Convective Hot Air Drying of Chilli Pepper: Process Optimization and Modelling the Drying Kinetics and Quality Attributes of Dried Product

: The objectives of this study were to evaluate the individual and interactive effects of air velocity, relative humidity, drying temperature, and drying time on the cabinet hot air drying and quality attributes of chilli pepper as well as to determine the optimum process conditions using the rotatable central composite design (RCCD) of response surface methodology (RSM). The drying kinetics was also modelled. Four factors with three levels of RCCD were utilized: air velocity (0.5-1.5 m/s), relative velocity (65-75%), drying temperature (50-70 o C), and drying time (180-360 min). Product moisture content (PMC), total plate count (TPC), protein content (PC), and carbohydrate content (CC) were evaluated as the quality attributes (responses). The results showed that the drying experimental data significantly ( p ≤ 0.001) and adequately fitted into second-order quadratic regression models with 2 R (>0.95) to describe and predict all the responses. Drying time and drying temperature are the most significant drying conditions that exerted more pronounced linear and interactive effects on the dried chilli pepper quality attributes. The predicted optimum process conditions for the production of dried chilli pepper with minimum PMC and TPC as well as maximum PC and CC were obtained to be: drying temperature, 69.98 o C, air velocity, 1.46 m/s, relative humidity, 66.57%, and drying time, 359.86 min. Four empirical models (Page, Newton, Logarithmic, and Henderson and Pabis) were fitted to the drying data and the Page model with 2 R (>0.95) best fitted the data to describe the drying kinetics. (nutritional (protein and carbohydrate contents) and microbial qualities) of dried chilli were evaluated. These drying process conditions that would result in optimum quality attributes of the dried chilli pepper were optimized using rotatable central composite design of the response surface methodology. This study is majorly different from existing few reported studies that have only focused on the investigation of the single effects of temperature, pretreatment and type of drying methods on ascorbic acid, color and capsuin of the dried chilli pepper.


