Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach

Abstract

Background: Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains.

Methods: This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study.

Results: Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and ‎‎1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively.

Conclusions: As a proof of concept, NIRS technology coupled with appropriate ‎multivariate classification models enables fast detection of citric acid-adulterated ‎lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.

Keywords: Adulteration; Benchtop FT-NIR, Lime Juice; Chemometrics; Citric Acid; Portable SW-NIR.

Figure 1.. The median NIR spectra (solid lines) and the range between minimum and maximum intensity (shaded areas) obtained from lime juice samples in the benchtop FT-NIRS (boxed areas are excluded from further evaluation) (A); and portable SW-NIRS (B); SNV transformed spectra of the samples acquired in benchtop FT-NIRS (C); SNV in combination with second derivative transformed spectra of the samples acquired in portable SW-NIRS (D). FT-NIRS, Fourier-transformation near-infrared‎ spectroscopy; SW-NIRS, short wave near-infrared‎ spectroscopy; SNV, standard normal variate.

Figure 2.. Principal component analysis score plot of genuine and adulterated samples with PC1, PC2, and PC3 based on the data obtained from benchtop FT-NIRS (A); and portable SW-NIRS (B). Outliers were excluded from the plots. PC, principal component; FT-NIRS, Fourier-transformation near-Infrared‎ spectroscopy; SW-NIRS, short wave near-infrared‎ spectroscopy.

Figure 3.. The ROC plot of the generated models based on the benchtop FT-NIRS (A); and portable SW-NIRS (B) data. ROC, receiver operator characteristic; FT-NIRS, Fourier-transformation near-infrared‎ spectroscopy; SW-NIRS, short wave near-infrared spectroscopy; C, calibration; CV, cross-validation.