Standardizing Indigenous Beverage Production: A Comparative Evaluation of Automated and Traditional Zobo Processing Methods Based on NAFDAC Nutritional Compliance ^†

Abstract

This study compares the nutritional consistency and regulatory compliance of automated versus traditional zobo juice production processes, utilizing the nutritional compliant requirements specified by Nigeria’s food consumption regulatory body as benchmarks. The automated system exhibits innovation in precise ingredients measurement, PLC smart temperature regulation, two-stage in-line filtration process, enhanced stirring mechanism, and pasteurization chamber. Five production batches from each method were analyzed for key nutrients, including energy, carbohydrates, sugars, fiber, protein, sodium, vitamin C, and iron. The automated method yielded juice with energy values ranging from 55.86 to 60.12 kcal, closely aligning with the NAFDAC nutritional requirements compliance of 60.00 kcal. In contrast, the traditional method showed greater variability, with energy values ranging from 45.85 to 58.12 kcal. Similarly, the automated process demonstrated lower mean deviation from the NAFDAC carbohydrate requirement (15.00 g), with a standard deviation (SD) of 0.23, compared to 1.57 for the traditional process. ANOVA results revealed statistically significant differences between the two methods across most nutrient parameters, with the automated method consistently exhibiting greater precision. Sensory evaluations further indicated that juice produced through automation remained acceptable for up to six days, compared to only three days for traditionally prepared juice. These findings highlight the advantages of automation in enhancing nutritional accuracy, minimizing batch-to-batch variability, and improving adherence to regulatory standards. This study supports the adoption of automated methods in local beverage production to achieve higher product quality and consistency.

1. Introduction

Zobo is a popular Nigerian beverage produced from the petals of Hibiscus sabdariffa, valued for its unique taste, low cost, and numerous health benefits [1]. Its low cost compared to other commercial soft drinks has made it a staple across diverse socioeconomic groups [2]. Beyond its sensory appeal, zobo contains essential nutrients and bioactive elements, which contribute to its therapeutic roles in managing blood pressure, cholesterol levels, blood sugar, liver function, and weight [3,4,5].

In traditional zobo processing, dried hibiscus petals are usually sorted and cleaned by hand, then boiled over open flames with natural ingredients like pineapple peels, ginger, and cloves. Following extraction, the mixture is allowed to cool before being sweetened, filtered through a cloth or mesh, and served cold. Notwithstanding its ease of use, this approach has a number of drawbacks, such as inaccurate ingredient measuring, a lack of hygienic control, a high labor cost, energy waste from prolonged heating, and uneven product quality [6].

A key limitation in the traditional process is its absence of controlled heating, which often results in the breakdown of thermolabile nutrients like vitamin C and anthocyanins [7]. Inadequate filtration and reuse of cloth filters pose additional risks of microbial contamination and reduced clarity [8].

Despite its nutritional appeal, traditional zobo processing is often undermined by lack of process standardization, poor temperature control, and unhygienic filtration practices [2]. Manual boiling without precise thermal regulation can result in degradation of heat-sensitive compounds like vitamin C and anthocyanins [9], or conversely, enable microbial instability due to inadequate pasteurization [10]. Using reusable cloth filters or basic sieves in traditional filtration compromises clarity and raises microbial safety concerns, particularly when not properly cleaned or sterilized [11]. Studies in Nigeria demonstrate that such practices frequently allow microbial counts to exceed acceptable safety thresholds [6,12].Traditional processes fail to effectively control shelf-life or microbial stability and are associated with rapid deterioration when not refrigerated [13].

Attempts have been made to diversify and mechanize the processing methods of zobo to offer options that cater to different needs, from immediate consumption to long-term storage [14,15].

Although the existing literature shows some progress in mechanizing zobo production, most efforts remain partial and small-scale, lacking the integration, intelligence, and scalability needed for modern commercial operations. In particular, the absence of precise process control, especially in temperature regulation and filtration during extraction and pasteurization, contributes to inconsistent quality, nutrient loss, and reduced shelf life [16,17]. This highlights a significant opportunity for innovation in developing a fully automated, smart, temperature-controlled zobo processing plant that effectively addresses these gaps.

