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Start date: 2020Subject: search.filters.subject.Principal component analysis

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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
    ;
    Techavuthiporn, Chairat
    ;
    Supapvanich, Suriyan
    ;
    Syukri, Daimon
    ;
    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
    ;
    Jongyingcharoen, Jiraporn Sripinyowanich
    ;
    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
    ;
    Hancherngchai, Kangsadan
    ;
    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.