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Modulation of functional properties, oxidative stability, and aroma evolution in cream-style salad dressing by Wolffia globosa powder incorporation
(2026-08-01)
Sinthusamran, Sirima
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Techavuthiporn, Chairat
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Supapvanich, Suriyan
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Syukri, Daimon
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Atika, Yusti
Wolffia globosa is a novel plant-derived protein rich in bioactive compounds with potential functionality in food products. This study evaluated dried Wolffia globosa powder (WP) as a partial egg yolk substitute (0–20%) in cream-style salad dressing, focusing on physicochemical properties, oxidative stability, and volatile compounds during storage. WP exhibited high protein content (32.31%), abundant chlorophylls and phenolics, and strong antioxidant capacity. Sensory evaluation identified 15% WP as the most acceptable formulation. WP incorporation altered color, reduced viscosity, increased droplet size, and enhanced electrostatic stability through a more negative ζ-potential. Nutritional quality improved via higher protein, fiber, and antioxidant levels. During 28 days at 30 °C, WP significantly suppressed lipid oxidation. GC–MS chemometric analysis revealed reduced oxidation-derived volatiles and preservation of freshness-related monoterpenes. These results demonstrate WP's effectiveness in regulating emulsion structure and oxidative pathways, highlighting its potential as a functional, plant-based ingredient for stable salad dressings.
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Thermochemical treatment of spent coffee grounds via torrefaction: A statistical evidence of biochar properties similarity between inert and oxidative conditions
(2024-03-01)
Pambudi, Suluh
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Jongyingcharoen, Jiraporn Sripinyowanich
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Saechua, Wanphut
This study used several statistical analyses to explore the impact of both inert and oxidative conditions on the characteristics of biochar derived from the torrefaction of spent coffee grounds (SCG). The study also considered variations in torrefaction temperature and residence time. Various fuel analyses were conducted, including high heating value (HHV), torrefaction index, proximate characteristics, thermogravimetric analysis (TGA), hygroscopicity, and Fourier-transform infrared spectroscopy (FTIR). The analysis of variance (ANOVA) revealed that the influence of both inert and oxidative conditions on HHV and mass yield was insignificant (p ≥ 0.05). Moreover, considering the same temperature and residence time, principal component analysis (PCA) and hierarchical cluster analysis (HCA) identified oxidative and inert conditions belonging to the same group or cluster. This finding indicated that neither atmospheric condition significantly affected the characteristics of the biochar measured in this research. Therefore, oxidative torrefaction offers significant advantages as it can produce biochar of comparable quality under inert conditions.
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Regional covariance matrix-based two-dimensional PCA for face recognition
(2020-01-01)
Titijaroonroj, Taravichet
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Hancherngchai, Kangsadan
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Rungrattanaubol, Jaratsri
Two-dimensional principal component analysis (2DPCA) is widely used in many applications, especially, face recognition. A key factor to improve the performance of the 2DPCA method comes from the efficiency of the covariance matrix. This paper believes that the effective eigenvector can be extracted when the effective covariance matrix is given. Therefore, the computing covariance matrix is a focus point in this paper. The set of the covariance matrix in the 2DPCA and its extensions is usually represented with a single directional correlation, which is then used to obtain a mean covariance matrix by using the average technique. This causes in obtaining the ineffective eigenvector since the covariance matrix is ineffective. In order to obtain the effective eigenvector, a regional covariance matrix-based on 2DPCA method (RCM-2DPCA) is proposed here. The contribution of this paper consists of two main parts including (i) regional matrix calculation for computing the two directional correlations and (ii) ELSSP conversion for extracting the effective representation of the covariance matrix. The experimental results show that the performance of the proposed method is higher than the baseline methods including 2DPCA, I-2DPCA, Bi2DPCA, 2D2PCA and ILM-2DPCA methods on a basis of three well-known datasets-ORL Face, Yale Face, and Yale Face extended B+ datasets.
