INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Empirical Investigation of Thin-Layer Dehydration of Guava Slices  
Edeani N. J1, Agu,; F. A2, Anyaene H. I3, Chukwuezie, O.C4.  
1Department of Chemical Engineering, University of Agriculture and Environmental Sciences,  
Umuagwo P.M.B. 1038 Owerri, Imo State.  
2Department of Chemical Engineering, Enugu State University Science and Technology, Agbani, Enugu  
State.  
3Department of Chemical Engineering, Cartias University Amorji-Nike, Enugu.  
4Department of Agriculture and Biosystem Engineering, University of Agriculture and Environmental  
Sciences, Umuagwo P.M.B. 1038 Owerri, Imo State.  
DOI: https://dx.doi.org/10.51244/IJRSI.2025.12110046  
Received: 18 November 2025; Accepted: 27 November 2025; Published: 05 November 2025  
ABSTRACT  
The main aim of the study was to analyze the drying process utilizing the thin-layer models suggested by Lewis,  
Page, and Henderson and Pabis. The guava fruit was meticulously prepared and subsequently diced into small  
pieces measuring 0.4 cm, 0.6 cm, and 0.8 cm for the drying process. Three distinct temperatures—60°C, 70°C,  
and 80°C—were employed during this drying session. The graph of moisture ratio against time showed the  
falling rate period. It was observed that drying temperature and slice thickness had effect on the rate of drying.  
A three-model statistical analysis was crucial to guarantee the reproducibility of the drying behavior. In all  
temperature ranges analyzed, the page model consistently offered the most compelling explanation for the drying  
process of guava fruit with highest value of R2 was 0.9898, RMSE of 0.03077 and SSE value of 0.009466 at  
drying temperature of 80oC and slice thickness of 0.6cm.  
Keywords: Thin-layer drying, guava fruit, hot air drying, drying models, temperature  
INTRODUCTION  
Guava (Psidium guajava L.) is a tropical fruit tree belonging to the Myrtaceae family [1] and is believed to have  
originated in Peru before spreading to other tropical and subtropical regions [2]. The tree thrives in a wide variety  
of climatic conditions and soil types, making it a widely cultivated fruit across many developing countries. Guava  
is nutritionally important: it is exceptionally rich in vitamin C [5], dietary fibre, antioxidants, and minerals, and  
its outstanding flavour has earned it the classification of a “super-fruit” [6,7,8].  
Despite its nutritional and economic value[9], guava fruit deteriorates rapidly due to its high moisture content,  
soft texture, and intense metabolic activity. The fruit’s succulent nature makes it highly susceptible to insect  
attack and microbial spoilage. Under ambient conditions, guava has a shelf life of only 2–3 days, after which it  
undergoes rapid quality degradation [10,11]. This poses major challenges for farmers, processors, distributors,  
and consumers, particularly in tropical regions where post-harvest losses are already significant. Consequently,  
there is a need for effective preservation technologies that extend shelf life while maintaining nutritional and  
sensory attributes.  
Drying is one of the most widely used methods for fruit preservation because it reduces water activity,  
concentrates nutrients, extends shelf life, and allows easier packaging, storage, and transportation[12,13, 14].  
Several mathematical models have been developed to describe the thin-layer drying behaviour of food materials  
[15]. Among these, thin-layer drying is particularly favoured due to its efficiency, uniform exposure of samples,  
minimal energy consumption, and reduced quality loss[16,17]. It also allows clear evaluation of parameters such  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
as slice thickness, temperature, air velocity, and relative humidity, which significantly influence the drying  
process.  
Understanding the thin-layer drying kinetics of guava is essential for optimizing drying systems, predicting  
drying behaviour, and guiding the design of industrial dryers. This study aims to generate experimental drying  
data and apply mathematical modelling techniques to explain the drying behaviour of guava slices. The models  
investigated include the Lewis model [18], Page model [19], and Henderson and Pabis model [20], which are  
widely used in fruit and vegetable drying studies.  
The research focuses on evaluating how slice thickness, drying duration and drying temperature affect moisture  
ratio, drying rate, and model performance. By identifying the most suitable thin-layer model, the study  
contributes to the development of energy-efficient drying processes and supports the potential commercialization  
of dried guava products. Ultimately, the findings are expected to help reduce post-harvest losses, enhance value  
addition in the guava value chain, and promote sustainable utilization of tropical fruits. Accurate modelling is  
vital for designing reliable dryers, minimizing nutrient degradation, and ensuring uniform product quality during  
dehydration.  
Although several studies [20, 21, 22, 23,24] have focused on drying kinetics of fruits, there is inadequate  
empirical data on thin-layer drying behaviour of guava slices at systematically varied slice thicknesses and  
temperature combinations. Furthermore, limited studies compare the predictive ability of Lewis, Page, and  
Henderson & Pabis models for guava under these specific conditions. This study addresses these gaps by  
experimentally evaluating drying behaviour, fitting models, and identifying the most suitable thin-layer model  
for guava dehydration.  