Introduction
In convective hot-air drying system, the combined microscopic and macroscopic mechanisms of moisture transfer that occurs during drying is often described by drying kinetics (Salehi and Kashaninejad, 2018). The kinetics of drying is often times affected by the followings: (i) drying conditions which include drying air temperature, air relative humidity, air velocity, sample size and thickness (Sturm et al., 2012), (ii) types of dryer (Salehi and Kashaninejad, 2018), and (iii) type of materials to be dried (Salehi and Kashaninejad, 2018). Drying is a complex, unsteady, nonlinear, and dynamic process that leads to different levels of quality change depending on factors such as fresh-wet material properties, dimensions, shapes, chemical composition, and process conditions and thus needs proper monitoring (Md Saleh et al., 2019). If drying is inadequate, it gives rise to microbial infection, while over drying or unfavorable drying process set-up result in drastic quality loss. The associated quality changes with dried products include physical, nutritional properties, sensory properties, textural properties, structural properties, and rehydration properties as well as microbiological properties (Omolola et al., 2017;Guiné, 2018).
Modeling and optimization are important steps required in any thermal process for increasing process efficiency (Taheri-Garavand et al., 2017). Thus, mathematical and empirical models are commonly utilized for the prediction of material drying kinetics and for optimizing the operating parameters and conditions (Salehi and Kashaninejad, 2018). Drying time and moisture (mass) transfer during drying have been predicted using several proposed mathematical and empirical drying models (Agarry, 2017;Salehi and Kashaninejad, 2018;Turan and Firatligil, 2019;Senadeera et al., 2020). The drying model is very essential for process optimization, improvement of product quality and equipment design. It is of paramount importance to select optimal drying process conditions that would ensure the preservation of physical, chemical, sensory, and nutritional qualities of dried products without compromising energy minimization and footprint of carbon on the surroundings (Abano et al., 2014). The determination of these optimal drying conditions can be achieved through the use of (i) classical optimization technique that involves varying one-factor-at-a-time (OFAT) while holding the other factors constant and (ii) statistical optimization referred to as response surface methodology (RSM) (Myers et al., 2016). The drawbacks of classical optimization (OFAT method) are that, it is time consuming, cumbersome and does not provide information on the interactive effects of the operating factors or variables (Bas and Boyaci, 2007). The shortcomings of this OFAT method are overcome by RSM.
The RSM consists of a group of mathematical and statistical methods that can be utilized to define the relationships between the response and the independent variables (Bas and Boyaci, 2007;Myers et al., 2016). It employs statistical design of experiment (DOE) which seeks to minimize or reduce the number of experiments required. It provide information on the single or individual effects, interaction effects and curvilinear/quadratic effects of study variables as well as generates a mathematical model that can be represented graphically. The RSM has been successfully applied in various drying operations such as in osmotic dehydration (Agarry and Owabor, 2012), vacuum drying (Šumić et al., 2016), spray drying (Shishir et al., 2016), microwave drying (Omolola et al., 2015) and hot air drying operations of food products (Abano et al., 2014;Zhao et al., 2017;Ganje et al., 2018).
Chilli pepper belongs to the Capsicum frutescens family and is one of the widely used varieties of pepper. Chilli pepper is a fruit that is highly nutritive and contains macronutrients and micronutrients such as vitamins, minerals, carbohydrate, fats, dietary fiber, and proteins (Olatunji and Afolayan, 2018). It can be used as spices and flavour in dried and ground form (Isiduro et al., 1995;Giuffrida et al., 2013) and as a seasoning and thickener for making soups and stew (Kordylas, 1991). Due to its been seasonal, short shelf life of about 2 to 3 days (Mihindukulasuriya and Jayasuriya, 2015), highly perishable, and high post-harvest loss or wastage, several researchers have in recent times investigated the hot air drying of chilli pepper using cabinet or tray dryer (Tunde-Akintunde and Ajala, 2010;Muhidin and Hensel, 2012;Saengrayap et al., 2016;Montoya-Ballesteros et al., 2017), vacuum heat pump dryer (Artnaseaw et al., 2010), rotary dryer (Mihindukulasuriya and Jayasuriya, 2015), and fluidized bed dryer (Mihindukulasuriya and Jayasuriya, 2013) at different drying temperature. In all of these studies, the research workers only investigated the effects of pretreatment, drying method and temperature on the retention of color, ascorbic acid, reducing sugar, and capsaicin contents of dried chilli pepper which have resulted in a variation of findings due to different varieties of chilli pepper with different characteristics responding differently to the drying process. When drying takes place at temperatures that seems not to be high enough, microorganisms' survival can be very significant and thus should be considered as an important quality parameter for study.
Few studies as ascertained from literature have been done to investigate and evaluate the effects of pretreatment, drying methods, and temperature on the microbial load (or survival of microorganisms) of dried fruits and vegetables such as green onions, tomato, carrots, apple and cabbage (Garcia et al., 2010;Sohail et al., 2011;Martinazzo et al., 2016;Bourdoux et al., 2016;Dauda et al., 2019;Ochida et al., 2019). However, the effects or impacts of drying process conditions such as air velocity and relative humidity on the chilli drying process as well as on the quality attributes like nutritional properties (i.e. protein and carbohydrate content) and microbiological properties like microbial load (total plate count) of the dried chilli pepper have not been evaluated. In addition, there is paucity of information on the interactive effect of these drying process conditions on the hot air drying process of chilli pepper and its post-quality attributes as well as the optimization of these conditions using RSM.
Minimizing quality changes of fruits and vegetables after drying is paramount for quality assurance and final evaluation of the finished dried product. The inadequacy of drying information related to the product quality of specific varieties of chilli pepper generates qualityrelated problems for the consumer and food market. Hence, due to these available research gaps, there is the need to evolve specific drying strategies that simultaneously minimize the quality degradation of red chilli pepper, while maximizing the production efficiency under optimum operating process conditions. The main purposes of this present study are therefore to (i) evaluate the independent and combined or interactive effects of drying temperature, air velocity, relative humidity, and drying time on the drying and quality attributes of dried chilli pepper, (ii) optimize the drying process conditions that would improve or maximize the dryproduct quality in relation to minimum moisture content and minimum total plate count (microbial load) as well as maximum protein and carbohydrate contents using RSM, and (iii) model the kinetics of drying using known empirical drying models in the literature.

