Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches

Aurum, Fawzan Sigma; Imaizumi, Teppei; Thammawong, Manasikan; Suhandy, Diding; Zaman, Muhammad Zukhrufuz; Purwanto, Edi; Praseptiangga, Danar; Nakano, K.

doi:10.1007/s00217-023-04281-2

Cited by 9 publications

(1 citation statement)

References 50 publications

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“…For example, Zhao et al employed HS-SPME/GC-MS-based metabolomics to identify distinctive markers in japonica rice and successfully discriminated between different varieties originating from diverse regions (Zhao et al, 2022). Furthermore, untargeted SPME-GC-MS-based volatile compounds ngerprinting combined with machine learning approaches have demonstrated the capability of predicting the origin of coffee (Aurum et al, 2023). Nevertheless, these methods typically necessitate preliminary analysis and identi cation of volatile compounds.…”

Section: Introductionmentioning

confidence: 99%

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Ji,

Ding

2023

Preprint

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Traditional fermented shrimp paste is wildly recognized for its distinctive flavor, exceptional nutritional value, and numerous health benefits. The volatile components of shrimp paste can be influenced by the production process and geographical environment, thereby reflecting its unique characteristics and quality. The objective of this study was to investigate the correlation between shrimp paste produced from different geographical origins and establish a rapid and accurate method for distinguishing them. To accomplish these objectives, the volatiles of shrimp paste from various regions were extracted using headspace solid-phase micro-extraction (HS-SPME) and subsequently analyzed by GC-MS. The data is preprocessed through MS-DIAL and then subjected to multivariate statistical analysis, wherein correlation analysis, principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and orthogonal partial least square-discriminant analysis (OPLS-DA) were collectively employed. The findings demonstrate that OPLS-DA exhibits a favorable discriminant region in the context of discriminant analysis. The integration of untargeted GC-MS metabolomics with multivariate statistical analysis offers a rapid and efficient approach for discriminating shrimp paste originating from diverse regions, which circumvents the time-intensive process of volatile compound identification.

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Section: Introductionmentioning

confidence: 99%

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Ji,

Ding

2023

Preprint

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Development of an Untargeted Method for the Analysis of the Volatile Profile of Onions via Solid‐Phase Microextraction Arrow‐Headspace‐Gas Chromatography‐Mass Spectrometry Using Design of Experiments

Böckstiegel,

Schaumann,

Egert

et al. 2024

J of Separation Science

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The distinctive aroma of onions, consisting primarily of sulfur‐containing compounds, is one of the reasons for the popularity of the vegetable. The rapid enzymatic and chemical reactions that occur after the destruction of onion bulb tissue render the analysis of the volatile profile challenging. Therefore, sample preparation is a crucial step in the analysis of the onion volatilome, but it often does not receive the necessary attention in the literature. In this work, we focused on two aspects: Firstly, we compared different sample preparation approaches to maximize the volatile yield and to enable the description of the onion volatile profile as it would emerge after a solid‐phase microextraction (SPME) Arrow sampling procedure. For headspace (HS)‐gas chromatography‐mass spectrometry analysis, onion juice with the addition of an ammonium sulfate solution proved to be the best option. Secondly, we optimized the HS sampling and desorption parameters (enrichment time, enrichment temperature, agitator speed, desorption time) for onion volatiles using the efficient design of experiments (DoE) approach. The optimal conditions for the analysis with HS‐SPME Arrow were an enrichment time of 75 min at 60°C with an agitator speed of 713 rpm and a desorption time of 120 s.

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Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Ji,

Ding

2023

Food Anal. Methods

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Traditional fermented shrimp paste is wildly recognized for its distinctive avor, exceptional nutritional value, and numerous health bene ts. The volatile components of shrimp paste can be in uenced by the production process and geographical environment, thereby re ecting its unique characteristics and quality. The objective of this study was to investigate the correlation between shrimp paste produced from different geographical origins and establish a rapid and accurate method for distinguishing them. To accomplish these objectives, the volatiles of shrimp paste from various regions were extracted using headspace solid-phase micro-extraction (HS-SPME) and subsequently analyzed by GC-MS. The data is preprocessed through MS-DIAL and then subjected to multivariate statistical analysis, wherein correlation analysis, principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and orthogonal partial least square-discriminant analysis (OPLS-DA) were collectively employed. The ndings demonstrate that OPLS-DA exhibits a favorable discriminant region in the context of discriminant analysis. The integration of untargeted GC-MS metabolomics with multivariate statistical analysis offers a rapid and e cient approach for discriminating shrimp paste originating from diverse regions, which circumvents the time-intensive process of volatile compound identi cation.

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Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches

Cited by 9 publications

References 50 publications

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Development of an Untargeted Method for the Analysis of the Volatile Profile of Onions via Solid‐Phase Microextraction Arrow‐Headspace‐Gas Chromatography‐Mass Spectrometry Using Design of Experiments

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

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