Year 2020, Volume 26, Issue 3, 339 - 348, 04.09.2020

Yousef ABBASPOUR-GİLANDEH Sajad SABZİ Farzad AZADSHAHRAKİ Rouhollah KARİMZADEH Elham ILBEYGİ Juan IGNACİO ARRİBAS

https://doi.org/10.15832/ankutbd.523574

Abstract

Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data

Abstract

Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using non-destructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network - Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92±0.040 and 0.89±0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.

Keywords

Spectroscopy, Color features, Non-destructive estimation, Artificial neural network, Regression, Metaheuristic algorithms

References

• Adebayo SE, Hashim N & Abdan K, Hanafi M (2016a). Application and potential ofbackscattering imaging techniques in agricultural and food processing—a review. journal of Food Engineering 169:155–164
• Adebayo SE, Hashim N, Abdan K, Hanafi M & Mollazade K (2016b). Prediction of quality attributes and ripeness classification of bananasusing optical properties. Scientia Horticulturae 212:171–182
• Ali MM, Hashim N, Bejo SK & Shamsudin R (2017). Quality evaluation ofwatermelon using laser-induced backscattering imaging during storage postharvest. Biology and Technology 123: 51–59
• Amoriello T, Ciccoritti R, Paliotta M & Carbone K (2018). Classification and prediction of early-to-late ripening apricot quality using spectroscopic techniques combined with chemometric tools. Scientia Horticulturae 240: 310–317
• Arendse E, Fawole OA, Magwaza LS, Nieuwoudt H & Opara UL (2018). Evaluation of biochemical markers associated with the development of husk scald and the use of diffuse reflectance NIR spectroscopy to predict husk scald in pomegranate fruit. Scientia Horticulturae 232:240–249
• Cardenas-Perez S, Chanona-Perez J, Mendez-Mendez JV, Calderon-Domı´nguez G, Lopez-Santiago R, Perea-Flores MJ & Arzate-Vazquez I (2017). Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system. Biosystem Engineering 159:46-58
• Cayuela JA (2008). Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biology and Technology 47: 75–80
• Clerici MTPS, Kallmann C, Gaspi FOG, Morgano MA, Martinez-Bustos F & Chang YK (2011). Physical, chemical and technological characteristics of Solanum lycocarpum A. St. - HILL (Solanaceae) fruit flour and starch. Food Research International 44: 2143–2150
• Costa G, Noferini M, Fiori G & Torrigiani P (2009). Use of vis/NIR spectroscopy to assess fruit ripening stage and improve management in post-harvest chain. Fresh Produce 1:35–41Eisenstecken D, Stürz B, Robatscher P, Lozano L, Angelo Zanella & Oberhuber M (2019). The potential of near infrared spectroscopy (NIRS) to trace apple origin: Study on different cultivars and orchard elevations. Postharvest Biology and Technology 147: 123–131
• Hu W, Sun D-W & Blasco J (2017). Rapid monitoring 1-MCP-induced modulation of sugars accumulation in ripening ‘Hayward’ kiwifruit by Vis/NIR hyperspectral imaging. Postharvest Biology and Technology 125: 168–180
• Jackman P, Sun D-W & Allen P (2009). Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Science 83:187–194
• Li JL, Sun DW & Cheng JH (2016). Recent Advances in Nondestructive Analytical Techniques for Determining the Total Soluble Solids in Fruits: A Review. Comprehensive Reviews in Food Science & Food Safety 15:897-911
• Martínez-Valdivieso D, Font R, Blanco-Díaz MT, Moreno-Rojas JM, Gómez P, Alonso-Moraga Á & Río-Celestino MD (2014). Application of near-infrared reflectance spectroscopy for predicting carotenoid content in summer squash fruit. Computers and Electronics in Agriculture 108: 71–79
• Mohammadi V, Kheiralipour K & Ghasemi-Varnamkhasti M (2015). Detecting maturity of persimmon fruit based on image processing technique. Scientia Horticulturae 184:123–128
• Ncama K, Tesfay SZ, Fawole OA, Opara UL & Magwaza LS (2017). Non-destructive prediction of ‘Marsh’ grapefruit susceptibility to postharvest rind pitting disorder using reflectanceVis/NIR spectroscopy. Scientia Horticulturae doi:https://doi.org/10.1016/j .scienta. 2017.12.028
• Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W & Theron IK (2007). Non- destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology 46:99–118
• Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S & Zaidi M (2006). The bees algo rithm-a novel tool for complex optimisation problems. Paper presented at the Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), Cardiff, UK
• Rossel RAV (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems 90:72–83
• Sabzi S, Abbaspour-Gilandeh Y & Garcia-Mateos G (2018). A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Information Processing in Agriculture 5: 162–172
• Sabzi S, Abbaspour-Gilandeh Y, Javadikia H & Havaskhan H (2015). Automatic Grading of Emperor Apples Based on Image Processing and ANFIS Tarim Bilimleri Dergisi — Journal of Agricultural Sciences 21:326-336
• Sabzi S & Arribas JI (2018). A visible-range computer-vision system for automated, non-intrusive assessment of the pH value in Thomson oranges. Computers in Industry 99: 69–82
• Sabzi S, Javadikia P, rabani H & Adelkhani A (2013). Mass modeling of Bam orange with ANFIS and SPSS methods for using in machine vision. Measurement 46: 3333–3341
• Seymour GB, Taylor JE & Tucker GA (1993). Biochemistry of Fruit Ripening. Springer Netherlands. doi:10.1007/978-94-011-1584-1
• Thompson A.K., Burden O.J. (1995). Harvesting and fruit care. In: Gowen S. (eds) Bananas and Plantains. World Crop Series. Springer, Dordrecht
• Wu D & Sun D-W (2013). Colour measurements by computer vision for food quality control—a review. Trends in Food Science & Technology 29:5–20
• Zhang B, Gu B, Tian G, Zhou J, Huang J & Xiong Y (2018). Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: A technical review. Trends in Food Science & Technology doi:https ://doi.org /10.1016/j.tifs.2018.09.018