Materials and equipment
The cabinet-tray dryer (65 cm x 55 cm x 90 cm) used for this experiment was self-designed and fabricated (Fig. 1). It was designed and fabricated using aluminum sheets lined with layer of 2.5 cm thick armaflex used as insulator and wrapped with 0.5 mm thick heat resistant aluminum foil to control heat loss by conduction. The drying chamber had slots for each drying tray which are perforated for effective airflow within the chamber. The dryer is made up of three sections, the energy source (electricity), blower and the drying cabinet sections. The energy source is located behind the dryer while the blower is located in the middle of the drying chamber and has a power rating of 0.5 hp. The blower helps in circulating heat for effective and efficient heat flow rate within the drying chamber. Humidification of the air entering into the drying chamber was done using a water aerosol (i.e. 1 L water trigger sprayer (Sprayon Model SO-075)) (Sigge et al., 1998) manually operated behind the air-blower until the desired relative air humidity is attained which was measured using a hygrometer (PCE-555 Model).
The velocity of the air in metres per seconds (m/s) delivered by the air-blower was measured with the use of a hot-wire anemometer (PCE-009 Model) linked to the air-blower. The inside and outside temperature of the dryer was checked using a mercury thermometer.
The fresh chilli pepper samples were purchased from local market at Idi-Oro (6.5219° N, 3.3565° E), Lagos State of South-West Nigeria. The samples were sorted, washed with tap water, cleaned with tissue paper and weighed. The samples were kept for some hours to achieve equilibrium temperature with the environment before usage as to obtain a better result. This is because the sample temperature could be higher than the temperature of the environment.

Preliminary drying procedure
The fresh chilli pepper samples with an average moisture content of 84.98% were not subjected to any form of pre-treatment. The chilli pepper samples were sliced into a thickness of 2 mm and 1 kg of the samples were weighed and loaded into the cabinet-tray dryer. The chilli pepper drying was carried out (using one factor-at a-time (OFAT) procedure) at a temperature of 40 o C, an air velocity of 1.5 m/s and a relative humidity of 60%, respectively. At intervals of 30 min, the samples were withdrawn to measure the weight until a constant weight was achieved.
This procedure was repeated for temperature (50 -70 o C), air velocity (0.3 -2.0 m/s), and relative humidity (65 -80%), respectively. The whole experimental tests carried out were done in triplicate (n = 3) and the mean values were utilized.

Design of drying experiment using rotatable central composite design (RCCD)
RSM of Design-Expert 6.0.8 (State Ease, USA) software which involved four factors with three levels rotatable central composite design (RCCD) was used. The values of the independent drying process variables used for the RCCD are: air velocity ( 1 X ), 0.

Product quality analysis
The properties of the dried product analyzed as indices for quality are PMC, PC, CC, and TPC.
The PMC of plantain samples was gravimetrically determined by the method of oven drying in which the samples were dried to constant weight at 105 o C (AOAC, 2015). Kjedahl nitrogen standard method (AOAC, 2015) and the phenol-sulphuric acid method (Dubois et al., 1956) was utilized for the determination of PC and CC, respectively. The TPC was performed using standard pour plate method (Harrigan and McCance, 1976). The initial values of PMC, PC, CC, and TPC in the fresh chilli pepper are 5.66 (kg moisture/kg dry matter or 84.98% wet basis), 3.09%, 5.56%, and 5.70 × 10 2 cfu/g, respectively.

Statistical analysis
The chilli pepper drying experimental data was analyzed using the Design-Expert 6.08 software ((state-Ease, Inc., Minneapolis MN, USA)). A quadratic polynomial model as given in Eq. (1) was used for the prediction of the response variables based on multiple linear regression analysis.
where Y = predicted response, o  = offset term (i.e. the constant that fixed the response at the experiment centre point), are the drying process independent variables. The quadratic polynomial model adequacy to fit the okra drying experimental data was assessed and evaluated using the variance analysis (ANOVA) and regression analysis with the following components: lack of fit, coefficient of variation (CV), coefficient of determination ( 2 ), Adjusted 2 , Predicted 2 , and Adequate Precision. The model terms were evaluated for each response by the probability value (i.e. p-value).

Optimization of drying process conditions and dried product quality attributes
In optimizing the drying process conditions for chilli pepper, the desirability function in numerical optimization tool of RSM in Design-Expert statistical software was applied. In carrying out the optimization process, the target criteria or objective was set as maximum value for the response variable (protein content) and minimum values for the response variables (moisture content and total plate count), while the values of the four independent variables were set at the ranges being studied. The predicted experimental drying process variables/conditions with the highest desirability were selected and recorded. The predicted and observed experimental results of the responses obtained at optimum drying process conditions were recorded.

