Application of adaptive neuro-fuzzy inference system (ANFIS) in predicting quality characteristics of stored apple fruit

Document Type : Research Paper

Authors

1 Biosystems Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Associate Professor, Biosystems Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Associate professor, Agricultural Engineering Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran.

4 Department of Oral and Maxillofacial Radiology Department, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran; Member of Dental Implants Research Center, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Ir

Abstract

Quality evaluations of apple fruit during storage can help producers to choose and optimize suitable storage conditions. The internal changes of this product during the storage period will be caused change in the quality characteristics of the fruit. Prediction of these changes and creating suitable storage conditions are important steps towards maintaining the nutritional and economic value of the product. In this research, some physicochemical characteristics of Golden Delicious apples were measured during storage at two temperatures of 0 and 4 °C for 0, 45, 90 and 135 days. These characteristics included CT number obtained by X-ray imaging, pH, firmness, density, total soluble solids index and fruits moisture content. Then, using adaptive neuro-fuzzy inference system (ANFIS) the mentioned characteristics changes during storage were determined and compared with the actual values. The ANFIS model inputs were color components L*, a* and b*, storage temperature and storage duration, and the outputs were the mentioned physicochemical characteristics. According to the results, in the best selected models, the values of R2, RMSE, MAPE and EF statistical parameters for CT number were 0.909, 24.331, 11.319% and 0.899, for fruit firmness were 0. 950, 1.862 N, 3.298% and 0.904, for pH value were 0.912, 0.134, 1.134% and 0.839, for density were 0.910, 0.045 g/cm3, 7.223% and 0.828, for soluble solids were 0.884, 0.537% Brix, 2.340% and 0.781 and for fruit moisture content were 0.945, 0.008, 0.729% and 0.893, respectively. These results show that it is possible to predict the characteristics of apple fruit under storage conditions with high accuracy. This prediction will be useful to determine and maintain the quality of the product during storage.

Keywords

Main Subjects

Application of adaptive neuro-fuzzy inference system (ANFIS) in predicting quality characteristics of stored apple fruit

EXTENDED ABSTRACT

Introduction

The cultivated area of ​​Iran's apple orchards is about 237,000 hectares, from which about 4 million tons of apples are produced, and it accounts for a significant share in the trade of agricultural products of this country. Apple is one of the fruits that can be stored for a relatively long time. Internal changes created by this product during the storage period cause a change in the quality characteristics of the fruit. Several researches have been conducted on the qualitative changes of apple fruit during storage. Quality evaluations of apple fruit during storage can help apple producers in choosing and optimizing suitable storage conditions. The Anticipating these changes and creating suitable storage conditions is an important step towards maintaining the nutritional and economic value of the product. Among the methods used in evaluating the quality of fruits is X-ray imaging and determining the relationship between the absorption properties of the rays and the quality parameters of the products. Also Fuzzy logic and fuzzy inference system are new and efficient techniques used in recent years to identify, classify and model complex nonlinear systems. This method can be used to predict and model the quality characteristics of food.

Materials and Methods

In this research, the physicochemical characteristics of Golden Delicious apples were recorded during storage at two temperatures of 0 and 4 degrees Celsius and the duration of storage in cold storage included 0, 45, 90 and 135 days. These characteristics included CT number obtained from X-ray imaging, pH, firmness, density, soluble solids and fruit moisture. In order to determine the mentioned physicochemical properties, 280 Golden Delicious apples were stored in temperature conditions of 0 and 4 °C and humidity of 85 ± 5% during four periods of time: zero (beginning of storage), 45, 90 and 135 days. At the end of each storage period, the physicochemical characteristics of apples were measured. In the next step, with the help of adaptive neuro-fuzzy inference system (ANFIS) and based on the inputs of color components L*, a* and b*, storage temperature and storage duration, the mentioned characteristics were extracted and compared with the actual values.

Results and Discussion

The implementation and comparison of different models of adaptive neuro-fuzzy inference system showed that in the best selected models, the values ​​of statistical parameters R2, RMSE, MAPE and EF for predicting CT number were 0.909, 24.331, 11.319% and 0.899, for fruit firmness were 0.950, 1.862 N, 3.298% and 0.904, for pH value were 0.912, 0.134, 0.134% and 0.839, for density were 0.910, 0.045 gr/m3, 7.223% and 0.828 respectively, for soluble solids were 0.884, 0.537% Brix, 2.340% and 0.781 and for fruit moisture were 0.945, 0.008 0, 0.729% and 0.893 respectively.

Conclusion

These results show that it is possible to predict the characteristics of apple fruit during storage with appropriate approximation and accuracy using color components and storage coordinates and the application of adaptive neuro-fuzzy inference system (ANFIS). Among these characteristics is the CT number, which is itself a function of other qualitative properties of apples.

