Discrimination of moldy peanuts with reference to aflatoxin using FTIR-ATR system

Kaya-Celiker, Hande; Mallikarjunan, P. Kumar; Schmale, David G.; Christie, María Elisa

doi:10.1016/j.foodcont.2014.03.045

Cited by 37 publications

(39 citation statements)

References 31 publications

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“…FTIR-ATR techniques have been applied in several different crud materials (Anjos, Campos, Ruiz, & Antunes, 2015;Anjos, Santos, Estevinho, & Caldeira, 2016;Droussi, D'orazio, Provenzano, Hafidi, & Ouatmane, 2009;Kaya-Celiker, Mallikarjunan, Schmale, & Christie, 2014). In fact, in honey it has been described as useful for the automated and highly sensitive botanical origin estimation (Gok, Severcan, Goormaghtigh, Kandemir, & Severcan, 2015), in the development of calibration models for sugar content estimation (Anjos et al, 2015) and for assessing the presence of adulterants (Gallardo-Velázquez, Osorio-Revilla, Loa, & Rivera-Espinoza, 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of FTIR-ATR spectroscopy on the bee pollen characterization

Anjos¹,

Santos²,

Dias

et al. 2017

Journal of Apicultural Research

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Bee pollen contains almost all nutrients required by the human organism as well as diverse health-promoting substances. However, its composition and nutritional value greatly depend on the botanical origin. As such, it is important to develop a rapid and non-expensive methodology that allows studying its characteristics, making labelling more objective and easier. The FTIR-ATR technique was used to predict some nutritional parameters in 126 bee pollen samples. FTIR-ATR spectrum obtained in the region between 4000 and 400 cm −1 with PLS Regression models were used to correlate spectral information with the data obtained using reference methods. In this first approach with pollen samples, good correlation models with appropriate accuracy were obtained for the evaluated parameters with r 2 varying from 74.8 to 97% and residual prediction deviation between 2.0 and 5.8. These results suggest that FTIR-ATR may be a useful technique for assessing bee pollen's composition.Aplicació n de la espectroscopía FTIR-ATR a la caracterizació n del polen de abeja El polen de abeja contiene casi todos los nutrientes requeridos por el organismo humano, así como diversas sustancias promotoras de la salud. Sin embargo, su composició n y valor nutricional dependen en gran medida del origen botánico. Como tal, es importante desarrollar una metodología rápida y econó mica que permita estudiar sus características, haciendo el etiquetado más objetivo y más fácil. La técnica FTIR-ATR se utilizó para predecir algunos parámetros nutricionales en 126 muestras de polen de abeja. Se utilizó el espectro FTIR-ATR obtenido en la regió n entre 4000 y 400 cm-1 con modelos de regresió n PLS para correlacionar informació n espectral con los datos obtenidos usando métodos de referencia. En este primer abordaje con muestras de polen, se obtuvieron buenos modelos de correlació n con una precisió n adecuada para los parámetros evaluados con r2 variando de 74,8% a 97% y una desviació n de predicció n residual entre 2,0 y 5,8. Estos resultados sugieren que el FTIR-ATR puede ser una técnica útil para evaluar la composició n del polen de abeja.

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Section: Introductionmentioning

confidence: 99%

Application of FTIR-ATR spectroscopy on the bee pollen characterization

Anjos¹,

Santos²,

Dias

et al. 2017

Journal of Apicultural Research

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“…7); two of the misclassified samples were false positive (misclassified as moldy, while they are toxic) and 5 of them were false negative (misclassified as toxic while true class was moldy). Apparently, similar secondary metabolites of aflatoxin contributed the spectra and led clustering of two different species together as reported elsewhere (Fischer et al 2006;Kaya-Celiker et al 2014, 2015. The performance analysis was conducted using 36 clean and infected peanut seeds.…”