Verification of predicted optimum process conditions
For verification of the predicted optimum drying process conditions as obtained using the numerical optimization tool, laboratory chilli pepper drying experiments were conducted under the predicted optimum drying process conditions obtained. The experimental observed values of the response variables (protein content, carbohydrate content, moisture content and total plate count) were recorded and then compared with the predicted values in order to check the validity of the models. The percentage error (%E) between the experimental value and the predicted value is calculated as provided in Eq. (2) (Agarry and Ogunleye, 2012): Where i Z is the predicted value and j Z is the experimental value.

Empirical modelling of the kinetics of chilli peper drying
The empirical drying models that were fitted to the chilli pepper kinetics data include the Page Logarithmic model: Henderson and Pabis model: and a , c , n , are empirical constants; k , drying constant; t , drying time; MR , moisture ratio.
The model that best fit the data was selected according to statistical criteria. The best of fit was determined using two parameters: highest values for coefficient of determination ( 2 R ) and smallest values for root mean square error (RMSE) using equations (8) -(9), respectively.
Where the experimental moisture ratio value, is the predicted moisture ratio value and, N is the number of observations. These modules (Eqs. (8) and (9)) have been used to evaluate the goodness of fit of different mathematical models (Agarry, 2017;Turan and Fıratlıgil, 2019).

Statistical modelling and analysis of variance (ANOVA)
The drying experimental data was fitted to the quadratic regression model (Eq. (1)) and the coefficients of the model were obtained using the Design Expert (6.08) software. The coefficients of the model equation for all the process variables and all the responses are provided in Table 3.
In order to test the fit of the model, variance analysis (ANOVA) where the Fischer test (Fvalue), probability value (p-value) and coefficient of determination ( 2 R ) (measures the degree of fit goodness) were determined and evaluated (  (> 35) which indicates that the noise to signal ratio for all the respective models are adequate.
The ANOVA for all the response quadratic regression models revealed that the models are highly significant, as evident from the very low probability value (< 0.0001) of the F -test. In addition, values of Prob > F less than 0.05 indicate that the model terms are significant and values greater than 0.1000 indicate that the terms of the model are insignificant.