Given these challenges, this study proposes and evaluates a fully integrated, smart-regulated zobo processing plant designed to improve efficiency, consistency, and compliance with national health and safety standards. The system incorporates innovation in automated temperature control, continuous two-stage filtration, homogenized mixing, and rapid cooling through a heat exchanger governed by a centralized programmable logic controller (PLC).

This paper presents a comparative analysis of zobo produced using this automated plant and the traditional method. The assessment is based on nutritional compliance with NAFDAC requirements, shelf life, sensory evaluation, and process consistency across five production batches. This study aims to demonstrate the advantages of automation in standardizing indigenous beverage production while promoting energy efficiency, hygienic safety, and economic scalability.

2. Materials and Methods

2.1. Materials

The materials used in this study include food-grade materials, and a range of processing and analytical equipment. All materials were selected based on food safety standards and relevance to automated and traditional zobo processing methods.

2.1.1. Raw Materials

Parameter	Specification
i. Dried zobo leaves (hibiscus petals)	50 g per L of water
ii. Ginger	15 g
iii. Cloves	5 g
iv. Pineapple	10 g
v. Water	900 g
vi. Citric acid	5 g

2.1.2. Zobo Processing Plant

i. 

Boiling, extraction, and blending tanks components (capacity: 150 L, material: food-grade stainless steel (SS316), double-walled jacketed tank, electric heater (2 kW), 50 mm thick Rockwool insulation).

ii. 

Stirring mechanism (power rating, 1 HP (0.75 kW, 220–240 V, single-phase).

iii. 

Two-stage in-line filtration units with filtrate discharge (mesh basket filter, 5–10 microns, SS316 housing with replaceable food-grade cartridges).

iv. 

Pasteurization chamber (plate heat exchanger (PHE), 150 L, SS316 corrugated plates, inlet 25 °C; outlet up to 90 °C, 0.5 mm thickness, 20 plates).

2.1.3. Sensors and Control Components

i. 

Flow meters (for liquid volume control), flow range: 0.1 to 100 L per minute (L/min), accuracy: ±1% of full scale.

ii. 

pH sensors (measurement range: pH 0–14 ±0.1, operating temperature: 0–80 °C.

iii. 

Pressure sensors (range: 0–10 ±0.5%, signal output: 4–20).

iv. 

Proximity sensors (sensing range: 10–50 mm, capacitive).

v. 

PLC (programmable logic controller) unit (I/O capacity: minimum 16 digital inputs/outputs and 4 analog inputs, MODBUS, Ethernet/IP).

vi. 

Temperature sensors: PT100 RTD temperature monitoring (up to 110 °C ± 0.01).

2.1.4. Experimental Design

This experimental setup enabled a structured comparison of the nutritional and sensory characteristics of zobo juice produced through automated and traditional methods, with the goal of validating the effectiveness and consistency of the automated system.

This study involved a comparative batch analysis to determine the nutritional consistency of zobo juice produced using the Automated Zobo Processing Machine and the traditional manual processing. The NAFDAC nutritional compliance requirements were used as the reference guide.

Five batched productions for both the automated zobo machine and the traditional zobo production methods, packaged to achieve adequate nutritional quality, maintaining standardized ingredients proportions and hygienic safety, were carried out. Proximate analysis was used to determine the product’s composition, and the results were compared with the NAFDAC nutritional compliance requirement. The water and material compositions in subsequent batch production were adjusted slightly to align the product’s composition with the NAFDAC nutrient-compliant composition. The pH of the batch production was monitored with a calibrated pH meter. The production was also subjected to continuous sensory evaluation.

2.1.5. Design Methodology

The Automated Zobo Processing plant was developed through a systematic design approach aimed at addressing the limitations of the traditional method. The following design methodology was adopted.

Material analysis, selection, and configuration to achieve PLC-controlled, insulated stainless steel tanks for boiling and extraction and ingredients blending, equipped with a motorized stirring mechanism for uniform extraction, and homogeneous mixing of sweeteners and natural preservatives. Integration of a pasteurization chamber consisting of plate heat exchanger rapidly cools the juice from 75 °C to around 30 °C to inhibit microbial growth.

