Zulkifli Husin
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Preferred name
Zulkifli Husin
Official Name
Zulkifli, Husin
Alternative Name
Husin, Zulkifli
Husin, Z.
Husin, Zulkifli Bin
Main Affiliation
Scopus Author ID
57201059019
Researcher ID
EXV-4088-2022

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Recent technology for food and beverage quality assessment: a review

2023-06-01 , Tan W.K. , Zulkifli Husin , Yasruddin M.L. , Ismail M.A.H.

Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.

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Automated Chilli Pesticide Residues Detection Using Odour Gas Sensors (OGS) and Deep Learning (DL) Algorithm

2023-01-01 , Tan W.K. , Hakin Ismail M.A. , Zulkifli Husin , Yasruddin M.L.

Detection of excessive pesticide residue detection is a serious problem for food regulators, suppliers, and consumers. It is very important to determine which chilli are contaminated with pesticides, and the current method of identifying and determining pesticide residues in chilli is still done using laboratory equipment. To overcome this problem, this study attempted to develop a method to detect pesticide residues in chilli samples using an eight different type of electronic nose based on a readily available metal oxide gas sensor. The proposed system used noise filtering, Long Short-Term Memory (LSTM) and Principal Component Analysis (PCA) algorithm along with a realtime data acquisition system that uses a computer to perform pesticide residue detection on the chilli sample. Two hundred forty samples of chilli sample with different pesticide concentrations went through the system and the accuracy rate achieved a success rate of 89.58% using the LSTM algorithm. The proposed method is expected to help the food processing industry to determine food contamination for producing clean and healthy food. The validation and feasibility of the proposed method for the determination of pesticide residues in chilli have been demonstrated by experiments.

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Automated tomato grading system using Computer Vision (CV) and Deep Neural Network (DNN) Algorithm

2022-01-01 , Tan Wei Keong , Muhammad Amir Hakim Ismail , Zulkifli Husin , Muhammad Luqman Yasruddin

The tomato grading is based on the skin colour at the grading stage. The evaluation of the colour used to classify tomatoes is very important, and the current methods of identifying and determining tomato varieties are still manual and prone to human error. The ability to automate tomato grading helps the food industry determine colour grades during the evaluation phase. Therefore, Computer Vision (CV) and Deep Neural Network (DNN) are utilised to grade tomatoes by determining their maturity colour. Three hundred tomatoes were selected and its maturity level are assigned by expertise. The tomato images are captured, processed and passed to the DNN classifier to determine the tomato grade. The proposed DNN classifier achieved the mAP percentage of 95.52%. This shows that the computer vision built into the DNN algorithm can provide an efficient implementation for predicting tomato grade.

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