Evaluation of the effect of drying process conditions on product quality attributes
After the carrying out of 30 experimental runs of the RCCD, the results of the statistical experiment are presented in Table 2.
Product moisture content and total plate count PMC is the most important property that is intimately associated with the entire quality and long shelf life of the food product (Shishir et al., 2016). Appropriate moisture or water content in food materials is very necessary to avoid microbial growth (i.e. microbial load) and product deterioration and degradation (Silva et al., 2017). Hence, low moisture contents are recommended (Silva et al., 2017). The TPC sometimes referred to as standard plate count is a widely used technique for evaluation of microorganisms in foods as it provides information concerning the total microbial load in foods (Brackett, 1993). Thus TPC (microbial) analysis was done on the dried products to determine whether the products are free from microorganisms or pathogens and hence, safe for consumption. Table 2 show the results for the PMC and TPC of the dried chilli pepper product after the drying process. From Table 2, it is observed that run numbers 1 and 2, 3 and 4, 5 and 6, 7 and 8, 9 and 10, 11 and 12, 13 and 14, and 15 and 16 were carried out at the same drying conditions of relative humidity, temperature, and drying time but at varying air velocities and the results shows that both the PMC and TPC decreases as the air velocity (A) gradually increased from low level of 0.5 m/s (coded value -1) to high level of 1.5 m/s (coded value +1) as better represented by a diagnostic perturbation plot presented in Fig. 2(a) and 2(b) for PC and TPC, respectively. This observation indicates that increase in air velocity results in increased moisture removal from the fresh sample and thus a decrease in the amount of moisture (PMC) retained in the dried product. The reduction in the moisture content led to the observed drastic reduction in the microbial load (TPC). Over the range of coded value -1 to coded value +1 of air velocity, the PMC and TPC did not changed over a wide range as observed in Fig. 2(a) and 2(b).
Run numbers 1 and 3, 2 and 4, 5 and 7, 6 and 8, 9 and 11, 10 and 12, 13 and 15, 14 and 16, and 19 and 20 were performed at the same drying conditions of air velocity, temperature and drying time but at different relative humidity and the results revealed that the PMC and TPC increases as the relative humidity (B) gradually increased from low level of 65% (coded value -1) to high level of 75% (coded value +1) as better represented by a diagnostic perturbation plot presented in Fig. 2(a) and 2(b) for PC and TPC, respectively. This observation showed that increase in relative humidity leads to decrease in moisture removal from the fresh samples and hence an increase in the amount of moisture retained in the dried product (i.e. higher PMC). The increase in the PMC led to the observed rise in the microbial load (TPC).
This observation of increased PMC due to increase in relative humidity has similarly been reported for the tray drying of green bell pepper (Sigge et al., 1998), celery leaves (Román and Hensel, 2001), and apple (Zlatanovic et al., 2013), respectively.
Effect of drying temperature was investigated at the same condition of air velocity, relative humidity and drying time (run numbers 1 and 5, 2 and 6, 3 and 7, 4 and 8, 9 and 13, 10 and 14, 11 and 15, 12 and 16, and 21 and 22) and the results demonstrated that increase in drying temperature (C) from low level of 50 o C (coded value -1) to high level of 70 o C (coded value +1) as better represented by a diagnostic perturbation plot presented in Fig. 2(a) and 2(b) for PC and TPC, respectively led to respective decrease in the PMC and TPC. It is observed from Fig. 2(a) and (b) that over the range of coded value -1 to coded value +1 of drying temperature, the PMC and TPC respectively changed over a wide range. This observation revealed that increase in the drying temperature resulted to increase in the amount of moisture removed from the fresh samples and thus a decrease in the amount of moisture or water retained in the dried product (i.e. lower PMC). The reduced PMC resulted in the observed decrease in the TPC. A decrease in moisture content due to increase in drying temperature has been   (Idah et al., 2010;Zhao et al., 2017;Senadeera et al., 2020).
Overall, from the results in Table 2, at temperature range of 50 -70 o C; air velocity range of 0.5-1.5 m/s; relative humidity range of 65-75%; and drying time range of 180-360 min; the TPC (microbial load or total viable bacterial count) correspondingly ranged from 0.468×10 2 -0.306×10 2 cfu/g; 0.473×10 2 -0.306×10 2 cfu/g; 0.465×10 2 -0.306×10 2 cfu/g; and 0.468×10 2 -0.306×10 2 cfu/g. These corresponding values are lower than the value of 5.70×10 5 cfu/g obtained for the fresh chilli pepper. The differences observed between the values for the dried product and the fresh product may be attributed to the synergistic effects of thermal inactivation and dehydration inactivation based on temperature intensity, drying environment characteristic (such as relative humidity, pressure, air velocity etc) and drying time (Bourdoux et al., 2016;Dauda et al., 2019). The high moisture content which invariably means high water activity of the fresh chilli pepper might be responsible for the high TPC or microbial load. This is because microorganisms thrive more in such favorable moisture environment. The TPC values of the dried product are within the accepted range of <10 5 cfu/g specified by WHO (1998) and International Commission on Microbiological Specification for Foods (ICMSF) (Van Schothorst, 1998). Thus the dried chilli pepper are within the limit acceptable for consumption.
It can be observed from Table 4 that air velocity, relative humidity, drying temperature, and drying time had significant effect (p ≤ 0.05) on both the PMC and TPC. Furthermore, it is observed that among all the drying process conditions, drying time exerted more pronounced linear effect (i.e. maximum effect) as indicated by higher regression coefficient value. That is, both the PMC and TPC are mostly and negatively influenced by drying time followed by the drying temperature, air velocity and positively influenced by relative humidity, respectively.
The quadratic regression equations for both the PMC and TPC relating to coded levels of drying process parameters were respectively obtained as Eq. (10)