Incorporation of two-stage in-line filtration units consisting of stainless steel meshes and sediment traps was conducted to improve purity and eliminate microbial contamination.

The automation framework was built around a PLC system programmed to control heating cycles, stirring durations, and flow regulation, all monitored with sensors. The design drawings are given in Appendix A.1 and Appendix A.2.

The entire system was designed in compliance with NAFDAC safety and hygiene standards, as well as international guidelines of [18,19].

To validate performance, five production batches were processed and evaluated for nutrient quality, microbial stability, and conformity with regulatory benchmarks. The results were analyzed using Python 3.11 via panda v2.x ANOVA and other statistical tools. “The schematic diagram of the plant layout” is provided in Figure A1 (see Appendix A.1) and Figure A2 (see Appendix A.2).

2.1.6. Automated Zobo Processing Plant Operating Principles

The zobo ingredients were selected according to specification, washed, and introduced into the boiling and extraction tank, following the general principles outlined in [20], for hygienic food processing. The PLC-regulated heating system heats the zobo ingredients to 95 °C within 30 min and maintains the temperature for 15 min. A motor-assisted mechanism is activated to stir the mixture for optimum extraction. The extract is discharged through a flow-regulated line and subjected to a two-stage filtration process equipped with filtrate traps. Filtrates are removed through valves along the flow line. The mixture then moves into a heat-insulated blending tank, where sweeteners and natural preservatives were added at a controlled temperature of 75 °C. Homogeneous mixing is ensured using an automated stirrer before the product is rapidly cooled to approximately 30 °C using a plate heat exchanger, in accordance with thermal process controls specified by [19].

2.1.7. Batch Productions Techniques

This study employed a comparative batch production analysis to evaluate the nutritional consistency and quality of zobo juice produced using two different methods of the Automated Zobo Processing Machine and the Traditional Manual Processing Method. Evaluation of each production batch was benchmarked against NAFDAC’s established material and nutrient guidelines for zobo juice to ensure quality consistency. The compositions of the NAFDAC materials and nutritional compliance requirements are indicated in

Table 1. NAFDAC materials compliance composition for 1 L zobo juice.

S/n	Ingredients	Amount per 1 L (g)	Percentage Composition %
1	Dried zobo leaves	50.00	5.00
2	Water	900.00	90.00
3	Ginger	15.00	1.50
4	Clove	5.00	0.50
5	Pineapple juice	10.00	1.00
6	Natural honey	10.00	1.00
7	Lemon juice	5.00	0.50
8	Citric acid	5.00	0.50

and

Table 2. NAFDAC nutritional compliance zobo juice composition.

1	Nutrients	Amount per Serving (250 mL)	% Daily Value
2	Energy	60 kcal	3
3	Carbohydrate	15 g	5
4	Sugar (total)	14
5	Added sugar	12	24
6	Dietary fiber	0.6	2
7	Protein	0.3	<1%
8	Fat	0	0
9	Sodium	5	<1%
10	Potassium	50 mg	1%
11	Vitamin C	8 mg	13
12	Iron	I mg	6

[20].

A total of five production batches were carried out for each method under standardized processing conditions. Ingredients used across all batches were of the same source and quality, and were measured and prepared using consistent proportions. Hygienic safety and packaging standards were strictly adhered to for both production methods to ensure comparability.

2.1.8. Proximate Analysis

Each batch was subjected to proximate analysis to determine key nutritional parameters such as moisture content, crude protein, fat, fiber, ash, and carbohydrate content. The results were compared with NAFDAC nutritional specification guidelines to assess compliance and consistency.

To ensure closer alignment with the target nutritional profile, minor adjustments to the water and ingredient compositions were made in subsequent batch productions where deviations from the NAFDAC standard were observed. These adjustments were applied equally to both production methods to maintain experimental integrity.

Samples were analyzed for Vitamin C, Iron, Calcium, Magnesium, Sodium, Potassium, Total Sugar, and pH. Measuring equipment included a UV–visible spectrophotometer (sensitivity: ±0.01 absorbance units), digital pH meter (±0.01), and flame photometer (±0.1 ppm).

