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Modeling and Optimization of Maize Yield and Water Use Efficiency under Biochar, Inorganic Fertilizer and Irrigation Using Principal Component Analysis
(2024-10-01)
Faloye, Oluwaseun Temitope
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Ajayi, Ayodele Ebenezer
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Oguntunde, Philip Gbenro
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Kamchoom, Viroon
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Fasina, Abayomi
This study was conducted to predict the grain yield of a maize crop from easy-to-measure growth parameters and select the best treatment combinations of biochar, inorganic fertilizer, and irrigation for the maize grain yield and water use efficiency (WUE) using the Principal Component Analysis (PCA) technique. Two rates of biochar (0 and 20 t ha−1) and fertilizer (0 and 300 kg ha−1) were applied to the soil, with maize crop planted, and subjected to deficit irrigation at 60, 80, and 100% of full irrigation amounts (FIA). Maize growth parameters (number of leaves—NL, leaf area—LA, leaf area index—LAI, and plant height—PH) were measured weekly. The results showed that the developed principal component regression (PCR) from the easy-to-measure growth parameters were strong and moderate in predicting the maize yield and WUE, with coefficient of determination; r2 values of 0.92 and 0.56, respectively. Using the PCA technique, the integration of irrigation with the least amount of water (60% FAI) with biochar (20 t ha−1) and fertilizer (300 kg ha−1) produced the highest ranking on grain yield and water use efficiency. This optimization technique showed that with the adoption of the integrative approach, 40% of irrigation water could be saved for other agricultural purposes
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Evaluating Impacts of Biochar and Inorganic Fertilizer Applications on Soil Quality and Maize Yield Using Principal Component Analysis
(2024-08-01)
Faloye, Oluwaseun Temitope
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Ajayi, Ayodele Ebenezer
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Kamchoom, Viroon
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Akintola, Olayiwola Akin
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Oguntunde, Philip Gbenro
A 2-year field experiment was conducted to test the effects of individual and co-application of biochar and inorganic fertilizer on soil quality using the principal component analysis (PCA) technique. The dry season field experiments were performed with biochar applied at 0 and 20 t ha−1, and fertilizer at 300 and 0 kg ha−1 (control). The factorial combinations of the above-mentioned treatments were subjected to irrigation at 60, 80, and 100% of irrigation amounts (IAs). Soil hydro-physical and chemical properties and grain yield were determined at harvest. Results from the PCA indicated that the soil total nitrogen (N) and moisture content (MC) were the soil properties mostly affecting the grain yield. The amendments’ effects on the soil physico-chemical properties and maize yield were in the order control < biochar < fertilizer < biochar + fertilizer. The derived comprehensive soil quality index (CSQI) from the PCA showed that the soil quality increased by 76, 100, and 200% in treatments individually applied with biochar, inorganic fertilizer, and the co-applications. This study therefore showed that the PCA revealed the actual dynamics in soil properties, in terms of the SQI upon the soil amendment addition, as well as their relationship with maize yield under different weather conditions.
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A comprehensive study of sous-vide cooked Korat chicken breast processed by various conditions: texture, compositional/structural changes, and consumer acceptance
(2024-04-01)
Pongsetkul, Jaksuma
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Saengsuk, Nachomkamon
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Siriwong, Supatcharee
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Thumanu, Kanjana
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Yongsawatdigul, Jirawat
Korat chicken (KC) is a slow-growing crossbreed renowned for its excellent growth and firm texture. This study investigated the effect of various sous-vide (SV) conditions (60 and 70°C, 1–3 h) on their texture, protein structure and degradation, as well as consumer acceptability, with the traditional boiling served as control. Texture showed significant improvement under all SV conditions compared to the control, as demonstrated by increased water holding capacity (WHC), cooking loss, and decreased shear force, hardness, and chewiness (P < 0.05). These changes corresponded to the higher sensory scores (P < 0.05). Among the SV samples, increased temperatures and longer cooking times led to higher degradation of myofibrils and connective tissue, as evidenced by a decrease in water-, salt-soluble proteins, and soluble collagen (P < 0.05). These findings aligned with the scanning electron microscopy (SEM) results, which showed a looser muscle structure in the meat under more intense cooking conditions. Based on synchrotron radiation-based Fourier transform infrared (SR-FTIR) results, a gradual increase in antiparallel forms within the amide I bands (1,700–1,600 cm−1) of the total spectra with higher temperature and longer cooking times was observed (P < 0.05), while the fluctuations were observed in the changes of α-helix, β-sheet, and β-turn structures. This suggested that the antiparallel structure represented a looser configuration developing during intense SV cooking. Combined with the principal component analysis (PCA) results, the findings indicated that the suitable SV condition for KC breast meat was 70°C for varying durations (1–3 h), as it showed the strongest correlation with sensory scores, particularly in terms of tenderness. In summary, these findings provided a better understanding of molecular changes and discovered SV conditions to enhance the texture quality of the KC meat.
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Adenoma Dysplasia Grading of Colorectal Polyps Using Fast Fourier Convolutional ResNet (FFC-ResNet)
(2023-01-01)
Paing, May Phu
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Pintavirooj, Chuchart
Colorectal polyps are precursor lesions of colorectal cancer; hence, early detection and dysplasia grading of polyps are essential for determining cancer risk, the possibility of developing subsequent polyps, and follow-up recommendations. The significant contribution of this study is the development of an enhanced deep-learning model called Fast Fourier Convolutional ResNet (FFC-ResNet) to classify dysplasia grades of polyps. It is based on the ResNet-50 architecture and uses cross-feature fusion, which combines local features extracted by traditional spatial convolution with global features extracted by Fourier convolution. Due to the compensatory effect between local and global features, the learnability and performance of FFC-ResNet have increased. The proposed FFC-ResNet was developed and tested using UniToPatho, a dataset containing 7000 μm and 800 μm hematoxylin-and-eosin (H&E)-stained colorectal images. And a favorable performance of sensitivity 0.95, specificity 0.93, balance accuracy 0.94, precision 0.95, F1 score 0.95, and AUC 0.99 was obtained using 800 μm polyp patches.
