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A comparative study was carried out between a large number of mathematical models and artificial neural networks to estimate the drying curves. Diamente et al. model and modified two‐term exponential‐V model were determined as the best ones describing drying curves for natural and forced drying air systems, respectively. The ANNs with 4‐9‐9‐1 and 3‐9‐1 topologies, log‐sigmoid, and hyperbolic tangent sigmoid transfer functions, and Levenberg–Marquardt training algorithm presented the best results for the former and latter systems, respectively. Furthermore, it was found that ANN modeling had much better performance in prediction of drying curves with respect to statistical analyses. Moisture diffusivity values were obtained in a range of 0.932×10-10-11.976×10-10normalm2/s and 2.209×10-10-9.848×10-10normalm2/s for the systems, respectively. Activation energies were determined as 88,509, 60,344, and 44,806 W/kg for the former system and 36.88, 29.66, and 18.59 kJ/mol for the latter system with an increase in the thickness of samples. Practical applications The main aim of drying food products is the reduction of moisture content up to a certain level to achieve the smallest possible amount of the microbiological spoilage. This process has a considerable effect on the drying kinetics and quality of the dried product as well. Infrared (IR) heating, as an alternative drying method for agricultural products, is efficient to preserve the main characteristics and to shorten drying time. It is important to investigate the effect operating parameters of IR dryer on the mentioned cases. Our studies have clearly displayed that IR heating of pumpkin samples can result in high heating rate and fast drying. Therefore, this alternative approach could be employed as an energy saving drying method along with improved drying efficiency and better product quality in comparison with the common drying methods. Study results are useful for producers of not only dried pumpkins but also other agricultural products.

In this work, the effect of the radiation intensity, slice thickness, and the distance between slices and infrared lamps under natural drying air and the effect of slice thickness and air velocity under forced drying air on the moisture diffusion characteristics and the drying rate of kiwifruit slices during infrared drying were investigated. The drying of kiwifruit happened in the falling rate period, and no constant‐rate period was observed in the drying curves. One hundred models were fitted to the drying data. Among the models, the exponential dsecay function model and modified two‐term exponential‐V model and the artificial neural networks with 4‐5‐7‐1 and 3‐5‐5‐1 topologies, hyperbolic tangent sigmoid transfer function, and Levenberg‐Marquardt training algorithm presented the best results and showed the goodness of fit with the experimental data for the former and latter systems, respectively. The diffusivities varied between 1.216 × 10−10–8.997 × 10−10 m2⁄s and 2.567 × 10−10–10.335 × 10−10 m2⁄s for natural and forced drying air systems, respectively.

Carboxyl-functionalized molybdenum disulfide (COOH-MoS2) nanosheets were prepared through a facile low-temperature hydrothermal method. The phase transformation of metallic-1T to 2H-semiconductor COOH-MoS2 nanosheets was conducted through introducing Au thin film on the unclad optical fiber as a sensing layer in a low temperature. The developed structure successfully refined the loss of the semiconducting properties and poor adhesion of COOH-MoS2 on the unclad polymer optical fiber, which provided limited semiconductor potential as the sensing layers on the optical fiber surfaces. The sensing performance of the as-prepared structure was tested for quantitative detection of three different volatile organic carbons (VOCs) of ethanol, propanol, and methanol gases as well as cross-sensitivity to relative humidity. The operating principle was based on intensity variation of the evanescent wave in the sensing region. The response of the proposed sensing system shows maximum response and better linearity (R 2 = 0.999) to methanol at room temperature. Finally, the comparative experimental cross-sensitivity to relative humidity and methanol was also studied to evaluate the potential of sensing range.