Determination of the ADF and IVOMD Content of Sugarcane Using Near Infrared Spectroscopy Coupled with Chemometrics

Ozcan Cataltas, Kemal Tutuncu


Sugarcane is a plant whose quality parameters are required to be determined both for being one of the substances used in sugar production and for being used as animal feed. Near-infrared spectroscopy is a technique that has already been used for predicting the parameters of various plants and has gained popularity in recent years. This study proposes a near-infrared spectroscopy-based model for the rapid and effortless analysis of acid detergent fiber fraction and vitro organic matter digestibility parameters of the sugarcane plant. Partial least squares regression was combined with common preprocessing methods for modeling. This model yielded an R2CV value of 0.935 and 0.953 for the acid detergent fiber fraction and vitro organic matter digestibility parameters, respectively. Then, the spectra from three handheld spectrometers were combined using a proposed combination method to generate new spectra with higher spectral resolution. New models were built using these generated spectra and compared to the previous result. Obtained results showed that combining spectra from different spectrometers can improve model performance.


Near-infrared spectroscopy; Chemometrics; Partial least squares regression; Food analysis

Full Text:



Assis C, Ramos RS, Silva LA, Kist V, Barbosa MHP, and Teófilo RF (2017). Prediction of Lignin Content in Different Parts of Sugarcane Using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS). Applied Spectroscopy 71:2001-2012.

Barnes RJ, Dhanoa MS, and Lister SJ (1989). Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Applied Spectroscopy 43:772-777. 10.1366/0003702894202201

Ciurczak EW, Igne B, Workman Jr J, and Burns DA. 2021. Handbook of near-infrared analysis: CRC press.

Cui C and Fearn T (2018). Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration. Chemometrics and Intelligent Laboratory Systems 182:9-20.

De Girolamo A, Arroyo MC, Cervellieri S, Cortese M, Pascale M, Logrieco AF, and Lippolis V (2020). Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study. LWT 127:109368.

Frank IE, and Friedman JH (1993). A Statistical View of Some Chemometrics Regression Tools. Technometrics 35:109-135. 10.2307/1269656

Fu L, Sun J, Wang S, Xu M, Yao K, Cao Y, and Tang N (2022). Identification of maize seed varieties based on stacked sparse autoencoder and near‐infrared hyperspectral imaging technology. Journal of Food Process Engineering 45. 10.1111/jfpe.14120

Geladi P, MacDougall D, and Martens H (1985). Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat. Applied Spectroscopy 39:491-500. 10.1366/0003702854248656

Genis HE, Durna S, and Boyaci IH (2021). Determination of green pea and spinach adulteration in pistachio nuts using NIR spectroscopy. LWT 136:110008.

Guo H, Chen J, Pan T, Wang J, and Cao G (2014). Vis-NIR wavelength selection for non-destructive discriminant analysis of breed screening of transgenic sugarcane. Analytical Methods 6:8810-8816. 10.1039/C4AY01833H

Kim S-Y, Hong S-J, Kim E, Lee C-H, and Kim G (2023). Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using Vis/NIR spectroscopy. Journal of Food Engineering 338:111254.

Laborde A, Puig-Castellví F, Jouan-Rimbaud Bouveresse D, Eveleigh L, Cordella C, and Jaillais B (2021). Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution. Food Control 119:107454.

Lemaitre G (2021). scikit-learn. Available at

Mishra P, Biancolillo A, Roger JM, Marini F, and Rutledge DN (2020). New data preprocessing trends based on ensemble of multiple preprocessing techniques. TrAC Trends in Analytical Chemistry 132:116045.

Mohamed H, Nagy P, Agbaba J, and Kamal-Eldin A (2021). Use of near and mid infra-red spectroscopy for analysis of protein, fat, lactose and total solids in raw cow and camel milk. Food Chemistry 334:127436.

Pérez-Marín D, Garrido-Varo A, and Guerrero JE (2007). Non-linear regression methods in NIRS quantitative analysis. Talanta 72:28-42.

Ryckewaert M, Chaix G, Héran D, Zgouz A, and Bendoula R (2022). Evaluation of a combination of NIR micro-spectrometers to predict chemical properties of sugarcane forage using a multi-block approach. Biosystems Engineering 217:18-25.

Savitzky A, and Golay MJE (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 36:1627-1639. 10.1021/ac60214a047

Schuler LP, Milne JS, Dell JM, and Faraone L (2009). MEMS-based microspectrometer technologies for NIR and MIR wavelengths. Journal of Physics D: Applied Physics 42:133001. 10.1088/0022-3727/42/13/133001

So S, Cherdthong A, and Wanapat M (2020). Improving sugarcane bagasse quality as ruminant feed with Lactobacillus, cellulase, and molasses. Journal of Animal Science and Technology 62:648.

Steidle Neto AJ, Toledo JV, Zolnier S, Lopes DdC, Pires CV, and Silva TGFd (2017). Prediction of mineral contents in sugarcane cultivated under saline conditions based on stalk scanning by Vis/NIR spectral reflectance. Biosystems Engineering 156:17-26.

Teye E, Anyidoho E, Agbemafle R, Sam-Amoah LK, and Elliott C (2020). Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A review. Infrared Physics & Technology 104:103127.

Wold S, Martens H, and Wold H (1983). The multivariate calibration problem in chemistry solved by the PLS method. In: Kågström B, and Ruhe A, editors. Matrix Pencils. Berlin, Heidelberg: Springer Berlin Heidelberg. p 286-293.

Yang J, Luo X, Zhang X, Passos D, Xie L, Rao X, Xu H, Ting KC, Lin T, and Ying Y (2022). A deep learning approach to improving spectral analysis of fruit quality under interseason variation. Food Control 140:109108.

Zgouz A, Héran D, Barthès B, Bastianelli D, Bonnal L, Baeten V, Lurol S, Bonin M, Roger J-M, Bendoula R, and Chaix G (2020). Dataset of visible-near infrared handheld and micro-spectrometers – comparison of the prediction accuracy of sugarcane properties. Data in Brief 31:106013.

Zhao X, Zhao D, Wang J, and Triantafilis J (2022). Soil organic carbon (SOC) prediction in Australian sugarcane fields using Vis–NIR spectroscopy with different model setting approaches. Geoderma Regional 30:e00566.



  • There are currently no refbacks.

Creative Commons Lisansı
Bu eser Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.