Protein and carbohydrate contents
Proteins are extremely important constituents of living cells in that they act as structural molecules, represent storage forms of carbon and nitrogen as well as regulate metabolism (Famurewa and Olumofin, 2015). Table 2 provides the results for the PC of the dried chilli pepper product after drying. It can be observed that run numbers 1 and 2, 3 and 4, 5 and 6, 7 and 8, 9 and 10, 11 and 12, 13 and 14, and 15 and 16 were performed at the same drying conditions of relative humidity, temperature, and drying time but at varying air velocities (A) and the results depicts that the PC and CC increases as the air velocity gradually increased from low level of 0.5 m/s (coded value -1) to high level of 1.5 m/s (coded value +1) as better represented by a diagnostic perturbation plot presented in Fig. 2(c) and 2(d) for PC and TPC, respectively. This increase may probably be as a result of increased moisture/water removal leading to solute/solid concentration of the PC and CC in the dried product (Babanovska-Milenkovska et al., 2016). Fig. 2(c) and 2(d) also show that over the range of coded value -1 (0.5 m/s) to coded value +1 (1.5 m/s) of air velocity, the PC and CC respectively did not changed over a wide range.
Run numbers 1 and 3, 2 and 4, 5 and 7, 6 and 8, 9 and 11, 10 and 12, 13 and 15, 14 and 16, and 19 and 20 were performed at the same drying conditions of air velocity, temperature and drying time but at different relative humidity (B) and the results as provided in Table 2 revealed that the PC and CC respectively decreased in relation to steady increase in the relative humidity from low level of 65% (coded value -1) to high level of 75% (coded value +1) as better represented by a diagnostic perturbation plot presented in Fig. 2(c) and 2(d) for PC and CC, respectively. This observation may be due to the fact that there is a decrease in moisture removal from the fresh samples as a result of increased relative humidity and hence decrease in the degree or level of PC and CC solutes concentration.
Effect of drying temperature (C) was investigated at the same condition of air velocity, relative humidity and drying time (run numbers 1 and 5, 2 and 6, 3 and 7, 4 and 8, 9 and 13, 10 and 14, 11 and 15, 12 and 16, and 21 and 22) and the results as given in Table 2 demonstrates that an increased drying temperature from low level of 50 o C (coded value -1) to high level of the results indicates that the PC and CC increased with increase in drying time from low level of 180 min (coded value -1) to high level of 360 min (coded value +1) as better represented by a diagnostic perturbation plot presented in Fig. 2(c) and 2(d) for PC and TPC, respectively. Fig.   2(c) and 2(d) also illustrates that over the range of coded value -1 (180 min) to coded value +1 (360 min) of drying time, the respective PC and CC changed over a wide range. This observation is as a result of increased moisture removal from the fresh samples due to increase in the drying time and thereby causing the PC to be more concentrated.
Generally as observed from the results in Table 2 R values obtained for these PC and CC response models are 0.9886 and 0.9930 respectively, which indicates a good model fit.
The interaction effects of the drying process variables on PC and CC are respectively illustrated in the three dimensional (3D) response surface plot presented in Figs. 5 and 6. Figs. 5(a) and 6(a) shows the interaction effect between relative humidity and air velocity on PC and CC, respectively. The PC and CC are significantly (p ≤ 0.05) and positively influenced by the interaction of relative humidity and air velocity. Since the target is to respectively maximize PC and CC, Figs. 5(a) and 6(a) demonstrates that at a fixed drying temperature and drying time, decrease in relative humidity with simultaneous increase in the air velocity resulted in an increase in both the PC and CC. This shows that high air velocity and low relative humidity produces a dried product that is high in PC and CC, respectively.
The influence of the interaction between air velocity and drying temperature on the respective PC and CC is significantly (p ≤ 0.05) positive and this is depicted in Figs. 5(a) and 6(b), respectively. The 3D plots indicates that at a fixed relative humidity and drying time, the PC and CC of the dried chilli pepper respectively increased with increase in both the air velocity and drying temperature. The response plots shows that both the air velocity and drying temperature has individual significant positive influence on both the PC and CC of dried chilli pepper. However, the influence of drying temperature is more than that of air velocity as the individual coefficient value is higher for drying temperature (PC, 0.44; CC, 0.56) than for air velocity (PC, 0.22; CC, 0.29). Therefore, Figs. 5(b) and 6(b) suggest that at a fixed relative humidity and drying time, the air velocity and drying temperature has to be increased for a dried chilli pepper with higher PC and CC to be produced.  (Table 3). The 3D response plots in Figs. 5(c) and 6(c) displays that at a fixed relative humidity and drying temperature, both the PC and CC of the dried product increased as both the air velocity and drying time mutually increased. That is, dried product with both high PC and CC will be obtained at a high air velocity and high drying 5(e) and 6(e), respectively. There is a significant (p ≤ 0.05) positive mutual impact of drying temperature and drying time on both the PC and CC (Table 3). The plots in Figs. 5(e) and 6(e) respectively show that the drying of chilli pepper at a fixed air velocity and relative humidity, there is an increase in both the PC and CC of the dried chilli pepper as the drying temperature and drying time mutually increased. That is, a dried chilli pepper with high PC and CC will be obtained at both high temperature and high drying time. Drying time exerted more positive effect on both the PC and CC than the drying temperature as the individual linear coefficient is higher (PC, 0.78; CC, 1.01) than that of drying temperature (PC, 0.48; CC, 0.56).

