Journal of Applied Research on Science and Technology (JARST)

The influence of sago flour addition as a stabilizing agent on the qualities of goat milk ice cream: A study of physicochemical and sensory evaluation

2025-09-03T10:11:21+07:00

Goat milk ice cream is an innovative and creamy alternative to traditional dairy-based frozen desserts. The ice cream made from goat milk has a smoother texture than cow's milk and a distinct taste profile that is rich and tangy. It is a growing trend in the artisanal and health-conscious food sectors, known for being easier to digest for people who have mild lactose intolerance or sensitivity to cow's milk. A common challenge encountered in its production, aside from the processing methods, is the need for an efficient and effective stabilizer to maintain the stability of goat milk ice cream. This study aims to assess the impact of adding various levels of sago flour to goat milk ice cream. The research focused on the ice cream production process from goat milk, along with tests for viscosity, melting time, moisture content, protein content, and organoleptic properties. The results showed that ice cream with 6% sago flour had a significantly higher viscosity (176.87 ± 1.67) and a significantly slower melting rate (80.33 ± 4.22) compared to the control sample (0% sago flour). Meanwhile, the water content values of added sago flour in ice cream were not much different from those of non-added sago flour. Also, the organoleptic tests to ensure the condition of the goat milk ice cream and the preferable taste to the panelists indicated 6% added sago flour showed the highest values in taste and texture. The 6% concentration was selected as optimal because it showed the highest values of sensory scores for taste and texture. For the parameters of color and aroma, no significant differences were found among the treatments. Thus, sago flour could be added to the ice cream as a stabilizer, resulting in a significant enhancement of the quality of goat milk ice cream and the development of alternative products for lactose-intolerant consumers.

Effects of fermented crickets (Gryllus bimaculatus) with pineapple for supplementation in feeds on growth performance, immune function, antioxidant activities and microflora in broiler chickens

2025-09-01T14:26:13+07:00

The poultry industry is vulnerable to pathogenic bacteria, leading to antibiotic use for disease prevention and growth enhancement. However, overuse causes resistance and disrupts gut microbiota, affecting consumers. Therefore, discovering alternatives to antibiotics is crucial. Crickets are highly nutritious insects, and fermentation enhances their nutritional value and biological activities. This study evaluated the effects of fermented crickets supplementation, where the crickets were fermented together with pineapple using natural fermentation relying on endogenous microbial activity in raw materials (submerged fermentation), on growth performance, inflammatory responses, immune function, antioxidative activity, and gut microbiota in broiler chickens. This study used 315 one-day-old unsexed Arbor Acres broiler chicks, which were divided into three groups with seven replicates per group: a control (CT) and two treatments supplemented with 1% (FC1) or 3% (FC3) fermented crickets. Results showed that no significant growth differences among groups (P > 0.05). At 21 days, the FC3 group had lower serum IgY (1,008.50 µg/ml) and large intestine IgA levels (834.05 µg/g) than CT group (serum IgY: 1,652.03 µg/ml; large intestine IgA: 1,345.72 µg/g, P < 0.01). The FC3 group also had lower serum nitric oxide at 21 days (195 nmole/ml, P < 0.05) than FC1 group and reduced IL-6 at 42 days (853.91 pg/ml, P < 0.05) compared to the CT group. At 21 days, E. coli and Salmonella spp. were absent in the FC3 group, and lactic acid bacteria (LAB) levels increased significantly by day 42 (P < 0.05). Antioxidant activity in the FC3 group was higher than in the CT group at 21 days and the FC1 group at 42 days (P < 0.05). In summary, the FC3 group reduced pathogenic bacteria, lowered IgY and IgA, suppressed inflammatory markers, and enhanced antioxidant activity, suggesting potential to mitigate gut oxidative stress and inflammation in broilers.

Detection and classification of human teeth in photographs using convolution mask and the watershed algorithm

2025-10-01T09:05:43+07:00

This research presents a novel image-processing algorithm for classifying human teeth in digital photographs, utilizing a combination of convolution masks and the watershed segmentation algorithm. The primary objective is to accurately identify and classify different types of lower teeth in simulated images derived from the SimKit model, a standardized dental simulation framework. The study focuses on digital images of lower teeth captured using a conventional digital camera, simulating real-world photographic conditions. These images are preprocessed through a series of color space transformations and morphological operations designed to enhance the visibility and separation of dental structures from surrounding elements, such as gums or background noise. Since previous studies on photographic images of human teeth have been limited, this work addresses the gap by improving posterior-tooth detection, where conventional watershed methods are less effective. Building on prior use of features such as cusps, grooves, and ridges, the study further employed convolution masks to detect posterior teeth, while watershed segmentation remained effective for the anterior teeth. To implement the classification system, custom software was developed using MATLAB R2020b. This software applies convolution masks to enhance image features, followed by the watershed algorithm, which segments individual teeth and facilitates their classification based on morphological characteristics. The performance of the classification algorithm was quantitatively evaluated using error rate ratios, with the benchmark set at an acceptable error ratio not exceeding 1.00. The evaluation results indicate promising classification accuracy across different tooth categories: incisors exhibited an error ratio of less than or equal to 0.12; canines showed an error ratio of less than or equal to 0.36; premolars maintained a ratio of up to 1.00; and molars demonstrated error ratios not exceeding 0.75. The results confirm that the proposed method is capable of effectively identifying lower teeth in photographic images with high accuracy. This research contributes to the development of automated dental analysis systems and supports the efficient creation of comprehensive databases of tooth types derived from oral photographs, potentially aiding both clinical diagnostics and educational tools in dentistry.

Process simulation of fast pyrolysis of Wolffia globosa using aspen plus for sustainable bio-oil production

2025-10-01T16:20:56+07:00

Fast pyrolysis has emerged as an efficient thermo-chemical route for converting biomass into liquid fuels under moderate temperatures and short vapor residence times. This study presents a systematic Aspen Plus simulation framework for the fast pyrolysis of Wolffia globosa, a protein-rich aquatic plant with low lignin and high volatile matter content. The model employed a multi-stage RYield reactor configuration to represent progressive decomposition processes, integrated with separation and quenching units to preserve vapor quality and bio-oil composition. To establish model credibility, the framework was benchmarked against a published sawdust simulation and compared with reported pyrolysis trends of aquatic biomass. Simulations were performed across 400–600 °C with a fixed vapor residence time of 1.25 s. The results demonstrated that bio-oil yield increased with temperature, peaking at 58.62 wt% at 550 °C before slightly declining at higher temperatures due to secondary cracking. At this optimum condition, the simulated bio-oil contained approximately 89.25% organic compounds and 10.75% water by mass, indicating favorable properties for downstream upgrading. Gas yields increased monotonically with temperature, while char yields decreased, reflecting enhanced volatilization at higher thermal severity. The validated model showed less than 10% deviation from literature data and provides a replicable approach for simulating protein-rich aquatic biomass. Overall, this study highlights Wolffia globosa as a viable feedstock for sustainable bio-oil production and offers a transparent simulation methodology that can support future optimization and integration with circular wastewater treatment systems.