References

Abbaspour Gilandeh, Y., Jahanbakhshi, A., & Kaveh, M. (2020). Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS. Food Science and Nutrition. 8: 594-611.
Abdel-Sattar, M., Al-Obeed, R.S., Aboukarima, A.M., & Eshra, D.H. (2021). Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits. PLoS One. 30:16(7):e0251185.
Ahmad, F., Zaidi, S., & Arshad, M. (2021). Postharvest quality assessment of apple during storage at ambient temperature. Heliyon. 7(8): 1-13.
Akdemir, S., & Bal, E. (2020). Quality Changes in Apple in Evaporative Cooling Store.  Erwerbs-Obstbau. 62 (1): 61- 67.
Al-Mahasneh, M., Aljarrah, M., Rababah, T., & Alu’datt, M. (2016). Application of hybrid neural fuzzy system (ANFIS) in food processing and technology. Food Engineering Reviews. 8: 351-366.
Anon. (2004). Fruits and vegetable products – determination of soluble solids content–refractometric method. International Standard 217.
Anon. (2023). Agricultural statistics. The third volume: Report on garden, mushroom and greenhouse products. Ministry of Agricultural Jihad, Center for Statistics, Information and Communication Technology, Vice President of Statistics, Center for Statistics, Information and Communication Technology. [In Persian].
AOAC. (1999). Official Methods of Analysis. 16th Edition, 5th Revision, Association of Official Analytical Chemists, Washington DC.
AOAC. (2005). Association of Analytical Chemists. Method 923.03. In: Official Methods of Analysis, 21st Edition, AOAC International Publisher, Gaithersburg.
Aryaee, H., Zare, D., Ariaei, P., Mirdamadi, S., & Naghizadeh Raeisi, S. (2020). Sensory evaluation using fuzzy logic model and evaluation of physicochemical properties, antioxidant activity and total phenol of fruit juice prepared from mulberry during frozen storage. FSCT. 17 (106):47-61.
Azadshahraki, F., & Kafashan, J. (2016). Quality indicators of garden products and their measurement methods. Agricultural Engineering Research Institute. Knowledge Network and Promotional Media Office. Publication of Agricultural Education. Iran. 24 p. [In Persian].
Barcelon, E.G., Tojo, S., & Watanabe, K. (1999). X-ray computed tomography for internal quality evaluation of peaches. Journal of Agricultural Engineering Research. 73(4): 323-330.
Birle, S., Hussein, M., & Becker, T. (2013). Fuzzy logic control and soft sensing applications in food and beverage processes. Food Control. 29: 254-269.
Damyar, S., & Dastjerdi, R. (2014). Evaluation of fruit quality changes in apple cultivar Gala, related to ripening stage and storage time. Research Achievements for Field and Horticulture Crops. 3(3): 179-189. [In Persian].
Dobrzanski, B., & Rybczynski, R. (2002). Color change of apple as a result of storage, shelf-life and bruising. International AgropHysics, 16, 261-268.
Ebrahimi, M., Karimi, R., Daraei Garmakhany, A., Aghajani, N., & Shayeganfar, A. (2024). Development of an expert system to determine the measured characteristics of grape fruit stored in cold storage using fuzzy logic. Food Science and Technology. 21 (2):70-83.
Fazel, F., Golmohammadi, A., Shahgholi, G., & Ahmadi, E. (2020). Predictions of the apple bruise volume on the basis of impact energy or maximum contact force using adaptive neuro-fuzzy inference system (ANFIS). Acta Technologica Agriculturae. 23(3): 118-125.
Hadian-Deljou, M., & Sarikhani, H. (2013). Effect of salicylic acid on maintaining post-harvest quality of apple cv. Golabe-Kohanz. Journal of Crops Improvement. 14(2): 71-82. [In Persian].
Harker, F. R., Kupferman, E. M., Marin, A. B., Gunson, F. A. & Triggs, C. M. (2008). Eating quality standards for apples based on consumer preferences. Postharvest Biology and Technology. 50 (1): 70–87.
Haseth, T. T., Egelandsdal, B., Bjerke, F. & Sørheim, O. (2007). Computed tomography for quantitative determination of sodium chloride in ground pork and dry cured hams. Journal of Food Science. 72(8): 420-427.
Jamshidi, B., Arefi, A., & Minaei, S. (2017). Non-destructive prediction of apple firmness during storage based on dynamic speckle patterns. Journal of Agricultural Machinery. 7(1): 140-151. [In Persian].
Jingping, Z., Zheng, P. & Jian, W. (2003). Correlation between moisture of apples and values of CT[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2003, 19(2): 180-182.
Kaur, S., Randhawa, S., & Malhi, A. (2021). An efficient ANFIS based pre-harvest ripeness estimation technique for fruits. Multimedia Tools and Applications. 80. 1-31.
Kavdir, I., & Guyer, D. E. (2003). Apple Grading Using Fuzzy Logic.Turkish Journal of Agriculture. 27:375-382.
Kaveh, M., Abbaspour Gilandeh, Y., Amiri Chayjan, R., & Mohammadigol, R. (2019). Comparison of Mathematical Modeling, Artificial Neural Networks and Fuzzy Logic for Predicting the Moisture Ratio of Garlic and Shallot in a Fluidized Bed Dryer. Journal of Agricultural Machinery. 9(1): 99-112. [In Persian].