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Classification of Cotton Wool Spots Using Principal Components Analysis and Support Vector Machine
(2019-01-10)
Sreng, Syna
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Maneerat, Noppadol
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Win, Khin Yadanar
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Hamamoto, Kazuhiko
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Panjaphongse, Ronakorn
Diabetic retinopathy is a complication of the eye damage and can lead to being blindness if it is late for treatment. Microaneurysms, exudates, hemorrhages and cotton wool spots are the lesions associated with diabetic retinopathy. Numerous studies have been done on the detection of microaneurysms, and hemorrhages, as well as exudates whereas only a few research works for detection of cotton wool spots, mainly because of the fact that its appearances are difficult to filter out from the background and not clearly visible. In this paper, an algorithm is proposed to detect cotton wool spots based on integrating principal components analysis and support vector machine. First, preprocessing is performed to enhance the retinal images. Then adaptive thresholding method is used to roughly extract the cotton wool spot from the background. Support vector machine and principal components analysis are further applied respectively to select the important features from morphologies, first-order statistics, gray level occurrence matrix and lacunarity. The proposed method was evaluated with local and DIARETDB1 datasets containing 289 images. Given a success rate of accuracy 90.47 %, sensitivity 85.29%, and specificity 90.12% with the average computational time 16.47 seconds per image on cotton wool spots detection, this system performed better by comparing to the previous research works.
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A simplified and powerful image processing methods to separate Thai jasmine rice and sticky rice varieties
(2018-01-01)
Khondok, Piyoros
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Sakulkalavek, Aparporn
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Suwansukho, Kajpanya
A simplified and powerful image processing procedures to separate the paddy of KHAW DOK MALI 105 or Thai jasmine rice and the paddy of sticky rice RD6 varieties were proposed. The procedures consist of image thresholding, image chain coding and curve fitting using polynomial function. From the fitting, three parameters of each variety, perimeters, area, and eccentricity, were calculated. Finally, the overall parameters were determined by using principal component analysis. The result shown that these procedures can be significantly separate both varieties.
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PCA-based informative SNP selection for analyzing population structure
(2016-12-19)
Limpiti, Tulaya
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Intarapanich, Apichart
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Tongsima, Sissades
Phenotypic differences among individuals of the same species are the result of a set of genetic variations which can be observed in the DNA sequence. To conduct a population genetic study, a high throughput genotyping platform such as Single Nucleotide Polymorphism (SNP) array is popularly used to obtain a large set of SNPs for each individual. However, analyzing today's genotypic data can be computationally expensive due to its large size and complexity. Faulty substructure may also be detected if the data is noisy from redundant or non-informative SNPs. Considerable efforts have been done to extract a smaller informative SNP subset that still represents the same intrinsic structure of populations within a data set as the full panel of SNPs. This work describes a foundation of a PCA-based informative marker selection technique. The proposed technique is simple and efficient. It improves upon another spectral analysis technique called PCA-correlated SNPs. A new informativeness score based on a basis function expansion of the SNP variation patterns across individuals is introduced. Such score is computed for each SNP to select a subset of SNPs with the best scores. Using a bovine data set, we demonstrate that our technique is superior to the PCAcorrelated SNPs method, which requires accurate rank estimation to perform well. In contrast, our method is robust to the assumed rank of the data. High data representation accuracy is also achieved after a significant reduction of the number of SNPs while retaining information about the underlying population structure from the original data.
