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

Nichani, Kapil; Uhlig, Steffen; Colson, Bertrand; Hettwer, Karina; Simon, Kirsten; Bönick, Josephine; Uhlig, Carsten; Kemmlein, Sabine; Stoyke, M.; Gowik, Petra; Huschek, Gerd; Rawel, Harshadrai M.

doi:10.3390/foods12010141

Cited by 1 publication

(4 citation statements)

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“…For external validation of the model, artificial mixed spectra of spelt bread and flour, 11 untypical spelt, and six old wheat cultivars (which were not part of model training) were analyzed. The model was able to identify the nonconventional cultivars of wheat and spelt with a D value of 0.57 …”

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

“…The model was able to identify the nonconventional cultivars of wheat and spelt with a D value of 0.57. 218 Not only food products but also active pharma formulations and ingredients are under the threat of adulteration and fraud. One such example is where MS is used to develop a model to differentiate between wild and cultivated Cordyceps sinensis harvests.…”

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

“…Integration of artificial intelligence and neural network models has further improved the demand for efficiency of MS systems. Nichani et al 218 have developed a method for the differential identification of spelt and wheat. Nontargeted LC–MS/MS along with convolutional neural network (CNN) models was developed.…”

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

“…The model was able to identify the nonconventional cultivars of wheat and spelt with a D value of 0.57. 218 …”

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

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Recent Advances in the Detection of Food Toxins Using Mass Spectrometry

Ahuja,

Singh,

Paul

et al. 2023

Chem. Res. Toxicol.

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Edibles are the only source of nutrients and energy for humans. However, ingredients of edibles have undergone many physicochemical changes during preparation and storage. Aging, hydrolysis, oxidation, and rancidity are some of the major changes that not only change the native flavor, texture, and taste of food but also destroy the nutritive value and jeopardize public health. The major reasons for the production of harmful metabolites, chemicals, and toxins are poor processing, inappropriate storage, and microbial spoilage, which are lethal to consumers. In addition, the emergence of new pollutants has intensified the need for advanced and rapid food analysis techniques to detect such toxins. The issue with the detection of toxins in food samples is the nonvolatile nature and absence of detectable chromophores; hence, normal conventional techniques need additional derivatization. Mass spectrometry (MS) offers high sensitivity, selectivity, and capability to handle complex mixtures, making it an ideal analytical technique for the identification and quantification of food toxins. Recent technological advancements, such as high-resolution MS and tandem mass spectrometry (MS/MS), have significantly improved sensitivity, enabling the detection of food toxins at ultralow levels. Moreover, the emergence of ambient ionization techniques has facilitated rapid in situ analysis of samples with lower time and resources. Despite numerous advantages, the widespread adoption of MS in routine food safety monitoring faces certain challenges such as instrument cost, complexity, data analysis, and standardization of methods. Nevertheless, the continuous advancements in MS-technology and its integration with complementary techniques hold promising prospects for revolutionizing food safety monitoring. This review discusses the application of MS in detecting various food toxins including mycotoxins, marine biotoxins, and plant-derived toxins. It also explores the implementation of untargeted approaches, such as metabolomics and proteomics, for the discovery of novel and emerging food toxins, enhancing our understanding of potential hazards in the food supply chain.

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Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

“…The model was able to identify the nonconventional cultivars of wheat and spelt with a D value of 0.57. 218 …”

Section: Detection Of Food Fraud and Food Adulterationmentioning

confidence: 99%

See 2 more Smart Citations