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 หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Classification of Hom Mali Rice with Different Degrees of Milling Based on Physicochemical Measurements by Principal Component Analysis) ผู้เขียน: Areerat Imsil,  ดร.รณฤทธิ์ ฤทธิรณ, รองศาสตราจารย์ , Panmanas Sirisomboon, Varipat Areekul สื่อสิ่งพิมพ์:pdf AbstractThe 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 PC1 and PC2 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. |
หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Determination of the Effect of Recycling Treatment on Pulp Fiber Properties by Principal Component Analysis) ผู้เขียน:ดร.สมหวัง ขันตยานุวงศ์, รองศาสตราจารย์ สื่อสิ่งพิมพ์:pdf AbstractThe effect of recycling treatment on properties of hardwood bleached kraft pulp fibers was determined. Even though the morphological and physical properties of fibers were affected by recycling, there were no significant changes in most of the properties. Therefore, an application of principal component analysis for determining the effect of recycling treatment was attempted and demonstrated in this research. This application could graphically demonstrate the difference between never-recycled and recycled fibers as well as that between the recycled fibers themselves. Furthermore, the results of the analysis could also usefully reveal the correlation between properties of fibers. |
การประชุมวิชาการA pipeline extraction on forward-looking sonar images using the self-organizing mapผู้แต่ง:Tulsook, S., Dr.Teerasit Kasetkasem, Associate Professor , Dr.Yodyium Tipsuwan, Assistant Professor , Sugino, N., Chanwimaluang, T., Hoonsuwan, P., การประชุมวิชาการ: 
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 ที่มา:การประชุมทางวิชาการ ครั้งที่ 51 มหาวิทยาลัยเกษตรศาสตร์หัวเรื่อง:ลักษณะทางประสาทสัมผัสด้านกลิ่นของข้าวผัด |
หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Sensory Properties of Thai Fish Sauces and Their Categorization) ผู้เขียน:ปิติพร ฤทธิเรืองเดช, ดร.ธงชัย สุวรรณสิชณน์, รองศาสตราจารย์ สื่อสิ่งพิมพ์:pdf AbstractSensory characteristics of Thai fish sauces is one of the most important factors of consumer perception. This study aims to characterize sensory properties of Thai fish sauces, and to categorize Thai fish sauces based on the sensory properties. Twenty samples composing of 12 commercial Thai fish sauces (P) and 8 commercial Thai mixed fish sauces (M) were collected. Generic descriptive quantitative analysis with twelve trained panelists was used to determine and compare the sensory characteristics of samples. The results showed that there were fifteen sensory descriptors which were brown color, five aromatics (sweet, caramelized, fermented, fishy, and musty), four tastes (sweet, salty, bitter, and umami), and five aftertastes (sweet aftertaste, salty aftertaste, bitter aftertaste, caramelized flavor, and fishy flavor). P samples had significant difference in all of sensory characteristics from M samples (p<0.05). In addition, principal component analysis (PCA) could reduce those sensory attributes into two independent principal components, which accounted for 55.14% of the total variance, and could categorize samples into four groups. The first principal component (PC1) separated samples into two groups which high and low degree of fishy aromatic, fishy flavor, sweet aftertaste, caramelized flavor and umami taste. The second PC also separated samples from PC1 in another two groups which high and low degree of caramelized aromatic, sweet taste, sweet aromatic, salty taste, salty aftertaste, musty aromatic and fermented aromatic. |
หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Categorization of Coconut Milk Products by Their Sensory Characteristics) ผู้เขียน:เสาวภาคย์ วัฒนพาหุ, ดร.ธงชัย สุวรรณสิชณน์, รองศาสตราจารย์, ดร.วรรณี จิรภาคย์กุล, รองศาสตราจารย์ , Sumaporn Kasermsumran สื่อสิ่งพิมพ์:pdf AbstractCoconut milk is extensively used as a major ingredient of savory foods and desserts in many countries. The sensory characteristics of coconut milk have a substantial effect on consumer acceptance. The objectives of this investigation were to develop a lexicon of coconut milk products produced by different tempering processes and to categorize coconut milk products based on their sensory properties. Twelve samples consisting of two unheated, four pasteurized, three UHT, one sterilized and two spray dried were collected and determined. Eight highly trained panelists described the sensory characteristics of the coconut milk samples. The results demonstrated that there were 17 attributes: smoothness, overall coconut milk odor, sweetness of coconut milk odor, freshness odor, coconut oil odor, cooked odor nutty odor, overall coconut milk fl avor, freshness fl avor, oily fl avor, cooked fl avor, sweet fl avor, nutty fl avor, viscosity, fat feel, dryness, lip and mouth feel. All 17 sensory attributes were signifi cantly different (P < 0.05) among coconut milk samples. The results of hierarchical cluster analysis (HCA) and principal component analysis (PCA) demonstrated that PCA reduced the 17 sensory attributes into two independent principal components (PCs), which accounted for 87.30% of the explained variance. These two PCs with HCA could classify the 12 samples into fi ve groups related to their tempering processes. |
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ที่มา:The 12th National Conference on Statistics and Applied Statistics 2011. Hat Yai, Song khla. May 18-22,2011หัวเรื่อง:ตัวสถิติในการตรวจสอบค่าผิดปกติโดยใช้ 2 องค์ประกอบรอง |
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