UPLC-QTOF-MS coupled with machine learning to discriminate between NFC and FC orange juice
Food Control, ISSN: 0956-7135, Vol: 145, Page: 109487
2023
- 12Citations
- 13Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The substitution of not from concentrate (NFC) orange juice with from concentrate (FC) orange juice occurs in the market, damaging consumers' interests. An evaluation of the authenticity of NFC orange juice is critical. This study aimed to develop an approach using LC-MS-based metabolomics and machine learning to discriminate between NFC and FC orange juice. Combining principal component analysis and orthogonal projection to latent structures discriminant analysis, 11 differential compounds for NFC and FC orange juices discrimination were identified. Among them, limonin and hydroxymethylfurfural were higher in FC than in NFC samples, whereas the remaining nine compounds showed the opposite trend. During processing, concentration was the key step for the formation of the differential compounds. Therefore, these 11 compounds have great potential for discrimination between NFC and thermal concentrated FC orange juice processed by other sterilization methods. Based on these 11 differential compounds, random forest (RF), support vector machine (SVM), and partial least squares discriminant analysis machine models were used to identify NFC and FC orange juices. The SVM model was the most accurate model to discriminate between NFC and FC orange juices, with 100% accuracy for both the training and validation sets. Subsequently, the SVM model was used for commercial sample identification, and one NFC orange juice was mislabeled. Our results demonstrated that untargeted screening coupled with machine learning could be a powerful tool for the discrimination of NFC and FC juice.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0956713522006806; http://dx.doi.org/10.1016/j.foodcont.2022.109487; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85141285445&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0956713522006806; https://dx.doi.org/10.1016/j.foodcont.2022.109487
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know