Section: Discriminant Analysis and Classificationmentioning

confidence: 73%

Characterization of Invasion of Genus Aspergillus on Peanut Seeds Using FTIR-PAS

2015

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Aspergillus colonization on peanuts is a growing concern, as it results in reduced crop yields or livestock productivity due to consumption of contaminated feed, and most importantly, they are famous as causative agents of opportunistic infestation in man. In order to defeat and evaluate peanuts suffering from Aspergillus infection, it is essential to establish proper technique and track the quality of peanuts at both farm and market levels. This study focuses on usage of Fourier transform mid-infrared spectroscopy (FTIR) with a non-invasive reflectance apparatus, photoacoustic spectroscopy (PAS) to identify and separate infected peanuts based on spectral characteristics of infrared radiation on peanuts. Classes were defined as Bclean^representing no-aflatoxin/nomold in peanuts, Bmoldy^representing peanuts with nonaflatoxigenic Aspergillus strains, and lastly, Btoxicr epresenting peanuts infected with aflatoxigenic strains of Aspergillus spp. Classes were analyzed using discriminant analysis algorithm, and distance values were calculated in Mahalanobis distance units. The spectral ranges between 3600 and 2750, 1800 and 1480, and 1200 and 900 cm −1 were assigned as the key bands, and corresponding vibration modes and intensities were labeled. All healthy, clean peanuts (15 healthy/69 total peanut pods) were successfully separated from moldy ones. Separation was further detailed to distinguish peanuts infected with aflatoxin producing strains of Aspergillus flavus and Aspergillus parasiticus, and as a result, 87 % of samples were separated correctly as only moldy or toxic streams. Performance test was conducted with 36 different samples including toxic, moldy, and clean samples; 80.6 % correct classification was achieved.

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“…These methods required less detection time and preparation time, which could save solvents, samples and cost. In quantitative analysis, partial least‐squares (PLS) regression has usually been combined with IR to evaluate the content of AFB1 in paddy rice (Zhang et al ., ), red chilli powder (Tripathi & Mishra, ), etc ., and the content of AFTs in maize (Lee et al ., ), peanut paste (Kaya‐Celiker et al ., ), etc., through the developed regression models. However, PLS regression could bring false‐negative and false‐positive results in prediction intervals of AFB1 / AFTs content, which is not a risk in qualitative analysis (Montgomery, ; Brereton, ).…”

Section: Introductionmentioning

confidence: 99%

Rapid screen of aflatoxin‐contaminated peanut oil using Fourier transform infrared spectroscopy combined with multivariate decision tree

Yang

Zhang

et al. 2018

Int J of Food Sci Tech

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Summary Contamination of peanut oil is a great concern in the industry. FTIR spectroscopy combined with chemometrics could develop a rapid and nondestructive method to screen aflatoxin‐contaminated peanut oil. Aflatoxin B1 (AFB1)‐ and aflatoxin (AFT)‐positive peanut oils were screened by mid‐IR (MIR) with classification models established by a novel multivariate decision tree (MDT) method. Two discriminant functions were developed in the fingerprint region based on absorbance ratio and moving window Fisher discrimination analysis methods for the two complex nodes in each MDT model. Window adjustment reduced the total different spectral data points in modelling to 23. The true‐positive and true‐negative rate of calibration and validation could both reach up to 100%. These results would render the possibility of efficient and economical design of a portable high‐speed multitasking MIR instrument for screening AFB1‐ and AFT‐contaminated peanut oil.

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Discrimination of moldy peanuts with reference to aflatoxin using FTIR-ATR system

Cited by 37 publications

References 31 publications

Application of FTIR-ATR spectroscopy on the bee pollen characterization

Application of FTIR-ATR spectroscopy on the bee pollen characterization

Characterization of Invasion of Genus Aspergillus on Peanut Seeds Using FTIR-PAS

Rapid screen of aflatoxin‐contaminated peanut oil using Fourier transform infrared spectroscopy combined with multivariate decision tree

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