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Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data

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Abstract

In the present study sunset yellow (SY), a synthetic colour, which is a common adulterant in tea powders has been analysed using FT-IR spectral data coupled to machine learning tools for efficient classification and quantification of the SY adulteration. Earlier established real coded genetic algorithm (RCGA) was used as variable selection method to predict the key fingerprints of SY in the FT-IR spectra. Here, RCGA was used to select 20, 30, 40, 50 and 60 characteristic wavenumbers for SY. Classification was carried using support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB) classifiers. SVM classifier using 50 variables could give an accuracy of 0.90 amongst the three. Quantification of SY based on PLS (partial least squares), LS-SVM (least squares-SVM), RF and XGBoost were built on characteristic wavenumbers. Both RF and LS-SVM models were observed to be superior to PLS when coupled to RCGA obtained 20 fingerprint variables. Overall, RCGA-LS-SVM model resulted in lowest RMSECV (0.1956) with regression co-efficient values RC2 = 0.9989 and RP2 = 0.9979, when 50 fingerprint variables were used. These results demonstrated that FT-IR combined with RCGA-LS-SVM procedure could be a robust technique for rapid detection of SY in tea powder.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

All the computational codes will be made available upon request.

Abbreviations

ATR:

Attenuated total reflection

BOSS-LightGBM:

Bootstrapping soft shrinkage- light gradient boosting machine

CARS:

Competitive adaptive reweighted sampling

CatBoost:

Categorical boosting

DMC:

Dry matter content

DS:

Direct sampling

FDA:

Food and drug administration

FSSAI:

Food safety and standards authority of India

FT-IR:

Fourier transform infrared

GA:

Genetic algorithm

iPLS:

Interval partial least squares

KS:

Kennard and stone

KSPXY:

Kernel distance-based sample set partitioning based on joint X–Y distances

LightGBM:

Light gradient boosting machine

LS-SVM:

Least squares-support vector machine

LVs:

Latent variables

MAE:

Mean absolute error

mTry:

Number of input variables

NIPALS:

Nonlinear iterative partial least squares

nTree:

Number of regression trees

PCs:

Principal components

PCA:

Principal component analysis

PCR:

Principal component regression

PLS:

Partial least squares

RBF:

Radial basis function

RCGA:

Real coded genetic algorithm

RC 2 and RP 2 :

Regression coefficient of calibration and prediction

RF:

Random forest

RMSEC:

Root mean square of calibration

RMSECV:

Root mean square error of cross validation

RMSEP:

Root mean square error of prediction

RS:

Random sampling

SG:

Savitzky-Golay

SNV:

Standard normal variate

SPA:

Successive projections algorithm

SPXY:

Sample set partitioning based on joint X–Y distances

SVD:

Singular value decomposition

SVM:

Support vector machine

TLC:

Thin layer chromatography

SY:

Sunset yellow

XGB:

Extreme Gradient Boosting

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Acknowledgements

RA wish to express sincere thanks to Indian Council of Medical Research (ICMR) for granting SRF fellowship to carry out research work. The authors would like to thank Ms. Asha M of Central Instruments Facility & Services (CFTRI) and Mr. Punil HN of Microbiology & Fermentation Technology Dept. (CFTRI) for their assistance during experimentation. Authors also acknowledge the Director, CSIR-CFTRI, Mysuru for providing infrastructure and support during the research work.

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Authors and Affiliations

  1. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Sarma Mutturi

  2. Microbiology and Fermentation Technology Department, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India

    Rani Amsaraj & Sarma Mutturi

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  1. Rani Amsaraj
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  2. Sarma Mutturi
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RA carried out the experiments and wrote the original manuscript, SM conceived, supervised, and edited the manuscript.

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Correspondence to Sarma Mutturi.

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Amsaraj, R., Mutturi, S. Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data. J Food Sci Technol 60, 1530–1540 (2023). https://doi.org/10.1007/s13197-023-05694-3

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