Metabolic fingerprinting of hard and semi-hard natural cheeses using gas chromatography with flame ionization detector for practical sensory prediction modeling

Hiroshi Ochi, Takeshi Bamba, Hiroshige Naito, Keiji Iwatsuki, Eiichiro Fukusaki

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Metabolic fingerprinting using gas chromatography with flame ionization detector (GC/FID) was used to generate a practical metabolomics-based tool for quality evaluation of natural cheese. Hydrophilic low molecular weight components, relating to sensory characteristics, including amino acids, fatty acids, amines, organic acids, and saccharides, were extracted and derivatized prior to the analysis. Data on 12 cheeses, six Cheddar cheeses and six Gouda cheeses, were analyzed by multivariate analysis. Prediction models for two sensory attributes relating to maturation, "Rich flavor" and "Sour flavor", were constructed with 4199 data points from GC/FID, and excellent predictability was validated. Chromatograms from GC/FID and gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS) were comparable when the same column was used. Although GC/FID alone cannot identify peaks, the mutually complementary relationship between GC/FID and GC/MS does allow peak identification. Compounds contributing significantly to the sensory predictive models included lactose, succinic acid, l-lactic acid, and aspartic acid for "Rich flavor", and lactose, l-lactic acid, and succinic acid for "Sour flavor" Since similar model precision was obtained using GC/FID and GC/TOF-MS, metabolic fingerprinting using GC/FID, which is a relatively inexpensive instrument compared with GC/MS, is easy to maintain and operate, and is a valid alternative when metabolomics (especially using GC/MS) is to be used in a practical setting as a novel quality evaluation tool for manufacturing processes or final products.

Original languageEnglish
Pages (from-to)506-511
Number of pages6
JournalJournal of Bioscience and Bioengineering
Volume114
Issue number5
DOIs
Publication statusPublished - Nov 1 2012
Externally publishedYes

Fingerprint

Flame Ionization
Cheeses
Cheese
Gas chromatography
Gas Chromatography
Ionization
Detectors
Flavors
Metabolomics
Succinic Acid
Lactic acid
Lactose
Mass spectrometry
Acids
Lactic Acid
Mass Spectrometry
Organic acids
Fatty acids
Aspartic Acid
Amines

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

Cite this

Metabolic fingerprinting of hard and semi-hard natural cheeses using gas chromatography with flame ionization detector for practical sensory prediction modeling. / Ochi, Hiroshi; Bamba, Takeshi; Naito, Hiroshige; Iwatsuki, Keiji; Fukusaki, Eiichiro.

In: Journal of Bioscience and Bioengineering, Vol. 114, No. 5, 01.11.2012, p. 506-511.

Research output: Contribution to journalArticle

@article{e8e8edf4254242da99257c53acdf73e3,
title = "Metabolic fingerprinting of hard and semi-hard natural cheeses using gas chromatography with flame ionization detector for practical sensory prediction modeling",
abstract = "Metabolic fingerprinting using gas chromatography with flame ionization detector (GC/FID) was used to generate a practical metabolomics-based tool for quality evaluation of natural cheese. Hydrophilic low molecular weight components, relating to sensory characteristics, including amino acids, fatty acids, amines, organic acids, and saccharides, were extracted and derivatized prior to the analysis. Data on 12 cheeses, six Cheddar cheeses and six Gouda cheeses, were analyzed by multivariate analysis. Prediction models for two sensory attributes relating to maturation, {"}Rich flavor{"} and {"}Sour flavor{"}, were constructed with 4199 data points from GC/FID, and excellent predictability was validated. Chromatograms from GC/FID and gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS) were comparable when the same column was used. Although GC/FID alone cannot identify peaks, the mutually complementary relationship between GC/FID and GC/MS does allow peak identification. Compounds contributing significantly to the sensory predictive models included lactose, succinic acid, l-lactic acid, and aspartic acid for {"}Rich flavor{"}, and lactose, l-lactic acid, and succinic acid for {"}Sour flavor{"} Since similar model precision was obtained using GC/FID and GC/TOF-MS, metabolic fingerprinting using GC/FID, which is a relatively inexpensive instrument compared with GC/MS, is easy to maintain and operate, and is a valid alternative when metabolomics (especially using GC/MS) is to be used in a practical setting as a novel quality evaluation tool for manufacturing processes or final products.",
author = "Hiroshi Ochi and Takeshi Bamba and Hiroshige Naito and Keiji Iwatsuki and Eiichiro Fukusaki",
year = "2012",
month = "11",
day = "1",
doi = "10.1016/j.jbiosc.2012.06.002",
language = "English",
volume = "114",
pages = "506--511",
journal = "Journal of Bioscience and Bioengineering",
issn = "1389-1723",
publisher = "The Society for Biotechnology, Japan",
number = "5",