MATERIAL AND METHODS  
Theoretical Principle  
The thin layer drying process is governed by both physical and thermal properties of the material and the drying  
condition. Mathematical models used to explain and predict moisture reduction relating moisture ratio (MR) to  
drying time were Lewis, Page, and Henderson & Pabis because of their simplicity, accuracy, and wide usage in  
fruit drying research. Moisture ratio of the process is given as Equation 1  
−
−
=
=
1
0−  
Where:  
MR = moisture ratio  
Mt = moisture content at time‘t’ (% db.)  
M0 = initial moisture content (% db.)  
Me = equilibrium moisture content (% db.) K = drying constant(mins-1) t = drying time (mins)  
Since Me for hot air drying is assumed to be zero. Equation 1 becomes  
=
0
The drying curve obtained using the experimental data where fitted using three models  
The Lewis Model  
The Lewis model is the simplest thin-layer exponential model derived from Fick’s second laws given in Equation  
2:  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
−
=
2
The Page Model  
The Page model modifies the Lewis equation by introducing an empirical exponent n, which adjusts the drying  
curve to account for non-linear internal moisture diffusion  
=
−
K = drying constant (mins-1) t = drying time (mins)  
n = exponent n  
3
Henderson and Pabis Model  
−
=
4
a is a constant  
The accuracy of thin-layer drying models is determined using statistical indicators like Coefficient of  
2
Determination (R²) which measures goodness of fit, Sum of Squares Error (SSE( )) where lower values indicate  
more accurate predictions, Root Mean Square Error (RMSE) that measures deviation between predicted and  
experimental values whereas Adjusted R² measures level of correctness of predictors in model selection. Models  
with high R² and low SSE/RMSE are considered more suitable.  
The best fitted curve indexes used were given in Equation 5 to 7  
(MRpre,i−MRexp,i )2  
2
R = 1 − [∑Ni=1 (MRexp,i−MRexp,i)2]  
5
6
(MRexp,i−MRpre,i )2  
)
−
MRpre,i )2)1/2  
7
WhereMRexp,i = the ith experimental moisture ratio; MRpre,i =the ith predicted moisture ratio; N = number of  
observations: m = number of constants in the drying models; MRpre = mean of predicted moisture ratio  
Experimental Procedure  
Fresh guava fruits (1,000 g) were procured from New Market, Enugu, Nigeria. The fruits were sorted to remove  
damaged or overripe samples, washed thoroughly with clean water, and drained to remove surface moisture.  
Each fruit was then sliced using a sharp stainless-steel knife and grouped into three thickness categories: 0.4 cm,  
0.6 cm, and 0.8 cm, with the thickness of each slice verified using a Vernier caliper to ensure uniformity. A  
Zenithlab hot-air oven dryer was used for the drying experiments. For each slice thickness, 30 pieces of freshly  
cut guava were selected. The weights of the 30 slices were separately taken, and the mean value was recorded  
as the initial mass for that thickness category.  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Before loading the samples, the oven was preheated and stabilized at the selected drying temperatures of 60°C,  
70°C, and 80°C. The prepared guava slices were then placed in a single layer on drying trays to ensure uniform  
airflow and heat distribution.  
During drying, the samples were weighed every 30 minutes using a digital weighing balance. The slices were  
returned to the oven immediately after each weighing to minimize heat loss. The drying process for each batch  
continued until successive weight measurements showed no significant difference, indicating that the slices had  
reached a constant weight.  
The recorded weights and drying times were used to calculate the moisture removed and subsequently determine  
the moisture ratio, drying rate, and other drying parameters required for model fitting.  
