Discrimination of adulterants in UHT milk samples by NIRS coupled with supervision discrimination techniques

Luna, Aderval S.; Pinho, Jéssica S. A.; Machado, Luciana Cristina

doi:10.1039/c6ay01351a

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…This is considered a soft method because a sample can be classified into a class or not assigned to any class and it can be extended for multiple dimensions, in case that more than two classes were modelled. The outcomes for SIMCA model (as the same way of PLS-DA) are the samples assignments to each class for both calibration and validation steps (Berrueta et al, 2007;Luna, Pinho, & Machado, 2016). In a similar way used in PLS-DA, the number of components (N) was selected by a previous cross validation error approach, in which the algorithm was run with a variable number of components (from 1 to 20) and the number of components with minimum validation error was selected for each class.…”

Section: Soft Independent Modelling Class Analogy (Simca)mentioning

confidence: 99%

“…ACC, NER and ER are considered global parameters giving information of the overall classification for each algorithm step (calibration or validation). NER and ACC parameters take values from 0 to 1, indicating a perfect classification when the values are 1(Ballabio, Grisoni, & Todeschini, 2018;Luna et al, 2016).Another analysis to visualize the relation between SEN and SPEC is Receiver OperatingCharacteristic curves (ROC curves). The first ROC curve shows a plot of SEN and SPEC values varying the threshold limit (boundary limits of the classes), where the optimal threshold is the value showing the highest SEN and SPEC values.…”

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confidence: 99%

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Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

2019

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Quinoa flour has been receiving an increasing attention as a substitute for wheat flour in bread formulations due to immuno-nutritional features. This growing interest in quinoa has increased the demand and consequently the prices, being a target for possible adulterations with cheaper cereals. Fourier transform Mid-infrared spectroscopy (FT-MIR) was used in the present work as a fingerprinting technique to detect the presence of three adulterants (soybean, maize and wheat flours). Partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) models were used to classify pure from adulterated samples. 414 samples were measured, including pure quinoa flour, pure adulterant flours and adulterated quinoa flours using three different proportions (10, 5 and 1% w/w). PLS-DA showed better classification results than SIMCA, with error rates from 2 to 8% for the three strategies used to detect the presence of adulterants.

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Section: Soft Independent Modelling Class Analogy (Simca)mentioning

confidence: 99%

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confidence: 99%

Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

2019

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“…Hasil klasifikasi kopi Codot murni dan campuran dinyatakan dalam bentuk matriks konfusi, seperti yang disajikan di Tabel 2 (Cheah & Fang 2020) Berdasarkan matriks konfusi tersebut dapat dihitung empat parameter berikut, yaitu sensitivitas (SEN), spesifisitas (SPEC), presisi (PREC), dan akurasi (ACC) dengan rentang nilai antara 0100% yang dihitung dengan persamaan 1 sampai 4 sebagai berikut (Luna et al 2016):…”

Section: Metode Kemometrika Untuk Uji Keaslian Kopi Codot Lampungunclassified

Penggunaan UV-Vis Spektroskopi dan Kemometrika untuk Uji Keaslian Kopi Codot Lampung

Yulia

Ningtyas

Suhandy

2021

J. Ilmu. Pertan. Indones.

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Codot coffee from Tanggamus, Lampung is one of Indonesian specialty coffee with a very limited production. In this research, an authentication study for the Codot ground roasted coffee was conducted using UV-vis spectroscopy and chemometrics. A total of 330 samples of pure and adulterated Codot coffee was prepared. The adulterated Codot coffee samples were intentionally created by adding a regular coffee (non-Codot coffee) into pure Codot coffee samples with three levels of adulterations: low (10-20%), medium (30-40%), and high level (50-60%). All samples were 0,29 mm in particle size. The extraction procedure was performed with hot distilled water (98°C). The spectral data of coffee samples were acquired using a benchtop UV-visible spectrometer in the range of 190-1100 nm using a transmittance mode. The result showed that the pure and adulterated samples could be discriminated along PC1 and PC2 axis. The classification model was developed using LDA with 90,91% of accuracy could be obtained. The LDA model was used to classify the new samples and resulted in a sensitivity (SEN) of 100%, specificity (SPEC) of 76,67%, precision (PREC) of 78,13%, and accuracy (ACC) of 87,27% could be obtained. Using PLS regression, a PLS model was developed to quantify the percentages of Codot coffee adulteration and resulted in high of coefficient of determination both in calibration and validation (R2kal = 0,99 and R2val = 0,98). These results showed that UV-vis spectroscopy and chemometrics are suitable for authentication of Codot specialty coffee with RMSEP = 2,68% and RPD in prediction of 6,49. Keywords: authentication, LDA, PCA, PLS regression, UV-vis spectroscopy

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“…The so-called Coomans plot, showing the distances for each sample and the boundaries is obtained and a sample could be either included into a class or even not assigned to any class. For this reason, SIMCA is considered a soft model (Vanden Branden and Hubert 2005;Luna et al 2016). Moreover, a confusion matrix is calculated based on the number of well and misclassified samples.…”

Section: Soft Independent Modelling Class Analogy (Simca)mentioning

confidence: 99%

Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy

et al. 2019

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Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C-H, C-H 2 , C-H 3 ), alkene group (-CH=CH-), hydroxyl group (O-H) and Amides I and II vibrational modes.

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Discrimination of adulterants in UHT milk samples by NIRS coupled with supervision discrimination techniques

Abstract: A methodology was developed for distinguishing different ultra-high temperature (UHT) milk adulterants (water, urea, and formaldehyde) at various levels using NIR spectroscopy (NIRS) coupled with supervision discrimination techniques (SIMCA, SVM-DA, and PLS-DA).

Cited by 11 publications

References 23 publications

Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods

Penggunaan UV-Vis Spektroskopi dan Kemometrika untuk Uji Keaslian Kopi Codot Lampung

Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy

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