An innovative screening approach for orange juice authentication using dual portable/handheld NIR spectrometers and chemometrics

In this study, we explored the feasibility of utilizing two portable/handheld near-infrared (NIR) spectrometers in combination with class modeling techniques, namely data-driven soft independent modeling of class analogy (DD-SIMCA) and soft-partial least squares-discriminant analysis (soft-PLS-DA), as well as discrimination strategies of ensemble learning and hard-PLS-DA, for developing a screening method to authenticate orange juice samples and detect the presence of adulterants (the Brix to citric acid ratio in pulp-wash). The results obtained with both NIR spectrometers (Tellspec, 900-1700 nm and Neospectra, 1350-2550 nm) coupled with DD-SIMCA exhibited perfect sensitivity and specificity of 100% in both calibration and prediction sets. Additionally, when combined with the NIR Tellspec spectrometer, ensemble learning methods including gradient boosting tree (GBT) and adaptive boosting (Adaboost) demonstrated exceptional predictive capabilities, achieving the pre-diction of 'Brix to citric acid' ratio in pulp-wash with high sensitivity and specificity. These results surpassed the performance of hard-PLS-DA as commonly used technique in food authentication.