RESULTS AND DISCUSSION  
The impact of moisture ratio in relation to time  
Figures 1.1-1.9 showed the graphs of moisture ratio (MR) with time (t) of 04.cm, 0.6cm and 0.8cm thickness at  
temperatures of 60 OC, 70 OC and 80OC for experimental, Lewis model, Page model and Henderson & Pabis  
model whereas the summary is presented in Table 1  
Table 1 : Sample Regression Analysis for guava at 60,70,800 C for 0.4,0.6,0.8cm Thickness  
Thickn  
Model  
Name  
Temperat  
ure (0C)  
ess  
(cm)  
Regression  
Parameters  
Coefficients  
RMS  
E
R2  
SSE  
K
A
N
Lewis  
60oC  
0.00504  
0.850  
6
0.247  
6
0.138  
0.8  
1
0.00447  
0.874  
6
0.120  
7
0.189  
2
0.6  
0.4  
0.8  
8
0.00451  
0.855  
3
0.134  
3
0.103  
2
0.252  
6
0.127  
7
8
0.00589  
3
70oC  
0.903  
0.00564  
9
0.897  
9
0.108  
1.555  
0.140  
1
0.6  
0.4  
0.8  
0.6  
0.00481  
1
0.824  
7
0.290  
2
80oC  
0.106  
7
0.125  
2
0.00510  
7
0.890  
9
0.933  
6
0.074  
8
0.061  
52  
0.00455  
9
0.869  
8
0.115  
4
0.146  
5
0.00453  
1
0.00002  
7
0.00004  
1
0.4  
0.8  
0.6  
Page  
60oC  
1.9  
70  
1.8  
62  
0.966  
8
0.983  
7
0.067  
7
0.045  
3
0.055  
0
0.024  
6
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
0.00002  
1.9  
70  
0.974  
3
0.058  
7
0.044  
8
0.4  
0.8  
0.6  
0.4  
0.8  
0.6  
0.4  
0.8  
0.6  
0.4  
0.8  
0.6  
0.4  
0.8  
0.6  
0.4  
3
70oC  
0.00023  
1.6  
16  
1.7  
44  
2.3  
31  
1.6  
64  
1.4  
80  
1.7  
72  
0.975  
5
0.989  
3
0.986  
6
0.973  
4
0.989  
8
0.966  
7
0.917  
9
0.945  
2
0.927  
9
0.962  
2
0.965  
4
0.925  
2
0.947  
4
0.980  
0
0.927  
3
0.541  
1
0.036  
62  
0.044  
96  
0.055  
28  
0.030  
21  
0.061  
26  
0.106  
5
0.083  
04  
0.922  
4
0.067  
29  
0.065  
68  
0.106  
1
0.077  
7
0.043  
08  
0.090  
46  
0.127  
7
0.140  
1
0.290  
2
0.030  
56  
0.009  
47  
0.037  
52  
0.136  
1
0.082  
8
0.125  
8
0.049  
81  
0.047  
45  
0.123  
8
0.060  
4
0.018  
56  
0.081  
82  
4
0.00011  
3
0.00000  
4
80oC  
0.00015  
22  
0.00035  
6
0.00007  
3
60oC  
70oC  
0.00660  
1.3  
31  
1.3  
08  
1.3  
32  
1.3  
11  
1.3  
30  
1.4  
09  
1.2  
64  
1.2  
06  
Henders  
on and  
Pabis  
7
0.00587  
9
0.00596  
1
0.00763  
7
0.00741  
5
0.00632  
6
80oC  
0.00651  
9
0.00565  
1
0.00578  
6
1.2  
48  
From Table 1, the R2 value of 04.cm, 0.6cm and 0.8cm thickness at temperatures of 60 OCfor Lewis Model was  
0.8506 indicating a moderately good fit, but the model did not capture complex drying behaviour . Page Model  
with R² = 0.967 is an excellent fit, the model captures the curvature better than Lewis. R² for Henderson & Pabis  
was 0.918 which is good, better than Lewis but not as good as Page model. From the R2 values Page model fits  
guava drying data the best. Figures 1.1 to 1.3 of Plot of MR vs t of 0.8cm, 0.6cm and 0.4cm thickness at 600C  
showed generally exponential curve indicating a that moisture ratio decrease exponentially with time of drying[  
25,26,27].  
Page model flexibility with exponent (n) allows it to accurately model both early rapid drying and slower later  
stages.Lewis and Henderson & Pabis are simpler and could be used for approximate predictions, but for  
precision.  
Fig. 1.1 Plot of MR vs t at 0.8cm thickness FOR 600C  
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Experimental  
Lewis model  
1
Page model  
Henderson & Pabis model  
0.8  
0.6  
0.4  
0.2  
0
50  
100  
150  
200  
250  
300  
350  
400  
Fig. 1.2 Plot of MR vs t at 0.6cm thickness  
Experimental  
page model  
Lewis model  
1
0.8  
0.6  
0.4  
0.2  
0
Hendeson & Pabis Model  
50  
100  
150  
200  
t(mins)  
250  
300  
350  
400  
Fig. 1.3 Plot of MR vs t at 0.4cm thickness  
Also from Table 1 the values of R2 on drying 04.cm, 0.6cm and 0.8cm thickness at 70 OC for Lewis model gives  
a moderate fit (R² = 0.875) reasonable, but it may not capture the non-linear aspects of drying for this thinner  
slice. Page Model (R² = 0.9837) was excellent fit and very close to experimental data and better fit than Lewis  
due to the scaling factor while Henderson & Pabis Model R² = 0.945 was very good, but not as precise as Page.  