León, K., Mery, D., Pedreschi, F. and León, J. (2006). Color measurement in L∗a∗b∗ units from RGB digital images. Food Research International. 39(10): 1084–1091.
Li, S., Liu, Y., Niu, X., Tang, Y., Lan, H., & Zeng, Y. (2023). Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears. Agronomy. 13. 1670.
Ligus, M., & Peternek, P. (2018). etermination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia. 153:101-106.
Liu, W., Chen, J. Ji, & Ye, C. (2014). Optimal Color Design of Psychological Counseling Room by Design of Experiments and Response Surface Methodology. PLoS ONE. 9(3): e90646.
Liu, Y., Xiyue, N., Yurong, T., Shiyuan, L., Haipeng, L., & Hao, N. (2023). Internal Quality Prediction Method of Damaged Korla Fragrant Pears during Storage. Horticulturae. 9(6): 666.
Lu, R. (2004). Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biology and Technology. 31(2): 147-157.
Marzban, A., Elhami, B., & Bougari, E. (2021). Integration of life cycle assessment (LCA) and modeling methods in investigating the yield and environmental emissions final score (EEFS) of carp fish (Cyprinus carpio) farms. Environmental Science and Pollution Research. 28(15): 19234-19246.
Mohammadpour, H., Selahvarzi, Y., Oraee, A., & Tehranifar, A. (2022). Evaluation of rootstock and scion interactions on apple storage characteristics (Malus domestica Borkh). Plant Process and Function. 11(48): 4. [In Persian].
Onu, C. E., Igbokwe, P. K., Nwabanne, J. T., & Ohale, P. E. (2022). ANFIS, ANN, and RSM modeling of moisture content reduction of cocoyam slices. Journal of Food Processing and Preservation. 46, e16032.
Pahlavan, R., Omid, M., & Akram, A. (2012). Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy. 37(1): 171-176.
Papageorgiou, E.I., Aggelopoulou, K., Gemtos, T.A & Nanos, G.D. 2018. Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality. Applied Artificial Intelligence. 32(3): 253-280.
Park, Y., Jung, S., & Gorinstein, S. (2006). Ethylene treatment of ‘Hayward’kiwifruits (Actinidia deliciosa) during ripening and its influence on ethylene biosynthesis and antioxidant activity. Scientia Horticulturae. 108:22-28.
Peng, Y., & Lu, R. (2006 Improving apple fruit firmness predictions by effective correction of multispectral scattering images. Postharvest Biology and Technology. 31: 147-157.
Peng, Y., & Lu, R. (2007). Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images. Journal of Food Engineering. 82(2):142-152.
Razavi, M. S., Golmohammadi, A., Sedghi, R., & Asghari, A. (2020). Prediction of bruise volume propagation of pear during the storage using soft computing methods. Food Science & Nutrition. 8: 884–893.
Salmanizade, F., Nassiri, S., rahemi, M., & Jafari, A. (2013). Feasibility Study of X-ray Absorption Application as a Non-destructive Method for Determining Some Qualitative Parameters of Pomegranate Fruit. Journal of Horticultural Science. 27(3): 335-341. [In Persian].
Shinde, K.J., & Pardeshi, I. L. (2014). Fuzzy logic model for sensory evaluation of commercially valuable jam samples. Journal of Ready to Eat Food. 1(2): 78-84.
Veravrbeke, E.A., Verboven, P., Oostveldt, P., & Nicolai, B.M. (2003). Predication of moisture loss across the cuticle of apple (Malus sylvestris supsp. Mitis (Wallr.) during storage: part 2. Model simulations and practical applications. Postharvest Biotechnology. 30: 89-97.
Yavari, B., chaparzadeh, N., najavand, S., Minaieh, M., & Mohammadpour, A. (2014). The effect of cold storage time on some physiological properties of two apple cultivars. Plant Process and Function. 3(7): 115-124. [In Persian].
Yildirim, D., Yesiloghlu Cevher, E. & Gurkan, A.K. (2024). Estimation and Classification of Physical Parameters Pumpkins (Cucurbita pepo L.) Crop S by Soft Computing Tecniques. BIO Web of Conferences. 85.
Zandi, M., Ganjloo, A., & Bimakr, M. (2021). Applying Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network to the Prediction of Quality changes of Hawthorn Fruit (Crataegus pinnatifida) during Various Storage Conditions. Journal of Agricultural Machinery. 11(2): 343-357. [In Persian].
Zandi, M., Ganjloo, A., Bimakr, M., Nikoomanesh, N., & Moradi, N. (2021). Application of fuzzy logic and neural-fuzzy inference system (ANFIS) for prediction of physicochemical changes and quality classification of coated sweet lemon during storage. Iranian Food Science and Technology Research Journal. 17(2): 339-351. [In Persian].
Iranian Journal of Biosystems Engineering
Volume 54, Issue 4
January 2024
Pages 47-64

Files

History

  • Receive Date: 23 April 2024
  • Revise Date: 11 June 2024
  • Accept Date: 06 July 2024

Share

How to cite

Statistics