Discrimination of peanuts from bulk cereals and nuts by near infrared reflectance spectroscopy

Adulteration and allergenic food materials are a common problem all around the world. European legislation (EU) 1169/2011/EC requires labelling of food products with respect to the presence of allergenic components including nuts, cereals or any other food products. Therefore, rapid methods for anal...

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Veröffentlicht in:Biosystems engineering 2016-11, Vol.151, p.178-186
Hauptverfasser: Ghosh, Satyabrata, Mishra, Puneet, Mohamad, Siti Nur Hidayah, de Santos, Rosario Martín, Iglesias, Belén Diezma, Elorza, Pilar Barreiro
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container_end_page 186
container_issue
container_start_page 178
container_title Biosystems engineering
container_volume 151
creator Ghosh, Satyabrata
Mishra, Puneet
Mohamad, Siti Nur Hidayah
de Santos, Rosario Martín
Iglesias, Belén Diezma
Elorza, Pilar Barreiro
description Adulteration and allergenic food materials are a common problem all around the world. European legislation (EU) 1169/2011/EC requires labelling of food products with respect to the presence of allergenic components including nuts, cereals or any other food products. Therefore, rapid methods for analysis of food ingredients are required to enforce this legislation. Near Infrared Spectroscopy (NIRS) being fast and non-destructive could be a candidate technique. Present study investigates the potential of NIRS (896–1686 nm) and chemometrics to classify thirty different cereals and nineteen different nuts based on their spectral signatures. The aim was to perform the specificity analysis for peanuts to detect its presence in various food materials. As a first step, Principal Components (PCs) modelling was used to perform a primary classification. PCs provided a classification of the samples into five major groups as gluten, non-gluten, high fatty acid, high fibre and omega-3 fatty acid. To perform segregation within the class identified as nuts intermixed with oilseeds (high fatty acid class), Partial Least Square Discriminant Analysis (PLSDA) was performed. First two discriminant vectors obtained from PLSDA were successfully able to segregate a group identified as peanuts and pine nuts, from other nuts (almond) and cereals (sesame and flaxseed). However, to segregate peanuts from pine nuts, first and third discriminant vectors were used. Results concluded that NIRS combined with chemometrics is a robust method for specificity analysis of peanuts from different cereals and nuts. [Display omitted] •NIRS for classifying cereals and nuts.•Classified as gluten, non-gluten, high fatty acid, high fibre & omega-3 fatty acid.•Peanut was segregated from other nuts by PLSDA.•NIRS a robust technique for identification of peanut from other cereals and nuts.
doi_str_mv 10.1016/j.biosystemseng.2016.09.008
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European legislation (EU) 1169/2011/EC requires labelling of food products with respect to the presence of allergenic components including nuts, cereals or any other food products. Therefore, rapid methods for analysis of food ingredients are required to enforce this legislation. Near Infrared Spectroscopy (NIRS) being fast and non-destructive could be a candidate technique. Present study investigates the potential of NIRS (896–1686 nm) and chemometrics to classify thirty different cereals and nineteen different nuts based on their spectral signatures. The aim was to perform the specificity analysis for peanuts to detect its presence in various food materials. As a first step, Principal Components (PCs) modelling was used to perform a primary classification. PCs provided a classification of the samples into five major groups as gluten, non-gluten, high fatty acid, high fibre and omega-3 fatty acid. To perform segregation within the class identified as nuts intermixed with oilseeds (high fatty acid class), Partial Least Square Discriminant Analysis (PLSDA) was performed. First two discriminant vectors obtained from PLSDA were successfully able to segregate a group identified as peanuts and pine nuts, from other nuts (almond) and cereals (sesame and flaxseed). However, to segregate peanuts from pine nuts, first and third discriminant vectors were used. Results concluded that NIRS combined with chemometrics is a robust method for specificity analysis of peanuts from different cereals and nuts. 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To perform segregation within the class identified as nuts intermixed with oilseeds (high fatty acid class), Partial Least Square Discriminant Analysis (PLSDA) was performed. First two discriminant vectors obtained from PLSDA were successfully able to segregate a group identified as peanuts and pine nuts, from other nuts (almond) and cereals (sesame and flaxseed). However, to segregate peanuts from pine nuts, first and third discriminant vectors were used. Results concluded that NIRS combined with chemometrics is a robust method for specificity analysis of peanuts from different cereals and nuts. 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subjects Adulteration
Arachis hypogaea
Cereals
Chemometrics
Classification
Fatty acids
Food allergy
Foods
Legislation
Non-destructive
Nuts
Peanuts
Prunus dulcis
title Discrimination of peanuts from bulk cereals and nuts by near infrared reflectance spectroscopy
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