Optimum drying process conditions for chilli pepper and verification
Predicted optimum drying conditions for the production of dried chilli pepper were determined using the numerical optimization tool of Design Expert (6.08) statistical software based on desirability function. According to this tool, the target goal for air velocity, relative humidity, CC of 37.22%, respectively. The desirability function was 1.000 (Fig. 7).
Verification experiment was carried out at these predicted optimum drying process conditions to verify the minimum PMC, minimum TPC, maximum PC, and maximum CC, respectively. From the verification experiment, the minimum PMC, minimum TPC, maximum PC and maximum CC obtained for the dried chilli pepper were 0.87 kg moisture/kg dry weight, 29.90 cfu/g, 12.90% and 37%, respectively. The percent errors between predicted and validated experimental or actual values calculated according to Eq. (2) were found to be -3.45%, -2.04%, 0.23% and -0.59% which explicitly indicated that there are no significant differences between them.

Modelling the thin layer drying kinetics
The preliminary experimental results of moisture content variation with drying time at different air velocity, relative humidity and drying temperature were fitted to the four different drying model (Newton, Page, Logarithm, and Handerson and Pabis) equations given in Eq.
(6). The parameters of the different model equations were determined with the use of the nonlinear regression (NLR) tool of MATLAB 6.5 version computer software. The model that had the highest coefficient of determination ( ) and the lowest root mean square error (RMSE) was selected (Senadeera et al., 2020). The values of and RMSE obtained by the NLR analysis are presented in Table 5. The results in Table 5 show that at different air velocity, relative humidity and drying temperature, the value of was greater than 0.90, indicating a good fit (Shahzad et al., 2013;Afolabi and Agarry, 2014). However, the value for the Page model at the different air velocity, relative humidity and drying temperature was comparatively the highest and with the lowest RMSE value. Thus, the Page model may be proposed to be the best model to describe the drying kinetics or behavior of chilli pepper. Similar observation has been reported for the HAD of red pepper (Agarry, 2017), tomato (Doymaz, 2007), red chillies

Conclusion
Response surface methodology was effectively used to describe the effects of drying conditions in the retention of carbohydrate and protein (nutritional properties) and also, the decline of moisture content (physical property) and total plate count (microbial quality). The most significant drying conditions that exerted more pronounced linear (individual) and interactive effects on the quality attributes (moisture content, protein content, carbohydrate content and total plate count) of dried chilli pepper in the course of convective hot air drying are drying time and drying temperature while the least significant are air velocity as well as relative humidity. Second-order quadratic regression models can adequately predicts the quality attributes of dried chilli pepper with respect to drying process conditions of air velocity, relative humidity, temperature and drying time in the course of convective hot air drying. The kinetics of chilli pepper drying can most suitably be described by Page empirical drying model. The drying treatments generally retained the protein and carbohydrate contents (nutritional properties) of fresh chilli pepper as well as reduced the microbial load to the acceptable limit allowed for consumption. However, further studies will be performed in our laboratory to evaluate the effects of physical and chemical pretreatments, temperature and drying time on the nutritional and microbial qualities of different varieties of chilli pepper using different types of drying equipment.

Statement of novelty
The novelty of this present work lies in the fact that the individual and interactive or combined effects of air velocity, relative humidity, temperature and drying time on the quality attributes (nutritional (protein and carbohydrate contents) and microbial qualities) of dried chilli pepper were evaluated. These drying process conditions that would result in optimum quality attributes of the dried chilli pepper were optimized using rotatable central composite design of the response surface methodology. This study is majorly different from existing few reported studies that have only focused on the investigation of the single effects of temperature, pretreatment and type of drying methods on ascorbic acid, color and capsuin of the dried chilli pepper.