Mass modeling of potato cultivars with different shape index by physical characteristics. (19th July 2022)

• Record Type: Journal Article
• Authors: Altuntas, Ebubekir; Mahawar, Manoj Kumar
• Is Part Of: Journal of food process engineering. Volume 45:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-19
• Subjects: grading -- linear regression models -- post‐harvest machinery -- predictive modeling; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; 664.005
• DOI: 10.1111/jfpe.14126
• Languages: English
• ISSNs: 0145-8876

Abstract

Abstract: The correlation between physical parameters like linear dimensions, projected areas, volumes, and mass of potato cultivars is imperative for predicting the quality besides the development of post‐harvest machineries especially grading systems. Therefore, this investigation was envisaged to determine the correlation between the mass and properties like dimensions viz. length ( l ), width ( w ), thickness ( t ), geometric mean diameter ( Gmd ), first projected area ( FPA ), second projected area ( SPA ), third projected area ( TPA ), criteria area ( Cae ), oblate spheroid volume ( V obsp ), ellipsoid spheroid volume ( V ellsp ), and shape index (SI) of potato cultivars cv. Milva, Jelly, and Sante. Based on the SI, potato tubers were classified as round (100–160), oval (161–240), and long (241–340), respectively. The predictive modeling was done using 171 linear regression models and the models having the highest coefficient of determination ( R 2 ) and lowest regression standard error ( RSE ) and root mean square error ( RMSE ) were recommended. A total of 27 model equations based on dimensions and projected area were recommended for the estimation of the mass of all three potato tubers. These model equations find application for developing an effective grading setup, further augmenting its prospective utilization. Results revealed that the linear models based on lwt ( m = k 1 l + k 2 w + k 3 t + k 4 ) were recommended for all the SI of the cultivars with R 2 varied from .942 to .965 (cv. Milva), .949 to .975 (cv. Jelly), and .946 to .956 (cv. Sante). The regression models based on projected area ( m = k 1 FPA + k 2 SPA + k 3 TPA + k 4 ) were recommended with R 2 varied from .956 to .974 (cv. Milva), .959 to .982 (cv. Jelly), and .957 to .977 (cv. Sante). The detailed information about the recommended mass models based on the engineering properties of potatoes could be imperative for the efficient design of an integrated and automated grading system. Practical Applications: Horticultural commodities having uniform size and shape possess elevated demand, price, and the consumers' preference. Size‐based grading is predominantly performed to obtain the dimensional uniformity of the produce. However, the commodities with similar appearance and variation in mass project complexity while grading. Therefore, grading based on the mass of the produce has gained importance in deciding the design features of post‐harvest machineries. Mass‐based grading features are an important criteria of packaging as it assists in optimizing the packaging modules, minimize wastage during handling and transportation, thereby improving the marketing potential. More recently, the researchers have elaborated and recommended the use of mass modeling techniques for various fruits and vegetables. In this study, the physical properties of three different cultivars of potatoes were determined, and in addition, the different model equations were employed for mass prediction of potatoes. The results will definitely be beneficial to utilize mass as a grading parameter and the possible development of an automated grading mechanism on the mutual effect of mass and size of potatoes. The detailed information about the interaction between the mass and axial dimensions, volume, and projected area of potatoes is also elaborated.