2.1.9. Sensory Evaluation

The sensory attributes of each zobo sample were evaluated daily over a six-day period by a trained panel. Attributes such as flavor, aroma, and appearance were scored based on a structured scale ranging from “very good” to “bad”. Bottled samples of the zobo juice stored under ordinary environmental conditions were observed in line with the stated criteria for six days. The evaluation was graded according to the ranking indicated in

Table 3. Sensory evaluation ranking.

Assessment	Ranking
Very good	05
Good	04
Acceptable	03
Manageable	02
Bad	01

.

3. Results and Discussion

3.1. Nutritional Composition Analysis

Table 4. Average nutrient composition of zobo juice (per 250 mL).

	NAFDAC	Average Value		% Deviation		Standard Deviation
Nutrients		Auto	Trad	Auto	Trad	Auto	Trad
Energy (g)	60.00	58.02	53.42	3.30	11.00	2.602	9.060
Carbohydrate (g)	15.00	14.82	13.93	1.20	7.10	0.228	1.575
Sugar (g)	14.00	13.86	12.86	1.00	8.10	0.140	1.480
Fiber (g)	0.6	0.57	0.51	5.00	15.00	0.048	0.102
Protein (g)	0.3	0.25	0.20	16.70	33.30	0.059	0.127
Sodium (mg)	5.00	4.91	4.46	1.60	11.30	0.107	0.895
Vitamin C (mg)	8.00	7.87	7.10	1.16	11.30	0.212	0.991
Iron (mg)	1.00	0.90	0.73	10.00	27.00	0.133	0.235

compares the average nutritional values of zobo juice produced using the Automated Zobo Processing Machine and the traditional method against the NAFDAC-nutritional requirements compliance.

The automated method consistently achieved nutrient values closer to the NAFDAC nutritional requirements compliance, with deviations mostly within 0–5%, except for protein (16.7%) and iron (10%). The traditional method showed higher and more erratic deviations, with significant deficiencies in protein, iron, and fiber, likely due to uncontrolled heating and a lower nutrients extraction capacity compared to the automated method. Vitamin C retention was better preserved in the automated method, due to its smart temperature control and rapid cooling, which minimized thermal degradation of nutrients.

3.2. ANOVA Analysis

The Python 3.11 via panda v2.x ANOVA analysis was conducted to compare the nutrient compositions of zobo juice produced through the automated and traditional methods against the NAFDAC nutritional compliance requirements as indicated in

Table 5. ANOVA analysis of batch productions.

Nutrient	Method	NAFDAC Std	Mean	Std Dev	F-Value	p-Value
Energy	Automated	42.0	41.1	0.85	4.52	0.0241
	Traditional		38.9	1.24	5.01	0.0218
Carbohydrates	Automated	10.0	9.87	0.31	5.72	0.0175
	Traditional		9.15	0.42	4.89	0.0253
Sugars	Automated	7.0	6.82	0.22	7.83	0.0057
	Traditional		6.45	0.35	6.77	0.0090
Fiber	Automated	0.5	0.48	0.04	2.13	0.0833
	Traditional		0.42	0.07	6.01	0.0085
Protein	Automated	1.2	1.17	0.05	6.03	0.0156
	Traditional		1.05	0.09	7.22	0.0128
Sodium	Automated	8.0	8.12	0.61	1.91	0.1211
	Traditional		7.22	0.72	4.45	0.0392
Vitamin C	Automated	35.0	32.5	1.8	1.77	0.1258
	Traditional		28.4	2.2	6.14	0.0206
Iron	Automated	4.5	4.38	0.21	5.32	0.0290
	Traditional		3.87	0.33	12.11	0.0003

.

The automated method shows high consistency and partial conformity with NAFDAC standards, with about 62.5% of nutrients showing statistically significant deviation, but with relatively small magnitudes. The traditional method exhibits statistically significant deviations from the NAFDAC nutritional compliance requirements in 100% of tested parameters, indicating lower conformity and greater variability.

The automated process aligns more closely with NAFDAC nutritional compliance requirements than the traditional method. This is evidenced by fewer statistically significant deviations and higher p-values in certain nutrients, especially those susceptible to manual measurement errors (e.g., sodium and vitamin C). Therefore, the automated method is more nutritionally reliable and consistent for NAFDAC zobo nutritional compliance requirements productions.

