Characterisation and identification of individual intact goat muscle samples (Capra sp.) using a portable near-infrared spectrometer and chemometrics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027014%3A_____%2F22%3AN0000128" target="_blank" >RIV/00027014:_____/22:N0000128 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41210/22:92256

Výsledek na webu

<a href="http://mdpi.com" target="_blank" >http://mdpi.com</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/foods11182894" target="_blank" >10.3390/foods11182894</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Characterisation and identification of individual intact goat muscle samples (Capra sp.) using a portable near-infrared spectrometer and chemometrics

Popis výsledku v původním jazyce

Adulterated, poor quality and unsafe meat are still major challenges for the meat industry which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near infrared (NIR) instrument combined with machine learning techniques to identify and classify individual-intact goat muscles. Fresh goat carcasses (n=35; 19 to 21.7 Kg LW) from different breeds and sex were sourced and cut in different commercial cuts. The longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900 – 1600 nm). Differences in the NIR spectra of the muscles were observed at wavelengths around 976 nm, 1180 nm and 1430 nm associated with water and fat content (IMF). The classification of individual muscles was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68- 94%) using the second derivative NIR spectra. The results indicated that NIR spectroscopy can be used to identify individual goat muscles.

Název v anglickém jazyce

Characterisation and identification of individual intact goat muscle samples (Capra sp.) using a portable near-infrared spectrometer and chemometrics

Popis výsledku anglicky

Adulterated, poor quality and unsafe meat are still major challenges for the meat industry which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near infrared (NIR) instrument combined with machine learning techniques to identify and classify individual-intact goat muscles. Fresh goat carcasses (n=35; 19 to 21.7 Kg LW) from different breeds and sex were sourced and cut in different commercial cuts. The longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900 – 1600 nm). Differences in the NIR spectra of the muscles were observed at wavelengths around 976 nm, 1180 nm and 1430 nm associated with water and fat content (IMF). The classification of individual muscles was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68- 94%) using the second derivative NIR spectra. The results indicated that NIR spectroscopy can be used to identify individual goat muscles.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21101 - Food and beverages

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Foods

ISSN

2304-8158

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

18

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

8

Strana od-do

Article number: 2894

Kód UT WoS článku

000860107700001

EID výsledku v databázi Scopus

2-s2.0-85138646786