Passive reflectance sensing using regression and multivariate analysis to estimate biochemical parameters of different fruits kinds

Elsayed, Salah; El-Gozayer, Khadiga; Allam, Aida; Schmidhalter, Urs

doi:10.1016/j.scienta.2018.08.004

Cited by 7 publications

(11 citation statements)

References 23 publications

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“…Similar to this study's prediction of WQIs, Gad et al [45] discovered that PLSR could be utilized to estimate the DWQI and three surface water pollution indices in the Northern Nile Delta. Elsayed et al [84] found that the multivariate method of Principal Component Regression (PCR) and machine learning of Support Vector Machine Regression SVMR revealed accurate estimation and produced robust models for forecasting the WQIs in both (Cal.) and (Val.…”

Section: Using Partial Least Square Regression To Predict Wqis For Dr...mentioning

confidence: 99%

Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia

Osta

Masoud

Alqarawy

et al. 2022

Water

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Water shortage and quality are major issues in many places, particularly arid and semi-arid regions such as Makkah Al-Mukarramah province, Saudi Arabia. The current work was conducted to examine the geochemical mechanisms influencing the chemistry of groundwater and assess groundwater resources through several water quality indices (WQIs), GIS methods, and the partial least squares regression model (PLSR). For that, 59 groundwater wells were tested for different physical and chemical parameters using conventional analytical procedures. The results showed that the average content of ions was as follows: Na+ > Ca2+ > Mg 2+ > K+ and Cl− > SO42− > HCO32− > NO3− > CO3−. Under the stress of evaporation and saltwater intrusion associated with the reverse ion exchange process, the predominant hydrochemical facies were Ca-HCO3, Na-Cl, mixed Ca-Mg-Cl-SO4, and Na-Ca-HCO3. The drinking water quality index (DWQI) has indicated that only 5% of the wells were categorized under good to excellent for drinking while the majority (95%) were poor to unsuitable for drinking, and required appropriate treatment. Furthermore, the irrigation water quality index (IWQI) has indicated that 45.5% of the wells were classified under high to severe restriction for agriculture, and can be utilized only for high salt tolerant plants. The majority (54.5%) were deemed moderate to no restriction for irrigation, with no toxicity concern for most plants. Agriculture indicators such as total dissolved solids (TDS), potential salinity (PS), sodium absorption ratio (SAR), and residual sodium carbonate (RSC) had mean values of 2572.30, 33.32, 4.84, and −21.14, respectively. However, the quality of the groundwater in the study area improves with increased rainfall and thus recharging the Quaternary aquifer. The PLSR models, which are based on physicochemical characteristics, have been shown to be the most efficient as alternative techniques for determining the six WQIs. For instance, the PLSR models of all IWQs had determination coefficients values of R2 ranging between 0.848 and 0.999 in the Cal., and between 0.848 and 0.999 in the Val. datasets, and had model accuracy varying from 0.824 to 0.999 in the Cal., and from 0.817 to 0.989 in the Val. datasets. In conclusion, the combination of physicochemical parameters, WQIs, and multivariate modeling with statistical analysis and GIS tools is a successful and adaptable methodology that provides a comprehensive picture of groundwater quality and governing mechanisms.

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Section: Using Partial Least Square Regression To Predict Wqis For Dr...mentioning

confidence: 99%

Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia

Osta

Masoud

Alqarawy

et al. 2022

Water

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“…In this study, PCR was tested as alternative approaches to predict Water Quality Indices (WQIs). The PCR and SVMR predict a single model based on multiple response variables [51,96,97]. The two models were used to assess the WQIs as output data depending on the major ions and trace elements as input data for IWQ and based on several major ions as input data for the other five indices (Table 1).…”

Section: Using Principal Component Regression and Support Vector Machmentioning

confidence: 99%

“…Therefore, multivariable statistical regression methods, including the PCR, SVMR and SMLR, were tested as alternative approaches for predicting WQIs for irrigation. These approaches incorporate multiple independent variables into estimation models used to predict a single dependent variable [50][51][52]. The PCR is a linear regression that first decomposes data into a representation of the maximum variation and aims to optimize the model's estimated capacity; then, the optimum number of the latent factors is reversed against the response variable [52].…”

Section: Introductionmentioning

confidence: 99%

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Elsayed

Hussein

Moghanm

et al. 2020

Water

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Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

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“…Fruit consumption has risen dramatically in recent decades, owing to the fast expansion of the economy and the improvement of use livelihood. Consumers, on the other hand, have higher expectations for fruit attributes, including ripeness and total soluble solids (TSS) [10,11]. However, many fruit quality attributes that affect consumer acceptance and price are still tested using traditional methods that are either subjective or time-consuming, so it should come as no surprise that nondestructive and rapid measurement of fruit attributes has become a research hotspot [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…It was feasible to select the best bands for estimating measured parameters, compensating for the drawbacks of using the complete band set. On the other hand, an accurate estimation can only be accomplished by constructing appropriate indices [9,11,14]. For that, the benefit of the current work was the optimization of two band SRIs by integrating two bands produced by distinct 2-D contour maps.…”

Section: Introductionmentioning

confidence: 99%

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

et al. 2022

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The primary issues in collecting biochemical information in a large area using chemical laboratory procedures are low throughput, hard work, time-consuming, and requiring several samples. Thus, real-time and precise estimation of biochemical variables of various fruits using a proximal remote sensing based on spectral reflectance is critical for harvest time, artificial ripening, and food processing, which might be beneficial economically and ecologically. The main goal of this study was to assess the biochemical parameters of banana fruits such as chlorophyll a (Chl a), chlorophyll b (Chl b), respiration rate, total soluble solids (TSS), and firmness using published and newly developed spectral reflectance indices (SRIs), integrated with machine learning modeling (Artificial Neural Networks; ANN and support vector machine regression; SVMR) at different ripening degrees. The results demonstrated that there were evident and significant differences in values of SRIs at different ripening degrees, which may be attributed to the large variations in values of biochemical parameters. The newly developed two-band SRIs are more effective at measuring different biochemical parameters. The SRIs that were extracted from the visible (VIS), near-infrared (NIR), and their combination showed better R2 with biochemical parameters. SRIs combined with ANN and SVMR would be an effective method for estimating five biochemical parameters in the calibration (Cal.) and validation (Val.) datasets with acceptable accuracy. The ANN-TSS-SRI-13 model was built to determine TSS with greater performance expectations (R2 = 1.00 and 0.97 for Cal. and Val., respectively). Furthermore, the model ANN-Firmness-SRI-15 was developed for determining firmness, and it performed better (R2 = 1.00 and 0.98 for Cal. and Val., respectively). In conclusion, this study revealed that SRIs and a combination approach of ANN and SVMR models would be a useful and excellent tool for estimating the biochemical characteristics of banana fruits.

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Passive reflectance sensing using regression and multivariate analysis to estimate biochemical parameters of different fruits kinds

Cited by 7 publications

References 23 publications

Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia

Groundwater Suitability for Drinking and Irrigation Using Water Quality Indices and Multivariate Modeling in Makkah Al-Mukarramah Province, Saudi Arabia

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

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