RMSE = 0.083 was lower error than Lewis but higher than Page. Figures 1.4 to 1.6 of model fittings for 0.8cm,  
0.6cm and 0.4cm thickness at 700C  
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Fig. 1.4 Plot of MR vs t at 0.8cm thickness FOR 700C  
Experimental  
1
Lewis models  
Page model  
Henderson & Pabis model  
0.8  
0.6  
0.4  
0.2  
0
50  
100  
150  
200  
250  
300  
35  
Fig. 1.5 Plot of MR vs t at 0.6cm thickness  
experimetnal  
lewis  
1
0.8  
0.6  
0.4  
0.2  
0
page  
Henderson  
50  
100  
150  
200  
t1(mins)  
250  
300  
350  
Fig. 1.6 Plot of MR vs t at 0.4cm thickness  
From figures 1.4 to 1.6 showed Lewis with simple exponential, moderate fit, Page of best fit and captures non-  
linear drying while Henderson &Pabis was good fit, simpler than Page. It implies that Page model is the most  
accurate for predicting the drying of 0.6ꢀcm guava slices, Henderson & Pabis is a good alternative if simplicity  
is preferred whereas Lewis is the least precise but can give a rough estimate. Both 0.8ꢀcm and 0.6ꢀcm slices show  
that Page model consistently provides the best fit. The drying exponent (n) decreases slightly with thinner slices  
(from 1.97 to 1.862), which aligns with the idea that thinner slices dry faster and slightly less non-linearly.  
Also from Table 1 the values of R2 on drying 04.cm, 0.6cm and 0.8cm thickness at 80 OC. Lewis model is slightly  
improved compared to 70ꢀ°C (R² = 0.8979) due to faster drying at higher temperature. Page Model provides the  
best fit R² = 0.9898 which is excellent and RMSE = 0.0308 is very low error. Also (n = 1.48) at 80OC is lower  
than at 70ꢀ°C (1.744), suggesting drying becomes closer to exponential at higher temperature for 0.6ꢀcm slices.  
Henderson & Pabis Model is Good fit, slightly worse than Page but better than Lewis. Higher (k) than Lewis  
(0.004559 to 0.005651) reflects faster drying at 80ꢀ°C. From figures 1.7 to 1.9 below showed exponential  
decrease of moisture ratio with increase in time [28,29].  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
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Fig. 1.7 Plot of MR vs t at 0.8cm thickness FOR 800C  
1
Experimental  
Lewis model  
0.9  
Page model  
Hendrson and Pabis  
0.8  
0.7  
0.6  
0.5  
0.4  
0.3  
0.2  
50  
100  
150  
200  
250  
300  
350 t1(mins))  
Fig. 1.8 Plot of MR vs t at 0.6cm thickness 80OC  
Fig. 1.9 Plot of MR vs t at 0.4cm thickness 80OC  
By implication Page model remains the most accurate at High temperature reduces exponent (n) and drying  
becomes more exponential. Lewis improves slightly but is still the least accurate.  
CONCLUSION  
Across all temperatures (60–80°C) and thicknesses (0.4–0.8 cm) it can be concluded that Lewis model gave  
moderate fits with R² between 0.82 and 0.90, indicating that simple diffusion can explain part of the drying  
behaviour.The drying constant (k) increased with temperature, confirming faster moisture removal at higher  
thermal energy. Thinner slices (0.4 cm) had higher k values than thicker slices (0.8 cm), consistent with reduced  
diffusion path. However, the Lewis model underestimated the curvature of the drying data, showing that guava  
drying is not a simple first-order process.  
Across all temperatures and thicknesses, the Page model consistently gave the best fit with R² values between  
0.96 and 0.99, confirming its suitability for guava drying. The exponent n increased 1.5–2.4 indicates a strong  
non-linear moisture diffusion pattern, typical for fruits with high sugar and pectin. Very low SSE and RMSE  
values show that the model accurately follows the behaviour of your MR curves. The improvement over Lewis  
confirms that guava does not follow simple exponential decay.Thus, the Page model best represents the empirical  
drying kinetics of guava slices.  
For Henderson and Pabis Model Good fits were obtained, with R² between 0.92 and 0.95, showing better  
accuracy than Lewis.The coefficient a ≈ 1.2–1.4 indicates non-ideal initial conditions, likely due to guava’s soft  
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tissue, high initial surface moisture, rapid initial evaporation k values were consistently higher than in the Lewis  
model, reflecting better representation of the empirical data.Although it performs better than Lewis, it is still less  
accurate than the Page model. Drying of guava occurs entirely in the falling-rate period, controlled by internal  
moisture diffusion.  
Higher temperatures (70–80°C) increase the drying constant k, confirming enhanced diffusion. Thinner slices  
dry faster due to shorter diffusion paths while the drying mechanism is non-linear, validating advanced models  
like Page.  
The Page model provided the best theoretical and empirical description of moisture behaviour.  
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