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Geographical origin discrimination of Ethiopian sesame seeds by elemental analysis and chemometric tools. Food Chem X 2022; 17:100545. [PMID: 36845523 PMCID: PMC9943757 DOI: 10.1016/j.fochx.2022.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 11/29/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022] Open
Abstract
Origin discrimination of sesame seeds is becoming one of the important factors for the sesame seed trade in Ethiopia as it influences the market price. This study was undertaken to construct accurate geographical origin discriminant models for Ethiopian sesame seeds using multi-element analysis and statistical tools. The concentration of 12 elements (Na, Mg, Cr, Mn, Fe, Cu, Co, Ni, Zn, Cd, As and Pb) were determined in 93 samples which were collected from three main sesame seed-producing regions in Ethiopia, Gondar, Humera and Wollega. According to a one-way analysis of variance (ANOVA), the concentration of 10 elements showing a significant difference (p < 0.05) was taken for statistical analysis using principal component analysis (PCA) and linear discriminant analysis (LDA). PCA showed some clustering of samples according to their respective origins. Then, the follow-up LDA resulted in a 100 % correct origin classification rate for all 93 sesame seed samples obtained from three regions in Ethiopia.
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MUNIZ ADS, CARVALHO GADD, RAICES RSL, SOUZA SLQD. Organic vs conventional agriculture: evaluation of cadmium in two of the most consumed vegetables in Brazil. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.106721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Mokhtar A, El-Ssawy W, He H, Al-Anasari N, Sammen SS, Gyasi-Agyei Y, Abuarab M. Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield. FRONTIERS IN PLANT SCIENCE 2022; 13:706042. [PMID: 35310645 PMCID: PMC8928436 DOI: 10.3389/fpls.2022.706042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 01/18/2022] [Indexed: 05/12/2023]
Abstract
Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield.
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Affiliation(s)
- Ali Mokhtar
- Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, Egypt
- State Key Laboratory of Soil Erosion and Dry Land Farming on Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources at Northwest University of Agriculture and Forestry, Xianyang, China
- School of Geographic Sciences, East China Normal University, Shanghai, China
| | - Wessam El-Ssawy
- Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, Egypt
- Irrigation and Drainage Department, Agricultural Engineering Research Institute, Agricultural Research Center, Giza, Egypt
- *Correspondence: Wessam El-Ssawy,
| | - Hongming He
- State Key Laboratory of Soil Erosion and Dry Land Farming on Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources at Northwest University of Agriculture and Forestry, Xianyang, China
- School of Geographic Sciences, East China Normal University, Shanghai, China
| | - Nadhir Al-Anasari
- Department of Civil Engineering, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden
- Nadhir Al-Anasari,
| | - Saad Sh. Sammen
- Department of Civil Engineering, College of Engineering, University of Diyala, Baquba, Iraq
| | - Yeboah Gyasi-Agyei
- School of Engineering and Built Environment, Griffith University, Nathan, QLD, Australia
| | - Mohamed Abuarab
- Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, Egypt
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