Prediction of mass‐based process designing parameters of amla fruit using different modeling techniques

Abstract

The machine designing parameters such as size, volume, criteria projected area are important for quality evaluation at the time of packaging, grading, and sorting of fruits. All these designing parameters also depend on mass of the fruit sample. So, the correlation between mass and designing parameters could be useful for automation of the process industry. The research was inducted to predict the dimension, projected area, and volume of fruit as a function of fruit mass. Prediction modeling was done using different models, that is, mathematical model (MM), artificial neural network (ANN), and support vector regression (SVR). The mathematical model (linear, quadratic, and power) showed better prediction results than SVR modeling. In addition, the best results were achieved by neural network‐based prediction modeling. It showed the highest correlation coefficient of .884, .986, .992, .999 and the lowest error of 0.570, 0.241, 8.080, 0.059 for height, diameter, projected area, and volume, respectively. It is observed that the physical properties of fruit can be predicted using ANN‐based modeling which could be useful in automation and efficient designing of post‐harvest equipment. Practical Application The machine designing parameters are the essential and fundamental parameters for designing of grading, sorting, packaging, and handling equipments of various horticultural products including amla. Fruits and vegetables are non‐uniform in size and shape. So, minor changes in shape and size of fruits could decrease the efficiency of various processes and its equipment. The design parameters of fruits and vegetables are dependent on the mass of samples. So, accurate knowledge of mass and design parameters could help in designing the automatic grading system. Different models can be used to predict various designing parameter of amla or similar fruits. By using various models, fruit can be separated more accurately based on minor changes in shape and size rather than its mass. The ANN‐based prediction will be beneficial for the automation of the fruits grading system at an industrial scale.

PHYSICAL CHARACTERIZATION AND MASS MODELING BY GEOMETRICAL ATTRIBUTES OF BLACK SAPOTE (Diospyros nigra (J.F.Gmel.) Perr.)

“…The mass modeling of the D. nigra fruit, according to their physical properties, was performed using six empirical models: linear, quadratic, power, S-curve, exponential, and multiple linear (equations 1 to 6, respectively), which have been previously reported for the modeling of fruit mass (Mahawar et al, 2019;Pathak et al, 2019;Barbhuiya et al, 2020;Panda et al, 2020;Altuntas and Mahawar, 2021;Bibwe et al, 2022;Birania et al, 2022;Gaurav et al, 2022;Tomar and Pradhan, 2022;Panda et al, 2022;Sasikumar et al, 2021).…”

“…ex Forman, Psidium guajava L., Flacourtia jangomas (Lour.) Raeusch., Diospyros melanoxylon Roxb., Terminalia chebula Retz, and Citrus reticulata L. (Mahawar et al, 2019;Pathak et al, 2019;Barbhuiya et al, 2020;Panda et al, 2020;Altuntas and Mahawar, 2021;Bibwe et al, 2022;Birania et al, 2022;Gaurav et al, 2022;Tomar and Pradhan, 2022;Panda et al, 2022;Sasikumar et al, 2021). Fruits bruise during the transfer and storage stages due to the pressure exerted by heavy loads.…”

Rapid and non‐destructive determination of total phenolic contents using UV–NIR spectroscopy of dehydrated mushroom (Lentinus edodes)

“…The establishment of PLS model showed an efficient prediction when spectral reflection and measured textural properties were combined (Fan et al, 2016). However, BPNN, PLS, and SVM were successfully used for the efficient and precise non-destructive prediction of water contents in mushrooms (Younas, Mao, Liu, Murtaza, et al, 2021), carrots (Liu et al, 2016), and various fruits (Tomar & Pradhan, 2022). TPC of mushrooms was achieved through various quantitative models.…”

Mass modeling of potato cultivars with different shape index by physical characteristics

“…Mass modeling using regression analysis is an established technique to investigate the relationship between the dimensional attributes and mass. Literature reported the application of regression analysis for grading based on size, mass, and geometrical properties for a diversity of fruits and vegetables (Saini et al, 2022; Tomar & Pradhan, 2022). There has been a consistent series of publications focused on the mass modeling of different fruits, reiterating the importance and significance of the technique.…”