Biography
Prof. Nazmi Mat Nawi
Prof. Nazmi Mat Nawi
Universiti Putra Malaysia, Malaysia
Title: Detection and Classification of Pesticide Residue Presence On Round Cabbages Using Visible Shortwave Near Infrared Spectroscopy
Abstract: 

Pesticides have long been used in cabbage industry to control pests. The extensive use of pesticides for pest control in cabbage production can lead to environmental pollution and potentially adverse health effects. Thus, it has become imperative to accurately monitor the presence of pesticide residues on any agricultural produce. In this study, the potential of visible and shortwave near-infrared spectroscopy (VSWNIRS) in the range of 200 to 1100 nm, in combination with three classifier algorithm techniques, was investigated to classify pesticide residue into four categories of maximum residues limit (MRL) levels. A total of 60 cabbage samples were used. The sample were divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining one batch was not sprayed with the pesticide. The three batches of round cabbage were sprayed with three different concentrations levels of pesticide, namely low, medium and high with values of 0.08, 0.11 and 0.14 % volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using VSWNIRS with the wavelengths range between 200 and 1100 nm. Gas chromatography was used to determine the maximum residue limit (MRL) value of the samples. In this study, the performance of three typical classification methods namely artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR) were used in classifying residue level according to MRL levels. Overall, the LR model has the highest prediction capability (95.2%), followed by the ANN model (86.8%) and the SVM model (88.4%). These results demonstrated that the proposed spectroscopic measurement is promising technique for detecting and classifying pesticide present at different concentration on cabbage samples. The results also revealed that all three classification models showed promising methods for classifying pesticide residues.


Keywords: gas chromatography, spectroscopy, MRL, pesticide residues, classification, round cabbages
Biography: 
Dr. Nazmi Mat Nawi obtained his Bachelor of Engineering (biological and agricultural) from Universiti Putra Malaysia in 2005. Upon graduation, he worked in palm oil mill as an assistant mill engineer for about one year. In 2007, He did his Master of Engineering degree in University of Southern Queensland (USQ), majoring in agricultural engineering. In 2010, he pursued his PhD at the same university. He completed his PhD in 2014 with the research on precision agriculture for sugarcane industry. During his PhD studies, he investigated the application of optical sensor for producing a quality map across a sugarcane field. Since he reported for duty at UPM, he has involved in several research projects. His research interests are on agricultural mechanization and post-harvest engineering. He has been the principal investigator for several research projects. One of his current projects entitled “application of a portable optical sensor system for in-field detection of multi-residue pesticides on cabbages” was fully funded by Ministry of Higher Education, Malaysia. To date, he has published 31 journal papers indexed by Scopus with his current H-index is 7.