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Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage

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Abstract

Rapid test methods with portable devices along with standard chemical tests are necessary to determine raw syrup quality in the sugarcane agro-industries. On this account, a special e-nose device was developed to test the sugarcane syrup and its association with the odor emitted from it to determine the amount of sucrose (purity) in the sugarcane syrup. Samples were obtained from the farms of Hakim-Farabi agro-industry, including four varieties (CP57, CP69, IRC99-02, and CP48). Experiments included chemical tests to determine the percentage of purity (PTY) and refined sugar (RS) plus an electronic nose test. Partial least squares (PLS), principle component regression (PCR), multiple linear regression (MLR), and artificial neural network (ANN) methods were used to evaluate the correlation between the gained signals from the sensor array and chemical analysis results of the samples. In the case of PTY, among 8 sensors, MQ3, MQ5, and MQ9 had the highest response compared to the others, while regarding RS, all the sensors except for MQ8 indicated a great contribution. Also, all models for PTY and RS showed a good prediction performance. The results revealed that ANN model, with topology 8–1-2, outperformed others for prediction of the quality indices of sugarcane, with high correlation coefficients (R2 = 0.96 for RS; 0.99 for PTY), and relatively low RMSE values of 0.33 for RS; 0.4 for RTY. Finally, findings indicated that e-nose technique has the potential to become an authentic tool to assess chemical features of sugarcane syrup from e-nose system signals.

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Acknowledgements

The authors appreciate Dr. Alireza Sanaeifar to develop the e-nose device, and the R&D office of Hakim-Farabi agro-industry, for samples and academic supports. We are grateful to the research council of the Shahid Chamran University of Ahvaz (SCUA) financial support (the grant number SCU.AA98.585).

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Authors and Affiliations

  1. Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Hassan Zaki Dizaji & Abdullah Adibzadeh

  2. Department of Agricultural Machinery Engineering, Sonqor Agriculture Faculty, Razi University, Kermanshah, Iran

    Nahid Aghili Nategh

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  1. Hassan Zaki Dizaji
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  2. Abdullah Adibzadeh
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  3. Nahid Aghili Nategh
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Correspondence to Hassan Zaki Dizaji.

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Practical Applications: To start harvesting whose time depends on various factors, such as sugarcane variety, date of planting, and last year harvest conditions. Regarding sugarcane variety, the exact time for sugarcane harvest initiation is determined based on the daily monitoring of raw syrup quality by chemical measurements. On the other hand, sugarcane billet or its syrup cannot be stored in the factory and its sugars factors are decomposed quickly by storage. Therefore, rapid test methods with portable devices along with standard chemical tests seem to be necessary to determine RTY and RS in the sugarcane agro-industries. E-nose technique combined with the modeling methods, such as MLR, PCR, PLS, and ANN can be introduced as a rapid testing method to estimate the RTY and RS.

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Zaki Dizaji, H., Adibzadeh, A. & Aghili Nategh, N. Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage. J Food Sci Technol 58, 4149–4156 (2021). https://doi.org/10.1007/s13197-020-04879-4

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  • DOI: https://doi.org/10.1007/s13197-020-04879-4

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