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Effect of noise signals on partial discharge classification models
(2015-01-26)
Pattanadech, Norasage
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Nimsanong, Phethai
This document proposes the comparison of four statistical classification models for partial discharge (PD) classification as follows: k-nearest neighbors (KNN) model, probabilistic neural network (PNN) model, and other two statistical models using principal component analysis (PCA) for a data reduction approach combined with KNN and PNN models, so called, PCA-KNN model and PCA-PNN model. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated and measured in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF, and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of experimented data was used as a training data for the PD classification model. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. Besides, noise signals were generated with computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed KNN, PNN, PCA-KNN and PCA - PNN model can predict PD patterns without noise signal with the accuracy 100%. Noise signals with amplitude of 20% or more of peak value of PD signal have obviously influence the accuracy of PD pattern classification. The combination of PCA with KNN model can improve the ability of PD classification compared with KNN PD classification model. However, it seems that PCA provided negative effect when PCA was combined with PNN model and evaluated with the mixed-noise PD signals.
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Partial discharge classification using principal component analysis combined with self-organizing map
(2015-01-26)
Pattanadech, Norasage
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Nimsanong, Phethai
This document proposes a statistical classification model using principal component analysis (PCA) for a data reduction approach combined with self-organizing map (SOM) for a classification purpose, so called, PCA-SOM model compared with SOM model to classify partial discharge pattern (PD) into four categories listed as corona at high voltage side, corona at low voltage side, surface discharge, and internal discharge. PD signals were investigated by using ultra high frequency (UHF) measurement technique. 12 independent parameters, skewness and kurtosis of each period of the measured electromagnetic signal, were calculated. 80 experiments in total were performed. PCA-SOM PD classification model was constructed. Then, 60% of the experimented data was used as a training data for the PD classification model. Another 40% experimented data was utilized to evaluate the performance of the designed PD classification model. Besides, noise signals were generated with a computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed SOM model and PCA-SOM model can predict PD patterns without noise signal with the accuracy 100% of classification. The prediction ability of SOM for PD classification models decreased sharply when this model was tested by the mixed-PD signals with the noise level of 30% of the peak value of the PD signal. Whereas the PCA-SOM provided some degree accuracy reducing for PD pattern classification when it was verified with such data.
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Effect of training methods on the accuracy of PCA-KNN partial discharge classification model
(2015-01-26)
Pattanadech, Norasage
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Nimsanong, Phethai
The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were experimented in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 PD experiments in total were performed. The original independent variables for the classification model, skewness and kurtosis of each period of the captured signals, were calculated. To study the effect of training methods: two patterns for data training, odd/even and block training methods were investigated. In case of the block training method, the effect of training data number can be examined as well. Besides, noise signals were generated with the computer program and trained into the PD classification models. The peak of noise signal was set up at 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PCA-KNN PD classification model. It was found that the block data training method provided the higher accuracy PD classification compared with the odd/event data training method. The block training method with 80% training data/20% testing data gave the highest accuracy (95% correction) for PD classification without noise signal. However, this training technique provided the lowest accuracy (56.25% correction) for PD classification with the mixed noise-PD signals.
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Partial discharge classification using learning vector quantization network model
(2015-01-26)
Pattanadech, Norasage
;
Nimsanong, Phethai
This paper represents a partial discharge (PD) classification technique by using learning vector quantization (LVQ) network model. LVQ model was not only implemented with the PD test data but also combined with the principal component analysis (PCA), so called, PCA-LVQ for a data reduction. In this research work, both LVQ model and PCA-LVQ model were investigated. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of the experimented data was used as a training data for the PD classification models. Another 40% experimented data was used to evaluate the performance of the designed PD classification models both LVQ and PCA-LVQ models. Besides, noise signals were generated with computer program for testing the efficiency of these models. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed LVQ model can predict PD patterns without noise signal with the accuracy 100% whereas the PCA-LVQ model provided more than 87.5% accuracy of PD classification. The prediction ability of LVQ PD classification models decreased sharply especially for CHV when this model was tested by the mixed noise-PD signals. Whereas the prediction accuracy of PCA-LVQ model was more robust to the noise signals than LVQ model.
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A comparison of dimensionality reduction techniques in virtual screening
(2013-09-25)
Pasupa, Kitsuchart
Most of the screening methods have always struggled to deal with the high dimensionality of data in virtual screening task. One of the most commonly used techniques to reduce the high dimensional data is principal component analysis (PCA). PCA and its variants have been introduced and re-introduced to solve the problems in particular tasks in real world applications. In this paper, PCA and four variants of it are compared and analyzed together in virtual screening task in particular using fingerprint representation. Fingerprint is one of the most regularly used descriptors in virtual screening task. None of these methods have never been compared and studied together with high dimensional and binary-valued data elsewhere. The results show superiority of the variants of PCA to PCA on the most heterogeneous classes, while the methods are competitive to PCA on the homogeneous classes. Supervised PCA is found to be the best technique and is competitive to Fisher discriminant analysis. It should be noted that Fisher discriminant analysis uses all the provided information while Supervised PCA uses only few components. © 2013 Springer-Verlag.

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