Uporabnost NIR spektroskopije za napovedovanje kemične sestave mesa in mesnih izdelkov

Drevenšek, Andreja

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Title:	Uporabnost NIR spektroskopije za napovedovanje kemične sestave mesa in mesnih izdelkov
Authors:	ID Drevenšek, Andreja (Author) ID ČANDEK-POTOKAR, MARJETA (Mentor) More about this mentor... ID ŠKORJANC, DEJAN (Comentor)
Files:	UNI_Drevensek_Andreja_2010.pdf (193,72 KB) MD5: E8D4650010D38D914EA9BC53622AB1B3 PID: 20.500.12556/dkum/51fe3877-c531-4e91-aaa7-23e42ccda968
Language:	Slovenian
Work type:	Undergraduate thesis
Organization:	FKBV - Faculty of Agriculture and Life Sciences
Abstract:	Ugotavljali smo zanesljivost bližnje infrardeče spektroskopije (NIRS) za napovedovanje kemijske sestave mesa in mesnih izdelkov. Vzorcem smo kemijsko določili vsebnost intramuskularne maščobe (IMM) z ekstrakcijo po Soxhletu s predhodno hidrolizo (SIST ISO 1443, 2001), vsebnost vode (ISO 6496, 1999), vsebnost beljakovin (ISO 5983-2, 2005) ter izračunali razmerje med vodo in beljakovinami (VB). Z aparatom NIR Systems model 6500 (Silver Spring, MD, USA) smo posneli spektre vzorcev na območju valovnih dolžin vidne in NIR svetlobe (400-2500 nm) ter pripravili umeritvene enačbe (WinISI II programski paket) za različna spektralna območja (NIR, vidni ali celoten spekter) znotraj mišice, za različne mišice ter na vseh vzorcih skupaj. Točnost kalibracij smo ocenili s statističnimi parametri: determinacijski koeficient navzkrižne validacije (R2CV) in predikcije (R2P), standardna napaka navzkrižne validacije (SECV) in predikcije (SEP) ter razmerjem med standardnim odklonom referenčnih vrednosti in standardno napako (RPD). Točnost napovedovanja je bila pri vseh enačbah odlična za vsebnost IMM (RCV: 0,91- 0,98; RPD: 3,40-6,74). Za ostale lastnosti je bila točnost (z izjemo slabe točnosti kalibracij znotraj mišice longissimus dorsi) le malo slabša (za beljakovine RCV: 0,80-0,91, RPD: 2,26-3,29; za vodo RCV: 0,73-0,98, RPD: 1,92-7,55; za VB: RCV: 0,65-0,92, RPD: 1,68-3,43). Glede na različne kazalce kalibracijske statistike lahko zaključimo, da lahko z NIRS zelo točno napovedujemo kemično sestavo mesa in mesnih izdelkov (z izjemo kalibracij za vsebnost beljakovin znotraj mišice longissimus dorsi).
Keywords:	NIR spektroskopija, meso, mesni izdelki, kemijska sestava
Place of publishing:	Maribor
Year of publishing:	2010
PID:	20.500.12556/DKUM-15687
NUK URN:	URN:SI:UM:DK:BXFHEC25
Publication date in DKUM:	13.10.2010
Views:	4436
Downloads:	239
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Categories:	FKBV
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Secondary language

Language:	English
Title:	Ability of NIR spectroscopy to predict chemical composition of meat and meat products
Abstract:	The aim of this study was to determine the accuracy of predicting chemical composition of meat and meat products by near infrared spectroscopy (NIRS). For each sample we chemically determined intramuscular fat (IMF) content using Soxhlet extraction with hydrolysis (SIST ISO 1443, 2001), moisture content (ISO 6496, 1999), protein content (ISO 5983-2, 2005) and calculatedratio moisture-to-protein. Sample spectra were recorded with spectrophotometer NIR Systems model 6500 (Silver Spring, MD, USA) at the spectral range of visible and NIR wavelengths (400-2500 nm). Calibration equations were prepared using WinISI II software for various spectral ranges (NIR, visible or the whole spectrum) for a single muscle, several muscles and all samples together. The accuracy of calibration was assessed by the following statistical parameters: coefficient of determination in cross-validation (R2CV) and prediction (R2P), standard error of cross-validation (SECV) and prediction (SEP) and the ratio between standard deviation of reference values and standard error (RPD). All equations showed very good accuracy to predict IMF content (RCV: 0,91-0,98; RPD: 3,40-6,74). For other constituents the accuracy (with the exception of the low accuracy of calibrations within longissimus dorsi muscle sample group ) was slightly lower (for proteins RCV: 0,80-0,91, RPD: 2,26-3,29; moisture: RCV: 0,73-0,98, RPD: 1,92-7,55; moisture and protein ratio: RCV: 0,65-0,92, RPD: 1,68-3,43). Based on parameters of calibration statistics we can conclude that it is possible to accurately predict the chemical composition of meat and meat products with NIRS (with the exception of protein content in the longissimus dorsi sample group).
Keywords:	NIR spectroscopy, meat, meat products, chemical composition

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