}

TY - JOUR

T1 - Metabolic fingerprinting of hard and semi-hard natural cheeses using gas chromatography with flame ionization detector for practical sensory prediction modeling

AU - Ochi, Hiroshi

AU - Bamba, Takeshi

AU - Naito, Hiroshige

AU - Iwatsuki, Keiji

AU - Fukusaki, Eiichiro

PY - 2012/11/1

Y1 - 2012/11/1

N2 - Metabolic fingerprinting using gas chromatography with flame ionization detector (GC/FID) was used to generate a practical metabolomics-based tool for quality evaluation of natural cheese. Hydrophilic low molecular weight components, relating to sensory characteristics, including amino acids, fatty acids, amines, organic acids, and saccharides, were extracted and derivatized prior to the analysis. Data on 12 cheeses, six Cheddar cheeses and six Gouda cheeses, were analyzed by multivariate analysis. Prediction models for two sensory attributes relating to maturation, "Rich flavor" and "Sour flavor", were constructed with 4199 data points from GC/FID, and excellent predictability was validated. Chromatograms from GC/FID and gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS) were comparable when the same column was used. Although GC/FID alone cannot identify peaks, the mutually complementary relationship between GC/FID and GC/MS does allow peak identification. Compounds contributing significantly to the sensory predictive models included lactose, succinic acid, l-lactic acid, and aspartic acid for "Rich flavor", and lactose, l-lactic acid, and succinic acid for "Sour flavor" Since similar model precision was obtained using GC/FID and GC/TOF-MS, metabolic fingerprinting using GC/FID, which is a relatively inexpensive instrument compared with GC/MS, is easy to maintain and operate, and is a valid alternative when metabolomics (especially using GC/MS) is to be used in a practical setting as a novel quality evaluation tool for manufacturing processes or final products.

AB - Metabolic fingerprinting using gas chromatography with flame ionization detector (GC/FID) was used to generate a practical metabolomics-based tool for quality evaluation of natural cheese. Hydrophilic low molecular weight components, relating to sensory characteristics, including amino acids, fatty acids, amines, organic acids, and saccharides, were extracted and derivatized prior to the analysis. Data on 12 cheeses, six Cheddar cheeses and six Gouda cheeses, were analyzed by multivariate analysis. Prediction models for two sensory attributes relating to maturation, "Rich flavor" and "Sour flavor", were constructed with 4199 data points from GC/FID, and excellent predictability was validated. Chromatograms from GC/FID and gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS) were comparable when the same column was used. Although GC/FID alone cannot identify peaks, the mutually complementary relationship between GC/FID and GC/MS does allow peak identification. Compounds contributing significantly to the sensory predictive models included lactose, succinic acid, l-lactic acid, and aspartic acid for "Rich flavor", and lactose, l-lactic acid, and succinic acid for "Sour flavor" Since similar model precision was obtained using GC/FID and GC/TOF-MS, metabolic fingerprinting using GC/FID, which is a relatively inexpensive instrument compared with GC/MS, is easy to maintain and operate, and is a valid alternative when metabolomics (especially using GC/MS) is to be used in a practical setting as a novel quality evaluation tool for manufacturing processes or final products.

UR - http://www.scopus.com/inward/record.url?scp=84866368406&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866368406&partnerID=8YFLogxK

U2 - 10.1016/j.jbiosc.2012.06.002

DO - 10.1016/j.jbiosc.2012.06.002

M3 - Article

C2 - 22824260

AN - SCOPUS:84866368406

VL - 114

SP - 506

EP - 511

JO - Journal of Bioscience and Bioengineering

JF - Journal of Bioscience and Bioengineering

SN - 1389-1723

IS - 5

ER -