scholarly journals Estimation of Pesticide Residues on Leafy Vegetables Using a Developed Handheld Spectrometer

2021 ◽  
Vol 12 (6) ◽  
pp. 8163-8173
Keyword(s):  
Pesticide Residues ◽  
Agricultural Products ◽  
Leafy Vegetables ◽  
Stepwise Multiple Linear Regression ◽  
Detection Model ◽  
Vegetable Samples ◽  
Reference Measurements ◽  
Non Destructive ◽  
Oriental Mustard ◽  
Estimation Of Pesticide

In this study, a rapid and non-destructive detection model for pesticide residues on leafy vegetables was presented using a developed portable spectrometer. VIS/NIR spectra of three vegetable samples, including lettuce, oriental mustard, and bok choy, were analyzed at the range of 380 – 840 nm. Stepwise multiple linear regression (SMLR) models were developed based on chemical reference measurements and the spectral information of the leaf samples after performing the pre-processing method. Furthermore, a data acquisition interface was developed by Matlab GUI. Results of SMLR procedure indicated good performance for detection of indoxacarb and chlorantraniliprole with R2 ≥ 0.90. A fairly good model (0.90 > R2 > 0.80) was obtained for carbendazim in lettuce, whereas a poor model was found for emamectin-benzoate with R2 ≤ 0.80. It was concluded that pesticide residues on leafy vegetables could be predicted using our developed handheld spectrometer. It can also be generalized for the prediction of other pesticide components in agricultural products.

Non-destructive safety assessment of agricultural products using Vis/NIR spectroscopy

NIR news ◽  
2017 ◽  
Vol 28 (1) ◽  
pp. 4-8 ◽  
Author(s):  
Bahareh Jamshidi
Keyword(s):  
Pesticide Residues ◽  
Safety Assessment ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Agricultural Products ◽  
Safety Control ◽  
Development Method ◽  
Portable System ◽  
User Friendly ◽  
Non Destructive

Visible/near-infrared (Vis/NIR) spectroscopy can be used for fast and non-destructive safety assessment of agricultural products in terms of contamination by pesticide residues. This paper reports the development method and evaluation of a user-friendly portable system for the safety control of intact cucumbers by the absence/presence of pesticide residue based on Vis/NIR spectroscopy. The system’s software can also be generalized for the detection and monitoring of pesticide residues in other agricultural products if developing their appropriate models is feasible.

scholarly journals Monitoring of Pesticide Residues and Risk Assessment for Cereals and Leafy Vegetables of Certificated and General Agricultural Products

2011 ◽  
Vol 30 (4) ◽  
pp. 440-445 ◽  
Author(s):  
Hyo-Young Kim ◽  
Young-Hwan Jeon ◽  
Jeong-In Hwang ◽  
Ji-Hwan Kim ◽  
Ji-Woon Ahn ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Pesticide Residues ◽  
Agricultural Products ◽  
Leafy Vegetables

Monitoring of Pesticide Residues in Domestic Agricultural Products

2010 ◽  
Vol 39 (6) ◽  
pp. 902-908 ◽  
Author(s):  
Jung-Ah Do ◽  
Hee-Jung Lee ◽  
Yong-Woon Shin ◽  
Won-Jo Choe ◽  
Kab-Ryong Chae ◽  
...  
Keyword(s):  
Pesticide Residues ◽  
Agricultural Products

Comparative Analysis of Pesticide Residues in Agricultural Products in Circulation in Gyeonggi-do Before and After Positive List System Enforcement

2021 ◽  
Vol 36 (3) ◽  
pp. 239-247
Author(s):  
Seo-Hyeon Song ◽  
Ki-Yu Kim ◽  
Yun-Sung Kim ◽  
Kyong-Shin Ryu ◽  
Min-Seong Kang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Pesticide Residues ◽  
Agricultural Products ◽  
Positive List ◽  
Before And After

scholarly journals Representative commodity for six leafy vegetables based on the determination of six pesticide residues by gas chromatography

2019 ◽  
Vol 31 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Shiming Song ◽  
Huili Huang ◽  
Zhaojie Chen ◽  
Jie Wei ◽  
Cheng Deng ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Pesticide Residues ◽  
Leafy Vegetables

scholarly journals Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch

2021 ◽  
Vol 22 (10) ◽  
Author(s):  
Benyamin Lakitan ◽  
Kartika Kartika ◽  
Laily Ilman Widuri ◽  
Erna Siaga ◽  
Lya Nailatul Fadilah
Keyword(s):  
Regression Model ◽  
Leaf Area ◽  
Morphological Traits ◽  
Leafy Vegetables ◽  
Stem Cuttings ◽  
Total Leaf Area ◽  
Tropical Ecosystem ◽  
Total Leaf ◽  
Tropical Conditions ◽  
Non Destructive

Abstract. Lakitan B, Kartika K, Widuri LI, Siaga E, Fadilah LN. 2021. Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch. Biodiversitas 22: 4487-4495. Talinum paniculatum known as Java ginseng is an ethnic vegetable in Indonesia that has also been utilized as a medical plant. Young leaves are the primary economic part of T. paniculatum, which can be eaten fresh or cooked. This study was focused on characterizing morphological traits of T. panicultaum and developing a non-destructive yet accurate and reliable model for predicting total area per leaf cluster on each elongated branch per flush growth cycle. The non-destructive approach allows frequent and timely measurements. In addition, the developed model can be used as guidance for deciding the time to harvest for optimum yield. Results indicated that T. paniculatum flourished rapidly under wet tropical conditions, especially if they were propagated using stem cuttings. The plants produced more than 50 branches and more than 800 leaves, or on average produced more than 15 leaves per branch at the age of nine weeks after planting (WAP). The zero-intercept linear model using a combination of two traits of length x width (LW) as a predictor was accurate and reliable for predicting a single leaf area (R2 = 0.997). Meanwhile, the estimation of total area per leaf cluster was more accurate if three traits, i.e., number of leaves, the longest leaf, and the widest leaf in each cluster were used as predictors with the zero-intercept linear regression model (R2 = 0.984). However, the use of a single trait of length (L) and width (W) of the largest leaf within each cluster as a predictor in the power regression model exhibited moderately accurate prediction at the R2 = 0.883 and 0.724, respectively.

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