and function of
TANSIG-TANSIG-PURELIN (TANSIG-TANSIG-PURELIN (R𝑀𝑆𝐸=0.0289 and 𝑅²=0.9881 at 14 epochs). The consequences of estimation
for ANN model (𝑅²=0.9881) was better than the PLSR model (𝑅²=0.9783). Therefore, according to the results, it can
be said that NIRS has a high potential for predicting the amount of protein in
wheat. 

Anahtar Kelimeler

PLSR, NIRS, ANN, Wheat protein content

Kaynakça

• Aghajani N, Kashaninejad M, Dehghani AA, Garmakhany AD (2012). Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt. Qual Assur Saf Crop Food. 4:93–101.
• Amiri Chayjan R, Kaveh M (2014). Physical parameters and kinetic modeling of fix and fluid bed drying of terebinth seeds. J Food Process Preserv. 38:1307–20.
• Amiri Chayjan R, Salari K, Barikloo H (2012). Modelling moisture diffusivity of pomegranate seed cultivars under fixed, semi fluidized and fluidized bed using mathematical and neural network methods. Acta Sci Polym Technol Aliment.11(2):137–49.
• Bagchi TB, Sharma S, Chattopadhyay K (2016). Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran, Food Chem. 191, 21–27.
• Chen J, Zhu S, Zhao G. 2017. Rapid determination of total protein and wet gluten in commercial wheat flour using siSVR-NIR, Food Chem. 221, 1939–1946.
• Chen P, Jing Q (2017). A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images, Adv. Space Res. 59, 987–995.
• Dandan Y, Laijun S, Borui Z, Qian Zh,Wenyi T, Wenkai Ch (2018). Non-destructive prediction of protein content in wheat using NIRS, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 189, 463–472.
• Delwiche SR, Graybosch RA, Peterson CJ (1998). Predicting protein composition, biochemical properties, and dough-handling properties of hard red winter wheat flour by near-infrared reflectance. Cereal Chem. 75:412-416.
• Demuth H, Beale M, Hagan M (2007). Neural network toolbox 5. Natick, MA, USA: The MathWorks.
• Diker K, Bausch WC (2003). Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosystems Engineering, 85: 437–447.
• Feng W, Yao X, Zhu Y, Tian YC, Cao WX (2008). Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy, 28: 394–404.
• Galasso HL, Callier MD, Bastianelli D, Blancheton JP, Aliaume C (2017). The potential of near infrared spectroscopy (NIRS) to measure the chemical composition of aquaculture solid waste, Aquaculture 476,134–140.
• Haddad K, Rahman A, Zaman MA, Shrestha S (2013). Applicability of Monte Carlo cross validation technique for model development and validation using generalized least squares regression, J. Hydrol. 482, 119–128.
• He Y, Li XL, Shao YN (2006). Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model. Spectroscopy and Spectral Analysis, 26: 850–853.
• Heise HM, Winzen R (2006). Chemometrics in near-infrared spectroscopy. In: Siesler HW, Ozaki Y, Kawata S, Heise HM, editors. Near-infrared spectroscopy: principles, instruments, applications. Germany: Wiley-VCH; p. 125–162.
• Kaveh M, Amiri Chayjan R (2015). Mathematical and neural network modelling of terebinth fruit under fluidized bed drying. Res Agr Eng. 61(2):55–65.
• Li Y, Zhu Y, Tian Y, Yao X, Zhou C, Cao W (2006). Quantitative relationship between leaf nitrogen accumulation and canopy reflectance spectral. Scientia Agricultura Sinica, 32: 203–209.
• Liu Y, Sun X, Ouyang A (2010). Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCABPNN, LWT Food Sci. Technol. 43, 602–660.
• Mabood F, Gilani SA, Albroumi M, Alameri S, Nabhani M, Jabeen F, Hussain J, Harrasi A, Boqué R, Farooq S, Hamaed AM, Naureen Z, Khan A, Hussain Z (2017). Detection and estimation of super premium 95 gasoline adulteration with premium 91 gasoline using new NIR spectroscopy combined with multivariate methods, Fuel 197, 388–396.
• Magwaza LS, Naidoo SIM, Laurie SM, Laing MD, Shimelis H (2016). Development of NIRS models for rapid quantification of protein content in sweet potato [Ipomoea batatas (L.) LAM.], LWT, Food Science and Technology 72, 63–70.
