RESEARCH PROPOSAL
A MACHINE LEARNING APPROACH FOR QUALITY ASSURANCE ASSESSMENT
IN HQCF PRODUCTION INDUSTRY
High Quality Cassava Flour (HQCF) is the most viable product among the cassava products in
term of business and productivity. From its name, it is expected that the final product is of high
quality. However, as a result of the technology involved in the processing of this product, which
is an upgrade of the traditional cassava processing technology, the final product usually deviates
significantly from the expected quality. These deviations are majorly due to contaminations which
normally get into product during processing, thereby demeaning the potentials and productivity
that can obtained from this industry. The need for engineers to re-arrange the various machines
and equipment in HQCF industry to enhance automation has been suggested by Kolawole and
Agbetoye (2007) but most researches in this area focus more on just one or two operations rather
than a holistic research that considered all the processes involved. As a result of this, the quality
of HQCF obtained is usually compromised. In most cases the extent of the quality deviation results
into the production of low quality HQCF that poses health related challenges to the final consumer.
Hence, this research aims at using machine learning to control the quality of HQCF production.
Methodology
The methodology that will be applied for this research is as follow:
(1) Survey: Medium and Small Scale Enterprises (MSMEs) with standard (HACCP/QACCP)
system for controlling input materials and processing procedures to meet pre-determined quality
characteristics will be surveyed
(2) Data Collection: These data must contain data of different levels of HQCF contaminations
using non-destructive spectral imaging technology such as Rapid and Atomic Absorption
Spectrometry (AAS) analysis (Tao, et al., 2021).
(3) Data Integration and Model Development: Discriminating features will be extracted from
each category of the pure and contaminated flour.
(4) Training of the Model: Calibrated models will be built using state-of-art machine learning
algorithms including artificial neural network, support vector machine (SVM) and random forest
(RF)
(5) Validation of Developed Model: Calibrated models will be validated using unknown pure and
contaminated samples
(6) Implementation: Final developed robust model will be deployed in an automated HQCF
system.
Benefits
HQCF production industry will be significantly improved in terms of processing, production and
quality of product. The research will also help in eliminating associated problems such as loss of
production time, poor quality products, poor maintenance practice, poor management and high
rate of post-harvest losses from the industry.
More importantly, the success of this innovative research in HQCF industry will definitely develop
and enhance food processing industry as many food and Agro-Allied will most likely adopt the
system to improve productivity and quality of products from the various sectors.
References
(1) Kolawole, O.P. and Agbetoye, L., (2007). Engineering Research to Improve Cassava
Processing Technology, International Journal of Food Engineering, Volume 3 Issue 6,
Article 9.
(2) Tao, F., Liu, L., Kucha, C., and Ngadi, M.O. (2021) Rapid and Non-Destructive Detection
of Cassava Flour Adulterants in Wheat Flour Using a Handheld MicroNIR Spectometer,
Biosystem Engineering, March 2021, 203(10):34-43.