Drying Kinetic Model Analysis with dryingkineticmodels

Introduction

The dryingkineticmodels package fits 20 thin-layer drying kinetic models to experimental moisture ratio (MR) data, ranks them by statistical criteria, identifies the best model, performs residual diagnostics, and exports a formatted report to a Word document.

Input Data Format

The input must be an Excel file (.xlsx) or a data frame with at least two numeric columns:

First numeric column — Drying time
Second numeric column — Moisture ratio (MR), typically between 0 and 1

Example layout:

Time (min)	MR
0	1.00
30	0.74
60	0.51
90	0.32
120	0.18

Note: Ensure the columns are in the correct order before running the function. No column renaming is required.

Basic Usage

library(dryingkineticmodels)

# From an Excel file
result <- dryingkineticmodels("drying_data.xlsx")

# From a data frame
df <- data.frame(
  time = c(0, 30, 60, 90, 120),
  MR   = c(1.00, 0.74, 0.51, 0.32, 0.18)
)
result <- dryingkineticmodels(df)

What the Function Does

When called, dryingkineticmodels() automatically:

Reads and validates the input data
Fits all 20 thin-layer drying models using nonlinear least squares
Computes R², RMSE, MAE, χ², and RSS for each model
Ranks models by a composite score and prints the comparison table
Identifies the best model and reports its coefficients and fit statistics
Runs hypothesis tests on model parameters
Computes an ANOVA table for the best model
Performs four residual diagnostic tests
Generates diagnostic plots and a drying curve
Exports everything to a Word document

Output

The function returns an invisible list with three elements:

comparison_table — all model statistics, ranked
best_model — result list for the best-fitting model
anova_table — ANOVA table for the best model

# Access results programmatically
result$comparison_table
result$anova_table

The 20 Models

Model	Equation
Lewis	MR = exp(-k·t)
Page	MR = exp(-k·t^n)
Modified Page	MR = exp(-(k·t)^n)
Henderson & Pabis	MR = a·exp(-k·t)
Logarithmic	MR = a·exp(-k·t) + c
Two-Term	MR = a·exp(-k₀·t) + b·exp(-k₁·t)
Two-Term Exponential	MR = a·exp(-k·t) + (1-a)·exp(-(k·a·t))
Diffusion Approximation	MR = a·exp(-k·t) + (1-a)·exp(-(k·b·t))
Wang & Singh	MR = 1 + a·t + b·t²
Midilli-Kucuk	MR = a·exp(-k·t^n) + b·t
Modified Henderson & Pabis	MR = a·exp(-k·t) + b·exp(-g·t) + c·exp(-h·t)
Verma	MR = a·exp(-k·t) + (1-a)·exp(-g·t)
Weibull	MR = exp(-(t/α)^β)
Aghbashlo et al.	MR = exp(-k₁·t / (1 + k₂·t))
Jena & Das	MR = a·exp(-k·t^n) + b·t + c
Hii et al.	MR = a·exp(-k·t^n) + b·exp(-g·t^m)
Parabolic	MR = a + b·t + c·t²
Thompson	MR = exp((-a + √(a²+4b·t)) / 2b)
Demir et al.	MR = a·exp(-k·t^n) + b
Thin-Layer Exp-Linear	MR = a·exp(-k·t) + b·t + c

References

Goyal, R. K., Kingsly, A. R. P., Manikantan, M. R., & Ilyas, S. M. (2007). Mathematical modelling of thin layer drying kinetics of plum in a tunnel dryer. Journal of Food Engineering, 79(1), 176–180.