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Discrimination of Grading Pungency for Red Peppers Spice Using Electronic Nose Based on Mass Spectrometer  

Kang, Jin Hee (Department of Food Science and Technology, Seoul Women's University)
Son, Hee-Jin (Department of Food Science and Technology, Seoul Women's University)
Hong, Eun-Jeung (Department of Food Science and Technology, Seoul Women's University)
Noh, Bong-Soo (Department of Food Science and Technology, Seoul Women's University)
Publication Information
Food Engineering Progress / v.14, no.1, 2010 , pp. 35-40 More about this Journal
Abstract
Electronic nose (E-nose) was assessed for grading pungency of powdered red pepper. Complex pretreatments are not required for flavor analysis unlike HPLC or Scoville tests. Mild and pungent taste of powdered red pepper were mixed at various concentrations of 0, 25, 50, 75, and 100%. Those were analyzed using mass spectrometer-based E-nose. Discriminant function analysis (DFA) was conducted on E-nose data. The $R^{2}$ and F-value of dicriminant function first score (DF1) were 0.9946 and 355.65, respectively, when the samples were separated by a relative degree of pungent taste. DF1 value decreased with increasing the amount of powdered red pepper with a pungent taste. It is similar to the increase in the concentration of capsaicin. Increasing the amount of red pepper powder, dicriminant function second score (DF2) values were moved from the negative position into the positive position. The $R^{2}$ and F-value of DF1 were 0.9890, 165.17 and DF2 were 0.9219, 21.64. Also, the results by MS based E-nose agreed to that by HPLC. There is the potential to grade pungent taste of powdered red pepper using the E-nose.
Keywords
red pepper; capsaicin; pungency; mass spectrometer; electronic nose;
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1 Cozzolino D, Smyth HE, Lattey KA, Cynkar W, Janik L, Dambergs RG, Francis IL, Gishen M. 2006. Combining mass spectrometry based electronic nose, visible-near infrared spectroscopy and chemometrics to assess the sensory properties of Australian Riesling wines. Anal. Chim. Acta. 563: 319-324   DOI
2 Gan HL, Che Man YB, Tan CP, NorAini I, Nazimah SAH. 2005. Characterization of vegetable oils by surface acoustic wave sensing electronic nose. Food Chem. 89: 507-518
3 Hong EJ, Son HJ, Kang JH, Noh BS. 2009. Analysis of binding trimethylamine with rice-washed solution using electronic nose based on mass spectrometer. Korean J. Food Sci. Technol. 41: 509-514
4 Kim SA, Kim KS, Park JB. 2006. Changes of various chemical compounds by the difference of the degree ripening and harvestin factors in two single-harvested peppers(capsinum annuum, L.). Korean J. Food Sci. Technol. 38: 615-620
5 Ministry for Agriculture, Forestry and Fisheries Republic of Korea. 1990. Agriculture, Forestry and Fisheries Statistical Yearbook. Seoul, Korea, p.100
6 Todd PH, Beninger MG, Biftu T. 1977. Determination of pungency due to capcicum by gas-liquid chromatography. J. Food Sci. 42: 660-665   DOI
7 Choi SM, Jeon YS, Park KY. 2000. Comparison of quality of red pepper powders produced in Korea. Korean J. Food Sci. Technol. 32: 1251-1257
8 Lim CL, Son HJ, Hong EJ, Han KY, Choi JY, Cho IY, Kim GW, Noh BS. 2009. Changes in physicochemical characteristics during fermentation of traditional noble wine, Samhaeju, by different brewing methods. Korean J. Food Sci. Technol. 41: 151-156
9 Son HJ, Kang JH, Hong EJ, Lim CL, Choi JY, Noh BS. 2009. Authentication of sesame oil with addition of perilla oil using electronic nose based on mass spectrometery. Korean J. Food Sci. Technol. 41: 609-614
10 Hwang SY, An YH, Shin GM. 2001. A study on the quality of commercial red pepper powder. Korean J. Food Nutr. 14: 424-428
11 Massimo FM. 2004. Composition and properties of indonesian palm civet coffee (Kopi Luwak) and Ethiopian civet coffee. Food Res. Int. 37: 901-912   DOI   ScienceOn
12 Figen K, Neriman B, Murat b, Yaar H. 2002. Ground red peppers: Capsaicinoids content, scoville scores, and discrimination by an electronic nose. J. Agr. Food Chem. 50: 3257-3261   DOI   ScienceOn
13 Frick G, Dubois S, Chaubert C, Ampuero S. 2009. Identification by microscopy and MS-based electronic nose of a fraudulent addition to maize gluten. Biotechnol. Agron. Soc. 13: 45-50
14 Yu JO, Choi WS, Lee US. 2009. Determination of capsaicin and dihydrocapsaicin in various species of red peppers and their powdered products in market by GC-MS analysis. Food Eng. Prog. 13: 38-43
15 Chung SJ, Lim CL, Noh BS. 2008. Understanding the sensory characteristics of various types of milk using descriptive analysis and electronic nose. Korean J. Food Sci. Technol. 40: 47-55
16 Jeong EJ, Bang BH, Kim KP. 2005. The characteristics of kimchi by the degree of hotness of powdered red pepper. Korean J. Food Nutr. 18: 88-93
17 Park SH, Lim HS. 2003. Effects of red pepper, salt-fermented anchovy extracts and salt concentration on the tastes of kimchi. J. Korean Soc. Food Sci. Nutr. 32: 346-349   DOI
18 Ampuero S, Zesiger T, Bogdanov S, Gustafsson V, Lund$\acute{e}$n A, Bosset JO. 2002. Determination of trimethylamine in milk using an MS based electronic Nose. Eur. Food. Res Technol. 214: 163-167   DOI   ScienceOn
19 Ampuero S, Bogdanov S, Bosset JO. 2004. Classification of unifloral honeys with an MS-based electronic nose using different sampling modes: SHS, SPME and INDEX. Eur. Food. Res Technol. 218: 198-207   DOI   ScienceOn
20 Ku KH, Kim NY, Park JB, Park WS. 2001. Characteristics of color and pungency in the red pepper for kimchi. Korean J. Food Sci. Technol. 33: 231-237
21 Ku KH, Cho MH, Park WS. 2003. Characteristics analysis for the standardization of commercial kimchi. Korean J. Food Sci. Technol. 35: 316-319
22 Imm BY, Shon SS, Kim KN. 2003. Changes in perceived intensities of pungency of ramen soup. Korean J. Food Sci. Technol. 35: 623-627
23 Mar$\acute{i}$n S, Vinaixa M, Brezmes J, Llobet E, Vilanova X, Correig X, Ramos AJ, Sanchis V. 2007. Use of a MS-electronic nose for prediction of early fungal spoilage of bakery products. Int J. Food Microbiol. 114: 10-16   DOI   ScienceOn
24 Barnett D. 1999. The electronic nose and food assessment. Food Aust. 51: 226
25 Brudzwski K, OsoWski S, Markiewicz T. 2006. Classification of milk by means of an electronic nose and SVM neural network. Sensor Actuator B. 98: 291-298