Non-targeted authentication of black pepper using a local web platform: Development, validation and post-analytical challenges of a combined NIR spectroscopy and LASSO method

Black pepper is a valuable commodity susceptible to economically motivated adulterations. This contribution describes the proper development, standardization, validation and late-stage validation of a non-targeted method for authentication of black pepper by near-infrared spectroscopy (NIR) coupled to a least absolute shrinkage and selection operator (LASSO). Note that this is the first successful attempt to apply the LASSO method to infrared spectroscopy data. We analysed 150 diverse samples of black pepper supplied by a well-recognized spice trader. A first batch of samples (n = 116) were split into training and test sets and then normalized by multiplicative scatter correction (MSC). While the test set was withheld for further testing of the model, the training set was submitted to the LASSO method with the aim of retrieving the discriminant spectral features and classifying the samples as authentic, exogenously-adulterated or endogenously-adulterated. The model was tested on the test set, achieving an overall accuracy of 94% with very high sensitivity and specificity rates. Furthermore, an R-based local web platform was created for immediate prediction of the samples from raw data. The NIR user simply uploads the raw data to the local web application and MSC normalization on the stored training set and interrogation of the LASSO classifier are performed. The online application facilitates the model testing and enables clear visualization of the outcomes. After the creation of the local web platform, the classifier was then validated with a new batch of independent samples, resulting in the correct prediction of 33/34 samples. The robustness of this non-targeted method and its stability over time was further established, and a late-stage validation of the classifier was successfully performed by an inexperienced user. Finally, the LASSO classifier was successfully challenged with a sample from an international inter-laboratory proficiency test.