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. 2020 Spring;19(2):34-44.
doi: 10.22037/ijpr.2019.112328.13686.

Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration

Reza Jahani  1   2 , Hassan Yazdanpanah  2   1 , Saskia M van Ruth  3   4 , Farzad Kobarfard  2   5 , Martin Alewijn  3 , Arash Mahboubi  2   6 , Mehrdad Faizi  1 , Mohammad Hossein Shojaee AliAbadi  7 , Jamshid Salamzadeh  2   8
Affiliations

Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration

Reza Jahani et al. Iran J Pharm Res. 2020 Spring.

Abstract

The aim of this study is to investigate the novel application of a ‎handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec®) in the spectral range of 900-1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and k-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew's correlation ‎coefficient ‎value of 0.75 in the external validation set. In the k-NN model, the highest accuracy and Matthew's correlation ‎coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2nd-order derivative and 5th nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.

Keywords: Chemometrics; Food fraud; Lime juice; PLS-DA; Portable NIR; k-NN.

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Figures

Figure 1.
Figure 1.
A detailed workflow of data analysis
Figure 2
Figure 2
Mean NIR reflectance spectra of genuine and adulterated lime juice samples in the 900–1700 nm region
Figure 3
Figure 3
(A) PCA score plot of all genuine and adulterated samples with PC1 and PC2. (B) PC score plot of first PC; Wavelength regions with apparent separation power are highlighted
Figure 4
Figure 4
(A) Spectra of raw data and (B) different preprocessing techniques including SNV, ‎(C) second order derivative, and (D) MSC followed by second order derivative
See this image and copyright information in PMC

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