Essential terminology and considerations for validation of non-targeted methods

Nichani, Kapil; Uhlig, Steffen; Stoyke, M.; Kemmlein, Sabine; Ulberth, Franz; Haase, Ilka; Döring, Maik; Walch, Stephan G.; Gowik, Petra

doi:10.1016/j.fochx.2022.100538

Cited by 6 publications

(6 citation statements)

References 38 publications

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Section: Discussionmentioning

confidence: 99%

“…For instance, Ackermanns Bayernkoenig an old cultivar, is a cross between wheat and spelt wheat, which could explain why CNN identifies it as being closer to spelt than wheat. Overall, these samples proved to be "challenging samples" for the method with the CNN models in their current form [31]. On a similar line of inquiry, further investigations were made to determine whether old wheat cultivars, which were not part of the model building, can be distinguished from spelt (or wheat).…”

Section: External Validation With Other Wheat Cultivars (Old Wheat Cu...mentioning

confidence: 99%

“…Non-targeted methods (NTMs) are being increasingly developed and deployed in the detection of food fraud and ratifying the authenticity of food substances [28][29][30]. An NTM encompasses analytical measurement, resulting in, e.g., a highly resolved fingerprint (referred to herein as the wet lab procedure), followed by mathematical modeling and data evaluation (referred to as the dry lab procedure), without laying a special spotlight on predetermined analytes of interest [31].…”

Section: Introductionmentioning

confidence: 99%

“…The other important component of an NTM is the dry lab, which includes statistical modeling [31]. Given the complexity and size of the measurement data that is generated with LC-HRMS, there is a need to resort to contemporary machine learning methods like neural networks [35,36].…”

Section: Introductionmentioning

confidence: 99%

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Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Nichani

Uhlig²,

Colson³

et al. 2022

Foods

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Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

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Section: Discussionmentioning

confidence: 99%

Section: External Validation With Other Wheat Cultivars (Old Wheat Cu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Nichani

Uhlig²,

Colson³

et al. 2022

Foods

Self Cite

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“…Chemoinformatic databases with FTIR spectroscopic data for EVOO/VOO or other types of oil samples are necessary for fingerprint analyses [26,28]. However, given the lack of standard assessment protocols, confusion about the terminology and misuses, misinterpretation of the research findings are often encountered in the scientific literature [6,[29][30][31].…”

Section: Ftir Spectroscopy In the Quality Control Of Evoo/voomentioning

confidence: 99%

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives

2023

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Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a “proof-of-concept” study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

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