Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis

Eva Borràs; Jose Manuel Amigo Rubio; Franciscus Winfried J van der Berg; Richard Boqué; Olga Busto

doi:10.1016/j.foodchem.2013.12.032

Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis

Eva Borràs, Jose Manuel Amigo Rubio, Franciscus Winfried J van der Berg, Richard Boqué, Olga Busto

Food Analytics and Biotechnology

38 Citations (Scopus)

Abstract

In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR.

Original language	English
Journal	Food Chemistry
Volume	153
Pages (from-to)	15–19
Number of pages	5
ISSN	0308-8146
DOIs	https://doi.org/10.1016/j.foodchem.2013.12.032
Publication status	Published - 15 Jun 2014

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Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis. / Borràs, Eva; Amigo Rubio, Jose Manuel; van der Berg, Franciscus Winfried J et al.

In: Food Chemistry, Vol. 153, 15.06.2014, p. 15–19.

Research output: Contribution to journal › Journal article › Research › peer-review

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abstract = "In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR.",

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T1 - Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis

AU - Borràs, Eva

AU - Amigo Rubio, Jose Manuel

AU - van der Berg, Franciscus Winfried J

AU - Boqué, Richard

AU - Busto, Olga

PY - 2014/6/15

Y1 - 2014/6/15

N2 - In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR.

AB - In this study, near-infrared spectroscopy (NIR) coupled to chemometrics is used to develop a fast, simple, non-destructive and robust method for discriminating sweet and bitter almonds (Prunus amygdalus) by the in situ measurement of the kernel surface without any sample pre-treatment. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were built to discriminate both types of almonds, obtaining high levels of sensitivity and specificity for both classes, with more than 95% of the samples correctly classified and discriminated. Moreover, the almonds were also analysed by Raman spectroscopy, the reference technique for this type of analysis, to validate and confirm the results obtained by NIR.

U2 - 10.1016/j.foodchem.2013.12.032

DO - 10.1016/j.foodchem.2013.12.032

M3 - Journal article

C2 - 24491694

SN - 0308-8146

VL - 153

SP - 15

EP - 19

JO - Food Chemistry

JF - Food Chemistry

ER -

Fast and robust discrimination of almonds (Prunus amygdalus) with respect to their bitterness by using near infrared and partial least squares-discriminant analysis

Abstract

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