• Malegori C, Marques EJN, Freitas ST, Pimentel MF, Pasquini Casiraghi CE (2017). Comparing the analytical performances of micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms, Talanta 165, 112–116.
• Millar SJ (2003). The development of near-infrared (NIR) spectroscopy calibrations for the prediction of wheat and flour quality. The Home- Grown Cereals Authority Project Report No. 310. HGCA: London.
• Mohebbi M, Shahidi F, Fathi M, Ehtiati A, Noshad M (2011). Prediction of moisture content in pre- osmosed and ultrasounded dried banana using genetic algorithm and neural network. Food Bioprod Process. 89:362–66.
• Moreira SA, Sarraguça J, Saraiva DF, Carvalho R, Lopes JA (2015). Optimization of NIR spectroscopy based PLSR models for critical properties of vegetable oils used in biodiesel production, Fuel 150, 697–704.
• Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol. 46:99–118.
• Nouri M, Gomez C, Gorretta N, Roger JM (2017). Clay content mapping from airborne hyperspectral Vis-NIR data by transferring a laboratory regression model, Geoderma 298, 54–66.
• Porfire A, Filip C, Tomuta I (2017). High-throughput NIR-chemometricmethods for chemical and pharmaceutical characterization of sustained release tablets, J. Pharm. Biomed. Anal. 138, 0731–7085.
• Rasooli Sharabian V, Noboru N, Kazunobu I (2013). Optimal Vegetation Indices for Winter Wheat Growth Status Based on Multi-Spectral Reflectance, Environ. Control Biol. 51 (3), 105112.
• Rasooli Sharabian V, Noboru N, Kazunobu I (2014). Significant wavelengths for prediction of winter wheat growth status and grain yield using multivariate analysis, Engineering in Agriculture, Environment and Food 7, 14-21
• Shetty N, Gislum R (2011). Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR, Field Crop Res 120, 31–37.
• Sissons M, Osborne B, Sissons S (2006). Application of near infrared reflectance spectroscopy to a durum wheat breeding programme. J. Near Infrared Spectrosc. 14:17-25.
• Tang YL, Huang JF, Wang RC (2004). Study on estimating the contents of crude protein and crude starch in rice panicle and paddy by hyperspectral. Scientia Agricultura Sinica, 37: 1282–1287.
• Thomas JR, Oerther GF (1972). Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal, 64: 11–13.
• Wang ZJ, Wang JH, Liu LY, Huang WJ, Zhao CJ, Wang CZ (2004). Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crops Research, 90: 311–321.
• Wentzell PD, Montoto LV (2003). Comparison of principal components regression and partial least squares regression through generic simulations of complex mixtures. Chemometr. Intell. Lab. 65, 257–279.
• Williams PC, El-Haramen PJ, Ortis-Ferira G, Srivastava JP (1988). Preliminary observations of the determination of wheat strength by near-infrared reflectance. Cereal Chem. 65:109-114.
• Yi QX, Huang JF, Wang FM, Wang XZ, Liu ZY (2007). Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environmental Science and Technology, 41: 6770–6775.
• Yousefi G, Emam-Djomeh Z, Omid M, Askari GR (2014). Prediction of physicochemical properties of raspberry dried by microwave-assisted fluidized bed dryer using artificial neural network. Drying Technol. 32:4–12.
• Zhang H, Hu H, Zhang XB, Zhu LF, Zheng KF, Jin QY, Zeng FP (2011). Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network. Acta Physiologiae Plantarum, 33: 2461–2466.
• Zhang H, Li Z, Chen T, Qin B (2017). Quantitative determination of Auramine O by terahertz spectroscopy with 2DCOS-PLSR model, Spectrochim. Acta A Mol. Biomol. Spectrosc. 184, 335–341.
• Zhang M, Zhang S, Iqbal J (2013). Key wavelengths selection from near infrared spectra using Monte Carlo sampling–recursive partial least squares, Chemom. Intell. Lab. Syst. 128, 17–24.
• Zhang TQ, Song KL, Wang GX, Wang H, Hu FP (2012). Prediction of crude protein content in rice grain with canopy spectral reflectance, PLANT SOIL ENVIRON. 58, (11): 514–520.