Factorial Analysis on Processing Factors for Nitrogen, Phosphorus and Potassium Contents in Mushroom Waste - N., Zainol; Muhammad Faiz, Mohamed Ameen; Sunarti, Abd Rahman; Muhammad Adib, Rinalto; Nur Syahirah, Muhammad Nor (Universiti Malaysia Pahang, 2017)
The agriculture industry relies heavily on the use of bio-fertilizer and the main components in bio-fertilizer are nitrogen (N), phosphorous (P), and potassium (K). Thus, a study was conducted to identify the N, P, and K contents in mushroom waste (MW). These components are numerous building blocks that plants need for healthy growth. Therefore, by increasing the N, P and K contents in MW, it can be utilized to produce high and better quality of bio -fertilizer. Five independent factors, i.e., aging of waste (fresh 0 day & aged 14 days), waste pH (7& 8.5), composition [MW only & mixture of MW & spent medium (SM)], technique of drying (oven 50°C & sunlight), and MW size (powder & cut) were the affecting factors on N, P, and K contents in MW. A 25-1 fractional factorial design was used to investigate the effect of the independent factors as well as the interaction factors on the N, P, and K contents. The N, P, and K contents were measured using HACH spectrophotometer. The objective of this research is to identify the best combination of processing factors. Some of the independent factors were shown to have significant effects on the N, P and K contents. The results showed that the most significant factor in N content are MW size and aging of waste, while for P and K contents are technique of drying and MW size. The best condition was identified to maximize the amount of N, P, and K contents in MW. The identified conditions were the MW aged for 7 days, MW size at powder form, waste pH at 7, drying under sun light and the composition MW only. Based on the proposed best condition the N (12.08 mg/L), P (3.04 mg/L), and K (8.09 mg/L) contents were achieved. The results show that fractional factorial design was suitable in investigating the effect of a large number of factors with a minimum number of experiments.