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

Abstract:

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 prediction 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.