3.3. Batch Performance Analysis

Figure 1 shows the compliance with the NAFDAC nutritional requirements for the automated and traditional methods batches. Automated batches demonstrated higher convergence toward NAFDAC nutritional compliance requirements, while traditional batches fluctuated due to uncontrolled manual operations. This indicated the close alignment of the Auto to NAFDAC nutritional compliance requirements.

3.4. Sensory Evaluation and Shelf Life

Sensory evaluations were conducted over six days.

Table 6. Sensory scores (percentage of panel ratings).

Assessment	Day One		Day Two		Day Three		Day Four		Day Five		Day Six
	Auto	Trad	Auto	Trad	Auto	Trad	Auto	Trad	Auto	Trad	Auto	Trad
Very good (%)	100	100	100	70	100		20		10
Good (%)				30			80		90		60
Acceptable (%)						10					40
Manageable (%)						40
Bad (%)						40		100		100		100

summarizes the panel’s assessment.

The automated zobo maintained high acceptability for up to six days, with minor sensory degradation. The traditional zobo showed rapid spoilage by day four, losing all acceptability by day five, due to lack of pasteurization and microbial control. This confirms that automated thermal regulation and hygienic filtration significantly increase shelf life and consumer acceptance.

3.5. Process Accuracy and Stability

Ingredient measurement accuracy in the automated system was ±2% compared to higher subjective variation in manual processing. Temperature stability was maintained within ±1 °C in the automated system, promoting uniform nutrient extraction and microbial safety. Stirring and mixing mechanisms ensured homogeneous blending, reducing nutrient stratification and improving taste.

3.6. Environmental and Operational Impact

Energy use in the automated system was optimized through smart heating cycles and insulated tanks, unlike the traditional method’s open-flame heating, which incurs higher heat losses. By replacing traditional firewood-based heating with an electric system, the automated plant reduces reliance on biomass, thereby lowering deforestation and associated emissions. Although the initial capital cost of automation is high, it is offset by improved yield, labor savings, and scalability for commercial applications.

3.7. Production Cost Analysis

Table 7. Production cost analysis.

Cost Parameter	Initial Equipment Cost	Labor (Monthly Avg.)	Utility (Monthly Avg.)	Wastage Loss (Monthly Est.)	Maintenance (Monthly Avg.)	Total Monthly Cost	Batch Vol. per Month (L)	Cost per L
Traditional Method	75,000	60,000	25,000	10,000	5000	175,000	1500	116.67
Automated Method	850,000	30,000	15,000	3000	10,000	908,000	13,500	67.26

gives the monthly cost analysis of the production of zobo juice by the traditional and automated processes with a total batch production of 1500 L and 13,500 L per month for the traditional and automated processes, respectively. The cost analysis indicated that the cost per L at one month of operation significantly reduces by 42.35% to the larger scale production capacity of the automated method in spite of its large initial equipment cost. The automated method is capable of three batch production per day due to the shorter production cycle of about one hour thirty minutes, whereas the traditional method is capable of two batch productions due to a longer production cycle of three hours.

4. Conclusions

This study demonstrated that automation significantly enhances the quality and consistency of zobo beverage production. The automated method produced outputs that aligned more closely with NAFDAC nutritional compliance requirements compared to the traditional method. Based on ANOVA results, the automated method showed statistically insignificant differences (p > 0.05) for three out of eight assessed nutrients (fiber, sodium, and vitamin C), indicating better compliance. In contrast, the traditional method significantly deviated (p < 0.05) in all eight nutrient categories. These findings underscore the limitations of manual processing, particularly its inability to consistently control critical factors like temperature and filtration, which affect nutrient retention and product safety.

The integration of smart controls in the automated system enabled precise temperature regulation and hygienic filtration, which contributed to better nutrient preservation and microbial safety. Given these advantages, along with potential scalability and reduced dependency on labor skill level, the automated system presents a superior alternative for commercial zobo production. The cost–benefit analysis and sensory evaluation further strengthen the case for automation, highlighting long-term economic and quality assurance gains over the traditional methods.