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Polymer-coated single-walled carbon nanotubes for ethanol and dichloromethane discrimination
(2013-10-29)
Muangrat, Worawut
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Maolanon, Rungroj
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Pratontep, Sirapat
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Porntheeraphat, Supanit
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Wongwiriyapan, Winadda
Sensor response and pattern recognition of polymer-coated single-walled carbon nanotubes (SWNTs) were investigated. Printed circuit board (PCB) with Cu/Au interdigitated electrode was used as sensor platform. SWNTs network was firstly formed on PCB by drop-casting. For polymer-coated SWNTs preparation, poly(methyl methacrylate) (PMMA) and thiophene were employed as polymers to coat on SWNTs by spin coating; PMMA/SWNTs and thiophene/SWNTs. Raman spectra showed no obvious structure changes of SWNTs after polymer coating. Next, gas sensing test was conducted. Pristine SWNTs, PMMA/SWNTs and thiophene/SWNTs were exposed to vapors of ethanol and dichloromethane at room temperature. From normalized sensor response results, it was found that pristine SWNTs and PMMA/SWNTs showed the highest response to ethanol and dichloromethane vapors, respectively. In order to discriminate vapors between ethanol and dichloromethane, pattern recognition technique was utilized. Principal component analysis (PCA) results showed that pattern recognition of ethanol and dichloromethane vapors can be discriminated by using pristine SWNTs and polymer-coated SWNTs sensors. © (2013) Trans Tech Publications, Switzerland.
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Classification of Hom Mali rice with different degrees of milling based on physicochemical measurements by principal component analysis
(2011-09-01)
Imsil, Areerat
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Rittiron, Ronnarit
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Sirisomboon, Panmanas
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Areekul, Varipat
The effect of the degree of milling on the physicochemical properties of Hom Mali rice compared with low, intermediate and high amylose rice groups was investigated in order to differentiate Hom Mali rice from the other groups at different degrees of milling by principal component analysis (PCA). For all the rice groups, the apparent amylose content, alkali spreading value and pasting properties such as maximum viscosity, breakdown, final viscosity and setback viscosity increased with increases in the degree of milling except for the gel consistency which was reduced. Milled rice with a degree of milling of 15% showed the highest apparent amylose content, alkali spreading value and pasting properties compared with milled rice with degrees of milling of 10% and 5% and with brown rice. PCA could be applied to classify Thai rice varieties into four groups-Hom Mali, low, intermediate and high amylose rice groups-by two principal components (PCs). Rotated PC <inf>1</inf> and PC <inf>2</inf> using the Varimax method were better at explaining the variance of the parameters than the unrotated PCs. PCA clearly differentiated the classification of Rice Department 15 variety from the Pathum Thani 1 variety at the same degree of milling. Therefore, the stability of the degree of milling using PCA based on physicochemical measurements made it a preferable classification procedure for rice.
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Efficiency improvement for unconstrained face recognition by weightening probability values of modular PCA and Wavelet PCA
(2008-05-29)
Puyati, Wayo
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Walairacht, Aranya
Principal Component Analysis (PCA) is a well-known classical appearance-base method in face recognition. In the previous works, the preprocessing process significantly improved the recognition rate. Modular PCA and Wavelet PCA are the preprocessing processes of PCA, which increase the recognition rate of the original PCA. Modular PCA is suitable for the highvaried face database, while Wavelet PCA for the low-varied face database. In this paper, we propose the preprocessing method which combines between Modular PCA and Wavelet PCA with the weightening probability values. The experiments are compared among our propose method, Modular PCA, Wavelet PCA and original PCA with face database from Yale, ORL and UMIST. The experimental results show that the recognition rate of our method is higher compared to the other methods and also support variety of face database.
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White blood cell classification based on the combination of eigen cell and parametric feature detection
(2006-12-01)
Yampri, P.
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Pintavirooj, C.
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Daochai, S.
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Teartulakarn, S.
Numbers of white blood cells in different classes help doctors to diagnose patients. A technique for automating the differential count of white blood cell is presented. The proposed system takes an input, color image of stained peripheral blood smears. The process in general involves segmentation, feature extraction and classification. In this paper, features extracted from the segmented cell are motivated by the concept of the wellknown Eigen face which is performed on the pre-classified which blood cell based on parametric feature detection. The derived Eigen value and Eigen vector contributes to the important feature in the classification process. The results presented here are based on trials conducted with normal cells. For training the classifiers, a library set of 50 patterns is used. The tested data consists of 50 samples and produced correct classification rate close to 92 % © 2006 IEEE.

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