Table 1.
Application of metabolomics analysis techniques in food composition analysis, classification, adulteration, and traceability.
| Platform | Sample | Result/Objective | References |
|---|---|---|---|
| LC-MS | Legumes | Be able to differentiate between lentils, white beans and chickpeas. | Llorach et al. (2019) |
| LC-MS/MS | Almond | It can simultaneously determine various aflatoxin in almond. Without purification, high sensitivity (0.34–0.5 μg/kg) | Ouakhssase et al. (2021) |
| GC–MS | Black tea | To monitor the dynamic changes of metabolites during the processing. | Wu et al. (2019) |
| GC–MS | Saffron | Ketoisophorone and safranal identified as freshness versus and ageing marker. Safranal was identified as a marker to identify saffron adulteration. | Farag et al. (2020) |
| GC/GC-TOF-MS | Rice | Non targeted analysis of volatile metabolic compounds released during rice cooking. | Daygon et al. (2016) |
| HPLC-QTOF-MS/MS | Plantago depressa | Effective exploration of polyphenol spectrum of complex natural products. | Xu et al. (2020) |
| UPLC-Q/TOF-MS | Fish sauce | 46 metabolites were identified as the key chemical components of fish sauce flavor. | Wang et al. (2019) |
| UPLC-QTOF/MS | Pomegranate juice | It can detect 1 % apple juice and grape juice mixed in pomegranate juice. | Dasenaki et al. (2019) |
| UPLC-ESI-MS/MS | Pork | Determine the source of pork by using more than 100 lipid metabolites. | Mi et al. (2019) |
| DESI-MSI 3D imaging | Beef | Beef tissue can be directly tested for steroid ester injections. | De Rijke et al. (2013) |
| DESI-MSI | Potato | Clarification of the distribution of the potato toxins α-chaconine and α-lonokinin, based on m/z 852 and m/z 868. | Cabral et al. (2013) |
| ESI-MS/MS | Coffee bean | Simultaneous determination of pesticides and mycotoxins in green coffee beans | Reichert et al. (2018) |
| TOF-SIMS imaging | Chicken | Higher concentrations of vitamin E were found in the fat of chickens fed soybean oil and flaxseed oil. | Marzec et al. (2016) |
| MALDI-MSI | Chocolate | Differentiate between different chocolate producers and cocoa varieties. | De Oliveira et al. (2018) |
| MALDI-MSI | Strawberry | The distribution of flavan-3-ols, organic acids, anthocyanins and ellagic glycosides in strawberry was found. | Enomoto et al. (2020); Enomoto (2021) |
| MALDI-MSI | Pork | Phosphatidylcarnosine is most widely distributed in the spine and lumbar muscles. | Enomoto et al. (2021) |
| MALDI-MSI | Persimmon epidermis | During the drying, the concentration of vitamin A1 increased, the vitamins B1 and B6 unchanged. | Shikano et al. (2020) |
| MALDI-MSI | Rice | The molecular types of lysophosphatidylcholine and the distribution of unsaturated fatty acids in rice were explored, and it was found that the content of lysophosphatidylcholine would affect the flavor of rice wine. | Zaima et al. (2014) |
| NMR | Chicken breast | Differentiation of Korean Chicken Breast with Free Amino Acids. | Kim, Ko & Jo (2021) |
| 1H NMR | Celery | Identification of the origin of celery using amino acids, organic acids and mannitol. | Lau et al. (2020) |
| 1H NMR | Milk powder | Use of low molecular weight metabolites to differentiate between milk powder types. | Zhao et al. (2017) |
| 1H NMR | Olive oils | Differentiation of olive oils from different regions by fatty acyl. | Ün & Ok (2018) |
| 1H NMR | Rice | Comparison of multiple metabolite levels to distinguish the origin of Chinese rice. | Huo et al. (2017) |
| 1H NMR | Olive oil | Fatty acyl is an important metabolite marker that can aid to determine shelf life of olive oil. | Ün & Ok (2018) |
| 1H NMR | Duck breast | Anserine, aspartic acid, and carnosine were correlated with quality, and nicotinamide with cooking degree. | Wang et al. (2020) |
| 13C NMR | Essential oils | Identification of impurities such as vegetable oil. | Truzzi et al. (2021) |
| FT-IR and NMR | Saffron | Proposed some metabolomics markers for product shelf life, authenticity and quality of saffron. | Consonni et al. (2016) |
| NIR | Honey | Determination of hydroxybenzoic acid in honey. | Tahir et al. (2020) |
| NIR | Fruits | Qualitative and quantitative analysis of anthocyanins. | Teng et al. (2020) |
| NIR | Cantaloupe | Distinguish different varieties of cantaloupe with 100 % accuracy. | Németh et al. (2019) |
| NIR | Truffle | Identification of adulteration of cheap truffle raw materials. | Segelke et al. (2020) |
| NIR | Hungarian honey | Identifying the botanical origin of Hungarian honey with 99 % accuracy. | Bodor et al. (2021) |
| NIR | Beef | 100 % probability of detecting the presence of adulterants such as pork, fat and offal. | Morsy & Sun (2013) |
| NIR | Rice | Capable of detecting more than 5 % of other rice. | Liu et al. (2020) |