Intelligent assessment of tea quality employing visible-near infrared spectra combined with a hybrid variable selection strategy.

• Assessing tea rank by Vis-NIR spectra and a hybrid variable selection strategy. • Feature data are selected by the GA, IRIV, VCPA, VCPA-GA, and VCPA-IRIV. • Authentication models for tea ranks are created using LSSVM, RF, and BPNN. • The VCPA-IRIV-LSSVM model obtains superior performance. Tea is one of the most consumed and popular beverages around the world. Authenticating tea quality is essential to resist counterfeit goods and prevent fraud. This study innovatively aims to use a visible-near infrared (Vis-NIR) sensor combined with a variable combination population analysis (VCPA)-based hybrid variable selection algorithm to realize the authentication of tea quality. First, the spectra of 700 tea samples from seven different grades are acquired using a Vis-NIR sensor. Second, the raw spectra acquired are preprocessed by Savitzky-Golay convolution smoothing combined with first derivative (SG1), standard normal variate transformation (SNV), and multiplicative scatter correction (MSC), and the best pretreating technique is determined by support vector machine (SVM) algorithm. Third, the VCPA-based hybrid method optimizes the variable space of the optimal pretreated spectra data in the first step, and a genetic algorithm (GA) and iteratively retaining informative variables (IRIV) are utilized to execute further optimization and shrink the variable in the second step. Finally, authentication models of tea quality are constructed employing least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN) approaches based on the selected variables. The results show that the VCPA-IRIV-LSSVM model obtains the best predictive performance with an accuracy of 97.40% in the predictive process. Our findings demonstrate that it is feasible to apply the inexpensive vis-NIR sensor to assess quality attributes in tea. Also, the VCPA-based hybrid method can search the combination of the optimal wavelength and improve the identification accuracy and robustness of the model and can assist sensor manufacturers in developing a cheap and user-friendly smartphone-coupled micro spectroscopic sensor for authentication of tea quality. [ABSTRACT FROM AUTHOR]